10951 lines (10950 with data), 2.0 MB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_csv(\"../transformed_data/X_train.csv\")\n",
    "y = pd.read_csv(\"../transformed_data/y_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X.isnull().sum() > 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB, BernoulliNB\n",
    "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
    "from xgboost import XGBClassifier \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7238, 192)\n",
      "(1810, 192)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, train_size=.8, random_state=42)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
    "\n",
    "def cross_validate_calculate_scores(model, X, y):\n",
    "    scores = ['roc_auc', 'accuracy', 'precision', 'recall', 'f1']\n",
    "    results = []\n",
    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "    for score_name in scores:  \n",
    "        score_results = cross_val_score(model, X, y, cv=skf, scoring=score_name)\n",
    "        results.append(score_results)\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, roc_auc_score\n",
    "\n",
    "def calculate_scores(X_train, X_test, y_train, y_test, model):\n",
    "    \n",
    "    val_scores = cross_validate_calculate_scores(model, X_test, y_test)\n",
    "    \n",
    "    sns.boxplot(data=val_scores)\n",
    "    plt.title(\"Cross validation scores for \" + str(model.__class__.__name__))\n",
    "    plt.xticks([0, 1, 2, 3, 4], ['roc_auc', 'accuracy', 'precision', 'recall', 'f1'])\n",
    "    \n",
    "    \n",
    "    model.fit(X_train, y_train)\n",
    "    y_predict = model.predict(X_test)\n",
    "    \n",
    "    accuracy = accuracy_score(y_test, y_predict)\n",
    "    recall = recall_score(y_test, y_predict)\n",
    "    precision = precision_score(y_test, y_predict)\n",
    "    f1 = f1_score(y_test, y_predict)\n",
    "    auc = roc_auc_score(y_test, y_predict)\n",
    "    gini = 2 * auc - 1\n",
    "    \n",
    "    score_names = ['roc_auc', 'accuracy', 'precision', 'recall', 'f1', 'gini']\n",
    "    scores = [auc, accuracy, precision, recall, f1, gini]\n",
    "    \n",
    "    plt.figure(figsize=(8, 5))\n",
    "    ax = sns.barplot(x= score_names, y=scores, color='palegreen')\n",
    "    plt.title('Scores on test dataset for ' + str(model.__class__.__name__))\n",
    "    \n",
    "    for i, score in enumerate(scores):\n",
    "        ax.text(i, score, f'{score:.2f}', ha='center', va='bottom')\n",
    "        \n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "def optimize_params(model, params_grid, X_train, y_train):\n",
    "    grid = GridSearchCV(estimator=model, param_grid=params_grid, scoring='recall', cv=5, n_jobs=-1)\n",
    "    grid_results= grid.fit(X_train, y_train) \n",
    "\n",
    "    print(\"%s best recall_score: %f, using: %s\" %(model.__class__.__name__, grid_results.best_score_, grid_results.best_params_))\n",
    "    best_params = grid_results.best_params_\n",
    "\n",
    "    if model.__class__.__name__ in (\"LogisticRegression\", \"DecisionTreeClassifier\", \"GradientBoostingClassifier\", \"RandomForestClassifier\", \"SVC\", \"XGBClassifier\"):\n",
    "        model = model.__class__(random_state=42, **best_params)\n",
    "    else:\n",
    "        model = model.__class__(**best_params)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression best recall_score: 0.963587, using: {'C': 10.0, 'penalty': 'l1', 'solver': 'liblinear'}\n"
     ]
    }
   ],
   "source": [
    "y_train = np.ravel(y_train)\n",
    "\n",
    "lg = LogisticRegression(random_state=42)\n",
    "\n",
    "params_grid = {\"C\":np.logspace(-3,3,7), \"penalty\":[\"l2\", \"l1\"], \"solver\": [\"liblinear\"]}\n",
    "\n",
    "model1 = optimize_params(lg, params_grid, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_scores(X_train, X_test, y_train, y_test, model1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier best recall_score: 0.895109, using: {'ccp_alpha': 0, 'criterion': 'gini', 'max_depth': 9, 'max_features': 'sqrt', 'min_samples_leaf': 3}\n"
     ]
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "params_grid = {\"criterion\": [\"gini\", \"entropy\"],\n",
    "               \"max_depth\": np.arange(1, 11),\n",
    "               \"min_samples_leaf\": np.arange(1, 6),\n",
    "               \"max_features\": [\"sqrt\", \"log2\"],\n",
    "               \"ccp_alpha\": [0, 0.05, 0.1]}\n",
    "\n",
    "model2 = optimize_params(dtc, params_grid, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_scores(X_train, X_test, y_train, y_test, model2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB best recall_score: 0.982065, using: {'var_smoothing': 2.310129700083158e-09}\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "\n",
    "params_grid = {'var_smoothing': np.logspace(0,-9, num=100)}\n",
    "model3 = optimize_params(gnb, params_grid, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_scores(X_train, X_test, y_train, y_test, model3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BernoulliNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BernoulliNB best recall_score: 0.830435, using: {'alpha': 0.03511191734215131, 'binarize': 1, 'fit_prior': False}\n"
     ]
    }
   ],
   "source": [
    "bnb = BernoulliNB()\n",
    "\n",
    "params_grid = {\"alpha\": np.logspace(0,-9, num=100),\n",
    "               \"binarize\": [1],\n",
    "               \"fit_prior\": [False, True]}\n",
    "\n",
    "model4 = optimize_params(bnb, params_grid, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_scores(X_train, X_test, y_train, y_test, model4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingClassifier best recall_score: 0.965217, using: {'criterion': 'friedman_mse', 'loss': 'exponential', 'n_estimators': 100, 'subsample': 0.8}\n"
     ]
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "params_grid = {\"loss\": [\"log_loss\", \"exponential\"],\n",
    "               \"criterion\": [\"friedman_mse\", \"squared_error\"],\n",
    "               \"subsample\": [0.8],\n",
    "               \"n_estimators\": [100]}\n",
    "\n",
    "model5 = optimize_params(gbc, params_grid, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nefOhuRknxu86JNvJW3vlMwEzs8Hg77oxMyu5ETOib29v5+mXd+Kl6R9udVWaavzy62lvf32rq2FmJeYRvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyY2Y++ghfV3A+OXXD/p29NJ6YGj+QzZ9BYLvozezwTNiAn1HR8eQbWvFit8AMO2NQxGAXz+kbTOz3z4jJtAP5bc79m6rs7NzyLZpZjZYPEdvZlZyDvTWp56eHubMmcPatWtbXRUzGyAHeuvTwoULWbZsGQsXLmx1VcxsgBzoraaenh4WLVpERLBo0SKP6s1GKAd6q2nhwoWkX4qErVu3elRvNkI50FtNN998M5s2bQJg06ZN3HTTTS2ukZkNhAO91XTUUUcxZswYAMaMGcPRRx/d4hqZ2UCMmPvoB6qzs5Ourq5+rbNixQqg//fud3R0DOn9/oNt9uzZLFq0CIBRo0Yxe/bsFtfIzAbCI/oqJkyYwIQJE1pdjZZra2tj1qxZSGLWrFnssccera6SmQ1A6Uf0ZRpht8Ls2bNZtWqVR/NmI1jpA73tmLa2Ni6++OJWV8PMdoCnbszMSs6B3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOQc6M3MSs6B3sys5BzozcxKzoHezKzkGgr0ko6R9LCkLknnV0nfTdK1kpZJulvSQYW0P5V0v6QHJP1ZE+tuZmYNqBvoJY0GLgFmAdOBkyVNr8j2OWBpRLwVOAX4el73IOATwKHA24APS5rWvOqbmVk9jYzoDwW6ImJlRGwErgKOrcgzHbgFICIeAqZK2hM4ELgzIl6MiM3AT4Hjm1Z7MzOrq5FAPwV4vPC6Oy8rug84AUDSocB+QDtwP3C4pD0kTQQ+BOxTbSOSzpK0WNLiNWvW9K8VZmZWUyOBXlWWRcXri4DdJC0F5gBLgM0R8SDwZeBm4MekC8LmahuJiEsjYkZEzJg8eXKD1Tczs3oaCfTdbD8KbweeLGaIiPURcVpEHEyao58MPJrTLouId0TE4cAzwIpmVNzMbDjo6elhzpw5rF27ttVVqamRQH8PME3S/pLGAicB1xUzSJqU0wDOBG6LiPU57XX5776k6Z0rm1V5M7NWW7hwIcuWLWPhwoWtrkpNdX9hKiI2SzoHuBEYDcyPiAcknZ3T55E+dL1C0hZgOXBGoYgfStoD2AR8OiKebXYjftt0dna+8qPd/fHiiy8SUTnrNjgkMXHixH6vN2vWLP/8o40YPT09LFq0iIhg0aJFzJ49e1j+tnJDPyUYETcAN1Qsm1d4fgdQ9bbJiHjvjlTQrK51MOrWIfjfv+fz350Hf1Os49W3PNiws3DhwlcGT1u3bmXhwoWce+65La7Vq/k3Y0eguXPnetSbdXR0DGi97u5uNmzY0K91Nryc8k8YNaFf602YMIH29vZ+rcOUgbfNhs7NN9/Mpk2bANi0aRM33XSTA71Zsw30gtfZ2UlXV1e/1unu7gbod9Du6OjwhbmkjjrqKG644QY2bdrEmDFjOProo1tdpao0VHO2/TFjxoxYvHhxq6thZtannp4eTjrpJDZu3Mi4ceO46qqrWjZHL+neiJhRLc1famZmNkBtbW3MmjULScyaNWtYfhALnroxM9shs2fPZtWqVcyePbvVVanJgd7MbAe0tbVx8cUXt7oaffLUjZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck1FOglHSPpYUldks6vkr6bpGslLZN0t6SDCml/LukBSfdLulLS+GY2wMzM+lY30EsaDVwCzAKmAydLml6R7XPA0oh4K3AK8PW87hRgLjAjIg4CRgMnNa/6ZmZWTyMj+kOBrohYGREbgauAYyvyTAduAYiIh4CpkvbMaTsBEyTtBEwEnmxKzc3MrCGNBPopwOOF1915WdF9wAkAkg4F9gPaI+IJ4KvAr4HVwHMRcVO1jUg6S9JiSYvXrFnTv1aYmVlNjQR6VVkWFa8vAnaTtBSYAywBNkvajTT63x/YG3iNpI9X20hEXBoRMyJixuTJkxutv5mZ1bFTA3m6gX0Kr9upmH6JiPXAaQCSBDyaHx8EHo2INTntGuD3gH/b4ZqbmVlDGhnR3wNMk7S/pLGkD1OvK2aQNCmnAZwJ3JaD/6+Bd0mamC8ARwAPNq/6ZmZWT90RfURslnQOcCPprpn5EfGApLNz+jzgQOAKSVuA5cAZOe0uSVcDvwA2k6Z0Lh2UlpiZWVWKqJxub70ZM2bE4sWLW10NM7MRQ9K9ETGjWpr/M9bMrOQc6M3MSs6B3sys5Bq5vdLMSqyzs5Ourq5+rdPd3Q1Ae3t7v9br6Ohg7ty5/VrHdpwDvZn124YNG1pdhT4N5OIF6QI2VG2bMGFCvy+UMLCLpe+6MbN+6w00nZ2dLa5JdSeccAI9PT2trsagaGtr45prrnnV8r7uuvGI3sxKZ9KkSQMamb/88sts3bq1X+v05h81qn8feY4aNYpx48b1ax1IbesvB3ozK5358+cPaL2yfl7hQG9mlpX1g2LfXmlmVnIO9GZmJedAb2ZWcg70ZmYl50BvZlZyDvRmZiXnQG9mVnIO9GZmJedAb2ZWcg70ZmYl50BvZlZyDvRmZiXnQG9mVnIO9GZmJedAb2ZWcg70ZmYl50BvZlZyDvRmZiXnQG9mVnIO9GZmJedAb2ZWcg70ZmYl50BvZlZyDvRmZiXnQG9mVnIO9GZmJddQoJd0jKSHJXVJOr9K+m6SrpW0TNLdkg7Kyw+QtLTwWC/pz5rcBjMz68NO9TJIGg1cAhwFdAP3SLouIpYXsn0OWBoRx0t6c85/REQ8DBxcKOcJ4NrmNsHMzPrSyIj+UKArIlZGxEbgKuDYijzTgVsAIuIhYKqkPSvyHAE8EhGP7WCdzcysHxoJ9FOAxwuvu/OyovuAEwAkHQrsB7RX5DkJuLLWRiSdJWmxpMVr1qxpoFpmZtaIRgK9qiyLitcXAbtJWgrMAZYAm18pQBoLfAT4Qa2NRMSlETEjImZMnjy5gWqZmVkj6s7Rk0bw+xRetwNPFjNExHrgNABJAh7Nj16zgF9ExNM7VFszM+u3Rkb09wDTJO2fR+YnAdcVM0ialNMAzgRuy8G/18n0MW1jZmaDp+6IPiI2SzoHuBEYDcyPiAcknZ3T5wEHAldI2gIsB87oXV/SRNIdO58chPqbmVkdjUzdEBE3ADdULJtXeH4HMK3Gui8Ce+xAHc3MbAf4P2PNzErOgd7MrOQc6M3MSs6B3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOQa+ocpMxv+Ojs76erqGpJtrVixAoC5c+cOyfY6OjqGbFtl5EBvVhK33nora3t6GFs/6w7r/WraB5cuHfRtbQS6u7sd6HeAA71ZiYwF9mp1JZpsdasrUAIO9GYl0d7ezrqeHs6o+hMSI9dlBJPaK3/HyPrDH8aamZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVXEOBXtIxkh6W1CXp/Crpu0m6VtIySXdLOqiQNknS1ZIekvSgpMOa2QAzM+tb3UAvaTRwCTALmA6cLGl6RbbPAUsj4q3AKcDXC2lfB34cEW8G3gY82IyKm5lZYxoZ0R8KdEXEyojYCFwFHFuRZzpwC0BEPARMlbSnpF2Bw4HLctrGiFjXrMqbmVl9jQT6KcDjhdfdeVnRfcAJAJIOBfYD2oE3AGuAyyUtkfSvkl5TbSOSzpK0WNLiNWvW9LMZZmZWSyOBXlWWRcXri4DdJC0F5gBLgM3ATsA7gH+JiLcDLwCvmuMHiIhLI2JGRMyYPHlyg9U3M7N6dmogTzewT+F1O/BkMUNErAdOA5Ak4NH8mAh0R8RdOevV1Aj0ZmY2OBoZ0d8DTJO0v6SxwEnAdcUM+c6asfnlmcBtEbE+Ip4CHpd0QE47AljepLqbmVkD6o7oI2KzpHOAG4HRwPyIeEDS2Tl9HnAgcIWkLaRAfkahiDnAd/KFYCV55G9mZkOjkakbIuIG4IaKZfMKz+8AptVYdykwY+BVNDOzHeH/jDUzKzkHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5JzoDczKzkHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5JzoDczK7mGvr3SzEaGp4DLXvUDcM23Nv/dY9C3lNo0aQi2U2YO9GYl0dHRMWTbWrNiBQCTplX9dvKmmsTQtq2MHOjNSmLu3LkDWq+zs5Ourq4m16a6jo6OAdfTBs6B3sz6bcKECa2ugvWDA73ZbzmPsMvPd92YmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWckpYvC/AKm/JK0BHmtxNdqAnhbXYbhwX2zjvtjGfbHNcOiL/SJicrWEYRnohwNJiyNiRqvrMRy4L7ZxX2zjvthmuPeFp27MzErOgd7MrOQc6Gu7tNUVGEbcF9u4L7ZxX2wzrPvCc/RmZiXnEb2ZWck50JuZlVypA72SUrfRho6kGZI6+0jfW9LVQ1mn4UzSqZK+kZ9fIOkzra5Ts0iaK+lBST+UdIekl4dz+0oXBCVNzTvgm8AvgMsk3S/pl5I+Vsh3Xl52n6SL+ijvE5Luyfl+KGliXr5A0omFfM/3t+yRSlIpfplM0uj+5I+IxRFR8+eYIuLJiDixVvpI4QFSQ/438CHgU8Bc4KutrU7fyrozDwCuAL4EtANvA44E/lHSXpJmAccB74yItwFf6aOsayLikJzvQeCMvjbcz7KbTtKPJN0r6QFJZ+Vlx0j6Rb7w3JKX7Szp8nxBWibpD/Ly4gXrREkL8vMFkr4m6b+AL0s6VNLPJS3Jfw/I+UZL+mqh3DmSjpB0baHcoyRdM8j9MFXSQ5IW5npcLWmipFWS/o+k24GPSjo6j8h+IekHknbO6x+S23WfpLsl7SJppqTrc/r7JC3NjyU5faqk+3P6+EL/LpH0/rz8VEnXSPqxpBWShvT4qKXKAOkLeYCzTNIXC/lOycvuk/TtvOz3Jd2V2/mfkvZsVTuGgqR5wBuA64A/joh7gE2trVXfSjEyq+KxiLhT0j8BV0bEFuBpST8FDgHeB1weES8CRMQzfZR1kKQvAZOAnYEb62z7yH6UPRhOj4hnJE0A7pH078C3gMMj4lFJu+d8XwCei4i3AEjarYGy3wQcGRFbJO2ay9ws6UjgH4A/AM4C9gfentN2B54FLpE0OSLWAKcBlzexzbUcAJwRET+TNJ80CgN4KSLeI6kNuCa36QVJnwXOze/Cvgd8LCLuyW3dUFH2Z4BP57J3Bl6qSP80QES8RdKbgZskvSmnHQy8HXgZeFjSxRHxeFNbPjAHkPbNj4ATgUMBAddJOhxYC/w18O6I6CkcS7cD74qIkHQmcB7wF0Nd+aESEWdLOgZ4f0S0+msPGlLWQP9C/qsa6QIava90AXBcRNwn6VRgZl6+mfyOSJKAsQMoezDMlXR8fr4PKfDeFhGPwnYXniOBk3pXiohnGyj7B/miCfBaYKGkaaT2jimUOy8iNhe3l0d/H5d0OXAYcMoA29cfj0fEz/LzfyO9xYYUxAHeBUwHfpZ2IWOBO0gBb3UeqRER63MbimX/DPiapO+Q3vV1V6S/B7g4r/+QpMdIF0qAWyLiuVzmcmA/YDgE+t4B0leBo4ElefnOwDTSO+Ore4Nb4VhqB74naS9SHz46tNW2eso6ddPrNuBjeTphMnA4cDdwE3C6ts23795HGbsAqyWNAf64sHwV8Lv5+bFsC3T9KbupJM0kBdrD8rTREuA+ql94al2QisvGV6S9UHj+d8B/RcRBwO8X8tYq93Lg48DJpAvG5r7a0iSV9eh9XRwI3BwRB+fH9Ig4gwYu1hFxEXAmMAG4M4/ai2oNMiCN5HttYfgMuIr9cmGhXzoi4jJq98vFwDfyu8NP8urjxlqs7IH+WmAZKdj9BDgvIp6KiB+T5tcWS1pKehteyxeAu4CbgYcKy78FvE/S3cA7ySdJP8tuttcCz0bEiznwvAsYl+u5P2x34bkJOKd3xcLUzdOSDlT6MO54anst8ER+fmph+U3A2cof2PZuLyKeBJ4EPk96lzQU9pV0WH5+MmmKoehO4N2SOgDyHP6bSPt5b0mH5OW7qOIDaElvjIhfRsSXgcVAZaC/jTwwyGXuCzzcvKYNqhtJg5XezyumSHodcAvwh5L2yMt7j6XisTB7qCtrDYgIP0ryIAX1RaSL2w+AW0lTTbPYNrq/OefdGVgI3J+Xn5CXnwg8ktf9BrAgL18AnFjY1mHAr0hTGH8HrMrLdwK+BizP5Z5TWOck4M4h6oupuQ7zcn/8EJhIeifWVsj3AeCenGcZ8JG8/BDSheC+/Hfn3JfX5/SLC313Ze77qcD9OX187rNf5r5/f15+Kmn027v964GZw+DYeaXu+fWf5rr/kjSd9ca8fHah3b3HxrHASuC/gX8Ebq1sK3AB8JlWt7OJ/bWK9NXErwe6gfXAuvx811bXr/Lhr0CwIaN0T/WSSNMAg72tqaSgfNBgb8tsuBsuc4MtJ+kS4N0Vi78eEUNxd0jpSbqXNL1V2rsxzIYrj+jNzEqu7B/Gmpn91nOgNzMrOQd6M7OSc6A3Mys5B3ozs5L7/391wri087gqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_scores(X_train, X_test, y_train, y_test, model5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier best recall_score: 0.934783, using: {'ccp_alpha': 0, 'criterion': 'gini', 'max_depth': 8, 'max_features': 'sqrt', 'min_samples_leaf': 3}\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(random_state=42)\n",
    "\n",
    "params_grid = {\"criterion\": [\"gini\", \"entropy\", \"log_loss\"],\n",
    "               \"max_depth\": np.arange(1, 9),\n",
    "               \"min_samples_leaf\": np.arange(1, 6),\n",
    "               \"max_features\": [\"sqrt\", \"log2\"],\n",
    "               \"ccp_alpha\": [0, 0.05, 0.1]}\n",
    "\n",
    "model6 = optimize_params(rfc, params_grid, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_scores(X_train, X_test, y_train, y_test, model6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC best recall_score: 0.966304, using: {'C': 1, 'degree': 2, 'gamma': 'auto', 'kernel': 'poly'}\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(random_state=42)\n",
    "\n",
    "params_grid = {'C': [0.1, 1, 5, 10],\n",
    "               'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
    "               'gamma': ['scale', 'auto'],\n",
    "               'degree': [2, 3, 4]}\n",
    "\n",
