Datasets
Models
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"WIP"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/arham/anaconda3/envs/DataScience/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
" from pandas.core import (\n"
]
}
],
"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_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"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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_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",
"\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",
"Target = 'NObeyesdad'\n",
"features = train_df.columns.drop(Target)\n",
"\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",
"\n",
"X_train = train_df[features]\n",
"y_train = train_df[Target]\n",
"X_val = val_df[features]\n",
"y_val = val_df[Target]\n",
"X_test = test_df[features]\n",
"y_test = test_df[Target]\n",
"\n",
"#combine X_train and y_train as one dataframe\n",
"tr = pd.concat([X_train, y_train], axis=1)\n",
"te = pd.concat([X_test, y_test], axis =1)\n",
"va = pd.concat([X_val, y_val], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No path specified. Models will be saved in: \"AutogluonModels/ag-20240421_174637\"\n"
]
},
{
"ename": "TypeError",
"evalue": "AbstractTabularLearner.__init__() got an unexpected keyword argument 'hyperparameters'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[6], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mautogluon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtabular\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TabularPredictor\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Train AutoGluon model with zero-shot HPO\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m predictor \u001b[38;5;241m=\u001b[39m \u001b[43mTabularPredictor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTarget\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearner_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhyperparameters\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mzero\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhyperparameters_extra\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mNN\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnum_epochs\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m}\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m predictor\u001b[38;5;241m.\u001b[39mfit(train_data\u001b[38;5;241m=\u001b[39mtr, tuning_data\u001b[38;5;241m=\u001b[39mva, time_limit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Evaluate on validation data\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/DataScience/lib/python3.10/site-packages/autogluon/tabular/predictor/predictor.py:255\u001b[0m, in \u001b[0;36mTabularPredictor.__init__\u001b[0;34m(self, label, problem_type, eval_metric, path, verbosity, log_to_file, log_file_path, sample_weight, weight_evaluation, groups, **kwargs)\u001b[0m\n\u001b[1;32m 252\u001b[0m learner_kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlearner_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mdict\u001b[39m())\n\u001b[1;32m 253\u001b[0m quantile_levels \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquantile_levels\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 255\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_learner: AbstractTabularLearner \u001b[38;5;241m=\u001b[39m \u001b[43mlearner_type\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath_context\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 258\u001b[0m \u001b[43m \u001b[49m\u001b[43mfeature_generator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 259\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_metric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_metric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[43m \u001b[49m\u001b[43mproblem_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproblem_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 261\u001b[0m \u001b[43m \u001b[49m\u001b[43mquantile_levels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquantile_levels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 262\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 263\u001b[0m \u001b[43m \u001b[49m\u001b[43mweight_evaluation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight_evaluation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 264\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 265\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mlearner_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_learner_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_learner)\n\u001b[1;32m 268\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_trainer: AbstractTrainer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/DataScience/lib/python3.10/site-packages/autogluon/tabular/learner/default_learner.py:31\u001b[0m, in \u001b[0;36mDefaultLearner.__init__\u001b[0;34m(self, trainer_type, **kwargs)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, trainer_type\u001b[38;5;241m=\u001b[39mAutoTrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 31\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer_type \u001b[38;5;241m=\u001b[39m trainer_type\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclass_weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"\u001b[0;31mTypeError\u001b[0m: AbstractTabularLearner.__init__() got an unexpected keyword argument 'hyperparameters'"
]
}
],
"source": [
"from autogluon.tabular import TabularPredictor\n",
"\n",
"# Train AutoGluon model with zero-shot HPO\n",
"predictor = TabularPredictor(label='Target', learner_kwargs={'hyperparameters': 'zero', 'hyperparameters_extra': {'NN': {'num_epochs': 10}}})\n",
"predictor.fit(train_data=tr, tuning_data=va, time_limit=None)\n",
"\n",
"# Evaluate on validation data\n",
"performance = predictor.evaluate(va)\n",
"\n",
"# Make predictions on test data\n",
"y_pred = predictor.predict(te)\n",
"\n",
"# Print evaluation metrics\n",
"print(performance)\n"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": []
}
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