{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f62b569-40d3-4fbe-a7e8-eae2c17b4650",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f206848-d082-4ffd-82db-665f2edbbf0f",
   "metadata": {},
   "source": [
    "#### Feature selection – variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a4f882d-fa66-4176-8861-411a9ecec123",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "data = load_wine()\n",
    "X = data.data\n",
    "y = data.target\n",
    "\n",
    "df = pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9060d9-5987-4e74-b5c0-8f16cc850621",
   "metadata": {},
   "source": [
    "##### Fairer Comparison of Variance With Feature Normalization\n",
    "Often, it is not fair to compare the variance of a feature to another. The reason is that as the values in the distribution get bigger, the variance grows exponentially. In other words, the variances will not be on the same scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7f5dc62-8b4e-47c4-b639-ba1698125138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.000618</td>\n",
       "      <td>2.336348</td>\n",
       "      <td>2.366517</td>\n",
       "      <td>19.494944</td>\n",
       "      <td>99.741573</td>\n",
       "      <td>2.295112</td>\n",
       "      <td>2.029270</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>1.590899</td>\n",
       "      <td>5.058090</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>2.611685</td>\n",
       "      <td>746.893258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.811827</td>\n",
       "      <td>1.117146</td>\n",
       "      <td>0.274344</td>\n",
       "      <td>3.339564</td>\n",
       "      <td>14.282484</td>\n",
       "      <td>0.625851</td>\n",
       "      <td>0.998859</td>\n",
       "      <td>0.124453</td>\n",
       "      <td>0.572359</td>\n",
       "      <td>2.318286</td>\n",
       "      <td>0.228572</td>\n",
       "      <td>0.709990</td>\n",
       "      <td>314.907474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.030000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>278.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.362500</td>\n",
       "      <td>1.602500</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.742500</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.782500</td>\n",
       "      <td>1.937500</td>\n",
       "      <td>500.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.050000</td>\n",
       "      <td>1.865000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2.355000</td>\n",
       "      <td>2.135000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>673.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.677500</td>\n",
       "      <td>3.082500</td>\n",
       "      <td>2.557500</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>3.170000</td>\n",
       "      <td>985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.830000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0           1           2           3           4           5   \\\n",
       "count  178.000000  178.000000  178.000000  178.000000  178.000000  178.000000   \n",
       "mean    13.000618    2.336348    2.366517   19.494944   99.741573    2.295112   \n",
       "std      0.811827    1.117146    0.274344    3.339564   14.282484    0.625851   \n",
       "min     11.030000    0.740000    1.360000   10.600000   70.000000    0.980000   \n",
       "25%     12.362500    1.602500    2.210000   17.200000   88.000000    1.742500   \n",
       "50%     13.050000    1.865000    2.360000   19.500000   98.000000    2.355000   \n",
       "75%     13.677500    3.082500    2.557500   21.500000  107.000000    2.800000   \n",
       "max     14.830000    5.800000    3.230000   30.000000  162.000000    3.880000   \n",
       "\n",
       "               6           7           8           9           10          11  \\\n",
       "count  178.000000  178.000000  178.000000  178.000000  178.000000  178.000000   \n",
       "mean     2.029270    0.361854    1.590899    5.058090    0.957449    2.611685   \n",
       "std      0.998859    0.124453    0.572359    2.318286    0.228572    0.709990   \n",
       "min      0.340000    0.130000    0.410000    1.280000    0.480000    1.270000   \n",
       "25%      1.205000    0.270000    1.250000    3.220000    0.782500    1.937500   \n",
       "50%      2.135000    0.340000    1.555000    4.690000    0.965000    2.780000   \n",