    "model7 = optimize_params(svc, params_grid, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3aPu0cVmbSx9QfdgOGjSIQYMGrVx/4403ss8++7D99tt3TaMLsJ+aYz81r61RhDvvvHOVMi2jCAceeOAqowi9evWiX79+nHDCCcyZM4d9992XCy64gDe/+c1dvRur6TCMI+Ia4GBgu4hYBPwbsBlAZk4FZgBHAAuApcAJG6qxWt1VS64q3YQudcw2x5RuArDqpY/+/fszffp0rr766lXKLFmyhC222II+ffqsdn0d4JprrunxQ4r2U3Psp+atzyjCa6+9xj333MPkyZMZO3YskyZN4txzz+Xzn/98VzW/XR2GcWau8dWt7w47pdNaVNuUQqa7BIyatz6XPgCWLl3KT3/6Uy6++OJSu9Al7Kfm2E/NW59RhKVLlzJgwADGjh0LwPjx49u8AayEaOsooyuMHj061/SrTYZxczalfgIPXKRN3fLly9lzzz2ZOXMm/fv3Z8yYMVx99dUMHz58ZZnGUYRLL72UX/ziF0ybNg2Ad77znVx22WUMHjyYs846i5dffpnzzjuvy9ofEXdn5ujWy3v2tzJINQ9apJ5hfUcRJk+ezDHHHMOyZcvYddddV55Bl2YYS1rFpnTg4qhU87rTAd763JA7atQo1jQqW8om8UMRkiR1Z54ZS5I2qE1pFGFdRxA8M5YkqTDDWJKkwgxjSZIKM4wlSSrMMJYkqTDDWJKkwgxjSZIKM4wlSSrMMJYkqTDDWJKkwgxjSZIKM4wlSSrMMJYkqTDDWJKkwgxjSZIKM4wlSSrMMJYkqTDDWJKkwgxjSZIKM4wlSSrMMJYkqTDDWJKkwgxjSZIKM4wlSSrMMJYkqTDDWJKkwgxjSZIKM4wlSSrMMJYkqTDDWJKkwgxjSZIKM4wlSSrMMJYkqTDDWJKkwgxjSZIKM4wlSSrMMJYkqTDDWJKkwpoK44gYFxEPRsSCiDijjfVbR8SPI2JORNwfESd0flMlSeqZOgzjiOgFTAEOB4YBEyJiWKtipwDzMnMkcDDwnxHRp5PbKklSj9TMmfF+wILMfDgzlwHTgaNalUlgq4gIYEvgeWB5p7ZUkqQeqpkw7g8sbJhfVC9rdBEwFHgS+A0wKTNf75QWSpLUwzUTxtHGsmw1fxhwL7AjMAq4KCL6rlZRxMkRMTsiZi9evHgtmypJUs/UTBgvAnZqmB9AdQbc6ATguqwsAB4BhrSuKDMvyczRmTm6X79+69pmSZJ6lGbC+C5gj4gYVN+UdTRwfasyjwPvBYiI7YHBwMOd2VBJknqq3h0VyMzlEXEqcDPQC7g8M++PiIn1+qnA54ErI+I3VMPap2fmsxuw3ZIk9RgdhjFAZs4AZrRaNrXh8ZPAoZ3bNEmSNg1+A5ckSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhTYVxRIyLiAcjYkFEnNFOmYMj4t6IuD8ift65zZQkqefq3VGBiOgFTAHeBywC7oqI6zNzXkOZbYCvAeMy8/GIeNsGaq8kST1OM2fG+wELMvPhzFwGTAeOalXmr4DrMvNxgMx8pnObKUlSz9VMGPcHFjbML6qXNdoT2DYi/jci7o6I49qqKCJOjojZETF78eLF69ZiSZJ6mGbCONpYlq3mewP7Au8HDgM+GxF7rvakzEsyc3Rmju7Xr99aN1aSpJ6ow2vGVGfCOzXMDwCebKPMs5n5MvByRNwKjAR+2ymtlCSpB2vmzPguYI+IGBQRfYCjgetblfkR8M6I6B0RWwBjgfmd21RJknqmDs+MM3N5RJwK3Az0Ai7PzPsjYmK9fmpmzo+Im4D7gNeByzJz7oZsuCRJPUUzw9Rk5gxgRqtlU1vNnwec13lNkyRp0+A3cEmSVJhhLElSYYaxJEmFGcaSJBVmGEuSVJhhLElSYYaxJEmFGcaSJBVmGEuSVJhhLElSYYaxJEmFGcaSJBVmGEuSVJhhLElSYYaxJEmFGcaSJBVmGEuSVJhhLElSYYaxJEmFGcaSJBVmGEuSVJhhLElSYYaxJEmFGcaSJBVmGEuSVJhhLElSYYaxJEmFGcaSJBVmGEuSVJhhLElSYYaxJEmFGcaSJBVmGEuSVJhhLElSYYaxJEmFGcaSJBVmGEuSVJhhLElSYYaxJEmFGcaSJBVmGEuSVJhhLElSYYaxJEmFNRXGETEuIh6MiAURccYayo2JiBURMb7zmihJUs/WYRhHRC9gCnA4MAyYEBHD2in3JeDmzm6kJEk9WTNnxvsBCzLz4cxcBkwHjmqj3CeB7wPPdGL7JEnq8ZoJ4/7Awob5RfWylSKiP/BBYGrnNU2SpE1DM2EcbSzLVvPnA6dn5oo1VhRxckTMjojZixcvbrKJkiT1bL2bKLMI2KlhfgDwZKsyo4HpEQGwHXBERCzPzB82FsrMS4BLAEaPHt060CVJ2iQ1E8Z3AXtExCDgCeBo4K8aC2TmoJbHEXElcEPrIJYkSW3rMIwzc3lEnEp1l3Qv4PLMvD8iJtbrvU4sSdJ6aObMmMycAcxotazNEM7M49e/WZIkbTr8Bi5JkgozjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKswwliSpMMNYkqTCmgrjiBgXEQ9GxIKIOKON9cdExH31dFtEjOz8pkqS1DN1GMYR0QuYAhwODAMmRMSwVsUeAQ7KzL2AzwOXdHZDJUnqqZo5M94PWJCZD2fmMmA6cFRjgcy8LTNfqGfvAAZ0bjMlSeq5mgnj/sDChvlF9bL2nATc2NaKiDg5ImZHxOzFixc330pJknqwZsI42liWbRaMeDdVGJ/e1vrMvCQzR2fm6H79+jXfSkmSerDeTZRZBOzUMD8AeLJ1oYjYC7gMODwzn+uc5kmS1PM1c2Z8F7BHRAyKiD7A0cD1jQUiYmfgOuDjmfnbzm+mJEk9V4dnxpm5PCJOBW4GegGXZ+b9ETGxXj8V+BzwVuBrEQGwPDNHb7hmS5LUczQzTE1mzgBmtFo2teHxJ4BPdG7TJEnaNPgNXJIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhhrEkSYUZxpIkFWYYS5JUmGEsSVJhTYVxRIyLiAcjYkFEnNHG+oiIC+v190XEPp3fVEmSeqYOwzgiegFTgMOBYcCEiBjWqtjhwB71dDLw9U5upyRJPVYzZ8b7AQsy8+HMXAZMB45qVeYoYFpW7gC2iYgdOrmtkiT1SM2EcX9gYcP8onrZ2paRJEltiMxcc4GIjwCHZeYn6vmPA/tl5icbyvw38MXM/GU9PxP4TGbe3aquk6mGsQEGAw921o50ku2AZ0s3YiNhXzXHfmqefdUc+6k53bWfdsnMfq0X9m7iiYuAnRrmBwBPrkMZMvMS4JImtllERMzOzNGl27ExsK+aYz81z75qjv3UnI2tn5oZpr4L2CMiBkVEH+Bo4PpWZa4Hjqvvqt4feDEzn+rktkqS1CN1eGacmcsj4lTgZqAXcHlm3h8RE+v1U4EZwBHAAmApcMKGa7IkST1LM8PUZOYMqsBtXDa14XECp3Ru04rotkPo3ZB91Rz7qXn2VXPsp+ZsVP3U4Q1ckiRpw/LrMCVJKqzHhXF9E1mP2y9tnCJidERcuIb1O0bE97qyTRuriDg+Ii6qH58VEaeVblNXiIhPRcT8iPh+RNweEa9uKvu+LiLi3yPikA7KfKCtr3Yuqalrxt1dRAwEbgR+BhwA3BsRY4AEvpCZ19blPgN8HHgduDEz23wxIuJvqP4eug/VTWkfz8ylEXElcENmfq8u91Jmbrk2dfdUEdE7M5eXbseGFhG9MnNFs+UzczYwew3rnwTGd0bbuquICKpLYq+XbstG6u+pvnL4ZWAX4C+Ltqaby8zPNVHmelb/q6CietIZ5GBgGvAFqr9zHgkcApwXETtExOFUb+KxmTkS+PIa6rouM8fU5eYDJ61pw2tZd5eLiB9GxN0RcX/9xSstP/5xT0TMqb+khYjYMiKuiIjf1D/48eF6+UsNdY2vD0qIiCsj4r8i4mfAlyJiv4i4LSJ+Xf87uC7XKyK+0lDvJyPivRHxg4Z63xcR13Vdr6wuIgZGxAMR8c26nd+LiC0i4tGI+FxE/BL4SEQcWp+h3BMR342IlgOyMfV+z4mIWRGxVUQcHBE31OsPioh76+nX9fqBETG3Xv/nDf3/64h4d738+Ii4LiJuioiHIqJbvb/aUu/X/Ij4GnAP8NmIuKvu17Mbyh1XL5sTEd+qlx0ZEXfWfXBLRGxfaj9Ki4ipwK5UwXFMZt4FvFa2Vd1HRHy2/j/704i4JiJOqz+XxtfrH42Is+v/q7+JiCH18pWjLN1Fjzgzrj2WmXdExFeBa+qzl6cj4ufAGOAg4IrMXAqQmc+voa4REfEFYBtgS6o/61qTQ9ai7hJOzMznI2Jz4K6I+BFwKfCuzHwkIt5Sl/ss1d+Ivx0gIrZtou49gUMyc0VE9K3rXB7VMNE5wIepRhkGAXvX694CvABMiYh+mbmY6s/hrujEfV5Xg4GTMvNXEXE51VkJwJ8y88CI2A64jmqfX46I04F/iIhzgWuBj2XmXXVfvNKq7tOAU+q6twT+1Gr9KQCZ+fb6Q+MnEbFnvW4UsDfwKvBgREzOzIV0b4OpXtcfUp397wcEcH1EvAt4DvgX4B2Z+WzD+/CXwP6ZmRHxCeAzwD92deO7g8ycGBHjgHdnZnf8NqliImI01efL3lRZdg9wdxtFn83MfSLi76n+D36i61rZvJ4Uxi/X/0Y764Nq2LoZVwJ/mZlzIuJ44OB6+XLq0YSICKph7LWtu4RPRcQH68c7UYXjrZn5CKxy8HAI1Ze6UC9/oYm6v9swbLs18M2I2IOqPzZrqHdqyzB2y/bqM6FjI+IKqssLx63j/nWmhZn5q/rxt4FP1Y+vrf/dn+rXy35VvQXoA9xOFTxP1WcuZOYfAOoyLX4F/FdEXEU1+rKo1foDgcn18x+IiMeoDnYAZmbmi3Wd86iGK7t7GLccIH8FOBT4db18S6pfeBsJfK8lZBrehwOAa6P6sZk+wCNd22xtJA4EfpSZrwBExI/bKdcy4nY38KGuaNi66EnD1C1uBT5WD432A94FzAJ+ApwYEVsANByFt2Ur4KmI2Aw4pmH5o8C+9eOjeCNs1qbuLhURB1OF4QH1EPqvgTm0ffDQ3kFF47I/b7Xu5YbHnwd+lpkjgCMbyrZX7xXAscAEqlDvDtecW7ezZb7xYO+nmTmqnoZl5kk0cUCWmedSHZVvDtzRMmTWoL0DSajOiFusYOM4kG7ssy829NnumfkN2u+zycBF9QjN37L6e06CNf9/adTyf6db/7/piWH8A+A+qsD5H6ofrPh9Zt5Edd1ldkTcSzVc0Z7PAncCPwUeaFh+KXBQRMwCxlJ/2Kxl3V1ta+CF+ga0IVRndm+i2o9BsMrBw0+AU1ue2DBM/XREDI3qLvUP0r6tgSfqx8c3LP8JMDEiejdur7556UngX6lGI7qDnSPigPrxBKoh00Z3AO+IiN0BorqmvCfV+2THqG4cpL4evMp//IjYLTN/k5lforqpq3UY30p98FfXuTPd78dU1sXNVAerLdfW+0fE24CZwEcj4q318pb3YeP76K+7urHaaPwSOLK+12JL4P2lG7Q+uu1RwtrIzEeBEfXjBP6pnlqXOxc4t4n6vg58vY3lT1OFWYsz17buAm6iCsL7qD7Y7wAWUw1VX1cH7DPA+6hufptS31C0AjibaojnDOAGqmHRuVTDjG35MtUw9T9QHQi1uIxquPW+iHiN6qCm5eaJq4B+mTmvc3Z3vc0H/joiLgYeonofrPyFssxcXF+6uCYi3lQv/tfM/G1EfAyYXF+bf4VqRKLRp6O6KWsFMI/qLwAaf/f7a8DUiPgN1SWR4zPz1VZD2RudzPxJRAwFbq/35SXg2Pprdf8D+HlErKAatTkeOAv4bkQ8QfV+HVSk4d1MRPwF1UFcX+D1iPg0MKzlksimpr4343qqE6/HqPrmxbKtWnd+A5eKqu9o/HU9bFm6LQOp/nRtROm2SOpYRGyZmS/VlwhvBU7OzHtKt2td9Igz43UVEVOAd7RafEFmdoe7enu8iLibaqh/k7xTVtJ6uyQihlHdV/DNjTWIwTNjSZKK64k3cEmStFExjCVJKswwliSpMMNYkqTCDGNJkgozjCVJKuz/A/CjHp/LQfMYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_scores(X_train, X_test, y_train, y_test, model7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_xg, X_test_xg = X_train.copy(), X_test.copy() \n",
    "\n",
    "X_train_xg.columns = [str(col).replace('[', '').replace(']', '').replace('<', '').replace('>', '') for col in X_train.columns]\n",
    "X_test_xg.columns = [str(col).replace('[', '').replace(']', '').replace('<', '').replace('>', '') for col in X_test.columns]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier best recall_score: 0.976630, using: {'gamma': 1, 'max_depth': 4, 'reg_alpha': 0, 'reg_lambda': 0.5, 'subsample': 0.8}\n"
     ]
    }
   ],
   "source": [
    "xgb = XGBClassifier(random_state=42)\n",
    "\n",
    "params_grid = param_grid = {'max_depth': np.arange(3, 11),\n",
    "                            'subsample': [0.8],\n",
    "                            'gamma': [0, 0.2, 0.5, 1],\n",
    "                            'reg_alpha': [0, 0.5, 1],\n",
    "                            'reg_lambda': [0, 0.5, 1]}\n",
    "\n",
