1973 lines (1972 with data), 142.4 kB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gloabl Experiment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Experiment: artifact_location='/Users/arham/Downloads/Projects/03-Experiments/mlruns/4', creation_time=1714075083201, experiment_id='4', last_update_time=1714075083201, lifecycle_stage='active', name='LightGBM', tags={}>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mlflow\n",
    "# Set the MLflow tracking URI to a new SQLite URI\n",
    "mlflow.set_tracking_uri(\"sqlite:///new_mlflow.db\")\n",
    "mlflow.set_experiment(\"LightGBM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "def load_data(path):\n",
    "    df = pd.read_csv(path)\n",
    "    train_df, test_df = train_test_split(df, test_size=0.35, random_state=42)\n",
    "    train_df, val_df,  = train_test_split(train_df, test_size=0.20, random_state=42)\n",
    "    train_df = train_df.drop(['id'], axis=1).drop_duplicates().reset_index(drop=True)\n",
    "    test_df = test_df.drop(['id'], axis=1).drop_duplicates().reset_index(drop=True)\n",
    "    val_df = val_df.drop(['id'], axis=1).drop_duplicates().reset_index(drop=True)\n",
    "    return train_df, val_df, test_df\n",
    "\n",
    "def encode_target(train):\n",
    "    target_key = {'Insufficient_Weight': 0, 'Normal_Weight': 1, 'Overweight_Level_I': 2, 'Overweight_Level_II': 3, 'Obesity_Type_I': 4,'Obesity_Type_II' : 5, 'Obesity_Type_III': 6}\n",
    "    train['NObeyesdad'] = train['NObeyesdad'].map(target_key)\n",
    "    return train\n",
    "\n",
    "def make_gender_binary(train):\n",
    "    train['Gender'] = train['Gender'].map({'Male':0, 'Female':1})\n",
    "\n",
    "def datatypes(train):\n",
    "    train['Weight'] = train['Weight'].astype(float)\n",
    "    train['Age'] = train['Age'].astype(float)\n",
    "    train['Height'] = train['Height'].astype(float)\n",
    "    return train\n",
    "\n",
    "# def age_binning(train_df):\n",
    "#     train_df['Age_Group'] = pd.cut(train_df['Age'], bins=[0, 20, 30, 40, 50, train_df['Age'].max()], labels=['0-20', '21-30', '31-40', '41-50', '50+'])\n",
    "#     return train_df\n",
    "\n",
    "def age_binning(df):\n",
    "    age_groups = []\n",
    "    for age in df['Age']:\n",
    "        if age <= 20:\n",
    "            age_group = 1\n",
    "        elif age <= 30:\n",
    "            age_group = 2\n",
    "        elif age <= 40:\n",
    "            age_group = 3\n",
    "        elif age <= 50:\n",
    "            age_group = 4\n",
    "        else:\n",
    "            age_group = 5\n",
    "        age_groups.append(age_group)\n",
    "    df['Age_Group'] = age_groups\n",
    "    return df\n",
    "\n",
    "def age_scaling_log(train_df):\n",
    "    train_df['Age'] = train_df['Age'].astype(float)\n",
    "    train_df['Log_Age'] = np.log1p(train_df['Age'])\n",
    "    return train_df\n",
    "\n",
    "def age_scaling_minmax(train_df):\n",
    "    train_df['Age'] = train_df['Age'].astype(float)\n",
    "    scaler_age = MinMaxScaler()\n",
    "    train_df['Scaled_Age'] = scaler_age.fit_transform(train_df['Age'].values.reshape(-1, 1))\n",
    "    return train_df, scaler_age\n",
    "\n",
    "def weight_scaling_log(train_df):\n",
    "    train_df['Weight'] = train_df['Weight'].astype(float)\n",
    "    train_df['Log_Weight'] = np.log1p(train_df['Weight'])\n",
    "    return train_df\n",
    "\n",
    "def weight_scaling_minmax(train_df):\n",
    "    train_df['Weight'] = train_df['Weight'].astype(float)\n",
    "    scaler_weight = MinMaxScaler()\n",
    "    train_df['Scaled_Weight'] = scaler_weight.fit_transform(train_df['Weight'].values.reshape(-1, 1))\n",
