rf+xgbhist_one_hot_encoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4aa5ad88",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.inspection import permutation_importance\n",
    "import time \n",
    "\n",
    "from Script import *\n",
    "\n",
    "df_train = pd.read_csv(\"Data/train.csv\")\n",
    "df_test  = pd.read_csv(\"Data/test.csv\")\n",
    "\n",
    "df_train = df_train.drop(\"i\", axis=1)\n",
    "y = df_train.pop(\"y\")\n",
    "df_train.insert(54,\"y\", y)\n",
    "df_test = df_test.drop([\"y\", \"i\"], axis = 1)\n",
    "\n",
    "\n",
    "df_train, df_test = preprocess_data(df_train, df_test, verbose=False)\n",
    "\n",
    "\n",
    "X = df_train.iloc[:, 0:-1]\n",
    "y = df_train.iloc[:, -1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3012e735",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_encoder(X):\n",
    "    X_2 = X.select_dtypes(include=['category'])\n",
    "    enc = OneHotEncoder(drop='first', handle_unknown='ignore')\n",
    "    enc.fit(X_2)\n",
    "    return enc\n",
    "\n",
    "_x_encoder = create_encoder(X)\n",
    "\n",
    "def prepare(X):\n",
    "    cat_col = X.select_dtypes(include=['category']).columns\n",
    "    aux_transformed = _x_encoder.transform(X[cat_col]).toarray()\n",
    "    X.drop(cat_col, axis=1)\n",
    "    X_onehot = pd.concat([X, pd.DataFrame(aux_transformed)], axis=1)\n",
    "    return X_onehot\n",
    "\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "onehot_pipe  = FunctionTransformer(prepare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d6438c39",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "clf1 = make_pipeline(onehot_pipe , SVC(gamma='auto', probability=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e0784f3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('functiontransformer',\n",
       "                 FunctionTransformer(func=<function prepare at 0x7fcd930ad5e0>)),\n",
       "                ('svc', SVC(gamma='auto', probability=True))])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "fdfb5b53",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:170: UserWarning: Found unknown categories in columns [0, 1, 2] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1.6382</td>\n",
       "      <td>1.5961</td>\n",
       "      <td>1.1843</td>\n",
       "      <td>1.1702</td>\n",
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       "      <th>1</th>\n",
       "      <td>-0.0305</td>\n",
       "      <td>-0.0323</td>\n",
       "      <td>-0.0259</td>\n",
       "      <td>-0.0274</td>\n",
       "      <td>-0.0317</td>\n",
       "      <td>-0.0339</td>\n",
       "      <td>-0.0362</td>\n",
       "      <td>-0.0357</td>\n",
       "      <td>-0.0240</td>\n",
       "      <td>-0.0263</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.0586</td>\n",
       "      <td>0.0872</td>\n",
       "      <td>0.0811</td>\n",
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       "      <td>0.1392</td>\n",
       "      <td>0.1721</td>\n",
       "      <td>0.1469</td>\n",
       "      <td>-0.0167</td>\n",
       "      <td>-0.0054</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0726</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0617</td>\n",
       "      <td>0.0595</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>0.1164</td>\n",
       "      <td>0.0911</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.0184</td>\n",
       "      <td>-0.0194</td>\n",
       "      <td>-0.0241</td>\n",
       "      <td>-0.0257</td>\n",
       "      <td>-0.0291</td>\n",
       "      <td>-0.0314</td>\n",
       "      <td>-0.0161</td>\n",
       "      <td>-0.0157</td>\n",
       "      <td>-0.0178</td>\n",
       "      <td>-0.0197</td>\n",
       "      <td>...</td>\n",
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       "      <th>5829</th>\n",
