310 lines (309 with data), 110.9 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 164,
   "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_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": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9952747150931159\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9040385327899222\n"
     ]
    }
   ],
   "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",
    "\n",
    "lgb_train = lgb.Dataset(X_train, y_train)\n",
    "params = {\n",
    "    'objective': 'multiclass',\n",
    "    'num_class': 7,\n",
    "    'metric': 'multi_logloss',\n",
    "    'boosting_type': 'gbdt',\n",
    "    'learning_rate': 0.01,\n",
    "    'num_leaves': 31,\n",
    "    'max_depth': -1,\n",
    "    'bagging_fraction': 0.8,\n",
    "    'feature_fraction': 0.8,\n",
    "    'verbosity': -1\n",
    "}\n",
    "\n",
    "model = lgb.train(params, lgb_train, num_boost_round=1000)\n",
    "y_pred = model.predict(X_train, num_iteration=model.best_iteration)\n",
    "y_pred = [np.argmax(y) for y in y_pred]\n",
    "accuracy = accuracy_score(y_train, y_pred)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "\n",
    "# feature importance\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "lgb.plot_importance(model, ax=ax)\n",
    "plt.show()\n",
    "\n",
    "# Validation\n",
    "X_val = val_df[features]\n",
    "y_val = val_df[Target]\n",
    "y_pred = model.predict(X_val, num_iteration=model.best_iteration)\n",
    "y_pred = [np.argmax(y) for y in y_pred]\n",
    "accuracy = accuracy_score(y_val, y_pred)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "projects-LtESSeSG-py3.10",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
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