    "model8 = optimize_params(xgb, params_grid, X_train_xg, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_scores(X_train_xg, X_test_xg, y_train, y_test, model8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n",
      "/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:770: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 761, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 216, in __call__\n",
      "    return self._score(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
      "    y_pred = method_caller(estimator, \"predict\", X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
      "    return getattr(estimator, method)(*args, **kwargs)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py\", line 214, in predict\n",
      "    neigh_dist, neigh_ind = self.kneighbors(X)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py\", line 752, in kneighbors\n",
      "    chunked_results = list(\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1717, in pairwise_distances_chunked\n",
      "    D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1889, in pairwise_distances\n",
      "    return _parallel_pairwise(X, Y, func, n_jobs, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 1430, in _parallel_pairwise\n",
      "    return func(X, Y, **kwds)\n",
      "  File \"/Users/julia/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/pairwise.py\", line 788, in haversine_distances\n",
      "    return DistanceMetric.get_metric(\"haversine\").pairwise(X, Y)\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 406, in sklearn.metrics._dist_metrics.DistanceMetric.pairwise\n",
      "  File \"sklearn/metrics/_dist_metrics.pyx\", line 1016, in sklearn.metrics._dist_metrics.HaversineDistance._validate_data\n",
      "ValueError: Haversine distance only valid in 2 dimensions\n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier best recall_score: 0.916304, using: {'leaf_size': 10, 'metric': 'cosine', 'n_neighbors': 2, 'weights': 'distance'}\n"
     ]
    }
   ],
   "source": [
    "knc = KNeighborsClassifier()\n",
    "\n",
    "params_grid = {\"n_neighbors\": np.arange(2, 9),\n",
    "                            \"weights\": [\"uniform\", \"distance\"],\n",
    "                            \"metric\": [\"cosine\", \"euclidean\", \"haversine\", \"manhattan\", \"minkowski\"],\n",
    "                            \"leaf_size\": np.arange(10, 51, 10)}\n",
    "\n",
    "model9 = optimize_params(knc, params_grid, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eaI6IJ4CvAo8Dq4HnIuLmna209Z2NGzf6moXZAFdPi15VxkXF50uByyQtJvXDLwK25L7744GDgbXAjySdEhHfecVMpLOAswDGjRtXb/2tG3y9wmz3VE+Lvg0YW/jcTEX3S0Ssi4jpEXEkqY9+NLACOBZYERHtEbEZuAH4w2oziYgrI2JyREwePXp095fEzMyqqifQLwAmSjpY0hDSxdS5xQySRuQ0SHfYzI+IdaQum6MkDZMk4BjggcZV38zMutJl101EbJF0DnAT6a6ZqyNiqaSzc/os4HDgWklbSRdpz8hpd0u6HvgdsIXUpXNlryyJmZlVVdd99BExD5hXMW5WYfhOYGKNaT8HfG4n6mhmZjvBz7oxMys5PwJhAOrJ/fA9tXz5cqBnd+z0RI/uv1/bR++MXZ//D+/9WbGWV97EbNZDDvQDUGtrK/+75HeMG7611+c1ZHMKoJseXdDr83p8/eBuT9PS0tILNamu46Q3cUzVXsrGGtO3y2bl5kA/QI0bvpXPTF7fdcYB5OKF3W8q9+Wvb/2bAhuo3EdvZlZyDvRmZiXnrpsBqK2tjReeH9yjro5d2WPPD2av/BA1M2sct+jNzEpuwLTofUvhds3NzWzasrqUF2OHdvNxyGbWtQET6FtbW1l0/zK2Dduv1+ell9LDOe95+Mlen9egDc/2+jzMbPc2YAI9wLZh+7Fp0vv7uxoNNXTZL3o03ePru99H/9SGQWzaWu2p0403dHBwwLBt3Zrm8fWDObSX6mO2OxtQgd6Snv6QZnBbG4P66CUig5uaut0Ncyj+kZBZb3CgH4D8ij4z6w7fdWNmVnIO9GZmJedAb2ZWcu6jt91ST36X0dPfV/To0ctmDeRAb1anpqam/q6CWY840NtuyS1s2524j97MrOQc6M3MSs6B3sys5BzozcxKzoHezKzk6rrrRtJxwGXAYOBbEXFpRfpI4GrgEGATcHpELMlpI4BvAUcAkdPubNQCmJk1Sk9+X9GW34rW3M2H+PXl7yu6DPSSBgNXAO8F2oAFkuZGxLJCtk8DiyPig5Jel/Mfk9MuA34ZESdKGgIM60lF29raGLThuR4/1ndXNWjDM7S1benvaphZD23soyfC7ox6WvRTgNaIeARA0nXA8UAx0E8CLgGIiAclTZB0ALAReBdwWk57CXipYbU3M2ugnrSwO6aZOXNmo6vTMPUE+jHAysLnNuBtFXnuBU4A7pA0BRgPNANbgXbgGklvAu4BzouIFypnIuks4CyAcePGvaISzc3NPPXiHqV88Uhz84H9XQ0zK7F6LsZWeyVRVHy+FBgpaTFwLrAI2EI6kbwF+I+IeDPwAnBhtZlExJURMTkiJo8ePbrO6puZWVfqadG3AWMLn5uBVcUMEbEOmA4gScCK/DcMaIuIu3PW66kR6M3MrHfU06JfAEyUdHC+mHoSMLeYQdKInAZwJjA/ItZFxJPASkmH5bRj2LFv38wGoDVr1nDuuefyzDPP9HdVrA5dBvqI2AKcA9wEPAD8MCKWSjpb0tk52+HAUkkPAlOB8wpFnAt8V9J9wJHAlxpYfzPrB3PmzOG+++5jzpw5/V0Vq0Nd99FHxDxgXsW4WYXhO4GJNaZdDEzueRXNbFeyZs0abrzxRiKCG2+8kWnTprH//vv3d7WsEwPqMcWDNjzbJ/fRa9M6AGLoPr0+r0EbngV8140NHHPmzCEi3Y+xbds25syZw/nnn9/PtbLODJhA39LS0mfzWr78eQAmHtIXAfjAPl02s511yy23sHnzZgA2b97MzTff7EC/ixswgb6nPxXuyU+ae8qvjLPdwXvf+17mzZvH5s2b2XPPPXnf+97X31WyLvihZlU0NTX5tXFmNUybNo10FzUMGjSIadOm9XONrCsDpkXfU25hmzXWqFGjmDp1KnPnzmXq1Km+EDsAlD7Qm1njTZs2jUcffdSt+QHCgd7Mum3UqFFcfvnl/V0Nq5P76M3MSs6B3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOR8e6WZlU5fPvpk+fLlQN/9OLMnj1pxoDez0mltbWXp/Q8wYtire31e215Kj4N44uHefwnL2g1P92g6B3ozK6URw17Ne153Un9Xo6Fue/C6Hk3nPnozs5JzoDczKzkHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5Kr6z56SccBlwGDgW9FxKUV6SOBq4FDgE3A6RGxpJA+GFgIPBER729Q3c3Mqmpra+O5Dc/3+L7zXdXaDU8TbRu7PV2XLfocpK8ApgKTgJMlTarI9mlgcUS8ETiVdFIoOg94oNu1MzOznVZPi34K0BoRjwBIug44HlhWyDMJuAQgIh6UNEHSARHxlKRm4M+AfwbOb2jtzcyqaG5uRi8+U8pfxo5p7v7L2Ovpox8DrCx8bsvjiu4FTgCQNAUYDzTntH8DPgls62wmks6StFDSwvb29jqqZWZm9agn0KvKuKj4fCkwUtJi4FxgEbBF0vuBpyPinq5mEhFXRsTkiJg8evToOqplZmb1qKfrpg0YW/jcDKwqZoiIdcB0AEkCVuS/k4C/kPSnwFBgH0nfiYhTGlB3MzOrQz0t+gXAREkHSxpCCt5zixkkjchpAGcC8yNiXUT8Y0Q0R8SEPN1/O8ibmfWtLlv0EbFF0jnATaTbK6+OiKWSzs7ps4DDgWslbSVdpD2jF+tsZmbdUNd99BExD5hXMW5WYfhOYGIXZdwO3N7tGpqZ2U7xi0fMrJTWbni6T34wtX7T7wEYPnRkr89r7YanGUP3b690oDez0mlpaemzeS1f/iwAYw7pfgDurjHs36Nlc6A3s9Lp6Yu6+/Kl4j15yXdPOdCbme2Epqam/q5Clxzozcyyvmph9zU/ptjMrOQc6M3MSs6B3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOQc6M3MSs6B3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOQc6M3MSs6B3sys5BzozcxKzoHezKzkHOjNzEqurkAv6ThJD0lqlXRhlfSRkn4i6T5Jv5V0RB4/VtJtkh6QtFTSeY1eADMz61yXgV7SYOAKYCowCThZ0qSKbJ8GFkfEG4FTgcvy+C3ABRFxOHAU8Ikq05qZWS+qp0U/BWiNiEci4iXgOuD4ijyTgFsBIuJBYIKkAyJidUT8Lo9/HngAGNOw2puZWZfqCfRjgJWFz228MljfC5wAIGkKMB5oLmaQNAF4M3B3D+tqZmY9UE+gV5VxUfH5UmCkpMXAucAiUrdNKkAaDvwY+D8Rsa7qTKSzJC2UtLC9vb2eupuZWR32qCNPGzC28LkZWFXMkIP3dABJAlbkPyTtSQry342IG2rNJCKuBK4EmDx5cuWJxMzMeqieFv0CYKKkgyUNAU4C5hYzSBqR0wDOBOZHxLoc9K8CHoiIrzey4mZmVp8uW/QRsUXSOcBNwGDg6ohYKunsnD4LOBy4VtJWYBlwRp78HcDHgPtztw7ApyNiXmMXw8zMaqmn64YcmOdVjJtVGL4TmFhlujuo3sdvZmZ9xL+MNTMrOQd6M7OSc6A3Mys5B3ozs5JzoDczKzkHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5Kr6xEIZrbrmzlzJq2trd2erq2tjY0bN/ZCjV6pqamJ5ubmrjNWaGlpYcaMGb1Qo92DA71ZSbS2tvLg4sUc2M3pXqTw8ohe9uILL7B2zZpuTfNkL9Vld+JAb1YiBwJnlOw5gle94j1H1l3uozczKzkHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5JzoDczKzkHejOzknOgNzMrOQd6M7OSc6A3Myu5ugK9pOMkPSSpVdKFVdJHSvqJpPsk/VbSEfVOa2ZmvavLQC9pMHAFMBWYBJwsaVJFtk8DiyPijcCpwGXdmNbMzHpRPS36KUBrRDwSES8B1wHHV+SZBNwKEBEPAhMkHVDntGZm1ovqCfRjgJWFz215XNG9wAkAkqYA44HmOqclT3eWpIWSFra3t9dXezMz61I9z6Ov9nDrygdEXwpcJmkxcD+wiPQug3qmTSMjrgSuBJg8ebIfQG3WTW1tbTxP+Z7fvhpY39bW39UY0OoJ9G3A2MLnZmBVMUNErAOmA0gSsCL/DetqWjMz6131BPoFwERJBwNPACcBHylmkDQC2JD74c8E5kfEOkldTmtmjdHc3MzaNWtK+YapET14z6xt12Wgj4gtks4BbgIGA1dHxFJJZ+f0WcDhwLWStgLLgDM6m7Z3FsXMzKqp652xETEPmFcxblZh+E5gYr3TmplZ3/EvY83MSs6B3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOQc6M3MSs6B3sys5BzozcxKzoHezKzkHOjNzEqurmfdmNnA8CR98zz6Z/L//Xt9TmmZRvTBfMrMgd6sJFpaWvpsXu3LlwMwYmLVZxk21Aj6dtnKyIHerCRmzJjR5/OaOXNmn83Tes599GZmJedAb2ZWcg70ZmYl50BvZlZyDvRmZiXnQG9mVnIO9GZmJedAb2ZWcg70ZmYlV1egl3ScpIcktUq6sEr6vpJ+LuleSUslTS+k/V0et0TS9yUNbeQCmJlZ57oM9JIGA1cAU4FJwMmSJlVk+wSwLCLeBBwNfE3SEEljgBnA5Ig4AhgMnNTA+puZWRfqadFPAVoj4pGIeAm4Dji+Ik8Ae0sSMBx4FtiS0/YAmiTtAQwDVjWk5mZmVpd6Av0YYGXhc1seV/QN4HBSEL8fOC8itkXEE8BXgceB1cBzEXFztZlIOkvSQkkL29vbu7kYZmZWSz2BXlXGVT7w+k+AxcBBwJHANyTtI2kkqfV/cE7bS9Ip1WYSEVdGxOSImDx69Og6q29mZl2pJ9C3AWMLn5t5ZffLdOCGSFqBFcDrgGOBFRHRHhGbgRuAP9z5apuZWb3qeR79AmCipIOBJ0gXUz9Skedx4Bjg15IOAA4DHiF9GzhK0jBgY86zsEF1N7MGmDlzJq2trd2aZnl+8Uh3n4Hf0tLSp8/Nt6TLQB8RWySdA9xEumvm6ohYKunsnD4L+CIwW9L9pOD+qYhYA6yRdD3wO9LF2UXAlb2zKGbWV5qamvq7CtYNiuj990t21+TJk2PhQjf8zczqJemeiJhcLc2/jDUzKzkHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5JzoDczKzkHejOzktslfzAlqR14rJ+rMQpY08912FV4XWzndbGd18V2u8K6GB8RVZ8IuUsG+l2BpIW1fmW2u/G62M7rYjuvi+129XXhrhszs5JzoDczKzkH+tr8lM3tvC6287rYzutiu116XbiP3sys5NyiNzMrOQd6M7OSK3WgV1LqZbS+I2mypJmdpB+U36hmgKTTJH0jD18k6e/7u06NImmGpAck/VjSnZJe3JWXr3RBUNKEvAH+nfQKw6skLZF0v6QPF/J9Mo+7V9KlnZT315IW5Hw/zu+/RdJsSScW8q3vbtkDlaR63jW8y5M0uDv5I2JhRNR84WlErIqIE2ulDxRuINXlb4E/Bf4GmAF8tX+r07mybszDgGuBi4Fm4E3AscC/SHqNpKnAB4C3RcSbgK90UtYNEfHWnO8B4IzOZtzNshtO0k8l3SNpqaSz8rjjJP0un3huzeOGS7omn5Duk/SXeXzxhHWipNl5eLakr0u6DfiypCmS/kfSovz/sJxvsKSvFso9V9Ixkn5SKPe9km7o5fUwQdKDkubkelwvaZikRyX9k6Q7gL+S9L7cIvudpB9JGp6nf2ternsl/VbS3pKOlvSLnP5uSYvz36KcPkHSkpw+tLB+F0l6Tx5/mqQbJP1S0nJJfbp/1FKlgfTZ3MC5T9LnC/lOzePulfTtPO7PJd2dl/O/JB3QX8vRFyTNAl4LzAU+GhELgM39W6vOlaJlVsVjEXGXpH8Fvh8RW4GnJP0KeCvwbuCaiNgAEBHPdlLWEZIuBkYAw0kvSe/Msd0ouzecHhHPSmoCFkj6GfBN4F0RsULSfjnfZ4HnIuINAJJG1lH2ocCxEbFV0j65zC2SjgW+BPwlcBZwMPDmnLYf8HvgCkmjI6IdmA5c08BlruUw4IyI+I2kq0mtMIBNEfFOSaOAG/IyvSDpU8D5+VvYD4APR8SCvKwbK8r+e+ATuezhwKaK9E8ARMQbJL0OuFnSoTntSODNwIvAQ5Iuj4iVDV3ynjmMtG1+CpwITAEEzJX0LuAZ4P8C74iINYV96Q7gqIgISWcCnwQu6OvK95WIOFvSccB7IqK/H3tQl7IG+hfyf9VIF1DvfaWzgQ9ExL2STgOOzuO3kL8RSRIwpAdl94YZkj6Yh8eSAu/8iFgBO5x4jgVO6pgoIn5fR9k/yidNgH2BOZImkpZ3z0K5syJiS3F+ufV3iqRrgLcDp/Zw+bpjZUT8Jg9/h/QVG1IQBzgKmAT8Jm1ChgB3kgLe6txSIyLW5WUolv0b4OuSvkv61tdWkf5O4PI8/YOSHiOdKAFujYjncpnLgPHArhDoOxpIXwXeByzK44cDE0nfjK/vCG6FfakZ+IGk15DW4Yq+rbZ1paxdNx3mAx/O3QmjgXcBvwVuBk7X9v72/TopY29gtaQ9gY8Wxj8K/EEePp7tga47ZTeUpKNJgfbtudtoEXAv1U88tU5IxXFDK9JeKAx/EbgtIo4A/ryQt1a51wCnACeTThhbOluWBqmsR8fnYkPglog4Mv9NiogzqONkHRGXAmcCTcBdudVeVKuRAakl32Eru06Dq7heLimsl5aIuIra6+Vy4Bv52+HHeeV+Y/2s7IH+J8B9pGD338AnI+LJiPglqX9toaTFpK/htXwWuBu4BXiwMP6bwLsl/RZ4G/kg6WbZjbYv8PuI2JADz1HAq3I9D4YdTjw3A+d0TFjounlK0uFKF+M+SG37Ak/k4dMK428Gzla+YNsxv4hYBawCPkP6ltQXxkl6ex4+mdTFUHQX8A5JLQC5D/9Q0nY+SNJb8/i9VXEBWtIhEXF/RHwZWAhUBvr55IZBLnMc8FDjFq1X3URqrHRcrxgj6dXArcCHJO2fx3fsS8V9YVpfV9bqEBH+K8kfKajfSDq5/Qi4ndTVNJXtrftbct7hwBxgSR5/Qh5/IvBwnvYbwOw8fjZwYmFebwf+l9SF8UXg0Tx+D+DrwLJc7jmFaU4C7uqjdTEh12FWXh8/BoaRvomNKuT7Y2BBznMf8Bd5/FtJJ4J78//heV3+IqdfXlh338/rfgKwJKcPzevs/rzu35PHn0Zq/XbM/xfA0bvAvvNy3fPn83Ld7yd1Zx2Sx08rLHfHvnE88Ajwa+BfgNsrlxW4CPj7/l7OBq6vR0mPJj4QaAPWAWvz8D79Xb/KPz8CwfqM0j3ViyJ1A/T2vCaQgvIRvT0vs13drtI32O8kXQG8o2L0ZRHRF3eHlJ6ke0jdW6W9G8NsV+UWvZlZyZX9YqyZ2W7Pgd7MrOQc6M3MSs6B3sys5BzozcxK7v8DdeNfkLL7rFwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_scores(X_train, X_test, y_train, y_test, model9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.base import BaseEstimator, TransformerMixin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLPTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, **params):\n",
    "        self.mlp = MLPClassifier(**params)\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        self.mlp.fit(X, y)\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        return self.mlp.predict_proba(X)\n",
    "    \n",
    "class CustomClassifierTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, classifier):\n",
    "        self.classifier = classifier\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        self.classifier.fit(X, y)\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        return self.classifier.predict_proba(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPTransformer(alpha=0.001, learning_rate_init=0.001, random_state=42)\n",
    "\n",
    "extra_trees = CustomClassifierTransformer(ExtraTreesClassifier(\n",
    "    bootstrap=False,\n",
    "    criterion='gini',\n",
    "    max_features=0.6,\n",
    "    min_samples_leaf=12,\n",
    "    min_samples_split=3,\n",
    "    n_estimators=100,\n",
    "    random_state=42)\n",
    ")\n",
    "\n",
    "gaussianNB = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('mlp', MLPTransformer()),\n",
       "                ('extra_trees',\n",
       "                 CustomClassifierTransformer(classifier=ExtraTreesClassifier(max_features=0.6,\n",
       "                                                                             min_samples_leaf=12,\n",
       "                                                                             min_samples_split=3,\n",
       "                                                                             random_state=42))),\n",
       "                ('gaussian_nb', GaussianNB())])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('mlp', mlp),\n",
    "    ('extra_trees', extra_trees),\n",
    "    ('gaussian_nb', gaussianNB)\n",
    "])\n",
    "\n",
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_scores(X_train, X_test, y_train, y_test, pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_models(models, X_train, X_test, y_train, y_test):\n",
    "    cross_val_scores = []\n",
    "    model_names = []\n",
    "    recall_scores = []\n",
    "    \n",
    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "    for model in models:\n",
    "        if model.__class__.__name__ == \"XGBClassifier\":\n",
    "            \n",
    "            X_train_xg, X_test_xg = X_train.copy(), X_test.copy() \n",
    "            X_train_xg.columns = [str(col).replace('[', '').replace(']', '').replace('<', '').replace('>', '') for col in X_train.columns]\n",
    "            X_test_xg.columns = [str(col).replace('[', '').replace(']', '').replace('<', '').replace('>', '') for col in X_test.columns]\n",
    "            \n",
    "            cross_val_scores.append(cross_val_score(model, X_train_xg, y_train, cv=skf, scoring=\"recall\"))\n",
    "            y_predict = model.predict(X_test_xg)\n",
    "        else:\n",
    "            cross_val_scores.append(cross_val_score(model, X_train, y_train, cv=skf, scoring=\"recall\"))\n",
    "            y_predict = model.predict(X_test)\n",
    "            \n",
    "    \n",
    "        model_names.append(model.__class__.__name__)\n",
    "        recall_scores.append(recall_score(y_test, y_predict))\n",
    "        \n",
    "        \n",
    "    sns.boxplot(data=cross_val_scores)\n",
    "    plt.title(\"Cross validation recall scores comparision\")\n",
    "    plt.xticks(np.arange(0, len(model_names), 1), model_names, rotation=60)\n",
    "    \n",
    "    \n",
    "    plt.figure(figsize=(8, 5))\n",
    "    ax = sns.barplot(x= model_names, y=recall_scores, color='palegreen')\n",
    "    plt.title('Recall scores on test dataset comparision')\n",
    "    plt.xticks(rotation=60)\n",
    "    \n",
    "    for i, score in enumerate(recall_scores):\n",
    "        ax.text(i, score, f'{score:.2f}', ha='center', va='bottom')\n",
    "        \n",
    "    plt.show()  \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = [model1, model2, model3, model4, model5, model6, model7, model8, model9, pipeline]\n",