       "      <td>-0.0276</td>\n",
       "      <td>-0.0296</td>\n",
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       "    <tr>\n",
       "      <th>5830</th>\n",
       "      <td>-0.0295</td>\n",
       "      <td>-0.0309</td>\n",
       "      <td>-0.0259</td>\n",
       "      <td>-0.0274</td>\n",
       "      <td>-0.0316</td>\n",
       "      <td>-0.0337</td>\n",
       "      <td>-0.0311</td>\n",
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       "      <td>-0.0253</td>\n",
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       "      <th>5831</th>\n",
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       "    <tr>\n",
       "      <th>5832</th>\n",
       "      <td>-0.0286</td>\n",
       "      <td>-0.0278</td>\n",
       "      <td>-0.0259</td>\n",
       "      <td>-0.0274</td>\n",
       "      <td>-0.0317</td>\n",
       "      <td>-0.0339</td>\n",
       "      <td>-0.0309</td>\n",
       "      <td>-0.0293</td>\n",
       "      <td>-0.0242</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>5833</th>\n",
       "      <td>-0.0287</td>\n",
       "      <td>-0.0224</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>-0.0164</td>\n",
       "      <td>-0.0254</td>\n",
       "      <td>-0.0160</td>\n",
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       "      <td>1.0</td>\n",
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       "<p>5834 rows × 788 columns</p>\n",
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      ],
      "text/plain": [
       "          x1      x2      x3      x4      x5      x6      x7      x8      x9  \\\n",
       "0     1.6382  1.5961  1.1843  1.1702  1.8396  1.8160  1.8586  1.8619  1.2993   \n",
       "1    -0.0305 -0.0323 -0.0259 -0.0274 -0.0317 -0.0339 -0.0362 -0.0357 -0.0240   \n",
       "2     0.0536  0.0586  0.0872  0.0811  0.1498  0.1392  0.1721  0.1469 -0.0167   \n",
       "3     0.0726  0.0490  0.0617  0.0595  0.1100  0.1060  0.1164  0.0911  0.0391   \n",
       "4    -0.0184 -0.0194 -0.0241 -0.0257 -0.0291 -0.0314 -0.0161 -0.0157 -0.0178   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "5829 -0.0276 -0.0296 -0.0230 -0.0239 -0.0300 -0.0313 -0.0309 -0.0300 -0.0228   \n",
       "5830 -0.0295 -0.0309 -0.0259 -0.0274 -0.0316 -0.0337 -0.0311 -0.0303 -0.0253   \n",
       "5831  0.0753  0.0590  0.0299  0.0422  0.0589  0.0787 -0.0221 -0.0139  0.1157   \n",
       "5832 -0.0286 -0.0278 -0.0259 -0.0274 -0.0317 -0.0339 -0.0309 -0.0293 -0.0242   \n",
       "5833 -0.0287 -0.0224 -0.0220 -0.0164 -0.0254 -0.0160 -0.0339 -0.0359 -0.0227   \n",
       "\n",
       "         x10  ...  724  725  726  727  728  729  730  731  732  733  \n",
       "0     1.1989  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1    -0.0263  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2    -0.0054  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3     0.0149  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4    -0.0197  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "5829 -0.0259  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "5830 -0.0277  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "5831  0.0947  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "5832 -0.0237  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "5833 -0.0117  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "\n",
       "[5834 rows x 788 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a4e49d39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9', 'c10', 'c11',\n",
      "       'c12', 'o1', 'o2'],\n",
      "      dtype='object')\n",
      "410\n",
      "204\n",
      "77\n",
      "4\n",
      "3\n",
      "2\n",
      "4\n",
      "2\n",
      "5\n",
      "4\n",
      "4\n",
      "4\n",
      "4\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "cat_col = df_test.select_dtypes(include=['category']).columns\n",
