test_shap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Textbook Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import numpy as np\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = shap.datasets.adult()\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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "cats = ['Workclass','Marital Status', 'Occupation', 'Relationship','Race','Sex','Country']\n",
    "nums = ['Age', 'Education-Num', 'Capital Gain', 'Capital Loss', 'Hours per week']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = ColumnTransformer(\n",
    "    transformers = [\n",
    "        ('cat', OneHotEncoder(),cats),\n",
    "        ('num' , StandardScaler(), nums)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Pipeline([\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', LogisticRegression(max_iter=10000))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                                  [&#x27;Workclass&#x27;,\n",
       "                                                   &#x27;Marital Status&#x27;,\n",
       "                                                   &#x27;Occupation&#x27;, &#x27;Relationship&#x27;,\n",
       "                                                   &#x27;Race&#x27;, &#x27;Sex&#x27;, &#x27;Country&#x27;]),\n",
       "                                                 (&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;Education-Num&#x27;,\n",
       "                                                   &#x27;Capital Gain&#x27;,\n",
       "                                                   &#x27;Capital Loss&#x27;,\n",
       "                                                   &#x27;Hours per week&#x27;])])),\n",
       "                (&#x27;classifier&#x27;, LogisticRegression(max_iter=10000))])</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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                                  [&#x27;Workclass&#x27;,\n",
       "                                                   &#x27;Marital Status&#x27;,\n",
       "                                                   &#x27;Occupation&#x27;, &#x27;Relationship&#x27;,\n",
       "                                                   &#x27;Race&#x27;, &#x27;Sex&#x27;, &#x27;Country&#x27;]),\n",
       "                                                 (&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;Education-Num&#x27;,\n",
       "                                                   &#x27;Capital Gain&#x27;,\n",
       "                                                   &#x27;Capital Loss&#x27;,\n",
       "                                                   &#x27;Hours per week&#x27;])])),\n",
       "                (&#x27;classifier&#x27;, LogisticRegression(max_iter=10000))])</pre></div></div></div><div class=\"sk-serial\"><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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;Workclass&#x27;, &#x27;Marital Status&#x27;, &#x27;Occupation&#x27;,\n",
       "                                  &#x27;Relationship&#x27;, &#x27;Race&#x27;, &#x27;Sex&#x27;, &#x27;Country&#x27;]),\n",
       "                                (&#x27;num&#x27;, StandardScaler(),\n",
       "                                 [&#x27;Age&#x27;, &#x27;Education-Num&#x27;, &#x27;Capital Gain&#x27;,\n",
       "                                  &#x27;Capital Loss&#x27;, &#x27;Hours per week&#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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Workclass&#x27;, &#x27;Marital Status&#x27;, &#x27;Occupation&#x27;, &#x27;Relationship&#x27;, &#x27;Race&#x27;, &#x27;Sex&#x27;, &#x27;Country&#x27;]</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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;Education-Num&#x27;, &#x27;Capital Gain&#x27;, &#x27;Capital Loss&#x27;, &#x27;Hours per week&#x27;]</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-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=10000)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('cat', OneHotEncoder(),\n",
       "                                                  ['Workclass',\n",
       "                                                   'Marital Status',\n",
       "                                                   'Occupation', 'Relationship',\n",
       "                                                   'Race', 'Sex', 'Country']),\n",
       "                                                 ('num', StandardScaler(),\n",
       "                                                  ['Age', 'Education-Num',\n",
       "                                                   'Capital Gain',\n",
       "                                                   'Capital Loss',\n",
       "                                                   'Hours per week'])])),\n",
       "                ('classifier', LogisticRegression(max_iter=10000))])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sub = shap.sample(X_train,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex = shap.Explainer(model.predict_proba, X_sub)\n",
    "shap_values = ex(X_test.iloc[0:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x650 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_index = 1\n",
    "data_index = 1\n",
    "shap.plots.waterfall(shap_values[data_index,:,class_index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fresh Example, own transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID  AGE  GENDER  LTV\n",
      "0   1   22    Male  0.5\n",
      "1   2   33  Female  0.3\n",
      "2   3   50    Male  0.1\n",
      "3   4   70    Male  0.9\n",
      "4   5   24  Female  1.0\n",
      "5   6   28    Male  0.6\n",
      "6   7   29  Female  0.1\n",
      "7   8   30    Male  0.9\n",
      "8   9   18  Female  1.0\n",
      "9  10   60    Male  0.6\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Specify the file path\n",
    "file_path = 'data/dummy_data.csv'\n",
    "\n",
    "# Load the CSV file into a DataFrame\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Print the DataFrame\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "# define the transformer\n",
    "class AgeImputer(BaseEstimator, TransformerMixin):\n",
    "    \n",
    "    def __init__(self, age_coef, gender_coef):\n",
    "        print('Initialising transformer...')\n",
    "        self.age_coef = age_coef\n",
    "        self.gender_coef = gender_coef\n",
    "        \n",
    "    def fit(self, X, y = None):\n",
    "        self.mean_age = round(X['Age'].mean())\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        print('replacing impossible age values')\n",
    "        X.loc[(X['age'] > self.max_age) \n",
    "              |  (X['age'] < 0), 'age'] = self.mean_age\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "# Define the column transformer\n",
    "ct = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('one_hot_encoder', OneHotEncoder(), ['GENDER'])\n",
    "    ],\n",
    "    remainder='passthrough'\n",
    ")\n",
    "\n",
    "# Create the pipeline\n",
    "pipeline = Pipeline([\n",
    "    ('column_transformer', ct),\n",
    "    ('linear_regression', LinearRegression())\n",
    "])\n",
    "\n",
    "# Fit the pipeline on the training data\n",
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulated Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulated Dataset:\n",
      "X: [[-0.43066755  0.67287309 -0.72427983 -0.53963044 -0.65160035]\n",
      " [ 0.21164583 -0.84389686  0.53479393  0.82584805  0.68195297]\n",
      " [ 1.09267506  0.40910605  1.10009583 -0.94275087 -0.98150865]\n",
      " [ 1.51990078 -0.77336118  1.99805321  0.15513175 -0.3853136 ]\n",