       "75%      2.875000    0.437500    1.950000    6.200000    1.120000    3.170000   \n",
       "max      5.080000    0.660000    3.580000   13.000000    1.710000    4.000000   \n",
       "\n",
       "                12  \n",
       "count   178.000000  \n",
       "mean    746.893258  \n",
       "std     314.907474  \n",
       "min     278.000000  \n",
       "25%     500.500000  \n",
       "50%     673.500000  \n",
       "75%     985.000000  \n",
       "max    1680.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e004363-de4a-484f-a26d-6ce8c7b5318d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9808451e-1a9d-46af-b351-5b56d532a0ac",
   "metadata": {},
   "source": [
    "The above features all have different medians, quartiles, and ranges — completely different distributions. We cannot compare these features to each other.\n",
    "\n",
    "One method we can use is scale all features using the Robust Scaler which is not highly affected by outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af13f786-a507-4c9f-9e68-09373ec6fb7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "transformer = RobustScaler().fit(X)\n",
    "X_scaled = transformer.transform(X)\n",
    "df_scaled = pd.DataFrame(X_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49183306-03e4-47fc-ae62-a6a3c7777934",
   "metadata": {},
   "source": [
    "This method ensures that all variances are on the same scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80bb2df6-cf89-49cb-8305-04a6ec9ccf71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.381132\n",
       "1     0.569766\n",
       "2     0.623277\n",
       "3     0.603174\n",
       "4     0.565067\n",
       "5     0.350252\n",
       "6     0.357746\n",
       "7     0.552056\n",
       "8     0.668561\n",
       "9     0.605204\n",
       "10    0.458666\n",
       "11    0.331842\n",
       "12    0.422453\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e4dd5dc-c703-47f2-9868-d43491d1f2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True False  True  True  True  True  True False\n",
      "  True]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "selector = VarianceThreshold(threshold=0.35)\n",
    "# Learn variances from X_scaled\n",
    "_ = selector.fit(X_scaled)\n",
    "# Get a mask (or integer index if indices=True is set) of the features selected\n",
    "mask = selector.get_support()\n",
    "print(mask)\n",
    "# get the subset of features selected\n",
    "X_transformed = X_scaled[:, mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "650ba8f4-a5a3-4a81-995d-4456630d53f0",
   "metadata": {},
   "source": [
    "#### Feature selection – importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be26b201-5c8d-4c2d-ba58-d36100afc3d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature 12 (0.240954)\n",
      "2. feature 11 (0.162438)\n",
      "3. feature 6 (0.149339)\n",
      "4. feature 0 (0.127384)\n",
      "5. feature 9 (0.125520)\n",
      "6. feature 10 (0.072550)\n",
      "7. feature 5 (0.031640)\n",
      "8. feature 1 (0.027186)\n",
      "9. feature 4 (0.021927)\n",
      "10. feature 3 (0.013679)\n",
      "11. feature 8 (0.012320)\n",
      "12. feature 2 (0.010575)\n",
      "13. feature 7 (0.004489)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we use the scaled features\n",
    "X = X_scaled\n",
    "y = data.target\n",
    "\n",
    "# Feature Importance 1\n",
    "# Use ensemble method: The goal of ensemble methods is to combine the \n",
    "# predictions of several base estimators built with a given learning algorithm \n",
    "# in order to improve generalizability / robustness over a single estimator.\n",
    "# http://scikit-learn.org/stable/modules/ensemble.html\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "# Build an estimator (forest of trees) and compute the feature importances\n",
    "# n_estimators = number of trees in forest\n",
    "estimator = ExtraTreesClassifier(n_estimators=100, max_features=13, random_state=0)\n",
    "estimator.fit(X,y)\n",
    "# Lets get the feature importances. Features with high importance score higher.\n",
    "importances = estimator.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in estimator.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "for f in range(X.shape[1]):\n",
    "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(X.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(X.shape[1]), indices)\n",