    "compare_models(models, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics \n",
    "def roc_curve_plot(models, X_val, y_val):\n",
    "    plt.figure(0).clf()\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.title(\"ROC Curve for Different Models\")\n",
    "    for model in models:\n",
    "        if model.__class__.__name__ == 'SVC':\n",
    "            y_pred = model.decision_function(X_val)\n",
    "        elif model.__class__.__name__ == 'XGBClassifier':\n",
    "            X_val_xg = X_val.copy()\n",
    "            X_val_xg.columns = [str(col).replace('[', '').replace(']', '').replace('<', '').replace('>', '') for col in X_val.columns]\n",
    "            y_pred = model.predict_proba(X_val_xg)[:, 1]\n",
    "        else:\n",
    "            y_pred = model.predict_proba(X_val)[:, 1]\n",
    "        fpr, tpr, _ = metrics.roc_curve(y_val, y_pred)\n",
    "        auc = round(metrics.roc_auc_score(y_val, y_pred), 4)\n",
    "        model_name = model.__class__.__name__  \n",
    "        plt.plot(fpr, tpr, label=f\"{model_name}, AUC={auc}\")\n",
    "    plt.legend(framealpha=0.0)\n",
    "    plt.savefig('plot.png', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_curve_plot(models, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model                         Accuracy    Recall    Precision    F1 Score       AUC      Gini\n",
      "--------------------------  ----------  --------  -----------  ----------  --------  --------\n",
      "LogisticRegression            0.983421  0.9625       0.972002    0.967213  0.997842  0.995684\n",
      "DecisionTreeClassifier        0.945839  0.880435     0.904996    0.892135  0.967191  0.934383\n",
      "GaussianNB                    0.62545   0.979348     0.402707    0.57071   0.748597  0.497193\n",
      "BernoulliNB                   0.863634  0.830978     0.694735    0.756229  0.932058  0.864116\n",
      "GradientBoostingClassifier    0.989777  0.970109     0.989477    0.979667  0.997736  0.995472\n",
      "RandomForestClassifier        0.983144  0.934239     0.999412    0.965686  0.997189  0.994377\n",
      "SVC                           0.982869  0.963043     0.969363    0.966182  0.996626  0.993253\n",
      "XGBClassifier                 0.990467  0.97337      0.989002    0.981089  0.998129  0.996257\n",
      "KNeighborsClassifier          0.969329  0.916304     0.961336    0.93814   0.968248  0.936497\n",
      "Pipeline                      0.983282  0.970652     0.966535    0.970149  0.993639  0.987278\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from tabulate import tabulate\n",
    "\n",
    "def compare_models(models, X_train, X_test, y_train, y_test):\n",
    "    results = []\n",
    "    headers = ['Model', 'Accuracy', 'Recall', 'Precision', 'F1 Score', 'AUC', 'Gini']\n",
    "\n",
    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "    \n",
    "    for model in models:\n",
    "        if model.__class__.__name__ == \"XGBClassifier\":\n",
    "            X_train_xg, X_test_xg = X_train.copy(), X_test.copy() \n",
    "            X_train_xg.columns = [str(col).replace('[', '').replace(']', '').replace('<', '').replace('>', '') for col in X_train.columns]\n",
    "            X_test_xg.columns = [str(col).replace('[', '').replace(']', '').replace('<', '').replace('>', '') for col in X_test.columns]\n",
    "            X_train_temp, X_test_temp = X_train_xg, X_test_xg\n",
    "        else:\n",
    "            X_train_temp, X_test_temp = X_train, X_test\n",
    "        \n",
    "        recall_scores = cross_val_score(model, X_train_temp, y_train, cv=skf, scoring=\"recall\")\n",
    "        accuracy_scores = cross_val_score(model, X_train_temp, y_train, cv=skf, scoring=\"accuracy\")\n",
    "        precision_scores = cross_val_score(model, X_train_temp, y_train, cv=skf, scoring=\"precision\")\n",
    "        f1_scores = cross_val_score(model, X_train_temp, y_train, cv=skf, scoring=\"f1\")\n",
    "        auc_scores = cross_val_score(model, X_train_temp, y_train, cv=skf, scoring=\"roc_auc\")\n",
    "        gini_scores = 2 * auc_scores - 1\n",
    "        \n",
    "        avg_recall = recall_scores.mean()\n",
    "        avg_accuracy = accuracy_scores.mean()\n",
    "        avg_precision = precision_scores.mean()\n",
    "        avg_f1 = f1_scores.mean()\n",
    "        avg_auc = auc_scores.mean()\n",
    "        avg_gini = gini_scores.mean()\n",
    "        \n",
    "        results.append([model.__class__.__name__, avg_accuracy, avg_recall, avg_precision, avg_f1, avg_auc, avg_gini])\n",
    "\n",
    "    print(tabulate(results, headers=headers, tablefmt='simple'))\n",
    "\n",
    "compare_models(models, X_train, X_test, y_train, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best models:\n",
    "- XGBClassifier\n",
    "- GradientBoostingClassifier\n",
    "- Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are choosing XGBClassifier as the best model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shap plots for the best model - XGBClassifier (train data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t=void 0;\"string\"==typeof this.props.plot_cmap?this.props.plot_cmap in je.colors?t=je.colors[this.props.plot_cmap]:(console.log(\"Invalid color map name, reverting to default.\"),t=je.colors.RdBu):Array.isArray(this.props.plot_cmap)&&(t=this.props.plot_cmap),this.colors=t.map((function(e){return q(e)})),this.brighterColors=[1.45,1.6].map((function(t,n){return e.colors[n].brighter(t)}));var n=ze(\",.4\");null!=this.props.ordering_keys&&null!=this.props.ordering_keys_time_format?(this.parseTime=Tr(this.props.ordering_keys_time_format),this.formatTime=Cr(this.props.ordering_keys_time_format),this.xtickFormat=function(e){return\"object\"==Za(e)?this.formatTime(e):n(e)}):(this.parseTime=null,this.formatTime=null,this.xtickFormat=n),this.xscale=De(),this.xaxis=dn().scale(this.xscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.xtickFormat(t)})).tickPadding(-18),this.ytickFormat=n,this.yscale=De(),this.yaxis=pn(an,undefined).scale(this.yscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.ytickFormat(e.invLinkFunction(t))})).tickPadding(2),this.xlabel.node().onchange=function(){return e.internalDraw()},this.ylabel.node().onchange=function(){return e.internalDraw()},this.svg.on(\"mousemove\",(function(t){return e.mouseMoved(t)})),this.svg.on(\"click\",(function(){return alert(\"This original index of the sample you clicked is \"+e.nearestExpIndex)})),this.svg.on(\"mouseout\",(function(t){return e.mouseOut(t)})),window.addEventListener(\"resize\",this.redraw),window.setTimeout(this.redraw,50)}},{key:\"componentDidUpdate\",value:function(){this.draw()}},{key:\"mouseOut\",value:function(){this.hoverLine.attr(\"display\",\"none\"),this.hoverx.attr(\"display\",\"none\"),this.hoverxOutline.attr(\"display\",\"none\"),this.hoverxTitle.attr(\"display\",\"none\"),this.hovery.attr(\"display\",\"none\"),this.hoveryOutline.attr(\"display\",\"none\"),this.hoverGroup1.attr(\"display\",\"none\"),this.hoverGroup2.attr(\"display\",\"none\")}},{key:\"mouseMoved\",value:function(e){var t,n,r=this;this.hoverLine.attr(\"display\",\"\"),this.hoverx.attr(\"display\",\"\"),this.hoverxOutline.attr(\"display\",\"\"),this.hoverxTitle.attr(\"display\",\"\"),this.hovery.attr(\"display\",\"\"),this.hoveryOutline.attr(\"display\",\"\"),this.hoverGroup1.attr(\"display\",\"\"),this.hoverGroup2.attr(\"display\",\"\");var a=function(e,t){if(e=function(e){let t;for(;t=e.sourceEvent;)e=t;return e}(e),void 0===t&&(t=e.currentTarget),t){var n=t.ownerSVGElement||t;if(n.createSVGPoint){var r=n.createSVGPoint();return r.x=e.clientX,r.y=e.clientY,[(r=r.matrixTransform(t.getScreenCTM().inverse())).x,r.y]}if(t.getBoundingClientRect){var a=t.getBoundingClientRect();return[e.clientX-a.left-t.clientLeft,e.clientY-a.top-t.clientTop]}}return[e.pageX,e.pageY]}(e,this.svg.node())[0];if(this.props.explanations){for(t=0;t<this.currExplanations.length;++t)(!n||Math.abs(n.xmapScaled-a)>Math.abs(this.currExplanations[t].xmapScaled-a))&&(n=this.currExplanations[t]);this.nearestExpIndex=n.origInd,this.hoverLine.attr(\"x1\",n.xmapScaled).attr(\"x2\",n.xmapScaled).attr(\"y1\",0+this.topOffset).attr(\"y2\",this.height),this.hoverx.