      " [-0.45390127 -2.18347304  0.24472415  2.59123946 -0.48423407]\n",
      " [-1.46361184  0.37531604 -1.79532002  0.25415746 -1.24778318]\n",
      " [ 0.88948365  0.80742726  0.73019848 -1.28568005  0.13074058]\n",
      " [-1.11327862  1.89033108 -1.92487377 -1.5598485   0.18645431]\n",
      " [ 0.63174629 -0.88541844  1.02703224  0.68057323  0.54709738]\n",
      " [-0.88602706 -0.83311649 -0.7173148   1.31217492  0.44381943]\n",
      " [ 0.56372286 -1.47487037  1.15509316  1.35536951 -0.2176812 ]\n",
      " [-1.02754411 -0.32929388 -1.05383855  0.82600732 -0.05952536]\n",
      " [ 0.19375402 -0.96314239  0.55600276  0.96423311 -0.20219265]\n",
      " [-0.05396947  0.15985512 -0.11708689 -0.15013844  0.82541635]\n",
      " [ 0.96335953  0.65992405  0.86561977 -1.15806823  0.46210347]\n",
      " [-1.09939128 -0.47936995 -1.08324727  1.02255619 -0.92693047]\n",
      " [-0.64760425  1.34700796 -1.20592943 -1.17762637  0.85243333]\n",
      " [-1.37111696  0.50688537 -1.73597973  0.06845616  0.77463405]\n",
      " [ 1.30373602 -0.22210005  1.56010259 -0.34898484  0.06980208]\n",
      " [-0.52878508  0.94621814 -0.9311259  -0.79371387 -0.11473644]\n",
      " [ 1.7240021  -1.22823431  2.38869353  0.55942643 -1.43586215]\n",
      " [-0.56603681 -1.41693293 -0.14994102  1.80474148  1.03246526]\n",
      " [ 0.70385192 -1.29826331  1.25291095  1.09885263  1.09877685]\n",
      " [ 0.17593    -1.01695056  0.55448398  1.03110238  0.30729952]\n",
      " [ 0.04668349 -0.24878048  0.13979096  0.25058844  0.61037027]\n",
      " [-1.03260487 -0.62804104 -0.95548162  1.15466088 -0.47494531]\n",
      " [ 0.52576893 -0.82741596  0.88629356  0.66530077 -0.6929096 ]\n",
      " [-1.10644979 -0.32066954 -1.14658127  0.85239186 -1.43014138]\n",
      " [ 1.1374615   0.24412056  1.20852705 -0.78284083 -0.20812225]\n",
      " [-0.81296687  1.85571007 -1.57127256 -1.65830375 -0.47193187]\n",
      " [-0.83806958  1.89792447 -1.61453273 -1.69302842 -2.02514259]\n",
      " [ 1.17077461 -2.65461268  2.25661188  2.36867367 -0.56018104]\n",
      " [ 0.08714206  0.0291022   0.08896214 -0.07133524  0.08187414]\n",
      " [ 0.4057089  -0.46809422  0.62453032  0.32725188  1.30547881]\n",
      " [ 0.65574086  1.3939833   0.25995672 -1.82037691  0.66213067]\n",
      " [-0.87306613  1.65095783 -1.56826626 -1.40735658  0.04557184]\n",
      " [ 0.71768635  1.30857636  0.36016958 -1.75518644 -1.9520878 ]\n",
      " [ 0.55831966 -1.31089865  1.09180466  1.17869556 -0.02090159]\n",
      " [ 0.64701888  1.44901594  0.23085877 -1.87653774  2.13303337]\n",
      " [ 0.88439534 -2.68317954  1.94087643  2.52983424 -0.59157139]\n",
      " [ 1.13446163  0.26843823  1.19664076 -0.80804463  0.28099187]\n",
      " [ 0.55253544  1.6253749   0.06194498 -2.02632079 -0.49300093]\n",
      " [-0.79383636  1.74355119 -1.51042899 -1.54446032  0.67959775]\n",
      " [ 1.306542   -0.23938468  1.56931739 -0.3313761   0.58831721]\n",
      " [ 0.28247903 -1.19137182  0.73644435  1.17329352  0.81286212]\n",
      " [ 0.97687358 -1.80921926  1.74147706  1.53313849 -0.82899501]\n",
      " [-1.13714057 -0.24038388 -1.20946429  0.77861318  1.26691115]\n",
      " [-1.45612516  2.48904785 -2.52343407 -2.05832072  1.0889506 ]\n",
      " [-1.03398089 -0.56156012 -0.98021491  1.08266027  1.76545424]\n",
      " [-0.84321213 -0.70426956 -0.71352532  1.15199146 -0.33450124]\n",
      " [-0.69102126 -1.22532865 -0.35885569  1.65214494  0.91786195]\n",
      " [-1.29267194 -0.1173572  -1.42922002  0.71479373  0.40498171]\n",
      " [-1.69200458  0.95057302 -2.25553963 -0.27062383 -0.70766947]\n",
      " [ 1.33970234 -2.24075406  2.30450019  1.83991037  0.89959988]\n",
      " [ 1.46098002 -0.55818081  1.85605469 -0.05319823 -0.60021688]\n",
      " [ 0.36783185 -0.21800395  0.49429823  0.07123641 -1.86726519]\n",
      " [-0.7635164   1.43140126 -1.36716377 -1.21722043 -0.73036663]\n",
      " [-0.79864786 -1.07525068 -0.53355799  1.53703587 -3.24126734]\n",
      " [ 0.50230075  1.77872961 -0.04862909 -2.17105282 -0.44004449]\n",
      " [ 0.68720469 -1.45724683  1.28938375  1.28008347  0.8496021 ]\n",
      " [ 0.36205368 -0.46281432  0.57304248  0.34129395 -0.31026676]\n",
      " [-0.94116325  1.63631177 -1.64060704 -1.36045573  0.06428002]\n",
      " [-0.99325144  1.69607038 -1.72067112 -1.40210053  0.21645859]\n",
      " [ 1.22541983  0.01066604  1.38991764 -0.56772453 -0.58936476]\n",
      " [-0.756006    1.62748917 -1.42695838 -1.43483867  2.14394409]\n",
      " [-0.51103632 -1.60499282 -0.02185231  1.98522348 -1.51936997]\n",
      " [ 0.44205183  1.94742094 -0.17593681 -2.3279946  -0.62269952]\n",
      " [-1.3731412   2.40437346 -2.39955005 -2.00347738 -0.79252074]\n",
      " [ 0.97650004  0.63067073  0.89075877 -1.13207427  1.44127329]\n",
      " [-0.68933585  1.39175475 -1.26898369 -1.20757158  2.31465857]\n",
      " [ 0.78025563 -2.0302233   1.59488828  1.86378908  1.2776649 ]\n",
      " [ 0.50047696 -1.46447269  1.07954187  1.3727107   0.81350964]\n",
      " [ 0.6635226  -1.44723231  1.25896077  1.2798899   0.02100384]\n",
      " [ 1.12210055  0.254643    1.18739045 -0.78736523  1.16316375]\n",
      " [-0.14951801  0.58846525 -0.3751207  -0.57500215 -0.66178646]\n",
      " [-0.32683618  0.8834691  -0.6795874  -0.81680628 -0.48536355]\n",
      " [ 0.77813969 -1.76254901  1.4991983   1.57233676  0.11732738]\n",
      " [ 0.30446853 -0.9809438   0.68811892  0.93343952  0.35701549]\n",
      " [ 0.30877317 -0.56811307  0.54914434  0.480502    0.74729361]\n",
      " [-0.74920203 -1.20727346 -0.43131517  1.65882246 -1.20029641]\n",
      " [ 0.99630897  0.53104737  0.94800532 -1.03223274  0.1990597 ]\n",
      " [ 1.28131775 -0.06692233  1.48052803 -0.50833095  0.11351735]\n",
      " [-1.19300559 -0.07873421 -1.32933233  0.6273745   0.86575519]\n",
      " [ 0.1094067   0.30759353  0.01722979 -0.38566776 -0.71530371]\n",
      " [-0.97763681 -0.54561186 -0.92169432  1.03967019  1.6324113 ]\n",
      " [-0.39769634  1.17059844 -0.86023823 -1.09831681  0.63391902]\n",
      " [ 1.05688647  0.40238353  1.06173727 -0.91916686  1.58601682]\n",
      " [-0.67826129 -1.09662143 -0.38919817  1.50575249 -1.26088395]\n",
      " [-0.63922566  1.18674111 -1.14054824 -1.00634985  0.68626019]\n",
      " [-0.74717988 -1.03371682 -0.48949935  1.46830827 -0.65332923]\n",
      " [ 0.32423734  2.23245441 -0.40925706 -2.58590856  0.01023306]\n",
      " [-1.24868955  2.35790248 -2.24181979 -2.00918545 -1.07774478]\n",
      " [ 0.46093758  1.89691056 -0.1368559  -2.2813861  -1.2378155 ]\n",
      " [-0.73426524  1.72251306 -1.43534875 -1.54851014  0.50498728]\n",
      " [-1.05463759 -0.55658056 -1.00544254  1.08659413 -0.25256815]\n",
      " [ 0.73594772  1.2031659   0.4176729  -1.64832073 -0.1517851 ]\n",
      " [ 0.84609208 -1.14052601  1.35970566  0.86199147  0.62962884]\n",
      " [-0.84467713  1.6431949  -1.5332749  -1.4117586  -1.61271587]\n",
      " [-1.2827436   0.12037048 -1.50077611  0.4505902   2.1221562 ]\n",
      " [-0.72216846 -1.12868571 -0.42795824  1.56070438 -1.02438764]]\n",
      "y: [0 1 0 0 1 1 0 0 1 1 1 1 1 1 0 1 0 1 0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 1 0 0 0\n",
      " 1 0 1 0 0 0 0 1 1 1 0 1 1 1 1 1 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 1 1 0\n",
      " 0 0 1 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 0 0 1 0 1 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "# Simulate a binary classification dataset\n",
    "X_log, y_log = make_classification(n_samples=100, n_features=5, random_state=42)\n",
    "\n",
    "# Print the simulated dataset\n",
    "print(\"Simulated Dataset:\")\n",