    "plt.xlim([-1, X.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ee6f6d3e-689e-4933-9c89-af70d789385a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True, False, False, False, False, False, True, False, False, True, False, True, True]\n",
      "[1, 3, 8, 5, 6, 4, 1, 9, 7, 1, 2, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "# Feature Importance 2\n",
    "from sklearn.feature_selection import RFE\n",
    "estimator = ExtraTreesClassifier(n_estimators=100, random_state=0)\n",
    "# keep the 5 most informative features\n",
    "selector = RFE(estimator, n_features_to_select=5, step=1)\n",
    "selector = selector.fit(X, y)\n",
    "print(list(selector.support_))\n",
    "print(list(selector.ranking_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018200ef-78f6-4cf1-a43f-0f128e29aa47",
   "metadata": {},
   "source": [
    "#### Feature selection – correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60c5f13e-728d-4fb7-b1fe-9c919faca46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import cm as cm\n",
    "\n",
    "plt.figure()\n",
    "# convert list of wine observations to a pandas dataframe \n",
    "# num of columns = num of features\n",
    "# find pairwise correlation of columns\n",
    "# default correlation method = pearson\n",
    "df = pd.DataFrame(X)\n",
    "df['y'] = y\n",
    "corr = df.corr(method='pearson')\n",
    "cmap = cm.get_cmap('jet', 30)\n",
    "_, ax = plt.subplots( figsize = ( 12 , 10 ) )\n",
    "sns.heatmap(corr, cmap = cmap, square=True, cbar_kws={ 'shrink' : .9 }, ax=ax, annot = True, annot_kws = { 'fontsize' : 12 }, xticklabels=corr.columns, yticklabels=corr.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "884b451e-acc9-4d60-82d0-c098f674f0d0",
   "metadata": {},
   "source": [
    "#### Feature selection - Forward selection/Backward elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ccdc46a-9988-447e-831c-4085d78a3d62",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.2s finished\n",
      "\n",
      "[2022-10-06 12:27:30] Features: 1/5 -- score: 0.7696078431372548[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.2s finished\n",
      "\n",
      "[2022-10-06 12:27:30] Features: 2/5 -- score: 0.9212418300653595[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.2s finished\n",
      "\n",
      "[2022-10-06 12:27:30] Features: 3/5 -- score: 0.9493464052287581[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s finished\n",
      "\n",
      "[2022-10-06 12:27:31] Features: 4/5 -- score: 0.9552287581699346[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sequential Forward Selection (k=5):\n",
      "Selected features: (1, 4, 6, 9, 12)\n",
      "Prediction score: 0.9663398692810456\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.1s finished\n",
      "\n",
      "[2022-10-06 12:27:31] Features: 5/5 -- score: 0.9663398692810456"
     ]
    }
   ],
   "source": [
    "# Example 1\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=4)\n",
    "\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "# Sequential Forward Selection\n",
    "sfs = SFS(knn, \n",
    "           k_features=5, \n",
    "           forward=True, \n",
    "           floating=False, \n",
    "           verbose=2,\n",
    "           scoring='accuracy',\n",
    "           cv=10)\n",
    "\n",
    "sfs = sfs.fit(X, y)\n",
    "\n",
    "print('\\nSequential Forward Selection (k=5):')\n",
    "print('Selected features:',sfs.k_feature_idx_)\n",
    "print('Prediction score:',sfs.k_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85551814-ccee-4a2f-b692-d50eb1de63f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sequential Backward Selection (k=5):\n",
      "Selected features: (0, 2, 8, 9, 12)\n",
      "Prediction (CV) score: 0.9607843137254901\n"
     ]
    }
   ],
   "source": [
    "# Example 2\n",
    "# Sequential Backward Selection\n",
    "sbs = SFS(knn, \n",
    "          k_features=5, \n",
    "          forward=False, \n",
    "          floating=False, \n",
    "          scoring='accuracy',\n",
    "          cv=10)\n",
    "sbs = sbs.fit(X, y)\n",
    "\n",
    "print('\\nSequential Backward Selection (k=5):')\n",
    "print('Selected features:',sbs.k_feature_idx_)\n",
    "print('Prediction (CV) score:',sbs.k_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e522d03e-a20a-41bc-8913-8dbbf873fbe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   feature_idx  \\\n",
