demo_ml_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import polars as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "sys.path.append('/kaggle/input/machine-learning-template')\n",
    "from ml_template.utils import KFold_Sampler, multiple_table\n",
    "from ml_template.utils.confusion_matrix_pretty_print import pp_matrix_from_data\n",
    "from ml_template.utils.eval_utils import *\n",
    "from ml_template.model.ml_model import *\n",
    "from ml_template.model import *\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from category_encoders import MEstimateEncoder\n",
    "\n",
    "\n",
    "from nltk.tokenize import (word_tokenize, MWETokenizer)\n",
    "#!pip install pyspellchecker\n",
    "#from spellchecker import SpellChecker\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "pd.set_option('display.max_columns', None)\n",
    "ori_data_df = pd.read_csv('./data/regression_example/html2023-spring-final-project/train.csv')\n",
    "ori_test_df = pd.read_csv('./data/regression_example/html2023-spring-final-project/test.csv')\n",
    "test_partial_ans = pd.read_csv('./data/regression_example/test_partial_answer.csv')\n",
    "data_df = ori_data_df.copy()\n",
    "test_df = ori_test_df.copy()\n",
    "combine_df = pd.concat([data_df,test_df])\n",
    "label_column = 'Danceability'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " [Text(0, 0, \"Finneas O'Connell\"),\n",
       "  Text(1, 0, 'Louis Bell'),\n",
       "  Text(2, 0, 'Juicy J'),\n",
       "  Text(3, 0, 'Noah \"40\" Shebib'),\n",
       "  Text(4, 0, 'Mike Dean'),\n",
       "  Text(5, 0, 'Yeti Beats'),\n",
       "  Text(6, 0, 'Terrace Martin'),\n",
       "  Text(7, 0, 'Ludwig Göransson'),\n",
       "  Text(8, 0, 'Ricky Reed'),\n",
       "  Text(9, 0, 'J. Cole')])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax =  plt.subplots(1,1,figsize=(10,5))\n",
    "sns.boxplot(combine_df,x='Composer',y=label_column)\n",
    "plt.xticks(rotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_df = ori_test_df[ori_test_df.id.isin(test_partial_ans.id)]\n",
    "val_df[label_column] = test_partial_ans[label_column].values\n",
    "test_df = test_df[~test_df.id.isin(test_partial_ans.id)]\n",
    "data_df['set'] = 'train'\n",
    "val_df['set'] = 'val'\n",
    "test_df['set'] = 'test'\n",
    "combine_df = pd.concat([data_df,val_df,test_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import category_encoders as ce\n",
    "# _data_df = combine_df[(combine_df.set=='train') | (combine_df.set == 'val')].copy().drop(columns=['set'])\n",
    "_data_df = combine_df[(combine_df.set=='train') ].copy().drop(columns=['set'])\n",
    "\n",
    "artist_loo_encoder = ce.LeaveOneOutEncoder()\n",
    "artist_loo_encoder.fit(_data_df['Artist'], _data_df.Danceability)\n",
    "\n",
    "composer_loo_encoder = ce.LeaveOneOutEncoder()\n",
    "composer_loo_encoder.fit(_data_df['Composer'], _data_df.Danceability)\n",
    "\n",
    "\n",
    "album_type_loo_encoder = ce.LeaveOneOutEncoder()\n",
    "album_type_loo_encoder.fit(_data_df['Album_type'], _data_df.Danceability)\n",
    "\n",
    "channel_loo_encoder = ce.LeaveOneOutEncoder()\n",
    "channel_loo_encoder.fit(_data_df['Channel'], _data_df.Danceability)\n",
    "\n",
    "Composer_Album_type_te = _data_df.groupby(['Composer','Album_type']).Danceability.mean()\n",
    "Composer_Album_type_te=Composer_Album_type_te.reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Energy', 'Key', 'Loudness', 'Speechiness', 'Acousticness',\n",
       "       'Instrumentalness', 'Liveness', 'Valence', 'Tempo', 'Duration_ms',\n",
       "       ...\n",
       "       'Artist_Tove Lo', 'Artist_Travis Scott', 'Artist_Ty Segall',\n",
       "       'Artist_Tyler, The Creator', 'Artist_Unknown Mortal Orchestra',\n",
       "       'Artist_Usher', 'Artist_Vampire Weekend', 'Artist_Zedd',\n",
       "       'Artist_deadmau5', 'Keyword'],\n",
       "      dtype='object', length=130)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def label_encoder(df,columns):\n",
    "    df = pd.get_dummies(df,columns=columns)\n",
    "    return df\n",
    "\n",
    "def keyword_search(df,keywords):\n",
    "    df['Keyword'] = df['Title'].apply(lambda x: any([ k in x.lower()  for k in keywords]))\n",