    "    return train_df, scaler_weight\n",
    "\n",
    "def height_scaling_log(train_df):\n",
    "    train_df['Log_Height'] = np.log1p(train_df['Height'])\n",
    "    return train_df\n",
    "\n",
    "def height_scaling_minmax(train_df):\n",
    "    scaler_height = MinMaxScaler()\n",
    "    train_df['Scaled_Height'] = scaler_height.fit_transform(train_df['Height'].values.reshape(-1, 1))\n",
    "    return train_df, scaler_height\n",
    "\n",
    "def make_gender_binary(train):\n",
    "    train['Gender'] = train['Gender'].map({'Female':1, 'Male':0})\n",
    "    return train\n",
    "\n",
    "def fix_binary_columns(train):\n",
    "    Binary_Cols = ['family_history_with_overweight','FAVC', 'SCC','SMOKE']\n",
    "    # if yes then 1 else 0\n",
    "    for col in Binary_Cols:\n",
    "        train[col] = train[col].map({'yes': 1, 'no': 0})\n",
    "    return train\n",
    "\n",
    "def freq_cat_cols(train):\n",
    "    # One hot encoding\n",
    "    cat_cols = ['CAEC', 'CALC']\n",
    "    for col in cat_cols:\n",
    "        train[col] = train[col].map({'no': 0, 'Sometimes': 1, 'Frequently': 2, 'Always': 3})\n",
    "    return train\n",
    "\n",
    "def Mtrans(train):\n",
    "    \"\"\"\n",
    "    Public_Transportation    8692\n",
    "    Automobile               1835\n",
    "    Walking                   231\n",
    "    Motorbike                  19\n",
    "    Bike                       16\n",
    "    \"\"\"\n",
    "    # train['MTRANS'] = train['MTRANS'].map({'Public_Transportation': 3, 'Automobile': 5, 'Walking': 1, 'Motorbike': 4, 'Bike': 2})\n",
    "    # dummify column\n",
    "    train = pd.get_dummies(train, columns=['MTRANS'])\n",
    "    return train\n",
    "\n",
    "\n",
    "def other_features(train):\n",
    "    train['BMI'] = train['Weight'] / (train['Height'] ** 2)\n",
    "    # train['Age'*'Gender'] = train['Age'] * train['Gender']\n",
    "    polynomial_features = PolynomialFeatures(degree=2)\n",
    "    X_poly = polynomial_features.fit_transform(train[['Age', 'BMI']])\n",
    "    poly_features_df = pd.DataFrame(X_poly, columns=['Age^2', 'Age^3', 'BMI^2', 'Age * BMI', 'Age * BMI^2', 'Age^2 * BMI^2'])\n",
    "    train = pd.concat([train, poly_features_df], axis=1)\n",
    "    return train\n",
    "\n",
    "\n",
    "def test_pipeline(test, scaler_age, scaler_weight, scaler_height):\n",
    "    test = datatypes(test)\n",
    "    test = encode_target(test)\n",
    "    test = age_binning(test)\n",
    "    test = age_scaling_log(test)\n",
    "    test['Scaled_Age'] = scaler_age.transform(test['Age'].values.reshape(-1, 1))\n",
    "    test = weight_scaling_log(test)\n",
    "    test['Scaled_Weight'] = scaler_weight.transform(test['Weight'].values.reshape(-1, 1))\n",
    "    test = height_scaling_log(test)\n",
    "    test['Scaled_Height'] = scaler_height.transform(test['Height'].values.reshape(-1, 1))\n",
    "    test = make_gender_binary(test)\n",
    "    test = fix_binary_columns(test)\n",
    "    test = freq_cat_cols(test)\n",
    "    test = Mtrans(test)\n",
    "    test = other_features(test)\n",
    "\n",
    "    return test\n",
    "\n",
    "def train_model(params, X_train, y_train):\n",
    "    lgb_train = lgb.Dataset(X_train, y_train)\n",
    "    model = lgb.train(params, lgb_train, num_boost_round=1000)\n",
    "    return model\n",
    "\n",
    "def evaluate_model(model, X_val, y_val):\n",
    "    y_pred = model.predict(X_val)\n",
    "    y_pred = [np.argmax(y) for y in y_pred]\n",
    "    accuracy = accuracy_score(y_val, y_pred)\n",
    "    return accuracy\n",
    "\n",
