Feature Importance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1356c763",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from pandas import Series, DataFrame\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, LassoCV, Ridge\n",
    "from sklearn.feature_selection import RFE,RFECV\n",
    "from lofo import LOFOImportance, Dataset, plot_importance\n",
    "sns.set()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "777f0b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>b</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        b  lstat  medv  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('BostonHousing.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ed2491b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crim       0\n",
       "zn         0\n",
       "indus      0\n",
       "chas       0\n",
       "nox        0\n",
       "rm         0\n",
       "age        0\n",
       "dis        0\n",
       "rad        0\n",
       "tax        0\n",
       "ptratio    0\n",
       "b          0\n",
       "lstat      0\n",
       "medv       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6ec1ce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n",
       "       'ptratio', 'b', 'lstat', 'medv'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "062ecf75",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.medv\n",
    "X = df.drop('medv', axis=1) # drop column \"Price\" from the dataset \n",
    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a48c0e2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (test data) : 4.8354\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"RMSE (test data) : %.4f\" % np.sqrt(np.mean((y_test-y_pred)**2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4867d0f",
   "metadata": {},
   "source": [
    "# 1. Filter Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ddaa4a",
   "metadata": {},
   "source": [
    "**It is an approach that, if the correlation result is below a threshold that we define, we will not use those features.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "036ef913",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = df_train.corr()\n",
    "f, ax = plt.subplots(figsize=(15, 12))\n",
    "_ = sns.heatmap(corrmat, linecolor = 'white', annot=True, cmap = 'magma', linewidths = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c4d2bdaa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medv       1.000000\n",
       "lstat     -0.741302\n",
       "rm         0.670963\n",
       "ptratio   -0.524866\n",
       "indus     -0.498042\n",
       "tax       -0.464379\n",
       "nox       -0.449965\n",
       "age       -0.413612\n",
       "rad       -0.393258\n",
       "crim      -0.392849\n",
       "zn         0.387510\n",
       "b          0.329432\n",
       "dis        0.280202\n",
       "chas       0.156214\n",
       "Name: medv, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation with target\n",
    "\n",
    "corr = df_train.corr()['medv']\n",
    "corr[np.argsort(abs(corr), axis=0)[::-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5b699e",
   "metadata": {},
   "source": [
    "**Let's say threshold is 0.40:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f3406e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (test data) : 5.4781\n"
     ]
    }
   ],
   "source": [
    "cols = ['indus', 'nox', 'rm', 'age', 'tax','ptratio', 'lstat']\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train[cols], y_train)\n",
    "y_pred = model.predict(X_test[cols])\n",
    "print(\"RMSE (test data) : %.4f\" % np.sqrt(np.mean((y_test-y_pred)**2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2cd2905",
   "metadata": {},
   "source": [
    "**Let's say threshold is 0.30:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d796f0f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (test data) : 5.1759\n"
     ]
    }
   ],
   "source": [
    "cols = ['indus', 'nox', 'rm', 'age', 'tax','ptratio', 'lstat','b','zn','crim','rad']\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train[cols], y_train)\n",
    "y_pred = model.predict(X_test[cols])\n",
    "print(\"RMSE (test data) : %.4f\" % np.sqrt(np.mean((y_test-y_pred)**2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84ecd91e",
   "metadata": {},
   "source": [
    "# 2. Wrapper Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2a4f86",
   "metadata": {},
   "source": [
    "## <span style=\"color:blue\">Recursive Feature Elimination (RFE)</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f16dc726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a2107f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True False  True  True  True  True  True\n",
      "  True]\n",
      "['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax', 'ptratio', 'b', 'lstat']\n"
     ]
    }
   ],
   "source": [
    "a = 1 \n",
    "score_list =[]\n",
    "\n",
    "for n in range(1,len(df.columns)):\n",
    "    X_trn, X_tst, y_trn, y_tst = train_test_split(X_train, y_train, test_size = 0.2, random_state = 0)\n",
    "    model = LinearRegression()\n",
    "    rfe = RFE(model,n_features_to_select=n)\n",
    "    X_trn_rfe = rfe.fit_transform(X_trn,y_trn)\n",
    "    X_tst_rfe = rfe.transform(X_tst)\n",
    "    model.fit(X_trn_rfe,y_trn)\n",
    "    score = model.score(X_tst_rfe,y_tst)\n",
    "    score_list.append(score)\n",
    "\n",
    "model = LinearRegression()\n",
    "rfe = RFE(model, n_features_to_select=score_list.index(max(score_list))+1)                 \n",
    "X_rfe = rfe.fit_transform(X_train, y_train)  \n",
    "model.fit(X_rfe,y_train)              \n",
    "print(rfe.support_)\n",
    "print(X_train.columns.to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3f11fbb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (test data) : 4.8326\n"
     ]
    }
   ],
   "source": [
    "use_cols = ['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'dis', 'rad', 'tax', 'ptratio', 'b', 'lstat']\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train[use_cols], y_train)\n",
    "y_pred = model.predict(X_test[use_cols])\n",
    "print(\"RMSE (test data) : %.4f\" % np.sqrt(np.mean((y_test-y_pred)**2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a0eb81",
   "metadata": {},
   "source": [
    "## <span style=\"color:blue\">RFECV (combination of RFE and Cross-Validation)</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4d45b90b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features: 13\n",
      "\n",
      "Best features:\n",
      "----------------\n",
      "['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax', 'ptratio', 'b', 'lstat']\n"
     ]
    }
   ],
   "source": [
    "selector = RFECV(estimator=model, step=1, cv=5, scoring='neg_root_mean_squared_error')\n",
    "selector = selector.fit(X_train, y_train)    \n",
    "print(f'Optimal number of features: {selector.n_features_}')\n",
    "print()\n",
    "print(f'Best features:')\n",
    "print(\"----------------\")\n",
    "print(f'{X_train.columns[selector.support_].tolist()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b9a2fb0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(selector.grid_scores_) + 1), selector.grid_scores_.mean(axis=1))\n",
    "plt.axvline(x=selector.n_features_, color='gray', alpha=0.6);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "784de85f",
   "metadata": {},
   "source": [
    "# 3. Embedded Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a7da674",
   "metadata": {},
   "source": [
    "## <span style=\"color:blue\">LOFO (Leave-one-feature-out)</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d9f34d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = Dataset(df=df_train, target='medv', features=[col for col in df.columns if col != 'medv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "42ebfa91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b769941fb88c4a5d8c35241fd20f07f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/13 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lofo_imp = LOFOImportance(dataset, model=model,scoring='neg_mean_squared_error')\n",
    "importance_df = lofo_imp.get_importance()\n",
    "plot_importance(importance_df, figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e10b6fd5",
   "metadata": {},
   "source": [
    "LOFO removes **INDUS** and **AGE**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "23633eb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (test data) : 4.8335\n"
     ]
    }
   ],
   "source": [
    "cols = ['crim', 'zn', 'chas', 'nox', 'rm', 'dis', 'rad', 'tax', 'ptratio', 'b', 'lstat'] \n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train[cols], y_train)\n",
    "y_pred = model.predict(X_test[cols])\n",
    "print(\"RMSE (test data) : %.4f\" % np.sqrt(np.mean((y_test-y_pred)**2)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}