    "print(\"X:\", X_log)\n",
    "print(\"y:\", y_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.13981491, 0.27771589, 0.23357655, 0.02947656, 0.01793922,\n",
       "       0.35491711, 0.148741  , 0.05461358, 0.10862162, 0.10086966,\n",
       "       0.14710616, 0.23601403, 0.10375676, 0.25349522, 0.05233625,\n",
       "       0.28583859, 0.04113383, 0.12960915, 0.17583344, 0.09043705,\n",
       "       0.24859884, 0.0534787 , 0.2520358 , 0.2199018 , 0.15945052,\n",
       "       0.20338365, 0.06612701, 0.16719077, 0.21221742, 0.31565302,\n",
       "       0.28293611, 0.03361155, 0.09084631, 0.25612295, 0.207467  ,\n",
       "       0.24087046, 0.12692957, 0.13875597, 0.08546767, 0.15799859,\n",
       "       0.13131052, 0.10772601, 0.09067595, 0.10692129, 0.49029884,\n",
       "       0.22999699, 0.04731202, 0.08508655, 1.        , 0.013423  ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "scale = 1 / np.max(gamma_values)\n",
    "y_gamma = gamma_values * scale\n",
    "y_gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import gamma\n",
    "\n",
    "x_gamma = gamma.rvs(gamma_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x_gamma, y_gamma)\n",
    "plt.xlabel('x_gamma')\n",
    "plt.ylabel('y_gamma')\n",
    "plt.title('Relationship between x_gamma and y_gamma')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Dataset:\n",
      "X_combined: [[-4.30667552e-01  6.72873085e-01 -7.24279827e-01 -5.39630436e-01\n",
      "  -6.51600348e-01  4.91517154e-06]\n",
      " [ 2.11645831e-01 -8.43896860e-01  5.34793935e-01  8.25848048e-01\n",
      "   6.81952971e-01  3.29338194e-78]\n",
      " [ 1.09267506e+00  4.09106051e-01  1.10009583e+00 -9.42750868e-01\n",
      "  -9.81508651e-01  3.18019858e-06]\n",
      " [ 1.51990078e+00 -7.73361182e-01  1.99805321e+00  1.55131754e-01\n",
      "  -3.85313597e-01  5.12635893e-02]\n",
      " [-4.53901271e-01 -2.18347304e+00  2.44724154e-01  2.59123946e+00\n",
      "  -4.84234073e-01  4.84401141e-03]\n",
      " [-1.46361184e+00  3.75316036e-01 -1.79532002e+00  2.54157464e-01\n",
      "  -1.24778318e+00  4.91517154e-06]\n",
      " [ 8.89483645e-01  8.07427260e-01  7.30198478e-01 -1.28568005e+00\n",
      "   1.30740577e-01  8.44419977e-04]\n",
      " [-1.11327862e+00  1.89033108e+00 -1.92487377e+00 -1.55984850e+00\n",
      "   1.86454315e-01  4.42427704e-01]\n",
      " [ 6.31746295e-01 -8.85418435e-01  1.02703224e+00  6.80573226e-01\n",
      "   5.47097381e-01  5.86964389e-02]\n",
      " [-8.86027064e-01 -8.33116491e-01 -7.17314796e-01  1.31217492e+00\n",
      "   4.43819428e-01  6.29501687e-01]\n",
      " [ 5.63722858e-01 -1.47487037e+00  1.15509316e+00  1.35536951e+00\n",
      "  -2.17681203e-01  2.46449909e-06]\n",
      " [-1.02754411e+00 -3.29293881e-01 -1.05383855e+00  8.26007319e-01\n",
      "  -5.95253561e-02  2.40090547e-01]\n",
      " [ 1.93754016e-01 -9.63142392e-01  5.56002755e-01  9.64233110e-01\n",
      "  -2.02192652e-01  8.44419977e-04]\n",
      " [-5.39694662e-02  1.59855122e-01 -1.17086894e-01 -1.50138438e-01\n",
      "   8.25416349e-01  1.53892764e-02]\n",
      " [ 9.63359529e-01  6.59924045e-01  8.65619770e-01 -1.15806823e+00\n",
      "   4.62103474e-01  3.29338194e-78]\n",
      " [-1.09939128e+00 -4.79369952e-01 -1.08324727e+00  1.02255619e+00\n",
      "  -9.26930472e-01  6.54135484e-01]\n",
      " [-6.47604249e-01  1.34700796e+00 -1.20592943e+00 -1.17762637e+00\n",
      "   8.52433335e-01  7.94812610e-02]\n",
      " [-1.37111696e+00  5.06885374e-01 -1.73597973e+00  6.84561582e-02\n",
      "   7.74634053e-01  3.18019858e-06]\n",
      " [ 1.30373602e+00 -2.22100055e-01  1.56010259e+00 -3.48984841e-01\n",
      "   6.98020850e-02  8.44419977e-04]\n",
      " [-5.28785077e-01  9.46218141e-01 -9.31125899e-01 -7.93713869e-01\n",
      "  -1.14736441e-01  4.91517154e-06]\n",
      " [ 1.72400210e+00 -1.22823431e+00  2.38869353e+00  5.59426431e-01\n",
      "  -1.43586215e+00  7.94812610e-02]\n",
      " [-5.66036812e-01 -1.41693293e+00 -1.49941022e-01  1.80474148e+00\n",
      "   1.03246526e+00  2.16713315e-03]\n",
      " [ 7.03851925e-01 -1.29826331e+00  1.25291095e+00  1.09885263e+00\n",
      "   1.09877685e+00  2.53805088e-02]\n",
      " [ 1.75930005e-01 -1.01695056e+00  5.54483984e-01  1.03110238e+00\n",
      "   3.07299521e-01  2.78559322e-04]\n",
      " [ 4.66834932e-02 -2.48780485e-01  1.39790957e-01  2.50588435e-01\n",
      "   6.10370265e-01  1.52223351e-08]\n",
      " [-1.03260487e+00 -6.28041044e-01 -9.55481620e-01  1.15466088e+00\n",
      "  -4.74945311e-01  2.28602013e-03]\n",
      " [ 5.25768931e-01 -8.27415959e-01  8.86293561e-01  6.65300774e-01\n",
      "  -6.92909595e-01  2.52063708e-01]\n",
      " [-1.10644979e+00 -3.20669541e-01 -1.14658127e+00  8.52391857e-01\n",
      "  -1.43014138e+00  1.00187058e-02]\n",
      " [ 1.13746150e+00  2.44120562e-01  1.20852705e+00 -7.82840833e-01\n",
      "  -2.08122250e-01  4.50115429e-05]\n",
      " [-8.12966870e-01  1.85571007e+00 -1.57127256e+00 -1.65830375e+00\n",
      "  -4.71931866e-01  2.91033718e-03]\n",
      " [-8.38069584e-01  1.89792447e+00 -1.61453273e+00 -1.69302842e+00\n",
      "  -2.02514259e+00  1.79209371e-02]\n",
      " [ 1.17077461e+00 -2.65461268e+00  2.25661188e+00  2.36867367e+00\n",
      "  -5.60181040e-01  3.21420469e-02]\n",
      " [ 8.71420582e-02  2.91021981e-02  8.89621380e-02 -7.13352412e-02\n",
      "   8.18741394e-02  1.00187058e-02]\n",
      " [ 4.05708897e-01 -4.68094222e-01  6.24530321e-01  3.27251881e-01\n",
      "   1.30547881e+00  1.08707055e-01]\n",
      " [ 6.55740858e-01  1.39398330e+00  2.59956720e-01 -1.82037691e+00\n",
      "   6.62130675e-01  1.52223351e-08]\n",
      " [-8.73066134e-01  1.65095783e+00 -1.56826626e+00 -1.40735658e+00\n",
      "   4.55718399e-02  4.65881759e-03]\n",
      " [ 7.17686348e-01  1.30857636e+00  3.60169578e-01 -1.75518644e+00\n",
      "  -1.95208780e+00  2.40090547e-01]\n",
      " [ 5.58319659e-01 -1.31089865e+00  1.09180466e+00  1.17869556e+00\n",
      "  -2.09015940e-02  3.21420469e-02]\n",
      " [ 6.47018879e-01  1.44901594e+00  2.30858774e-01 -1.87653774e+00\n",
      "   2.13303337e+00  1.08707055e-01]\n",
      " [ 8.84395338e-01 -2.68317954e+00  1.94087643e+00  2.52983424e+00\n",
      "  -5.91571389e-01  6.54135484e-01]\n",
      " [ 1.13446163e+00  2.68438233e-01  1.19664076e+00 -8.08044635e-01\n",
      "   2.80991868e-01  3.29338194e-78]\n",
      " [ 5.52535445e-01  1.62537490e+00  6.19449780e-02 -2.02632079e+00\n",
      "  -4.93000935e-01  3.21420469e-02]\n",
      " [-7.93836362e-01  1.74355119e+00 -1.51042899e+00 -1.54446032e+00\n",
      "   6.79597749e-01  6.29501687e-01]\n",
      " [ 1.30654200e+00 -2.39384676e-01  1.56931739e+00 -3.31376097e-01\n",
      "   5.88317206e-01  6.54135484e-01]\n",
      " [ 2.82479031e-01 -1.19137182e+00  7.36444353e-01  1.17329352e+00\n",
      "   8.12862119e-01  1.29198893e+00]\n",
      " [ 9.76873584e-01 -1.80921926e+00  1.74147706e+00  1.53313849e+00\n",
      "  -8.28995011e-01  4.58125309e-05]\n",
      " [-1.13714057e+00 -2.40383883e-01 -1.20946429e+00  7.78613181e-01\n",
      "   1.26691115e+00  9.07606177e-02]\n",
      " [-1.45612516e+00  2.48904785e+00 -2.52343407e+00 -2.05832072e+00\n",
      "   1.08895060e+00  1.29198893e+00]\n",
      " [-1.03398089e+00 -5.61560120e-01 -9.80214910e-01  1.08266027e+00\n",
      "   1.76545424e+00  3.16637528e-02]\n",
      " [-8.43212128e-01 -7.04269558e-01 -7.13525325e-01  1.15199146e+00\n",
      "  -3.34501236e-01  9.07606177e-02]\n",
      " [-6.91021261e-01 -1.22532865e+00 -3.58855688e-01  1.65214494e+00\n",
      "   9.17861947e-01  2.40090547e-01]\n",
      " [-1.29267194e+00 -1.17357197e-01 -1.42922002e+00  7.14793729e-01\n",