    "    return df\n",
    "\n",
    "def target_encode(df):\n",
    "    df['Artist_te'] = artist_loo_encoder.transform(df['Artist'])\n",
    "    df['Composer_te'] = composer_loo_encoder.transform(df['Composer'])\n",
    "    df['Album_type_te'] = album_type_loo_encoder.transform(df['Album_type'])\n",
    "    # df['Channel_te'] = channel_loo_encoder.transform(df['Channel'])\n",
    "    return df\n",
    "\n",
    "def baseline_process(df,feature_columns):\n",
    "    df['Album_type'] = df['Album_type'].fillna('album')\n",
    "    df['Composer'] = df['Composer'].fillna('unknow')\n",
    "    df['Description'] = df['Description'].fillna('')\n",
    "    df['Title'] = df['Title'].fillna('')\n",
    "    df['Album'] = df['Album'].fillna('')\n",
    "    df['Licensed'] = df['Licensed'].fillna(df['Licensed'].median()).astype(int)\n",
    "    df['official_video'] = df['official_video'].fillna(df['official_video'].median()).astype(int)\n",
    "    for col in feature_columns:\n",
    "        if df[col].dtype != object:\n",
    "            df[col] = df[col].fillna(df[col].median())\n",
    "    df = target_encode(df)\n",
    "    df = label_encoder(df,['Album_type','Composer','Artist'])\n",
    "    keywords = ['dance','dancing','party']\n",
    "    df = keyword_search(df,keywords)\n",
    "    return df\n",
    "\n",
    "drop_feature_columns = ['Danceability', 'Description', 'Title',\\\n",
    "                         'set', 'Uri', 'Url_spotify',\\\n",
    "                        'Url_youtube','id','Track','Album',\\\n",
    "                        'Channel']\n",
    "feature_columns = data_df.columns.drop(drop_feature_columns)\n",
    "combine_df = baseline_process(combine_df,feature_columns)\n",
    "feature_columns = combine_df.columns.drop(drop_feature_columns)\n",
    "feature_columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = combine_df[combine_df.set=='test'].copy().drop(columns=['set'])\n",
    "val_df = combine_df[combine_df.set=='val'].copy().drop(columns=['set'])\n",
    "data_df = combine_df[(combine_df.set=='train') | (combine_df.set == 'val')].copy().drop(columns=['set'])\n",
    "# data_df = combine_df[(combine_df.set=='train')].copy().drop(columns=['set'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, AdaBoostClassifier\n",
    "from sklearn.model_selection import StratifiedKFold, RepeatedStratifiedKFold\n",
    "from sklearn.metrics import accuracy_score,precision_recall_fscore_support, roc_auc_score, f1_score, recall_score, precision_score, mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression, BayesianRidge, LassoLars, SGDRegressor, SGDOneClassSVM, PassiveAggressiveRegressor, PassiveAggressiveClassifier, TweedieRegressor,MultiTaskElasticNet,HuberRegressor, QuantileRegressor, TheilSenRegressor\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n",
    "from sklearn.svm import SVR, SVC\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from xgboost import XGBRegressor, XGBClassifier\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor, GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel, CompoundKernel, RBF, Sum, Matern, Exponentiation, PairwiseKernel\n",
    "from sklearn.neural_network import MLPRegressor, MLPClassifier\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.neighbors import KNeighborsRegressor,KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from catboost import CatBoostRegressor ,CatBoostClassifier\n",
    "import lightgbm as lgb\n",
    "from glob import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, sys\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from collections import Counter\n",
    "import copy\n",
    "from tabpfn import TabPFNClassifier\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import QuantileTransformer, PowerTransformer, StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ML_Weighted_Model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 51\u001b[0m\n\u001b[1;32m     48\u001b[0m model \u001b[39m=\u001b[39m Stack_Ensemble_Model(model_dict,stack_model\u001b[39m=\u001b[39mstack_model,stack_training_split\u001b[39m=\u001b[39m\u001b[39m0.85\u001b[39m)\n\u001b[1;32m     49\u001b[0m \u001b[39m#model = Mean_Ensemble_Model(model_dict)\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[39m#model=XGBRegressor(n_estimators=500,max_depth=4,colsample_bytree=0.8)\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m model\u001b[39m.\u001b[39;49mfit(data_df[feature_columns]\u001b[39m.\u001b[39;49mvalues,data_df[label_column]\u001b[39m.\u001b[39;49mvalues)\n",