    "print(cat_col)\n",
    "for i in cat_col:\n",
    "    print(len(df_test[i].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "33b8c2fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "418\n",
      "202\n",
      "73\n",
      "4\n",
      "3\n",
      "2\n",
      "4\n",
      "2\n",
      "5\n",
      "4\n",
      "4\n",
      "4\n",
      "4\n",
      "19\n"
     ]
    }
   ],
   "source": [
    "for i in cat_col:\n",
    "    print(len(X[i].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "93420d0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting Classifiers\n",
      "Hard Voting \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aux stop\n",
      "hard voting nan\n",
      "aux stop\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py:1675: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
      "10 fits failed out of a total of 10.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "10 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 681, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/ensemble/_voting.py\", line 324, in fit\n",
      "    return super().fit(X, transformed_y, sample_weight)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/ensemble/_voting.py\", line 74, in fit\n",
      "    self.estimators_ = Parallel(n_jobs=self.n_jobs)(\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/joblib/parallel.py\", line 1043, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/joblib/parallel.py\", line 779, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 211, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/ensemble/_base.py\", line 42, in _fit_single_estimator\n",
      "    estimator.fit(X, y)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/pipeline.py\", line 394, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/svm/_base.py\", line 190, in fit\n",
      "    X, y = self._validate_data(\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/base.py\", line 576, in _validate_data\n",
      "    X, y = check_X_y(X, y, **check_params)\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py\", line 956, in check_X_y\n",
      "    X = check_array(\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py\", line 792, in check_array\n",
      "    _assert_all_finite(array, allow_nan=force_all_finite == \"allow-nan\")\n",
      "  File \"/home/antonio/miniconda3/envs/TFgpu/lib/python3.9/site-packages/sklearn/utils/validation.py\", line 114, in _assert_all_finite\n",
      "    raise ValueError(\n",
      "ValueError: Input contains NaN, infinity or a value too large for dtype('float64').\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Fitting Classifiers\")\n",
    "eclf1 = VotingClassifier(estimators=[\n",
    "        ('svm', clf1)], voting='hard')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(\"Hard Voting \")\n",
    "hard_scores = cross_val_score(eclf1, X, y, cv=10)\n",
    "\n",
    "print(\"aux stop\")\n",
    "\n",
    "\n",
    "# import joblib\n",
    "# name_of_model = \"rf+svm\"\n",
    "\n",
    "# joblib.dump(hard_scores, \"Temp/\"+name_of_model+\"_hard_scores\"+\".joblib\")\n",
    "# joblib.dump(hard_scores, \"Temp/\"+ name_of_model+\"_soft_scores\"+ \".joblib\")\n",
    "\n",
    "print(\"hard voting\", hard_scores.mean())\n",
    "\n",
    "\n",
    "print(\"aux stop\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8df3a49f",
   "metadata": {},
   "outputs": [
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       "      <td>-0.0312</td>\n",
       "      <td>-0.0259</td>\n",
       "      <td>-0.0274</td>\n",
       "      <td>-0.0317</td>\n",
       "      <td>-0.0339</td>\n",
       "      <td>-0.0300</td>\n",
       "      <td>-0.0292</td>\n",
       "      <td>-0.0248</td>\n",
       "      <td>-0.0287</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0169</td>\n",