      "13  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)   \n",
      "12     (0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)   \n",
      "11        (0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12)   \n",
      "10            (0, 2, 3, 5, 6, 7, 8, 9, 10, 12)   \n",
      "9                (0, 2, 3, 5, 7, 8, 9, 10, 12)   \n",
      "8                   (0, 2, 3, 5, 8, 9, 10, 12)   \n",
      "7                      (0, 2, 3, 8, 9, 10, 12)   \n",
      "6                          (0, 2, 3, 8, 9, 12)   \n",
      "5                             (0, 2, 8, 9, 12)   \n",
      "\n",
      "                                            cv_scores avg_score  \\\n",
      "13  [0.8888888888888888, 0.9444444444444444, 0.888...  0.937908   \n",
      "12  [0.8333333333333334, 0.9444444444444444, 0.888...  0.949673   \n",
      "11  [0.9444444444444444, 0.9444444444444444, 0.944...   0.96634   \n",
      "10  [0.9444444444444444, 0.9444444444444444, 0.944...  0.971895   \n",
      "9   [0.9444444444444444, 0.9444444444444444, 1.0, ...  0.971895   \n",
      "8   [0.9444444444444444, 0.8888888888888888, 1.0, ...  0.977778   \n",
      "7   [0.9444444444444444, 0.8888888888888888, 1.0, ...  0.977778   \n",
      "6   [0.9444444444444444, 0.8888888888888888, 1.0, ...  0.972222   \n",
      "5   [1.0, 0.8333333333333334, 0.9444444444444444, ...  0.960784   \n",
      "\n",
      "                                 feature_names  ci_bound   std_dev   std_err  \n",
      "13  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)  0.034803  0.046859   0.01562  \n",
      "12     (0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)  0.043108  0.058041  0.019347  \n",
      "11        (0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12)  0.020424    0.0275  0.009167  \n",
      "10            (0, 2, 3, 5, 6, 7, 8, 9, 10, 12)  0.020885   0.02812  0.009373  \n",
      "9                (0, 2, 3, 5, 7, 8, 9, 10, 12)  0.020885   0.02812  0.009373  \n",
      "8                   (0, 2, 3, 5, 8, 9, 10, 12)   0.02737  0.036851  0.012284  \n",
      "7                      (0, 2, 3, 8, 9, 10, 12)   0.02737  0.036851  0.012284  \n",
      "6                          (0, 2, 3, 8, 9, 12)  0.027679  0.037268  0.012423  \n",
      "5                             (0, 2, 8, 9, 12)  0.037224  0.050118  0.016706  \n"
     ]
    }
   ],
   "source": [
    "# Example 3\n",
    "print(pd.DataFrame.from_dict(sbs.get_metric_dict()).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e194b8d2-4b6d-4d31-ac15-a220c33d2fc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.2s finished\n",
      "\n",
      "[2022-10-06 12:27:33] Features: 1/5 -- score: 0.7696078431372548[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.3s finished\n",
      "\n",
      "[2022-10-06 12:27:33] Features: 2/5 -- score: 0.9212418300653595[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.2s finished\n",
      "\n",
      "[2022-10-06 12:27:34] Features: 3/5 -- score: 0.9493464052287581[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.2s finished\n",
      "\n",
      "[2022-10-06 12:27:34] Features: 4/5 -- score: 0.9552287581699346[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.1s finished\n",
      "\n",
      "[2022-10-06 12:27:34] Features: 5/5 -- score: 0.9663398692810456"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 4\n",
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
    "\n",
    "sfs = SFS(knn, \n",
    "          k_features=5, \n",
    "          forward=True, \n",
    "          floating=False, \n",
    "          scoring='accuracy',\n",
    "          verbose=2,\n",
    "          cv=10)\n",
    "\n",
    "sfs = sfs.fit(X, y)\n",
    "\n",
    "fig1 = plot_sfs(sfs.get_metric_dict(), kind='std_dev')\n",
    "\n",
    "plt.ylim([0.65, 1])\n",
    "plt.title('Sequential Forward Selection (w. StdDev)')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da492c73-a737-476b-afe0-a3065acba288",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best combination (ACC: 0.972): (1, 4, 6, 9, 10, 11, 12)\n",
      "\n",
      "all subsets:\n",
      " {1: {'feature_idx': (6,), 'cv_scores': array([0.61111111, 0.66666667, 0.88888889, 0.88888889, 0.72222222,\n",
      "       0.66666667, 0.94444444, 0.77777778, 0.82352941, 0.70588235]), 'avg_score': 0.7696078431372548, 'feature_names': ('6',)}, 2: {'feature_idx': (6, 9), 'cv_scores': array([0.77777778, 0.83333333, 0.88888889, 0.94444444, 1.        ,\n",