    "def objective(trial, X_train, y_train):\n",
    "    params = {\n",
    "        'objective': 'multiclass',\n",
    "        'num_class': 7,\n",
    "        'metric': 'multi_logloss',\n",
    "        'boosting_type': 'gbdt',\n",
    "        'learning_rate': trial.suggest_loguniform('learning_rate', 0.005, 0.5),\n",
    "        'num_leaves': trial.suggest_int('num_leaves', 10, 1000),\n",
    "        'max_depth': trial.suggest_int('max_depth', -1, 20),\n",
    "        'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.6, 0.95),\n",
    "        'feature_fraction': trial.suggest_uniform('feature_fraction', 0.6, 0.95),\n",
    "        'verbosity': -1\n",
    "    }\n",
    "\n",
    "    n_splits = 5\n",
    "    kf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
    "    scores = []\n",
    "\n",
    "    for train_index, val_index in kf.split(X_train, y_train):\n",
    "        X_tr, X_val = X_train.iloc[train_index], X_train.iloc[val_index]\n",
    "        y_tr, y_val = y_train.iloc[train_index], y_train.iloc[val_index]\n",
    "\n",
    "        model = train_model(params, X_tr, y_tr)\n",
    "        accuracy = evaluate_model(model, X_val, y_val)\n",
    "        scores.append(accuracy)\n",
    "\n",
    "    return np.mean(scores)\n",
    "\n",
    "def optimize_hyperparameters(X_train, y_train, n_trials=2):\n",
    "    study = optuna.create_study(direction='maximize')\n",
    "    study.optimize(lambda trial: objective(trial, X_train, y_train), n_trials=n_trials)\n",
    "    return study.best_params\n",
    "\n",
    "def New_Test_Instances_Pipeline(test, scaler_age, scaler_weight, scaler_height):\n",
    "    test = datatypes(test)\n",
    "    test = age_binning(test)\n",
    "    test = age_scaling_log(test)\n",
    "    test['Scaled_Age'] = scaler_age.transform(test['Age'].values.reshape(-1, 1))\n",
    "    test = weight_scaling_log(test)\n",
    "    test['Scaled_Weight'] = scaler_weight.transform(test['Weight'].values.reshape(-1, 1))\n",
    "    test = height_scaling_log(test)\n",
    "    test['Scaled_Height'] = scaler_height.transform(test['Height'].values.reshape(-1, 1))\n",
    "    test = make_gender_binary(test)\n",
    "    test = fix_binary_columns(test)\n",
    "    test = freq_cat_cols(test)\n",
    "    test = Mtrans(test)\n",
    "    test = other_features(test)\n",
    "\n",
    "    return test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/Users/arham/Downloads/Projects/01-Dataset/01-Data-for-model-building/train.csv'\n",
    "train_df, val_df, test_df = load_data(path)\n",
    "\n",
    "# train test val pipeline\n",
    "train_df = datatypes(train_df)\n",
    "train_df = encode_target(train_df)\n",
    "train_df = age_binning(train_df)\n",
    "train_df, scaler_age = age_scaling_minmax(train_df)\n",
    "train_df = age_scaling_log(train_df)\n",
    "train_df, scaler_weight = weight_scaling_minmax(train_df)\n",
    "train_df = weight_scaling_log(train_df)\n",
    "train_df, scaler_height = height_scaling_minmax(train_df)\n",
    "train_df = height_scaling_log(train_df)\n",
    "train_df = make_gender_binary(train_df)\n",
    "train_df = fix_binary_columns(train_df)\n",
    "train_df = freq_cat_cols(train_df)\n",
    "train_df = Mtrans(train_df)\n",
    "train_df = other_features(train_df)\n",
    "val_df = test_pipeline(val_df, scaler_age, scaler_weight, scaler_height)\n",
    "test_df = test_pipeline(test_df, scaler_age, scaler_weight, scaler_height)\n",
    "\n",
    "\n",
    "# target & predictors\n",
    "Target = 'NObeyesdad'\n",
    "features = ['Gender', 'Age', 'Height', 'Weight', 'family_history_with_overweight',\n",