      "File \u001b[0;32m~/Project/Machine-Learning-Template/ml_template/model/ml_model.py:43\u001b[0m, in \u001b[0;36mStack_Ensemble_Model.fit\u001b[0;34m(self, data, label)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit\u001b[39m(\u001b[39mself\u001b[39m, data, label):\n\u001b[1;32m     37\u001b[0m   split_dict\u001b[39m=\u001b[39m{\u001b[39m0.1\u001b[39m:(\u001b[39m10\u001b[39m,\u001b[39m1\u001b[39m),\n\u001b[1;32m     38\u001b[0m               \u001b[39m0.2\u001b[39m:(\u001b[39m5\u001b[39m,\u001b[39m1\u001b[39m),\n\u001b[1;32m     39\u001b[0m               \u001b[39m0.15\u001b[39m:(\u001b[39m6\u001b[39m,\u001b[39m1\u001b[39m),\n\u001b[1;32m     40\u001b[0m               \u001b[39m0.25\u001b[39m:(\u001b[39m4\u001b[39m,\u001b[39m1\u001b[39m),\n\u001b[1;32m     41\u001b[0m               \u001b[39m0.3\u001b[39m:(\u001b[39m3\u001b[39m,\u001b[39m1\u001b[39m),\n\u001b[1;32m     42\u001b[0m               \u001b[39m0.5\u001b[39m:(\u001b[39m2\u001b[39m,\u001b[39m1\u001b[39m),}\n\u001b[0;32m---> 43\u001b[0m   \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstack_model,ML_Weighted_Model) \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstack_model\u001b[39m.\u001b[39mload_stacking ):\n\u001b[1;32m     45\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstack_training_split \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m split_dict\u001b[39m.\u001b[39mkeys():\n\u001b[1;32m     46\u001b[0m       model_data, model_label, stack_model_data, stack_model_label \u001b[39m=\u001b[39m KFold_Sampler(data,label,n_splits\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m)\u001b[39m.\u001b[39mget_multi_fold(n_fold\u001b[39m=\u001b[39m\u001b[39mint\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstack_training_split\u001b[39m*\u001b[39m\u001b[39m100\u001b[39m))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ML_Weighted_Model' is not defined"
     ]
    }
   ],
   "source": [
    "model_lgb = lgb.LGBMRegressor(objective='regression',\n",
    "                            learning_rate=0.05, n_estimators=3000,\n",
    "                           )\n",
    "\n",
    "model_lgb_depth = lgb.LGBMRegressor(objective='regression',\n",
    "                            learning_rate=0.1, n_estimators=3000,\n",
    "                            max_bin = 55, bagging_fraction = 0.8,\n",
    "                            bagging_freq = 10, feature_fraction = 0.2319,\n",
    "                            feature_fraction_seed=9, bagging_seed=9,\n",
    "                            min_data_in_leaf =6, min_sum_hessian_in_leaf = 11,\n",
    "                            is_unbalance=False,verbosity=-1)\n",
    "\n",
    "model_dict = {\n",
    "              #'RF_depth_None':RandomForestRegressor(n_estimators=1300,max_depth=5),\n",
    "              'LGB_Reg_Depth':model_lgb_depth,\n",
    "              'LGB_None':model_lgb,\n",
    "              # 'MLP':MLPRegressor(activation = \"relu\", alpha = 0.1, hidden_layer_sizes = (100,10),\n",
    "              #               learning_rate = \"constant\", max_iter = 3000, random_state = 1000),\n",
    "              #'XGB_31_3':XGBRegressor(n_estimators=500,max_depth=4),\n",
    "              #'XGB_310_3':XGBRegressor(n_estimators=31,max_depth=4),\n",
    "              #'XGB_4':XGBRegressor(n_estimators=500,max_depth=4,colsample_bytree=0.8),\n",
    "              #'XGB_2':XGBRegressor(n_estimators=500,max_depth=2,colsample_bytree=0.8),\n",
    "              'XGB_None':XGBRegressor(n_estimators=500,colsample_bytree=0.8),\n",
    "              'XGB_robust':XGBClassifier(n_estimators=31,max_depth=4,reg_lambda=5,num_parallel_tree=5,gamma=0.2,colsample_bytree=0.5, colsample_bylevel=0.8, colsample_bynode=0.8),\n",
    "              'XGB_310':XGBRegressor(n_estimators=310),\n",
    "              # 'GPR':GaussianProcessRegressor(),\n",
    "              \"Cat\":CatBoostRegressor(\n",
    "                          learning_rate=1e-5, \n",
    "                          loss_function='RMSE',verbose=0),\n",