      "   4.04981711e-01  4.65881759e-03]\n",
      " [-1.69200458e+00  9.50573016e-01 -2.25553963e+00 -2.70623832e-01\n",
      "  -7.07669466e-01  3.21420469e-02]\n",
      " [ 1.33970234e+00 -2.24075406e+00  2.30450019e+00  1.83991037e+00\n",
      "   8.99599875e-01  2.53805088e-02]\n",
      " [ 1.46098002e+00 -5.58180811e-01  1.85605469e+00 -5.31982332e-02\n",
      "  -6.00216877e-01  4.84401141e-03]\n",
      " [ 3.67831850e-01 -2.18003951e-01  4.94298226e-01  7.12364098e-02\n",
      "  -1.86726519e+00  4.84401141e-03]\n",
      " [-7.63516402e-01  1.43140126e+00 -1.36716377e+00 -1.21722043e+00\n",
      "  -7.30366632e-01  4.49630925e-01]\n",
      " [-7.98647864e-01 -1.07525068e+00 -5.33557995e-01  1.53703587e+00\n",
      "  -3.24126734e+00  2.12631626e-03]\n",
      " [ 5.02300750e-01  1.77872961e+00 -4.86290939e-02 -2.17105282e+00\n",
      "  -4.40044487e-01  2.52063708e-01]\n",
      " [ 6.87204686e-01 -1.45724683e+00  1.28938375e+00  1.28008347e+00\n",
      "   8.49602097e-01  3.07995168e-13]\n",
      " [ 3.62053684e-01 -4.62814324e-01  5.73042476e-01  3.41293945e-01\n",
      "  -3.10266757e-01  3.54247372e-01]\n",
      " [-9.41163249e-01  1.63631177e+00 -1.64060704e+00 -1.36045573e+00\n",
      "   6.42800191e-02  1.00187058e-02]\n",
      " [-9.93251439e-01  1.69607038e+00 -1.72067112e+00 -1.40210053e+00\n",
      "   2.16458590e-01  2.78559322e-04]\n",
      " [ 1.22541983e+00  1.06660430e-02  1.38991764e+00 -5.67724525e-01\n",
      "  -5.89364757e-01  3.29338194e-78]\n",
      " [-7.56006002e-01  1.62748917e+00 -1.42695838e+00 -1.43483867e+00\n",
      "   2.14394409e+00  3.29338194e-78]\n",
      " [-5.11036324e-01 -1.60499282e+00 -2.18523104e-02  1.98522348e+00\n",
      "  -1.51936997e+00  3.93473325e-01]\n",
      " [ 4.42051834e-01  1.94742094e+00 -1.75936814e-01 -2.32799460e+00\n",
      "  -6.22699520e-01  1.22087254e-05]\n",
      " [-1.37314120e+00  2.40437346e+00 -2.39955005e+00 -2.00347738e+00\n",
      "  -7.92520738e-01  8.44419977e-04]\n",
      " [ 9.76500035e-01  6.30670730e-01  8.90758772e-01 -1.13207427e+00\n",
      "   1.44127329e+00  1.52223351e-08]\n",
      " [-6.89335853e-01  1.39175475e+00 -1.26898369e+00 -1.20757158e+00\n",
      "   2.31465857e+00  1.00187058e-02]\n",
      " [ 7.80255630e-01 -2.03022330e+00  1.59488828e+00  1.86378908e+00\n",
      "   1.27766490e+00  4.91517154e-06]\n",
      " [ 5.00476965e-01 -1.46447269e+00  1.07954187e+00  1.37271070e+00\n",
      "   8.13509636e-01  4.50115429e-05]\n",
      " [ 6.63522604e-01 -1.44723231e+00  1.25896077e+00  1.27988990e+00\n",
      "   2.10038416e-02  2.91033718e-03]\n",
      " [ 1.12210055e+00  2.54643002e-01  1.18739045e+00 -7.87365232e-01\n",
      "   1.16316375e+00  8.88612632e-17]\n",
      " [-1.49518014e-01  5.88465251e-01 -3.75120699e-01 -5.75002146e-01\n",
      "  -6.61786465e-01  3.16637528e-02]\n",
      " [-3.26836178e-01  8.83469102e-01 -6.79587396e-01 -8.16806281e-01\n",
      "  -4.85363548e-01  4.84401141e-03]\n",
      " [ 7.78139695e-01 -1.76254901e+00  1.49919830e+00  1.57233676e+00\n",
      "   1.17327383e-01  1.29198893e+00]\n",
      " [ 3.04468528e-01 -9.80943802e-01  6.88118916e-01  9.33439522e-01\n",
      "   3.57015486e-01  8.88612632e-17]\n",
      " [ 3.08773169e-01 -5.68113067e-01  5.49144337e-01  4.80502003e-01\n",
      "   7.47293605e-01  2.40090547e-01]\n",
      " [-7.49202028e-01 -1.20727346e+00 -4.31315171e-01  1.65882246e+00\n",
      "  -1.20029641e+00  2.28602013e-03]\n",
      " [ 9.96308974e-01  5.31047366e-01  9.48005316e-01 -1.03223274e+00\n",
      "   1.99059696e-01  1.15504682e-06]\n",
      " [ 1.28131775e+00 -6.69223319e-02  1.48052803e+00 -5.08330948e-01\n",
      "   1.13517345e-01  4.58125309e-05]\n",
      " [-1.19300559e+00 -7.87342078e-02 -1.32933233e+00  6.27374505e-01\n",
      "   8.65755194e-01  2.28602013e-03]\n",
      " [ 1.09406705e-01  3.07593533e-01  1.72297863e-02 -3.85667758e-01\n",
      "  -7.15303709e-01  1.42299196e-13]\n",
      " [-9.77636806e-01 -5.45611863e-01 -9.21694317e-01  1.03967019e+00\n",
      "   1.63241130e+00  7.72379263e-07]\n",
      " [-3.97696342e-01  1.17059844e+00 -8.60238232e-01 -1.09831681e+00\n",
      "   6.33919022e-01  2.46449909e-06]\n",
      " [ 1.05688647e+00  4.02383534e-01  1.06173727e+00 -9.19166865e-01\n",
      "   1.58601682e+00  4.84401141e-03]\n",
      " [-6.78261288e-01 -1.09662143e+00 -3.89198171e-01  1.50575249e+00\n",
      "  -1.26088395e+00  3.93473325e-01]\n",
      " [-6.39225662e-01  1.18674111e+00 -1.14054824e+00 -1.00634985e+00\n",
      "   6.86260190e-01  1.42299196e-13]\n",
      " [-7.47179884e-01 -1.03371682e+00 -4.89499351e-01  1.46830827e+00\n",
      "  -6.53329233e-01  5.86964389e-02]\n",
      " [ 3.24237337e-01  2.23245441e+00 -4.09257060e-01 -2.58590856e+00\n",
      "   1.02330610e-02  3.18019858e-06]\n",
      " [-1.24868955e+00  2.35790248e+00 -2.24181979e+00 -2.00918545e+00\n",
      "  -1.07774478e+00  7.94812610e-02]\n",
      " [ 4.60937578e-01  1.89691056e+00 -1.36855905e-01 -2.28138610e+00\n",
      "  -1.23781550e+00  2.28602013e-03]\n",
      " [-7.34265238e-01  1.72251306e+00 -1.43534875e+00 -1.54851014e+00\n",
      "   5.04987279e-01  3.18019858e-06]\n",
      " [-1.05463759e+00 -5.56580559e-01 -1.00544254e+00  1.08659413e+00\n",
      "  -2.52568151e-01  1.03613098e-01]\n",
      " [ 7.35947717e-01  1.20316590e+00  4.17672896e-01 -1.64832073e+00\n",
      "  -1.51785095e-01  1.08707055e-01]\n",
      " [ 8.46092076e-01 -1.14052601e+00  1.35970566e+00  8.61991467e-01\n",
      "   6.29628842e-01  2.91033718e-03]\n",
      " [-8.44677131e-01  1.64319490e+00 -1.53327490e+00 -1.41175860e+00\n",
      "  -1.61271587e+00  1.15504682e-06]\n",
      " [-1.28274360e+00  1.20370482e-01 -1.50077611e+00  4.50590196e-01\n",
      "   2.12215620e+00  5.12635893e-02]\n",
      " [-7.22168462e-01 -1.12868571e+00 -4.27958242e-01  1.56070438e+00\n",
      "  -1.02438764e+00  3.93473325e-01]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create a mask for y_log equal to 0\n",
    "mask = y_log == 0\n",
    "\n",
    "# Sample values from X_gamma based on the mask\n",
    "X_combined = np.concatenate((X_log, np.random.choice(x_gamma, size=(X_log.shape[0], 1))), axis=1)\n",
    "\n",
    "# Print the combined dataset\n",
    "print(\"Combined Dataset:\")\n",
    "print(\"X_combined:\", X_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated Combined Dataset:\n",
      "X_combined: [[-4.30667552e-01  6.72873085e-01 -7.24279827e-01 -5.39630436e-01\n",
      "  -6.51600348e-01  4.91517154e-06]\n",
      " [ 2.11645831e-01 -8.43896860e-01  5.34793935e-01  8.25848048e-01\n",
      "   6.81952971e-01  2.46449909e-06]\n",
      " [ 1.09267506e+00  4.09106051e-01  1.10009583e+00 -9.42750868e-01\n",
      "  -9.81508651e-01  3.18019858e-06]\n",
      " [ 1.51990078e+00 -7.73361182e-01  1.99805321e+00  1.55131754e-01\n",
      "  -3.85313597e-01  5.12635893e-02]\n",
      " [-4.53901271e-01 -2.18347304e+00  2.44724154e-01  2.59123946e+00\n",
      "  -4.84234073e-01  2.40090547e-01]\n",
      " [-1.46361184e+00  3.75316036e-01 -1.79532002e+00  2.54157464e-01\n",
      "  -1.24778318e+00  6.54135484e-01]\n",
      " [ 8.89483645e-01  8.07427260e-01  7.30198478e-01 -1.28568005e+00\n",
      "   1.30740577e-01  8.44419977e-04]\n",
      " [-1.11327862e+00  1.89033108e+00 -1.92487377e+00 -1.55984850e+00\n",
      "   1.86454315e-01  4.42427704e-01]\n",
      " [ 6.31746295e-01 -8.85418435e-01  1.02703224e+00  6.80573226e-01\n",
      "   5.47097381e-01  5.20899181e-08]\n",
      " [-8.86027064e-01 -8.33116491e-01 -7.17314796e-01  1.31217492e+00\n",
      "   4.43819428e-01  8.86943881e-09]\n",
