wrappermethods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Different Wrapper methods to optimize our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Given a feature set and a model, we want to distinguish between important and unimportant features\n",
    "- Or even important combinations of features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gender  Senior Citizen  Partner  Dependents  Multiple Lines  \\\n",
      "0       1               0        0           0               0   \n",
      "1       0               0        0           1               0   \n",
      "2       0               0        0           1               1   \n",
      "3       0               0        1           1               1   \n",
      "4       1               0        0           1               1   \n",
      "\n",
      "   Online Security  Online Backup  Device Protection  Tech Support  \\\n",
      "0                1              1                  0             0   \n",
      "1                0              0                  0             0   \n",
      "2                0              0                  1             0   \n",
      "3                0              0                  1             1   \n",
      "4                0              1                  1             0   \n",
      "\n",
      "   Streaming TV  ...      CLTV  DSL_Service  month-to-month_contract  \\\n",
      "0             0  ... -0.981675            1                        1   \n",
      "1             0  ... -1.436462            0                        1   \n",
      "2             1  ...  0.821409            0                        1   \n",
      "3             1  ...  0.509483            0                        1   \n",
      "4             1  ...  0.794358            0                        1   \n",
      "\n",
      "   one-year_contract  two-year_contract  bank-transfer-auto_paymentmethod  \\\n",
      "0                  0                  0                                 0   \n",
      "1                  0                  0                                 0   \n",
      "2                  0                  0                                 0   \n",
      "3                  0                  0                                 0   \n",
      "4                  0                  0                                 1   \n",
      "\n",
      "   credit-card-auto_paymentmethod  electronic-check_paymentmethod  \\\n",
      "0                               0                               0   \n",
      "1                               0                               1   \n",
      "2                               0                               1   \n",
      "3                               0                               1   \n",
      "4                               0                               0   \n",
      "\n",
      "   mailed-check_paymentmethod  Churn Value  \n",
      "0                           1            1  \n",
      "1                           0            1  \n",
      "2                           0            1  \n",
      "3                           0            1  \n",
      "4                           0            1  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "X = pd.read_excel(\"./Datasets/Filtered_features.xlsx\")\n",
    "\n",
    "print(X.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preparing the data by splitting it into Pandas Dataframe (for X) and Pandas Series (for y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = X[\"Churn Value\"]\n",
    "X = X.drop(columns=[\"Churn Value\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Before using a Wrapper method, we need to specify a machine learning model\n",
    "- We Will use our Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8034698521046644\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "model.fit(X, y)\n",
    "print(model.score(X, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequential Forward Floating Selection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                          floating=True, k_features=(18, 18),\n",
       "                          scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-181\" type=\"checkbox\" ><label for=\"sk-estimator-id-181\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SequentialFeatureSelector</label><div class=\"sk-toggleable__content\"><pre>SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          floating=True, k_features=(18, 18),\n",
       "                          scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-182\" type=\"checkbox\" ><label for=\"sk-estimator-id-182\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-183\" type=\"checkbox\" ><label for=\"sk-estimator-id-183\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          floating=True, k_features=(18, 18),\n",
       "                          scoring='accuracy')"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "# set up SFS Params\n",
    "sffs = SFS(model,\n",
    "           k_features=18,\n",
    "           forward=True,\n",
    "           floating=True,\n",
    "           scoring=\"accuracy\",\n",
    "           cv=0)\n",
    "sffs.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Online Security', 'Online Backup', 'Tech Support', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'CLTV', 'DSL_Service', 'month-to-month_contract', 'one-year_contract', 'two-year_contract', 'bank-transfer-auto_paymentmethod', 'credit-card-auto_paymentmethod', 'mailed-check_paymentmethod')\n",
      "0.8058873720136519\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\KIIT\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\core\\_methods.py:269: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\KIIT\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\core\\_methods.py:261: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print the tuple of features which were chosen\n",
    "print(sffs.subsets_[18][\"feature_names\"])\n",
    "# print the score of the model with selected features\n",
    "print(sffs.subsets_[18][\"avg_score\"])\n",
    "# plot the accuracy as a function of the number of features\n",
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
    "import matplotlib.pyplot as plt\n",
    "plot_sfs(sffs.get_metric_dict())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequential Backward Floating Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-62 {color: black;background-color: white;}#sk-container-id-62 pre{padding: 0;}#sk-container-id-62 div.sk-toggleable {background-color: white;}#sk-container-id-62 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-62 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-62 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-62 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-62 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-62 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-62 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-62 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-62 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-62 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-62 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-62 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-62 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-62 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-62 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-62 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-62 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-62 div.sk-item {position: relative;z-index: 1;}#sk-container-id-62 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-62 div.sk-item::before, #sk-container-id-62 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-62 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-62 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-62 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-62 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-62 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-62 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-62 div.sk-label-container {text-align: center;}#sk-container-id-62 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-62 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-62\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          forward=False, k_features=(13, 13),\n",
       "                          scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-184\" type=\"checkbox\" ><label for=\"sk-estimator-id-184\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SequentialFeatureSelector</label><div class=\"sk-toggleable__content\"><pre>SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          forward=False, k_features=(13, 13),\n",
       "                          scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-185\" type=\"checkbox\" ><label for=\"sk-estimator-id-185\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-186\" type=\"checkbox\" ><label for=\"sk-estimator-id-186\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SequentialFeatureSelector(cv=0, estimator=LogisticRegression(max_iter=1000),\n",
       "                          forward=False, k_features=(13, 13),\n",
       "                          scoring='accuracy')"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up SFS Params\n",
    "sbfs = SFS(model,\n",
    "           k_features=13,\n",
    "           forward=False,\n",
    "           floating=False,\n",
    "           scoring=\"accuracy\",\n",
    "           cv=0)\n",
    "sbfs.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Multiple Lines', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'CLTV', 'DSL_Service', 'month-to-month_contract', 'one-year_contract', 'two-year_contract', 'bank-transfer-auto_paymentmethod', 'credit-card-auto_paymentmethod', 'electronic-check_paymentmethod', 'mailed-check_paymentmethod')\n",
      "0.8034698521046644\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\KIIT\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\core\\_methods.py:269: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\KIIT\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\core\\_methods.py:261: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print the tuple of features which were chosen\n",
    "print(sbfs.subsets_[23][\"feature_names\"])\n",
    "# print the score of the model with selected features\n",
    "print(sbfs.subsets_[23][\"avg_score\"])\n",
    "# plot the accuracy as a function of the number of features\n",
    "plot_sfs(sbfs.get_metric_dict())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Senior Citizen', 'Partner', 'Dependents', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'DSL_Service', 'month-to-month_contract', 'two-year_contract', 'credit-card-auto_paymentmethod', 'electronic-check_paymentmethod']\n",
      "0.804749715585893\n"
     ]
    }
   ],
   "source": [
    "features_list = ('Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Multiple Lines', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'CLTV', 'DSL_Service', 'month-to-month_contract', 'one-year_contract', 'two-year_contract', 'bank-transfer-auto_paymentmethod', 'credit-card-auto_paymentmethod', 'electronic-check_paymentmethod', 'mailed-check_paymentmethod')\n",
    "\n",
    "# use Recursive feature Elimation frmo sklearn\n",
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "rfe = RFE(model,\n",
    "          n_features_to_select=15)\n",
    "rfe.fit(X,y)\n",
    "\n",
    "features = [f for (f, support) in zip (features_list, rfe.support_) if support]\n",
    "print(features)\n",
    "print(rfe.score(X, y))\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
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  "orig_nbformat": 4
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}