      "       0.94444444, 1.        , 1.        , 0.88235294, 0.94117647]), 'avg_score': 0.9212418300653595, 'feature_names': ('6', '9')}, 3: {'feature_idx': (4, 6, 9), 'cv_scores': array([0.72222222, 1.        , 0.94444444, 1.        , 1.        ,\n",
      "       0.94444444, 1.        , 1.        , 0.88235294, 1.        ]), 'avg_score': 0.9493464052287581, 'feature_names': ('4', '6', '9')}, 4: {'feature_idx': (4, 6, 9, 12), 'cv_scores': array([0.83333333, 1.        , 0.88888889, 0.94444444, 1.        ,\n",
      "       1.        , 1.        , 0.94444444, 0.94117647, 1.        ]), 'avg_score': 0.9552287581699346, 'feature_names': ('4', '6', '9', '12')}, 5: {'feature_idx': (1, 4, 6, 9, 12), 'cv_scores': array([0.83333333, 1.        , 0.88888889, 1.        , 1.        ,\n",
      "       1.        , 1.        , 1.        , 0.94117647, 1.        ]), 'avg_score': 0.9663398692810456, 'feature_names': ('1', '4', '6', '9', '12')}, 6: {'feature_idx': (1, 4, 6, 9, 11, 12), 'cv_scores': array([0.83333333, 1.        , 0.88888889, 1.        , 1.        ,\n",
      "       1.        , 1.        , 1.        , 0.94117647, 1.        ]), 'avg_score': 0.9663398692810456, 'feature_names': ('1', '4', '6', '9', '11', '12')}, 7: {'feature_idx': (1, 4, 6, 9, 10, 11, 12), 'cv_scores': array([0.88888889, 1.        , 0.88888889, 1.        , 1.        ,\n",
      "       1.        , 1.        , 1.        , 0.94117647, 1.        ]), 'avg_score': 0.9718954248366012, 'feature_names': ('1', '4', '6', '9', '10', '11', '12')}, 8: {'feature_idx': (0, 1, 4, 6, 9, 10, 11, 12), 'cv_scores': array([0.94444444, 1.        , 0.94444444, 0.94444444, 1.        ,\n",
      "       1.        , 1.        , 1.        , 0.94117647, 0.94117647]), 'avg_score': 0.9715686274509803, 'feature_names': ('0', '1', '4', '6', '9', '10', '11', '12')}, 9: {'feature_idx': (0, 1, 3, 4, 6, 9, 10, 11, 12), 'cv_scores': array([0.94444444, 1.        , 0.94444444, 0.94444444, 1.        ,\n",
      "       0.94444444, 1.        , 1.        , 0.94117647, 1.        ]), 'avg_score': 0.9718954248366012, 'feature_names': ('0', '1', '3', '4', '6', '9', '10', '11', '12')}, 10: {'feature_idx': (0, 1, 3, 4, 5, 6, 9, 10, 11, 12), 'cv_scores': array([0.88888889, 1.        , 0.94444444, 0.94444444, 1.        ,\n",
      "       0.94444444, 1.        , 1.        , 0.88235294, 1.        ]), 'avg_score': 0.9604575163398692, 'feature_names': ('0', '1', '3', '4', '5', '6', '9', '10', '11', '12')}, 11: {'feature_idx': (0, 1, 3, 4, 5, 6, 8, 9, 10, 11, 12), 'cv_scores': array([0.94444444, 1.        , 0.88888889, 0.94444444, 1.        ,\n",
      "       0.94444444, 1.        , 1.        , 0.88235294, 1.        ]), 'avg_score': 0.9604575163398692, 'feature_names': ('0', '1', '3', '4', '5', '6', '8', '9', '10', '11', '12')}, 12: {'feature_idx': (0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12), 'cv_scores': array([0.88888889, 0.94444444, 0.83333333, 0.94444444, 1.        ,\n",
      "       0.94444444, 1.        , 1.        , 0.88235294, 1.        ]), 'avg_score': 0.9437908496732026, 'feature_names': ('0', '1', '2', '3', '4', '5', '6', '8', '9', '10', '11', '12')}, 13: {'feature_idx': (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), 'cv_scores': array([0.88888889, 0.94444444, 0.88888889, 0.94444444, 1.        ,\n",
      "       0.88888889, 1.        , 1.        , 0.88235294, 0.94117647]), 'avg_score': 0.9379084967320261, 'feature_names': ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12')}}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 5\n",
    "knn = KNeighborsClassifier(n_neighbors=4)\n",
    "\n",
    "sfs_range = SFS(estimator=knn, \n",
    "           k_features=(2, 13),\n",
    "           forward=True, \n",
    "           floating=False, \n",
    "           scoring='accuracy',\n",
    "           cv=10)\n",
    "\n",
    "sfs_range = sfs_range.fit(X, y)\n",
    "\n",
    "print('best combination (ACC: %.3f): %s\\n' % (sfs_range.k_score_, sfs_range.k_feature_idx_))\n",
    "print('all subsets:\\n', sfs_range.subsets_)\n",
    "plot_sfs(sfs_range.get_metric_dict(), kind='std_err');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee85ff4-ce6b-4304-8ac3-204119c3cf1c",
   "metadata": {},
   "source": [