    "       'FAVC', 'FCVC', 'NCP', 'CAEC', 'SMOKE', 'CH2O', 'SCC', 'FAF', 'TUE',\n",
    "       'CALC', 'Age_Group', \n",
    "       'MTRANS_Automobile', 'MTRANS_Bike', 'MTRANS_Motorbike',\n",
    "       'MTRANS_Public_Transportation', 'MTRANS_Walking', 'BMI', 'Age^2',\n",
    "       'Age^3', 'BMI^2', 'Age * BMI', 'Age * BMI^2', 'Age^2 * BMI^2'] \n",
    "        #'Scaled_Age', 'Log_Age', 'Scaled_Weight', 'Log_Weight', 'Scaled_Height', 'Log_Height',\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/lightgbm/engine.py:172: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001675 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 3576\n",
      "[LightGBM] [Info] Number of data points in the train set: 10793, number of used features: 25\n",
      "[LightGBM] [Info] Start training from score -2.103541\n",
      "[LightGBM] [Info] Start training from score -1.893390\n",
      "[LightGBM] [Info] Start training from score -2.159762\n",
      "[LightGBM] [Info] Start training from score -2.113461\n",
      "[LightGBM] [Info] Start training from score -1.974767\n",
      "[LightGBM] [Info] Start training from score -1.867272\n",
      "[LightGBM] [Info] Start training from score -1.619963\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/lightgbm/engine.py:172: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000936 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 3576\n",
      "[LightGBM] [Info] Number of data points in the train set: 8634, number of used features: 25\n",
      "[LightGBM] [Info] Start training from score -2.104065\n",
      "[LightGBM] [Info] Start training from score -1.893344\n",
      "[LightGBM] [Info] Start training from score -2.159716\n",
      "[LightGBM] [Info] Start training from score -2.113607\n",
      "[LightGBM] [Info] Start training from score -1.974220\n",
      "[LightGBM] [Info] Start training from score -1.867526\n",
      "[LightGBM] [Info] Start training from score -1.619799\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/lightgbm/engine.py:172: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001324 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 3573\n",
      "[LightGBM] [Info] Number of data points in the train set: 8634, number of used features: 25\n",
      "[LightGBM] [Info] Start training from score -2.104065\n",
      "[LightGBM] [Info] Start training from score -1.893344\n",
      "[LightGBM] [Info] Start training from score -2.159716\n",
      "[LightGBM] [Info] Start training from score -2.112648\n",
      "[LightGBM] [Info] Start training from score -1.974220\n",
      "[LightGBM] [Info] Start training from score -1.867526\n",
      "[LightGBM] [Info] Start training from score -1.620385\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/lightgbm/engine.py:172: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000880 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 3572\n",
      "[LightGBM] [Info] Number of data points in the train set: 8634, number of used features: 25\n",
      "[LightGBM] [Info] Start training from score -2.103115\n",
      "[LightGBM] [Info] Start training from score -1.893344\n",
      "[LightGBM] [Info] Start training from score -2.159716\n",
      "[LightGBM] [Info] Start training from score -2.113607\n",
      "[LightGBM] [Info] Start training from score -1.975054\n",
      "[LightGBM] [Info] Start training from score -1.867526\n",
      "[LightGBM] [Info] Start training from score -1.619799\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/lightgbm/engine.py:172: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000655 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 3571\n",