    "              'SVM_2_RBF':SVR(C=1,kernel='rbf'),\n",
    "              'SVM_2_lin':SVR(C=1,kernel='linear'),\n",
    "              }\n",
    "qt=QuantileTransformer(n_quantiles=1000)\n",
    "st = StandardScaler()\n",
    "pt = PowerTransformer()\n",
    "data_df[feature_columns] = qt.fit_transform(data_df[feature_columns])\n",
    "val_df[feature_columns] = qt.transform(val_df[feature_columns])\n",
    "test_df[feature_columns] = qt.transform(test_df[feature_columns])\n",
    "num_model = len(list(model_dict.keys()))\n",
    "num_classes=1\n",
    "stack_model=ML_Weighted_Model(num_model,\n",
    "                              num_classes,\n",
    "                              lr=1e-2,\n",
    "                              epoch=200,\n",
    "                              model_reg=0.0,\n",
    "                              classes_reg=0.00,\n",
    "                              l1_norm=0.00)\n",
    "model = Stack_Ensemble_Model(model_dict,stack_model=stack_model,stack_training_split=0.85)\n",
    "#model = Mean_Ensemble_Model(model_dict)\n",
    "#model=XGBRegressor(n_estimators=500,max_depth=4,colsample_bytree=0.8)\n",
    "model.fit(data_df[feature_columns].values,data_df[label_column].values)\n",
    "#cv_model, cv_df = model.cross_validation_evaluate(data_df[feature_columns].values, data_df[label_column].values, evaluation_fn=mean_absolute_error,n_splits=10, n_repeats=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========== Model Classes Importance ============\n",
      "model\n",
      "LGB_None         0.173\n",
      "LGB_Reg_Depth    0.170\n",
      "XGB_310          0.156\n",
      "XGB_robust       0.130\n",
      "XGB_None         0.114\n",
      "SVM_2_RBF        0.101\n",
      "SVM_2_lin        0.082\n",
      "Cat              0.073\n",
      "Name: weights, dtype: float32\n",
      "                     weights\n",
      "class model                 \n",
      "0     LGB_None         0.173\n",
      "      LGB_Reg_Depth    0.170\n",
      "      XGB_310          0.156\n",
      "      XGB_robust       0.130\n",
      "      XGB_None         0.114\n",
      "      SVM_2_RBF        0.101\n",
      "      SVM_2_lin        0.082\n",
      "      Cat              0.073\n",
      "=========== Feature Importance ============\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  class                feature_name  importance  mean_importance\n",
      "0     0  Composer_Finneas O'Connell    0.011905         0.011905\n",
      "1     0                     Valence    0.008981         0.008981\n",
      "2     0                 Speechiness    0.007704         0.007704\n",
      "3     0                       Tempo    0.007630         0.007630\n",
      "4     0   Composer_Ludwig Göransson    0.006057         0.006057\n",
      "5     0                Acousticness    0.005075         0.005075\n",
      "6     0                      Energy    0.004663         0.004663\n",
      "7     0                    Loudness    0.004293         0.004293\n",
      "8     0                 Duration_ms    0.004206         0.004206\n",
      "9     0                    Liveness    0.004093         0.004093\n"
     ]
    }
   ],
   "source": [
    "stacking_analysis_df = weighted_stacking_analysis(model, feature_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjusting_error(df,threshold=0.4):\n",
    "    df['diff'] = (df[label_column].apply(lambda x: (x+0.5)//1) - df[label_column])\n",
    "    df['diff']=df['diff']*(df['diff'].abs()<=threshold).astype(int)\n",
    "    \n",
    "    df[label_column]  += df['diff']\n",
    "    df = df.drop(columns=['diff'])\n",
    "    return df\n",
    "def post_process(df, adjusting_threshold=0.4):\n",
    "    df[label_column] = df[label_column].apply(lambda x: 0 if x<0 else x).values\n",
    "    \n",
    "    df = adjusting_error(df,adjusting_threshold)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             model  eval_metric\n",
      "0          model_5     2.089912\n",
      "1          model_8     2.098558\n",
      "2          model_1     2.099393\n",
      "3         model_10     2.099836\n",
      "4   Ensemble Model     2.102947\n",
      "5          model_9     2.103216\n",
      "6          model_3     2.112509\n",