    "#### Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6fec1b43-018b-4728-aecf-52125956b4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TruncatedSVD explained variance ratio (first two components): [0.52875361 0.44845576]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA explained variance ratio (first two components): [0.92461872 0.05306648]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LDA explained variance ratio (first two components): [0.9912126 0.0087874]\n",
      "PCA explained variance ratio (first four components): [0.80582318 0.16305197 0.02134861 0.00695699]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "iris_data = iris.data\n",
    "iris_target = iris.target\n",
    "num_of_classes = len(set(iris_target))\n",
    "\n",
    "def print_scatter_plot(features, target, title):\n",
    "    # figure size\n",
    "    plt.figure(figsize=(5,5))\n",
    "    # customize gridness\n",
    "    plt.rc('grid', linestyle=\"--\", color='gray')\n",
    "    colors = ['r', 'g', 'b', 'm', 'c']\n",
    "    for i in range(num_of_classes):\n",
    "        ind = [j for j in range(target.size) if target[j] == i]\n",
    "        # plot the data observations\n",
    "        # Colormaps reference: http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "        plt.scatter(features[ind,0], features[ind,1], c=colors[i], s=10, label='%s'%iris.target_names[i])\n",
    "    plt.title(title+', 2 components')\n",
    "    # print the grid\n",
    "    plt.grid(True)\n",
    "    # print the lagend\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()        \n",
    "\n",
    "def print_variance_explained_plot(obj, n_components):\n",
    "    cum_var_exp = np.cumsum(obj.explained_variance_ratio_)\n",
    "    fig, ax = plt.subplots(figsize=(6, 4))\n",
    "    bars = ax.bar(range(n_components), obj.explained_variance_ratio_, alpha=0.5, align='center',\n",
    "                label='individual explained variance')\n",
    "    # show percentage of explained variance on top of each bar\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        ax.text(bar.get_x() + bar.get_width()/2., 1.05*height, '%.2f%%' % (height*100), \n",
    "                ha='center', va='bottom')\n",
    "    plt.step(range(n_components), cum_var_exp, where='mid',\n",
    "                 label='cumulative explained variance')\n",
    "    plt.ylabel('Explained variance ratio')\n",
    "    plt.xlabel('Principal components')\n",
    "    plt.xticks( range(n_components) )\n",
    "    plt.grid(True)\n",
    "    plt.legend(loc='best')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "# Truncated SVD\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "tsvd = TruncatedSVD(2)\n",
    "iris_tsvd = tsvd.fit_transform(iris_data)\n",
    "print_scatter_plot(iris_tsvd, iris_target, 'Truncated SVD')\n",
    "# Percentage of variance explained for each components\n",
    "print('TruncatedSVD explained variance ratio (first two components): %s'\n",
    "      % str(tsvd.explained_variance_ratio_))\n",
    "\n",
    "# PCA\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "iris_pca = pca.fit_transform(iris_data)\n",
    "print_scatter_plot(iris_pca, iris_target, 'PCA')\n",
    "# Percentage of variance explained for each components\n",
    "print('PCA explained variance ratio (first two components): %s'\n",
    "      % str(pca.explained_variance_ratio_))\n",
    "print_variance_explained_plot(pca,2)\n",
    "\n",
    "# Linear Discriminant Analysis\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "iris_lda = lda.fit(iris_data, iris_target).transform(iris_data)\n",
    "print_scatter_plot(iris_lda, iris_target, 'LDA')\n",
    "# Percentage of variance explained for each components\n",
    "print('LDA explained variance ratio (first two components): %s'\n",
    "      % str(lda.explained_variance_ratio_))\n",
    "\n",
    "# PCA on a regression problem\n",
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n",
    "n_components = 4\n",
    "pca = PCA(n_components=n_components)\n",
    "# train model with data to get the variance expained\n",
    "pca.fit(boston.data)\n",
    "# Percentage of variance explained for each components\n",
    "print('PCA explained variance ratio (first four components): %s'\n",
    "      % str(pca.explained_variance_ratio_))\n",
    "print_variance_explained_plot(pca, n_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1658512a-1f81-4eb2-81a3-9ea62a5322d5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}