       "      <td>-0.0133</td>\n",
       "      <td>-0.1744</td>\n",
       "      <td>-0.0219</td>\n",
       "      <td>-0.0085</td>\n",
       "      <td>0.1027</td>\n",
       "      <td>-4.173895</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5831</th>\n",
       "      <td>-0.0306</td>\n",
       "      <td>-0.0324</td>\n",
       "      <td>-0.0259</td>\n",
       "      <td>-0.0274</td>\n",
       "      <td>-0.0317</td>\n",
       "      <td>-0.0339</td>\n",
       "      <td>-0.0368</td>\n",
       "      <td>-0.0394</td>\n",
       "      <td>-0.0239</td>\n",
       "      <td>-0.0241</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0169</td>\n",
       "      <td>-0.0133</td>\n",
       "      <td>-0.1744</td>\n",
       "      <td>-0.0219</td>\n",
       "      <td>-0.0085</td>\n",
       "      <td>0.1027</td>\n",
       "      <td>-4.173895</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5832 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          x1      x2      x3      x4      x5      x6      x7      x8      x9  \\\n",
       "0    -0.0229 -0.0250 -0.0173 -0.0269 -0.0181 -0.0330 -0.0248 -0.0244 -0.0195   \n",
       "1    -0.0110 -0.0173 -0.0226 -0.0183 -0.0264 -0.0191  0.0136  0.0007 -0.0232   \n",
       "2    -0.0278 -0.0294 -0.0223 -0.0239 -0.0259 -0.0281 -0.0272 -0.0266 -0.0250   \n",
       "3    -0.0292 -0.0309 -0.0233 -0.0248 -0.0276 -0.0296 -0.0274 -0.0270 -0.0269   \n",
       "4    -0.0300 -0.0317 -0.0259 -0.0274 -0.0317 -0.0339 -0.0380 -0.0375 -0.0224   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "5827 -0.0303 -0.0317 -0.0259 -0.0274 -0.0317 -0.0339 -0.0314 -0.0307 -0.0262   \n",
       "5828 -0.0189  0.0030 -0.0236 -0.0248 -0.0280 -0.0296 -0.0296 -0.0256 -0.0114   \n",
       "5829 -0.0124 -0.0229 -0.0231 -0.0235 -0.0272 -0.0274 -0.0322 -0.0293 -0.0009   \n",
       "5830 -0.0287 -0.0312 -0.0259 -0.0274 -0.0317 -0.0339 -0.0300 -0.0292 -0.0248   \n",
       "5831 -0.0306 -0.0324 -0.0259 -0.0274 -0.0317 -0.0339 -0.0368 -0.0394 -0.0239   \n",
       "\n",
       "         x10  ...     x31     x32     x33      x34      x35     x36       x37  \\\n",
       "0    -0.0226  ... -0.0169 -0.0133 -0.1744  -0.0219  -0.0085  0.1027 -1.900400   \n",
       "1    -0.0268  ... -0.0169 -0.0133 -0.1744  -0.0219  -0.0085  0.1027 -2.244900   \n",
       "2    -0.0277  ... -0.0169 -0.0133 -0.1744  -0.0219  -0.0085  0.1027 -2.429100   \n",
       "3    -0.0296  ... -0.0169 -0.0133 -0.1744  -0.0219  -0.0085  0.1027 -2.429100   \n",
       "4    -0.0243  ... -0.0169 -0.0133 -0.1744  -0.0219  -0.0085  0.1027 -2.105500   \n",
       "...      ...  ...     ...     ...     ...      ...      ...     ...       ...   \n",
       "5827 -0.0286  ... -0.0169 -0.0069  2.0060  -0.0219  -0.0085  0.1027 -4.173895   \n",
       "5828  0.0195  ...  0.0314 -0.0027  3.9440  11.2800  14.2946  3.5971 -4.173895   \n",
       "5829 -0.0164  ... -0.0169 -0.0095  2.0060  11.2800  14.2946  3.5971 -4.173895   \n",
       "5830 -0.0287  ... -0.0169 -0.0133 -0.1744  -0.0219  -0.0085  0.1027 -4.173895   \n",
       "5831 -0.0241  ... -0.0169 -0.0133 -0.1744  -0.0219  -0.0085  0.1027 -4.173895   \n",
       "\n",
       "         x38  x39  x40  \n",
       "0     1.0000  1.0  0.0  \n",
       "1     1.0000  1.0  0.0  \n",
       "2     1.0000  1.0  0.0  \n",
       "3     1.0000  1.0  0.0  \n",
       "4     1.0000  1.0  0.0  \n",
       "...      ...  ...  ...  \n",
       "5827  0.7527  1.0  0.0  \n",
       "5828  0.7802  1.0  0.0  \n",
       "5829  1.0000  1.0  0.0  \n",
       "5830  1.0000  1.0  0.0  \n",
       "5831  1.0000  1.0  0.0  \n",
       "\n",
       "[5832 rows x 40 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xp = X.copy\n",
    "X.drop(cat_col, axis = 1)"
   ]
  }
 ],
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