      "7          model_4     2.115349\n",
      "8          model_2     2.123818\n",
      "9          model_7     2.128337\n",
      "10         model_6     2.146066\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.090936677108287"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Mean_Ensemble_Model(cv_model)\n",
    "eval_dict = model.evaluate(val_df[feature_columns],val_df[label_column], evaluation_fn = mean_absolute_error)\n",
    "pred = eval_dict['model_predict']['Ensemble Model']\n",
    "_df = pd.DataFrame()\n",
    "_df['id'] = val_df.id\n",
    "_df[label_column] = pred\n",
    "_df = post_process(_df)\n",
    "mean_absolute_error(_df[label_column],val_df[label_column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(test_df[feature_columns])\n",
    "submit_df = pd.read_csv('./data/regression_example/html2023-spring-final-project/sample_submission.csv')\n",
    "submit_df.loc[submit_df.id.isin(test_df.id),label_column] = pred\n",
    "submit_df.loc[submit_df.id.isin(val_df.id),label_column] = val_df[label_column].values\n",
    "submit_df = post_process(submit_df)\n",
    "submit_df.to_csv('ensemble_submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'SVM_2_RBF'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[36], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m _,model_predict_dict\u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mmodel_predicts(test_df[feature_columns])\n\u001b[1;32m      2\u001b[0m ensemble_pred \u001b[39m=\u001b[39mmodel_predict_dict[\u001b[39m'\u001b[39m\u001b[39mEnsemble Model\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m----> 3\u001b[0m SVM_pred \u001b[39m=\u001b[39mmodel_predict_dict[\u001b[39m'\u001b[39;49m\u001b[39mSVM_2_RBF\u001b[39;49m\u001b[39m'\u001b[39;49m]\n\u001b[1;32m      4\u001b[0m XGB_pred \u001b[39m=\u001b[39mmodel_predict_dict[\u001b[39m'\u001b[39m\u001b[39mXGB_None\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m      5\u001b[0m LGB_pred \u001b[39m=\u001b[39mmodel_predict_dict[\u001b[39m'\u001b[39m\u001b[39mLGB_Reg_Depth\u001b[39m\u001b[39m'\u001b[39m]\n",
      "\u001b[0;31mKeyError\u001b[0m: 'SVM_2_RBF'"
     ]
    }
   ],
   "source": [
    "_,model_predict_dict= model.model_predicts(test_df[feature_columns])\n",
    "ensemble_pred =model_predict_dict['Ensemble Model']\n",
    "SVM_pred =model_predict_dict['SVM_2_RBF']\n",
    "XGB_pred =model_predict_dict['XGB_None']\n",
    "LGB_pred =model_predict_dict['LGB_Reg_Depth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "submit_df = pd.read_csv('./data/regression_example/html2023-spring-final-project/sample_submission.csv')\n",
    "submit_df.loc[submit_df.id.isin(test_df.id),label_column] = ensemble_pred\n",
    "submit_df.loc[submit_df.id.isin(val_df.id),label_column] = val_df[label_column].values\n",
    "submit_df = post_process(submit_df)\n",
    "submit_df.to_csv('ensemble_submission.csv',index=False)\n",
    "    \n",
    "\n",
    "submit_df = pd.read_csv('./data/regression_example/html2023-spring-final-project/sample_submission.csv')\n",
    "submit_df.loc[submit_df.id.isin(test_df.id),label_column] = SVM_pred\n",
    "submit_df.loc[submit_df.id.isin(val_df.id),label_column] = val_df[label_column].values\n",
    "submit_df = post_process(submit_df)\n",
    "submit_df.to_csv('svm_submission.csv',index=False)\n",
    "    \n",
    "    \n",
    "\n",
    "submit_df = pd.read_csv('./data/regression_example/html2023-spring-final-project/sample_submission.csv')\n",
    "submit_df.loc[submit_df.id.isin(test_df.id),label_column] = XGB_pred\n",
    "submit_df.loc[submit_df.id.isin(val_df.id),label_column] = val_df[label_column].values\n",
    "submit_df = post_process(submit_df)\n",
    "submit_df.to_csv('xgb_submission.csv',index=False)\n",
    "\n",
    "\n",
    "submit_df = pd.read_csv('./data/regression_example/html2023-spring-final-project/sample_submission.csv')\n",
    "submit_df.loc[submit_df.id.isin(test_df.id),label_column] = LGB_pred\n",
    "submit_df.loc[submit_df.id.isin(val_df.id),label_column] = val_df[label_column].values\n",
    "submit_df = post_process(submit_df)\n",
    "submit_df.to_csv('lgb_submission.csv',index=False)\n",
    "    \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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