      " [ 5.63722858e-01 -1.47487037e+00  1.15509316e+00  1.35536951e+00\n",
      "  -2.17681203e-01  5.12635893e-02]\n",
      " [-1.02754411e+00 -3.29293881e-01 -1.05383855e+00  8.26007319e-01\n",
      "  -5.95253561e-02  2.12631626e-03]\n",
      " [ 1.93754016e-01 -9.63142392e-01  5.56002755e-01  9.64233110e-01\n",
      "  -2.02192652e-01  1.42299196e-13]\n",
      " [-5.39694662e-02  1.59855122e-01 -1.17086894e-01 -1.50138438e-01\n",
      "   8.25416349e-01  3.93473325e-01]\n",
      " [ 9.63359529e-01  6.59924045e-01  8.65619770e-01 -1.15806823e+00\n",
      "   4.62103474e-01  3.29338194e-78]\n",
      " [-1.09939128e+00 -4.79369952e-01 -1.08324727e+00  1.02255619e+00\n",
      "  -9.26930472e-01  1.08707055e-01]\n",
      " [-6.47604249e-01  1.34700796e+00 -1.20592943e+00 -1.17762637e+00\n",
      "   8.52433335e-01  7.94812610e-02]\n",
      " [-1.37111696e+00  5.06885374e-01 -1.73597973e+00  6.84561582e-02\n",
      "   7.74634053e-01  4.58125309e-05]\n",
      " [ 1.30373602e+00 -2.22100055e-01  1.56010259e+00 -3.48984841e-01\n",
      "   6.98020850e-02  8.44419977e-04]\n",
      " [-5.28785077e-01  9.46218141e-01 -9.31125899e-01 -7.93713869e-01\n",
      "  -1.14736441e-01  4.91517154e-06]\n",
      " [ 1.72400210e+00 -1.22823431e+00  2.38869353e+00  5.59426431e-01\n",
      "  -1.43586215e+00  7.94812610e-02]\n",
      " [-5.66036812e-01 -1.41693293e+00 -1.49941022e-01  1.80474148e+00\n",
      "   1.03246526e+00  6.29501687e-01]\n",
      " [ 7.03851925e-01 -1.29826331e+00  1.25291095e+00  1.09885263e+00\n",
      "   1.09877685e+00  7.72379263e-07]\n",
      " [ 1.75930005e-01 -1.01695056e+00  5.54483984e-01  1.03110238e+00\n",
      "   3.07299521e-01  2.91033718e-03]\n",
      " [ 4.66834932e-02 -2.48780485e-01  1.39790957e-01  2.50588435e-01\n",
      "   6.10370265e-01  4.65881759e-03]\n",
      " [-1.03260487e+00 -6.28041044e-01 -9.55481620e-01  1.15466088e+00\n",
      "  -4.74945311e-01  7.94812610e-02]\n",
      " [ 5.25768931e-01 -8.27415959e-01  8.86293561e-01  6.65300774e-01\n",
      "  -6.92909595e-01  1.15504682e-06]\n",
      " [-1.10644979e+00 -3.20669541e-01 -1.14658127e+00  8.52391857e-01\n",
      "  -1.43014138e+00  2.78559322e-04]\n",
      " [ 1.13746150e+00  2.44120562e-01  1.20852705e+00 -7.82840833e-01\n",
      "  -2.08122250e-01  4.50115429e-05]\n",
      " [-8.12966870e-01  1.85571007e+00 -1.57127256e+00 -1.65830375e+00\n",
      "  -4.71931866e-01  2.91033718e-03]\n",
      " [-8.38069584e-01  1.89792447e+00 -1.61453273e+00 -1.69302842e+00\n",
      "  -2.02514259e+00  1.79209371e-02]\n",
      " [ 1.17077461e+00 -2.65461268e+00  2.25661188e+00  2.36867367e+00\n",
      "  -5.60181040e-01  1.53892764e-02]\n",
      " [ 8.71420582e-02  2.91021981e-02  8.89621380e-02 -7.13352412e-02\n",
      "   8.18741394e-02  1.00187058e-02]\n",
      " [ 4.05708897e-01 -4.68094222e-01  6.24530321e-01  3.27251881e-01\n",
      "   1.30547881e+00  3.21420469e-02]\n",
      " [ 6.55740858e-01  1.39398330e+00  2.59956720e-01 -1.82037691e+00\n",
      "   6.62130675e-01  1.52223351e-08]\n",
      " [-8.73066134e-01  1.65095783e+00 -1.56826626e+00 -1.40735658e+00\n",
      "   4.55718399e-02  4.65881759e-03]\n",
      " [ 7.17686348e-01  1.30857636e+00  3.60169578e-01 -1.75518644e+00\n",
      "  -1.95208780e+00  2.40090547e-01]\n",
      " [ 5.58319659e-01 -1.31089865e+00  1.09180466e+00  1.17869556e+00\n",
      "  -2.09015940e-02  1.03613098e-01]\n",
      " [ 6.47018879e-01  1.44901594e+00  2.30858774e-01 -1.87653774e+00\n",
      "   2.13303337e+00  1.08707055e-01]\n",
      " [ 8.84395338e-01 -2.68317954e+00  1.94087643e+00  2.52983424e+00\n",
      "  -5.91571389e-01  4.84401141e-03]\n",
      " [ 1.13446163e+00  2.68438233e-01  1.19664076e+00 -8.08044635e-01\n",
      "   2.80991868e-01  3.29338194e-78]\n",
      " [ 5.52535445e-01  1.62537490e+00  6.19449780e-02 -2.02632079e+00\n",
      "  -4.93000935e-01  3.21420469e-02]\n",
      " [-7.93836362e-01  1.74355119e+00 -1.51042899e+00 -1.54446032e+00\n",
      "   6.79597749e-01  6.29501687e-01]\n",
      " [ 1.30654200e+00 -2.39384676e-01  1.56931739e+00 -3.31376097e-01\n",
      "   5.88317206e-01  6.54135484e-01]\n",
      " [ 2.82479031e-01 -1.19137182e+00  7.36444353e-01  1.17329352e+00\n",
      "   8.12862119e-01  4.42427704e-01]\n",
      " [ 9.76873584e-01 -1.80921926e+00  1.74147706e+00  1.53313849e+00\n",
      "  -8.28995011e-01  9.07606177e-02]\n",
      " [-1.13714057e+00 -2.40383883e-01 -1.20946429e+00  7.78613181e-01\n",
      "   1.26691115e+00  1.52223351e-08]\n",
      " [-1.45612516e+00  2.48904785e+00 -2.52343407e+00 -2.05832072e+00\n",
      "   1.08895060e+00  1.29198893e+00]\n",
      " [-1.03398089e+00 -5.61560120e-01 -9.80214910e-01  1.08266027e+00\n",
      "   1.76545424e+00  1.00187058e-02]\n",
      " [-8.43212128e-01 -7.04269558e-01 -7.13525325e-01  1.15199146e+00\n",
      "  -3.34501236e-01  8.88612632e-17]\n",
      " [-6.91021261e-01 -1.22532865e+00 -3.58855688e-01  1.65214494e+00\n",
      "   9.17861947e-01  5.38087969e-04]\n",
      " [-1.29267194e+00 -1.17357197e-01 -1.42922002e+00  7.14793729e-01\n",
      "   4.04981711e-01  2.28602013e-03]\n",
      " [-1.69200458e+00  9.50573016e-01 -2.25553963e+00 -2.70623832e-01\n",
      "  -7.07669466e-01  4.49630925e-01]\n",
      " [ 1.33970234e+00 -2.24075406e+00  2.30450019e+00  1.83991037e+00\n",
      "   8.99599875e-01  1.79209371e-02]\n",
      " [ 1.46098002e+00 -5.58180811e-01  1.85605469e+00 -5.31982332e-02\n",
      "  -6.00216877e-01  4.84401141e-03]\n",
      " [ 3.67831850e-01 -2.18003951e-01  4.94298226e-01  7.12364098e-02\n",
      "  -1.86726519e+00  4.84401141e-03]\n",
      " [-7.63516402e-01  1.43140126e+00 -1.36716377e+00 -1.21722043e+00\n",
      "  -7.30366632e-01  4.49630925e-01]\n",
      " [-7.98647864e-01 -1.07525068e+00 -5.33557995e-01  1.53703587e+00\n",
      "  -3.24126734e+00  3.53008939e-05]\n",
      " [ 5.02300750e-01  1.77872961e+00 -4.86290939e-02 -2.17105282e+00\n",
      "  -4.40044487e-01  2.52063708e-01]\n",
      " [ 6.87204686e-01 -1.45724683e+00  1.28938375e+00  1.28008347e+00\n",
      "   8.49602097e-01  1.22087254e-05]\n",
      " [ 3.62053684e-01 -4.62814324e-01  5.73042476e-01  3.41293945e-01\n",
      "  -3.10266757e-01  2.53805088e-02]\n",
      " [-9.41163249e-01  1.63631177e+00 -1.64060704e+00 -1.36045573e+00\n",
      "   6.42800191e-02  1.00187058e-02]\n",
      " [-9.93251439e-01  1.69607038e+00 -1.72067112e+00 -1.40210053e+00\n",
      "   2.16458590e-01  2.78559322e-04]\n",
      " [ 1.22541983e+00  1.06660430e-02  1.38991764e+00 -5.67724525e-01\n",
      "  -5.89364757e-01  3.29338194e-78]\n",
      " [-7.56006002e-01  1.62748917e+00 -1.42695838e+00 -1.43483867e+00\n",
      "   2.14394409e+00  3.29338194e-78]\n",
      " [-5.11036324e-01 -1.60499282e+00 -2.18523104e-02  1.98522348e+00\n",
      "  -1.51936997e+00  4.91517154e-06]\n",
      " [ 4.42051834e-01  1.94742094e+00 -1.75936814e-01 -2.32799460e+00\n",
      "  -6.22699520e-01  1.22087254e-05]\n",
      " [-1.37314120e+00  2.40437346e+00 -2.39955005e+00 -2.00347738e+00\n",
      "  -7.92520738e-01  8.44419977e-04]\n",
      " [ 9.76500035e-01  6.30670730e-01  8.90758772e-01 -1.13207427e+00\n",
      "   1.44127329e+00  1.52223351e-08]\n",
      " [-6.89335853e-01  1.39175475e+00 -1.26898369e+00 -1.20757158e+00\n",
      "   2.31465857e+00  1.00187058e-02]\n",
      " [ 7.80255630e-01 -2.03022330e+00  1.59488828e+00  1.86378908e+00\n",
      "   1.27766490e+00  5.86964389e-02]\n",
      " [ 5.00476965e-01 -1.46447269e+00  1.07954187e+00  1.37271070e+00\n",
      "   8.13509636e-01  2.95893037e-03]\n",
      " [ 6.63522604e-01 -1.44723231e+00  1.25896077e+00  1.27988990e+00\n",
      "   2.10038416e-02  3.07995168e-13]\n",