      "[LightGBM] [Info] Number of data points in the train set: 8635, number of used features: 25\n",
      "[LightGBM] [Info] Start training from score -2.103231\n",
      "[LightGBM] [Info] Start training from score -1.893459\n",
      "[LightGBM] [Info] Start training from score -2.159832\n",
      "[LightGBM] [Info] Start training from score -2.113723\n",
      "[LightGBM] [Info] Start training from score -1.975170\n",
      "[LightGBM] [Info] Start training from score -1.866892\n",
      "[LightGBM] [Info] Start training from score -1.619915\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/lightgbm/engine.py:172: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001269 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 3575\n",
      "[LightGBM] [Info] Number of data points in the train set: 8635, number of used features: 25\n",
      "[LightGBM] [Info] Start training from score -2.103231\n",
      "[LightGBM] [Info] Start training from score -1.893459\n",
      "[LightGBM] [Info] Start training from score -2.159832\n",
      "[LightGBM] [Info] Start training from score -2.113723\n",
      "[LightGBM] [Info] Start training from score -1.975170\n",
      "[LightGBM] [Info] Start training from score -1.866892\n",
      "[LightGBM] [Info] Start training from score -1.619915\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "\n",
      "Accuracy: 0.9020661539886964\n",
      "Precision: 0.9018236286221342\n",
      "Recall: 0.9020661539886964\n",
      "F1: 0.9018567099523006\n",
      "Recall for class 0: 0.9335443037974683\n",
      "Recall for class 1: 0.9037433155080213\n",
      "Recall for class 2: 0.7701492537313432\n",
      "Recall for class 3: 0.8267477203647416\n",
      "Recall for class 4: 0.8574821852731591\n",
      "Recall for class 5: 0.9641148325358851\n",
      "Recall for class 6: 0.9960474308300395\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import precision_recall_fscore_support, accuracy_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "import mlflow\n",
    "\n",
    "# Define focal loss function\n",
    "def focal_loss_lgb(y_true, y_pred, alpha=0.25, gamma=2.0):\n",
    "    \"\"\"\n",
    "    Focal loss for LightGBM, with support for one-vs-rest multiclass classification.\n",
    "    :param y_true: numpy.ndarray, true labels.\n",
    "    :param y_pred: numpy.ndarray, predicted scores.\n",
    "    :param alpha: float, balancing parameter (default=0.25).\n",
    "    :param gamma: float, focusing parameter (default=2.0).\n",
    "    :return: numpy.ndarray, focal loss.\n",
    "    \"\"\"\n",
    "    # Convert y_true to one-hot encoding\n",
    "    one_hot = np.eye(7)[y_true.astype(int)]\n",
    "    \n",
    "    # Calculate focal loss for each class\n",
    "    pt = one_hot * y_pred[:, None] + (1 - one_hot) * (1 - y_pred[:, None])\n",
    "    focal_loss = -alpha * (1 - pt) ** gamma * np.log(pt)\n",
    "    \n",
    "    # Sum the loss across classes\n",
    "    return np.mean(focal_loss, axis=1)\n",
    "\n",
    "# Define training data\n",
    "X_train = train_df[features]\n",
    "y_train = train_df[Target]\n",
    "\n",
    "# Define validation data\n",
    "X_val = val_df[features]\n",
    "y_val = val_df[Target]\n",
    "\n",
    "# Define test data\n",
    "X_test = test_df[features]\n",
    "y_test = test_df[Target]\n",
    "\n",
    "# Define parameters for LightGBM\n",
    "params = {\n",
    "    'objective': 'multiclass',\n",
    "    'num_class': 7,\n",
    "    'metric': 'multi_logloss',\n",
    "    'num_iterations': 300,  # Adjust this as needed\n",
    "}\n",
    "\n",
    "# Start MLflow run\n",
    "with mlflow.start_run(run_name=\"LGBM_with_OvR_and_Focal_Loss\"):\n",