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxOutline.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxTitle.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-18).text(n.count>1?n.count+\" averaged samples\":\"\"),this.hovery.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint))),this.hoveryOutline.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint)));for(var i,o,u=[],l=this.currPosOrderedFeatures.length-1;l>=0;--l){var s=this.currPosOrderedFeatures[l],c=n.features[s];o=5+(c.posyTop+c.posyBottom)/2,(!i||o-i>=15)&&c.posyTop-c.posyBottom>=6&&(u.push(c),i=o)}var f=[];i=void 0;var p,d=Ga(this.currNegOrderedFeatures);try{for(d.s();!(p=d.n()).done;){var h=p.value,v=n.features[h];o=5+(v.negyTop+v.negyBottom)/2,(!i||i-o>=15)&&v.negyTop-v.negyBottom>=6&&(f.push(v),i=o)}}catch(e){d.e(e)}finally{d.f()}var g=function(e){var t=\"\";return null!==e.value&&void 0!==e.value&&(t=\" = \"+(isNaN(e.value)?e.value:r.ytickFormat(e.value))),n.count>1?\"mean(\"+r.props.featureNames[e.ind]+\")\"+t:r.props.featureNames[e.ind]+t},y=this.hoverGroup1.selectAll(\".pos-values\").data(u);y.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(y).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),y.exit().remove();var m=this.hoverGroup2.selectAll(\".pos-values\").data(u);m.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(m).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[0]).text(g),m.exit().remove();var b=this.hoverGroup1.selectAll(\".neg-values\").data(f);b.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(b).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),b.exit().remove();var _=this.hoverGroup2.selectAll(\".neg-values\").data(f);_.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(_).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[1]).text(g),_.exit().remove()}}},{key:\"draw\",value:function(){var e=this;if(this.props.explanations&&0!==this.props.explanations.length){(0,Re.each)(this.props.explanations,(function(e,t){return e.origInd=t}));var t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var u=a.value;i.features.hasOwnProperty(u)||(i.features[u]={effect:0,value:0}),i.features[u].ind=u}}catch(e){o.e(e)}finally{o.f()}}}catch(e){r.e(e)}finally{r.f()}var l=this.xlabel.node().value,s=\"sample order by key\"===l&&null!=this.props.ordering_keys_time_format;if(this.xscale=s?Ya():De(),this.xaxis.scale(this.xscale),\"sample order by similarity\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.simIndex})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by output value\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return-e.outValue})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"original sample ordering\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.origInd})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by key\"===l)t=this.props.explanations,s?(0,Re.each)(t,(function(e,t){return e.xmap=n.parseTime(n.props.ordering_keys[t])})):(0,Re.each)(t,(function(e,t){return e.xmap=n.props.ordering_keys[t]})),t=(0,Re.sortBy)(t,(function(e){return e.xmap}));else{var c=(0,Re.findKey)(this.props.featureNames,(function(e){return e===l}));(0,Re.each)(this.props.explanations,(function(e,t){return e.xmap=e.features[c].value}));var f=(0,Re.sortBy)(this.props.explanations,(function(e){return e.xmap})),p=(0,Re.map)(f,(function(e){return e.xmap}));if(\"string\"==typeof p[0])return void alert(\"Ordering by category names is not yet supported.\");var d,h,v=(0,Re.min)(p),g=((0,Re.max)(p)-v)/100;t=[];for(var y=0;y<f.length;++y){var m=f[y];if(d&&!h&&m.xmap-d.xmap<=g||h&&m.xmap-h.xmap<=g){h||((h=(0,Re.cloneDeep)(d)).count=1);var b,_=Ga(this.usedFeatures);try{for(_.s();!(b=_.n()).done;){var w=b.value;h.features[w].effect+=m.features[w].effect,h.features[w].value+=m.features[w].value}}catch(e){_.e(e)}finally{_.f()}h.count+=1}else if(d)if(h){var x,k=Ga(this.usedFeatures);try{for(k.s();!(x=k.n()).done;){var S=x.value;h.features[S].effect/=h.count,h.features[S].value/=h.count}}catch(e){k.e(e)}finally{k.f()}t.push(h),h=void 0}else t.push(d);d=m}d.xmap-t[t.length-1].xmap>g&&t.push(d)}this.currUsedFeatures=this.usedFeatures,this.currPosOrderedFeatures=this.posOrderedFeatures,this.currNegOrderedFeatures=this.negOrderedFeatures;var E=this.ylabel.node().value;if(\"model output value\"!==E){var C=t;t=(0,Re.cloneDeep)(t);for(var T=(0,Re.findKey)(this.props.featureNames,(function(e){return e===E})),M=0;M<t.length;++M){var N=t[M].features[T];t[M].features={},t[M].features[T]=N,C[M].remapped_version=t[M]}this.currUsedFeatures=[T],this.currPosOrderedFeatures=[T],this.currNegOrderedFeatures=[T]}this.currExplanations=t,\"identity\"===this.props.link?this.invLinkFunction=function(e){return n.props.baseValue+e}:\"logit\"===this.props.link?this.invLinkFunction=function(e){return 1/(1+Math.exp(-(n.props.baseValue+e)))}:console.log(\"ERROR: Unrecognized link function: 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e.effect<0})),(function(e){return-e.effect})))||0;A=Math.max(A,2.2*Math.max(R,j))}this.yscale.domain([-A/2,A/2]).range([this.height-10,this.topOffset]),this.yaxisElement.attr(\"transform\",\"translate(\"+this.leftOffset+\",0)\").call(this.yaxis);for(var U=0;U<O;++U){var I,$=t[U].features,B=-((0,Re.sum)((0,Re.map)((0,Re.filter)($,(function(e){return e.effect<0})),(function(e){return-e.effect})))||0),W=void 0,V=Ga(this.currPosOrderedFeatures);try{for(V.s();!(I=V.n()).done;)$[W=I.value].posyTop=this.yscale(B),$[W].effect>0&&(B+=$[W].effect),$[W].posyBottom=this.yscale(B),$[W].ind=W}catch(e){V.e(e)}finally{V.f()}var H,q=B,Q=Ga(this.currNegOrderedFeatures);try{for(Q.s();!(H=Q.n()).done;)$[W=H.value].negyTop=this.yscale(B),$[W].effect<0&&(B-=$[W].effect),$[W].negyBottom=this.yscale(B)}catch(e){Q.e(e)}finally{Q.f()}t[U].joinPoint=q,t[U].joinPointy=this.yscale(q)}var Y=En().x((function(e){return e[0]})).y((function(e){return e[1]})),G=this.mainGroup.selectAll(\".force-bar-array-area-pos\").data(this.currUsedFeatures);G.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-pos\").merge(G).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[0]),G.exit().remove();var K=this.mainGroup.selectAll(\".force-bar-array-area-neg\").data(this.currUsedFeatures);K.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-neg\").merge(K).