      " [ 1.12210055e+00  2.54643002e-01  1.18739045e+00 -7.87365232e-01\n",
      "   1.16316375e+00  8.88612632e-17]\n",
      " [-1.49518014e-01  5.88465251e-01 -3.75120699e-01 -5.75002146e-01\n",
      "  -6.61786465e-01  3.16637528e-02]\n",
      " [-3.26836178e-01  8.83469102e-01 -6.79587396e-01 -8.16806281e-01\n",
      "  -4.85363548e-01  4.84401141e-03]\n",
      " [ 7.78139695e-01 -1.76254901e+00  1.49919830e+00  1.57233676e+00\n",
      "   1.17327383e-01  2.43640058e-04]\n",
      " [ 3.04468528e-01 -9.80943802e-01  6.88118916e-01  9.33439522e-01\n",
      "   3.57015486e-01  2.16713315e-03]\n",
      " [ 3.08773169e-01 -5.68113067e-01  5.49144337e-01  4.80502003e-01\n",
      "   7.47293605e-01  4.50115429e-05]\n",
      " [-7.49202028e-01 -1.20727346e+00 -4.31315171e-01  1.65882246e+00\n",
      "  -1.20029641e+00  8.44419977e-04]\n",
      " [ 9.96308974e-01  5.31047366e-01  9.48005316e-01 -1.03223274e+00\n",
      "   1.99059696e-01  1.15504682e-06]\n",
      " [ 1.28131775e+00 -6.69223319e-02  1.48052803e+00 -5.08330948e-01\n",
      "   1.13517345e-01  4.58125309e-05]\n",
      " [-1.19300559e+00 -7.87342078e-02 -1.32933233e+00  6.27374505e-01\n",
      "   8.65755194e-01  3.54247372e-01]\n",
      " [ 1.09406705e-01  3.07593533e-01  1.72297863e-02 -3.85667758e-01\n",
      "  -7.15303709e-01  1.42299196e-13]\n",
      " [-9.77636806e-01 -5.45611863e-01 -9.21694317e-01  1.03967019e+00\n",
      "   1.63241130e+00  3.16637528e-02]\n",
      " [-3.97696342e-01  1.17059844e+00 -8.60238232e-01 -1.09831681e+00\n",
      "   6.33919022e-01  2.46449909e-06]\n",
      " [ 1.05688647e+00  4.02383534e-01  1.06173727e+00 -9.19166865e-01\n",
      "   1.58601682e+00  4.84401141e-03]\n",
      " [-6.78261288e-01 -1.09662143e+00 -3.89198171e-01  1.50575249e+00\n",
      "  -1.26088395e+00  2.52063708e-01]\n",
      " [-6.39225662e-01  1.18674111e+00 -1.14054824e+00 -1.00634985e+00\n",
      "   6.86260190e-01  1.42299196e-13]\n",
      " [-7.47179884e-01 -1.03371682e+00 -4.89499351e-01  1.46830827e+00\n",
      "  -6.53329233e-01  3.52196910e-03]\n",
      " [ 3.24237337e-01  2.23245441e+00 -4.09257060e-01 -2.58590856e+00\n",
      "   1.02330610e-02  3.18019858e-06]\n",
      " [-1.24868955e+00  2.35790248e+00 -2.24181979e+00 -2.00918545e+00\n",
      "  -1.07774478e+00  7.94812610e-02]\n",
      " [ 4.60937578e-01  1.89691056e+00 -1.36855905e-01 -2.28138610e+00\n",
      "  -1.23781550e+00  2.28602013e-03]\n",
      " [-7.34265238e-01  1.72251306e+00 -1.43534875e+00 -1.54851014e+00\n",
      "   5.04987279e-01  3.18019858e-06]\n",
      " [-1.05463759e+00 -5.56580559e-01 -1.00544254e+00  1.08659413e+00\n",
      "  -2.52568151e-01  1.90431799e-11]\n",
      " [ 7.35947717e-01  1.20316590e+00  4.17672896e-01 -1.64832073e+00\n",
      "  -1.51785095e-01  1.08707055e-01]\n",
      " [ 8.46092076e-01 -1.14052601e+00  1.35970566e+00  8.61991467e-01\n",
      "   6.29628842e-01  3.18019858e-06]\n",
      " [-8.44677131e-01  1.64319490e+00 -1.53327490e+00 -1.41175860e+00\n",
      "  -1.61271587e+00  1.15504682e-06]\n",
      " [-1.28274360e+00  1.20370482e-01 -1.50077611e+00  4.50590196e-01\n",
      "   2.12215620e+00  1.29198893e+00]\n",
      " [-7.22168462e-01 -1.12868571e+00 -4.27958242e-01  1.56070438e+00\n",
      "  -1.02438764e+00  3.29338194e-78]]\n"
     ]
    }
   ],
   "source": [
    "# Create a mask for y_log equal to 1\n",
    "mask = y_log == 1\n",
    "\n",
    "# Replace the values in X_combined based on the mask\n",
    "X_combined[mask, -1] = x_gamma\n",
    "\n",
    "# Print the updated combined dataset\n",
    "print(\"Updated Combined Dataset:\")\n",
    "print(\"X_combined:\", X_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          X1        X2        X3        X4        X5            X6\n",
      "0  -0.430668  0.672873 -0.724280 -0.539630 -0.651600  4.915172e-06\n",
      "1   0.211646 -0.843897  0.534794  0.825848  0.681953  2.464499e-06\n",
      "2   1.092675  0.409106  1.100096 -0.942751 -0.981509  3.180199e-06\n",
      "3   1.519901 -0.773361  1.998053  0.155132 -0.385314  5.126359e-02\n",
      "4  -0.453901 -2.183473  0.244724  2.591239 -0.484234  2.400905e-01\n",
      "..       ...       ...       ...       ...       ...           ...\n",
      "95  0.735948  1.203166  0.417673 -1.648321 -0.151785  1.087071e-01\n",
      "96  0.846092 -1.140526  1.359706  0.861991  0.629629  3.180199e-06\n",
      "97 -0.844677  1.643195 -1.533275 -1.411759 -1.612716  1.155047e-06\n",
      "98 -1.282744  0.120370 -1.500776  0.450590  2.122156  1.291989e+00\n",
      "99 -0.722168 -1.128686 -0.427958  1.560704 -1.024388  3.293382e-78\n",
      "\n",
      "[100 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Convert X_combined to a DataFrame\n",
    "df_combined = pd.DataFrame(X_combined)\n",
    "\n",
    "# Assign column labels to the DataFrame\n",
    "df_combined.columns = ['X1', 'X2', 'X3', 'X4', 'X5', 'X6']\n",
    "\n",
    "# Print the updated DataFrame\n",
    "print(df_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, KBinsDiscretizer, FunctionTransformer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "# Define the transformations for each column\n",
    "transformations = [\n",
    "    ('normalize', StandardScaler(), ['X1', 'X2']),\n",
    "    ('encode', KBinsDiscretizer(n_bins=5, encode='ordinal'), ['X3', 'X4']),\n",
    "    ('rank_order', FunctionTransformer(lambda x: x.rank(), validate=False), ['X5', 'X6'])\n",
    "]\n",
    "\n",
    "# Create the column transformer\n",
    "ct = ColumnTransformer(transformers=transformations)\n",
    "ct.fit(df_combined)\n",
    "\n",
    "# Apply the transformations to the dataframe\n",
    "X_transformed = ct.fit_transform(df_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidIndexError",
     "evalue": "(slice(None, None, None), slice(None, 5, None))",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:173\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: '(slice(None, None, None), slice(None, 5, None))' is an invalid key",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidIndexError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[81], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LogisticRegression\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Select the features X1-X5\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m X_features \u001b[38;5;241m=\u001b[39m \u001b[43mdf_combined\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Create the logistic regression model\u001b[39;00m\n\u001b[1;32m      7\u001b[0m classifier \u001b[38;5;241m=\u001b[39m LogisticRegression()\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/pandas/core/indexes/base.py:3817\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m-> 3817\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_indexing_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3818\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/pandas/core/indexes/base.py:6059\u001b[0m, in \u001b[0;36mIndex._check_indexing_error\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   6055\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check_indexing_error\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m   6056\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(key):\n\u001b[1;32m   6057\u001b[0m         \u001b[38;5;66;03m# if key is not a scalar, directly raise an error (the code below\u001b[39;00m\n\u001b[1;32m   6058\u001b[0m         \u001b[38;5;66;03m# would convert to numpy arrays and raise later any way) - GH29926\u001b[39;00m\n\u001b[0;32m-> 6059\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n",