    "    # Fit and evaluate the model\n",
    "    model = lgb.LGBMClassifier(**params)\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # CV predictions of LightGBM\n",
    "    cv_predictions = cross_val_predict(model, X_train, y_train, cv=5)\n",
    "    accuracy_lgbm = accuracy_score(y_train, cv_predictions)\n",
    "    \n",
    "    # Compute precision, recall, and F1-score\n",
    "    precision_lgbm, recall_lgbm, f1_lgbm, _ = precision_recall_fscore_support(y_train, cv_predictions, average='weighted')\n",
    "    print(\"\\nAccuracy:\", accuracy_lgbm)\n",
    "    print(\"Precision:\", precision_lgbm)\n",
    "    print(\"Recall:\", recall_lgbm)\n",
    "    print(\"F1:\", f1_lgbm)\n",
    "    \n",
    "    mlflow.log_metric('accuracy', accuracy_lgbm)\n",
    "    mlflow.log_metric('precision', precision_lgbm)\n",
    "    mlflow.log_metric('recall', recall_lgbm)\n",
    "    mlflow.log_metric('f1', f1_lgbm)\n",
    "\n",
    "    # Predict on validation data\n",
    "    y_val_pred_lgbm = model.predict(X_val)\n",
    "    \n",
    "    # Compute precision, recall, and F1-score per class\n",
    "    precision_per_class, recall_per_class, f1_per_class, _ = precision_recall_fscore_support(y_val, y_val_pred_lgbm, average=None)\n",
    "    for i in range(len(recall_per_class)):\n",
    "        print(f\"Recall for class {i}: {recall_per_class[i]}\")\n",
    "        mlflow.log_metric(f'recall_class_{i}', recall_per_class[i])\n",
    "\n",
    "    # Log the model and tags\n",
    "    mlflow.lightgbm.log_model(model, 'model')\n",
    "    mlflow.set_tag('experiments', 'Arham A.')\n",
    "    mlflow.set_tag('model_name', 'LGBM')\n",
    "    mlflow.set_tag('preprocessing', 'Yes')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing Single Instance For Architecture Development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input_data = {\n",
    "#     \"id\": 6204,\n",
    "#     \"Gender\": \"Female\",\n",
    "#     \"Age\": 23.0,\n",
    "#     \"Height\": 1.581527,\n",
    "#     \"Weight\": 78.089575,\n",
    "#     \"family_history_with_overweight\": \"yes\",\n",
    "#     \"FAVC\": \"yes\",\n",
    "#     \"FCVC\": 2.0,\n",
    "#     \"NCP\": 2.070033,\n",
    "#     \"CAEC\": \"Sometimes\",\n",
    "#     \"SMOKE\": \"no\", \n",
    "#     \"CH2O\": 2.953192,\n",
    "#     \"SCC\": \"no\",\n",
    "#     \"FAF\": 0.118271,\n",
    "#     \"TUE\": 0.0,\n",
    "#     \"CALC\": \"no\",\n",
    "#     \"MTRANS\": \"Public_Transportation\"\n",
    "    \n",
    "# }\n",
    "\n",
    "# input_df = pd.DataFrame([input_data])\n",
    "# input_df = New_Test_Instances_Pipeline(input_df, scaler_age, scaler_weight, scaler_height)\n",
    "\n",
    "# # X input to have same columns as features\n",
    "# X_input = pd.DataFrame(columns=features)\n",
    "# # if input df does not have a column that is in features, add it with 0s at the same position\n",
    "# for col in features:\n",
    "#     if col not in input_df.columns:\n",
    "#         if col in ['MTRANS_Automobile', 'MTRANS_Bike', 'MTRANS_Motorbike', 'MTRANS_Public_Transportation', 'MTRANS_Walking']:\n",
    "#             X_input[col] = False\n",
    "#         else:\n",
    "#             X_input[col] = 0\n",
    "#     else:\n",
    "#         X_input[col] = input_df[col]\n",
    "#     # if MTRANS_Automobile, MTRANS_Bike, MTRANS_Motorbike, MTRANS_Public_Transportation, MTRANS_Walking are zero, make them False\n",
    "    \n",
    "# y_pred_proba = model.predict(X_input)\n",
    "# y_pred = np.argmax(y_pred_proba)\n",
    "\n",
    "# y_pred"
   ]
  }
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