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[1]),K.exit().remove();var Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var pe=t[le].xmapScaled;for(ue=le;pe+100>t[ue].xmapScaled&&ue<O-1;)++ue;le=ue}}}catch(e){ie.e(e)}finally{ie.f()}}var de=this.onTopGroup.selectAll(\".force-bar-array-flabels\").data(ee);de.enter().append(\"text\").attr(\"class\",\"force-bar-array-flabels\").merge(de).attr(\"x\",(function(e){return e[0]})).attr(\"y\",(function(e){return e[1]+4})).text((function(e){return e[2]})),de.exit().remove()}},{key:\"componentWillUnmount\",value:function(){window.removeEventListener(\"resize\",this.redraw)}},{key:\"render\",value:function(){var t=this;return e.createElement(\"div\",{ref:function(e){return t.wrapper=Jt(e)},style:{textAlign:\"center\"}},e.createElement(\"style\",{dangerouslySetInnerHTML:{__html:\"\\n          .force-bar-array-wrapper {\\n            text-align: center;\\n          }\\n          .force-bar-array-xaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-xaxis .domain {\\n            opacity: 0;\\n          }\\n          .force-bar-array-xaxis paths {\\n            display: none;\\n          }\\n          .force-bar-array-yaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-yaxis paths {\\n            display: none;\\n          }\\n          .tick line {\\n            stroke: #000;\\n            stroke-width: 1px;\\n            opacity: 0.4;\\n          }\\n          .tick text {\\n            fill: #000;\\n            opacity: 0.5;\\n            font-size: 12px;\\n            padding: 0px;\\n          }\\n          .force-bar-array-flabels {\\n            font-size: 12px;\\n            fill: #fff;\\n            text-anchor: middle;\\n          }\\n          .additive-force-array-xlabel {\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            margin-left: 80px;\\n            max-width: 300px;\\n          }\\n          .additive-force-array-xlabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-ylabel {\\n            position: relative;\\n            top: 0px;\\n            left: 0px;\\n            transform: rotate(-90deg);\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            max-width: 150px;\\n          }\\n          .additive-force-array-ylabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-hoverLine {\\n            stroke-width: 1px;\\n            stroke: #fff;\\n            opacity: 1;\\n          }\"}}),e.createElement(\"select\",{className:\"additive-force-array-xlabel\"}),e.createElement(\"div\",{style:{height:\"0px\",textAlign:\"left\"}},e.createElement(\"select\",{className:\"additive-force-array-ylabel\"})),e.createElement(\"svg\",{ref:function(e){return t.svg=Jt(e)},style:{userSelect:\"none\",display:\"block\",fontFamily:\"arial\",sansSerif:!0}}))}}])&&Xa(n.prototype,r),Object.defineProperty(n,\"prototype\",{writable:!1}),u}(e.Component);ni.defaultProps={plot_cmap:\"RdBu\",ordering_keys:null,ordering_keys_time_format:null};const ri=ni;window.SHAP={SimpleListVisualizer:He,AdditiveForceVisualizer:Ln,AdditiveForceArrayVisualizer:ri,React:e,ReactDom:t}})()})();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obliczanie wartości SHAP\n",
    "explainer = shap.TreeExplainer(model8)\n",
    "shap_values = explainer.shap_values(X_train_xg)\n",
    "\n",
    "# Generowanie wykresu SHAP\n",
    "shap.summary_plot(shap_values, X_train_xg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pierwsza obserwacja - przewidziana 1, w rzeczywistości jest 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.force_plot(explainer.expected_value, shap_values[0], X_train_xg.iloc[0], matplotlib=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_class = model8.predict(X_train_xg.iloc[[0]])[0]\n",
    "predicted_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label    1\n",
       "Name: 782, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_value = y_train.iloc[0]\n",
    "actual_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pierwsza obserwacja - przewidziana 0, w rzeczywistości jest 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.force_plot(explainer.expected_value, shap_values[1], X_train_xg.iloc[1], matplotlib=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_class = model8.predict(X_train_xg.iloc[[1]])[0]\n",
    "predicted_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label    0\n",
       "Name: 4198, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_value = y_train.iloc[1]\n",
    "actual_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = pd.read_csv(\"../validation_data/X_val.csv\")\n",
    "y_val = pd.read_csv(\"../validation_data/y_val.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.shape[1] == X_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = [model8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_curve_plot(best_model, X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_metrics(model, X_val, y_val):\n",
    "    if model.__class__.__name__ == 'XGBClassifier':\n",
    "        X_val_xg = X_val.copy()\n",
    "        X_val_xg.columns = [str(col).replace('[', '').replace(']', '').replace('<', '').replace('>', '') for col in X_val.columns]\n",
    "        predictions = model.predict(X_val_xg)\n",
    "        auc = roc_auc_score(y_val, model.predict_proba(X_val_xg)[:, 1])\n",
    "    else:\n",
    "        predictions = model.predict(X_val)\n",
    "    accuracy = accuracy_score(y_val, predictions)\n",
    "    recall = recall_score(y_val, predictions)\n",
    "    precision = precision_score(y_val, predictions)\n",
    "    f1 = f1_score(y_val, predictions)\n",
    "    if model.__class__.__name__ == 'SVC':\n",
    "        auc = roc_auc_score(y_val, model.decision_function(X_val))\n",
    "    elif model.__class__.__name__ != 'XGBClassifier':\n",
    "        auc = roc_auc_score(y_val, model.predict_proba(X_val)[:, 1])\n",
    "    gini = 2 * auc - 1\n",
    "    return accuracy, recall, precision, f1, auc, gini\n",
    "\n",
    "def print_metrics(models,  X_val, y_val):\n",
    "    results = pd.DataFrame(columns=['Model', 'Accuracy', 'Recall', 'Precision', 'F1 Score', 'AUC', 'Gini'])\n",
    "    for model in models:\n",
    "        accuracy, recall, precision, f1, auc, gini = calculate_metrics(model,  X_val, y_val)\n",
    "        results.loc[len(results)] = {'Model': model.__class__.__name__,\n",
    "                                        'Accuracy': accuracy,\n",
    "                                        'Recall': recall,\n",
    "                                        'Precision': precision,\n",
    "                                        'F1 Score': f1,\n",
    "                                        'AUC': auc,\n",
    "                                        'Gini': gini}\n",
    "    print(tabulate(results, headers='keys', tablefmt='simple'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Model            Accuracy    Recall    Precision    F1 Score       AUC      Gini\n",
      "--  -------------  ----------  --------  -----------  ----------  --------  --------\n",
      " 0  XGBClassifier    0.991493  0.977733     0.988741    0.983206  0.998599  0.997198\n"
     ]
    }
   ],
   "source": [
    "print_metrics(best_model, X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}