      "\u001b[0;31mInvalidIndexError\u001b[0m: (slice(None, None, None), slice(None, 5, None))"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Select the features X1-X5\n",
    "X_features = X_log[:, :5]\n",
    "\n",
    "# Create the logistic regression model\n",
    "classifier = LogisticRegression()\n",
    "\n",
    "# Fit the model on the selected features and target variable\n",
    "classifier.fit(X_features, y_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidIndexError",
     "evalue": "(slice(None, None, None), -1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:173\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: '(slice(None, None, None), -1)' is an invalid key",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidIndexError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[82], line 10\u001b[0m\n\u001b[1;32m      7\u001b[0m X_subset \u001b[38;5;241m=\u001b[39m df_combined[y_log \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# Fit the model on X6 and y_gamma\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m gamma_model\u001b[38;5;241m.\u001b[39mfit(\u001b[43mX_subset\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m), y_gamma)\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/pandas/core/indexes/base.py:3817\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m-> 3817\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_indexing_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3818\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/pandas/core/indexes/base.py:6059\u001b[0m, in \u001b[0;36mIndex._check_indexing_error\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   6055\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check_indexing_error\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m   6056\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(key):\n\u001b[1;32m   6057\u001b[0m         \u001b[38;5;66;03m# if key is not a scalar, directly raise an error (the code below\u001b[39;00m\n\u001b[1;32m   6058\u001b[0m         \u001b[38;5;66;03m# would convert to numpy arrays and raise later any way) - GH29926\u001b[39;00m\n\u001b[0;32m-> 6059\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n",
      "\u001b[0;31mInvalidIndexError\u001b[0m: (slice(None, None, None), -1)"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import GammaRegressor\n",
    "\n",
    "# Create the gamma regression model\n",
    "gamma_model = GammaRegressor()\n",
    "\n",
    "# Filter the subset where y_log = 1\n",
    "X_subset = X_combined[y_log == 1]\n",
    "\n",
    "# Fit the model on X6 and y_gamma\n",
    "gamma_model.fit(X_subset[:, -1].reshape(-1, 1), y_gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import TransformedTargetRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "\n",
    "\n",
    "# Create the pipeline\n",
    "model = Pipeline([\n",
    "    ('preprocessor', ct ),\n",
    "    ('classifier', classifier)#,\n",
    "    #('gamma_model', gamma_model)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.30667552e-01,  6.72873085e-01, -7.24279827e-01,\n",
       "        -5.39630436e-01, -6.51600348e-01,  4.91517154e-06],\n",
       "       [ 2.11645831e-01, -8.43896860e-01,  5.34793935e-01,\n",
       "         8.25848048e-01,  6.81952971e-01,  2.46449909e-06],\n",
       "       [ 1.09267506e+00,  4.09106051e-01,  1.10009583e+00,\n",
       "        -9.42750868e-01, -9.81508651e-01,  3.18019858e-06],\n",
       "       [ 1.51990078e+00, -7.73361182e-01,  1.99805321e+00,\n",
       "         1.55131754e-01, -3.85313597e-01,  5.12635893e-02],\n",
       "       [-4.53901271e-01, -2.18347304e+00,  2.44724154e-01,\n",
       "         2.59123946e+00, -4.84234073e-01,  2.40090547e-01],\n",
       "       [-1.46361184e+00,  3.75316036e-01, -1.79532002e+00,\n",
       "         2.54157464e-01, -1.24778318e+00,  6.54135484e-01],\n",
       "       [ 8.89483645e-01,  8.07427260e-01,  7.30198478e-01,\n",
       "        -1.28568005e+00,  1.30740577e-01,  8.44419977e-04],\n",
       "       [-1.11327862e+00,  1.89033108e+00, -1.92487377e+00,\n",
       "        -1.55984850e+00,  1.86454315e-01,  4.42427704e-01],\n",
       "       [ 6.31746295e-01, -8.85418435e-01,  1.02703224e+00,\n",
       "         6.80573226e-01,  5.47097381e-01,  5.20899181e-08],\n",
       "       [-8.86027064e-01, -8.33116491e-01, -7.17314796e-01,\n",
       "         1.31217492e+00,  4.43819428e-01,  8.86943881e-09],\n",
       "       [ 5.63722858e-01, -1.47487037e+00,  1.15509316e+00,\n",
       "         1.35536951e+00, -2.17681203e-01,  5.12635893e-02],\n",
       "       [-1.02754411e+00, -3.29293881e-01, -1.05383855e+00,\n",
       "         8.26007319e-01, -5.95253561e-02,  2.12631626e-03],\n",
       "       [ 1.93754016e-01, -9.63142392e-01,  5.56002755e-01,\n",
       "         9.64233110e-01, -2.02192652e-01,  1.42299196e-13],\n",
       "       [-5.39694662e-02,  1.59855122e-01, -1.17086894e-01,\n",
       "        -1.50138438e-01,  8.25416349e-01,  3.93473325e-01],\n",
       "       [ 9.63359529e-01,  6.59924045e-01,  8.65619770e-01,\n",
       "        -1.15806823e+00,  4.62103474e-01,  3.29338194e-78],\n",
       "       [-1.09939128e+00, -4.79369952e-01, -1.08324727e+00,\n",
       "         1.02255619e+00, -9.26930472e-01,  1.08707055e-01],\n",
       "       [-6.47604249e-01,  1.34700796e+00, -1.20592943e+00,\n",
       "        -1.17762637e+00,  8.52433335e-01,  7.94812610e-02],\n",
       "       [-1.37111696e+00,  5.06885374e-01, -1.73597973e+00,\n",
       "         6.84561582e-02,  7.74634053e-01,  4.58125309e-05],\n",
       "       [ 1.30373602e+00, -2.22100055e-01,  1.56010259e+00,\n",
       "        -3.48984841e-01,  6.98020850e-02,  8.44419977e-04],\n",
       "       [-5.28785077e-01,  9.46218141e-01, -9.31125899e-01,\n",
       "        -7.93713869e-01, -1.14736441e-01,  4.91517154e-06],\n",
       "       [ 1.72400210e+00, -1.22823431e+00,  2.38869353e+00,\n",
       "         5.59426431e-01, -1.43586215e+00,  7.94812610e-02],\n",
       "       [-5.66036812e-01, -1.41693293e+00, -1.49941022e-01,\n",
       "         1.80474148e+00,  1.03246526e+00,  6.29501687e-01],\n",
       "       [ 7.03851925e-01, -1.29826331e+00,  1.25291095e+00,\n",
       "         1.09885263e+00,  1.09877685e+00,  7.72379263e-07],\n",
       "       [ 1.75930005e-01, -1.01695056e+00,  5.54483984e-01,\n",
       "         1.03110238e+00,  3.07299521e-01,  2.91033718e-03],\n",
       "       [ 4.66834932e-02, -2.48780485e-01,  1.39790957e-01,\n",
       "         2.50588435e-01,  6.10370265e-01,  4.65881759e-03],\n",
       "       [-1.03260487e+00, -6.28041044e-01, -9.55481620e-01,\n",
       "         1.15466088e+00, -4.74945311e-01,  7.94812610e-02],\n",
       "       [ 5.25768931e-01, -8.27415959e-01,  8.86293561e-01,\n",
       "         6.65300774e-01, -6.92909595e-01,  1.15504682e-06],\n",
       "       [-1.10644979e+00, -3.20669541e-01, -1.14658127e+00,\n",
       "         8.52391857e-01, -1.43014138e+00,  2.78559322e-04],\n",
       "       [ 1.13746150e+00,  2.44120562e-01,  1.20852705e+00,\n",
       "        -7.82840833e-01, -2.08122250e-01,  4.50115429e-05],\n",
       "       [-8.12966870e-01,  1.85571007e+00, -1.57127256e+00,\n",
       "        -1.65830375e+00, -4.71931866e-01,  2.91033718e-03],\n",
       "       [-8.38069584e-01,  1.89792447e+00, -1.61453273e+00,\n",
       "        -1.69302842e+00, -2.02514259e+00,  1.79209371e-02],\n",
       "       [ 1.17077461e+00, -2.65461268e+00,  2.25661188e+00,\n",
       "         2.36867367e+00, -5.60181040e-01,  1.53892764e-02],\n",
       "       [ 8.71420582e-02,  2.91021981e-02,  8.89621380e-02,\n",
       "        -7.13352412e-02,  8.18741394e-02,  1.00187058e-02],\n",
       "       [ 4.05708897e-01, -4.68094222e-01,  6.24530321e-01,\n",
       "         3.27251881e-01,  1.30547881e+00,  3.21420469e-02],\n",
       "       [ 6.55740858e-01,  1.39398330e+00,  2.59956720e-01,\n",
       "        -1.82037691e+00,  6.62130675e-01,  1.52223351e-08],\n",
       "       [-8.73066134e-01,  1.65095783e+00, -1.56826626e+00,\n",
       "        -1.40735658e+00,  4.55718399e-02,  4.65881759e-03],\n",
       "       [ 7.17686348e-01,  1.30857636e+00,  3.60169578e-01,\n",
       "        -1.75518644e+00, -1.95208780e+00,  2.40090547e-01],\n",
       "       [ 5.58319659e-01, -1.31089865e+00,  1.09180466e+00,\n",
       "         1.17869556e+00, -2.09015940e-02,  1.03613098e-01],\n",
       "       [ 6.47018879e-01,  1.44901594e+00,  2.30858774e-01,\n",
       "        -1.87653774e+00,  2.13303337e+00,  1.08707055e-01],\n",
       "       [ 8.84395338e-01, -2.68317954e+00,  1.94087643e+00,\n",
       "         2.52983424e+00, -5.91571389e-01,  4.84401141e-03],\n",
       "       [ 1.13446163e+00,  2.68438233e-01,  1.19664076e+00,\n",
       "        -8.08044635e-01,  2.80991868e-01,  3.29338194e-78],\n",
       "       [ 5.52535445e-01,  1.62537490e+00,  6.19449780e-02,\n",
       "        -2.02632079e+00, -4.93000935e-01,  3.21420469e-02],\n",
       "       [-7.93836362e-01,  1.74355119e+00, -1.51042899e+00,\n",
       "        -1.54446032e+00,  6.79597749e-01,  6.29501687e-01],\n",
       "       [ 1.30654200e+00, -2.39384676e-01,  1.56931739e+00,\n",
       "        -3.31376097e-01,  5.88317206e-01,  6.54135484e-01],\n",
       "       [ 2.82479031e-01, -1.19137182e+00,  7.36444353e-01,\n",
       "         1.17329352e+00,  8.12862119e-01,  4.42427704e-01],\n",
       "       [ 9.76873584e-01, -1.80921926e+00,  1.74147706e+00,\n",
       "         1.53313849e+00, -8.28995011e-01,  9.07606177e-02],\n",
       "       [-1.13714057e+00, -2.40383883e-01, -1.20946429e+00,\n",
       "         7.78613181e-01,  1.26691115e+00,  1.52223351e-08],\n",
       "       [-1.45612516e+00,  2.48904785e+00, -2.52343407e+00,\n",
       "        -2.05832072e+00,  1.08895060e+00,  1.29198893e+00],\n",
       "       [-1.03398089e+00, -5.61560120e-01, -9.80214910e-01,\n",
       "         1.08266027e+00,  1.76545424e+00,  1.00187058e-02],\n",
       "       [-8.43212128e-01, -7.04269558e-01, -7.13525325e-01,\n",
       "         1.15199146e+00, -3.34501236e-01,  8.88612632e-17],\n",
       "       [-6.91021261e-01, -1.22532865e+00, -3.58855688e-01,\n",
       "         1.65214494e+00,  9.17861947e-01,  5.38087969e-04],\n",
       "       [-1.29267194e+00, -1.17357197e-01, -1.42922002e+00,\n",
       "         7.14793729e-01,  4.04981711e-01,  2.28602013e-03],\n",
       "       [-1.69200458e+00,  9.50573016e-01, -2.25553963e+00,\n",
       "        -2.70623832e-01, -7.07669466e-01,  4.49630925e-01],\n",
       "       [ 1.33970234e+00, -2.24075406e+00,  2.30450019e+00,\n",
       "         1.83991037e+00,  8.99599875e-01,  1.79209371e-02],\n",
       "       [ 1.46098002e+00, -5.58180811e-01,  1.85605469e+00,\n",
       "        -5.31982332e-02, -6.00216877e-01,  4.84401141e-03],\n",
       "       [ 3.67831850e-01, -2.18003951e-01,  4.94298226e-01,\n",
       "         7.12364098e-02, -1.86726519e+00,  4.84401141e-03],\n",
       "       [-7.63516402e-01,  1.43140126e+00, -1.36716377e+00,\n",
       "        -1.21722043e+00, -7.30366632e-01,  4.49630925e-01],\n",
       "       [-7.98647864e-01, -1.07525068e+00, -5.33557995e-01,\n",
       "         1.53703587e+00, -3.24126734e+00,  3.53008939e-05],\n",
       "       [ 5.02300750e-01,  1.77872961e+00, -4.86290939e-02,\n",
       "        -2.17105282e+00, -4.40044487e-01,  2.52063708e-01],\n",
       "       [ 6.87204686e-01, -1.45724683e+00,  1.28938375e+00,\n",
       "         1.28008347e+00,  8.49602097e-01,  1.22087254e-05],\n",
       "       [ 3.62053684e-01, -4.62814324e-01,  5.73042476e-01,\n",
       "         3.41293945e-01, -3.10266757e-01,  2.53805088e-02],\n",
       "       [-9.41163249e-01,  1.63631177e+00, -1.64060704e+00,\n",
       "        -1.36045573e+00,  6.42800191e-02,  1.00187058e-02],\n",
       "       [-9.93251439e-01,  1.69607038e+00, -1.72067112e+00,\n",
       "        -1.40210053e+00,  2.16458590e-01,  2.78559322e-04],\n",
       "       [ 1.22541983e+00,  1.06660430e-02,  1.38991764e+00,\n",
       "        -5.67724525e-01, -5.89364757e-01,  3.29338194e-78],\n",
       "       [-7.56006002e-01,  1.62748917e+00, -1.42695838e+00,\n",
       "        -1.43483867e+00,  2.14394409e+00,  3.29338194e-78],\n",
       "       [-5.11036324e-01, -1.60499282e+00, -2.18523104e-02,\n",
       "         1.98522348e+00, -1.51936997e+00,  4.91517154e-06],\n",
       "       [ 4.42051834e-01,  1.94742094e+00, -1.75936814e-01,\n",
       "        -2.32799460e+00, -6.22699520e-01,  1.22087254e-05],\n",
       "       [-1.37314120e+00,  2.40437346e+00, -2.39955005e+00,\n",
       "        -2.00347738e+00, -7.92520738e-01,  8.44419977e-04],\n",
       "       [ 9.76500035e-01,  6.30670730e-01,  8.90758772e-01,\n",
       "        -1.13207427e+00,  1.44127329e+00,  1.52223351e-08],\n",
       "       [-6.89335853e-01,  1.39175475e+00, -1.26898369e+00,\n",
       "        -1.20757158e+00,  2.31465857e+00,  1.00187058e-02],\n",
       "       [ 7.80255630e-01, -2.03022330e+00,  1.59488828e+00,\n",
       "         1.86378908e+00,  1.27766490e+00,  5.86964389e-02],\n",
       "       [ 5.00476965e-01, -1.46447269e+00,  1.07954187e+00,\n",
       "         1.37271070e+00,  8.13509636e-01,  2.95893037e-03],\n",
       "       [ 6.63522604e-01, -1.44723231e+00,  1.25896077e+00,\n",
       "         1.27988990e+00,  2.10038416e-02,  3.07995168e-13],\n",
       "       [ 1.12210055e+00,  2.54643002e-01,  1.18739045e+00,\n",
       "        -7.87365232e-01,  1.16316375e+00,  8.88612632e-17],\n",
       "       [-1.49518014e-01,  5.88465251e-01, -3.75120699e-01,\n",
       "        -5.75002146e-01, -6.61786465e-01,  3.16637528e-02],\n",
       "       [-3.26836178e-01,  8.83469102e-01, -6.79587396e-01,\n",
       "        -8.16806281e-01, -4.85363548e-01,  4.84401141e-03],\n",
       "       [ 7.78139695e-01, -1.76254901e+00,  1.49919830e+00,\n",
       "         1.57233676e+00,  1.17327383e-01,  2.43640058e-04],\n",
       "       [ 3.04468528e-01, -9.80943802e-01,  6.88118916e-01,\n",
       "         9.33439522e-01,  3.57015486e-01,  2.16713315e-03],\n",
       "       [ 3.08773169e-01, -5.68113067e-01,  5.49144337e-01,\n",
       "         4.80502003e-01,  7.47293605e-01,  4.50115429e-05],\n",
       "       [-7.49202028e-01, -1.20727346e+00, -4.31315171e-01,\n",
       "         1.65882246e+00, -1.20029641e+00,  8.44419977e-04],\n",
       "       [ 9.96308974e-01,  5.31047366e-01,  9.48005316e-01,\n",
       "        -1.03223274e+00,  1.99059696e-01,  1.15504682e-06],\n",
       "       [ 1.28131775e+00, -6.69223319e-02,  1.48052803e+00,\n",
       "        -5.08330948e-01,  1.13517345e-01,  4.58125309e-05],\n",
       "       [-1.19300559e+00, -7.87342078e-02, -1.32933233e+00,\n",
       "         6.27374505e-01,  8.65755194e-01,  3.54247372e-01],\n",
       "       [ 1.09406705e-01,  3.07593533e-01,  1.72297863e-02,\n",
       "        -3.85667758e-01, -7.15303709e-01,  1.42299196e-13],\n",
       "       [-9.77636806e-01, -5.45611863e-01, -9.21694317e-01,\n",
       "         1.03967019e+00,  1.63241130e+00,  3.16637528e-02],\n",
       "       [-3.97696342e-01,  1.17059844e+00, -8.60238232e-01,\n",
       "        -1.09831681e+00,  6.33919022e-01,  2.46449909e-06],\n",
       "       [ 1.05688647e+00,  4.02383534e-01,  1.06173727e+00,\n",
       "        -9.19166865e-01,  1.58601682e+00,  4.84401141e-03],\n",
       "       [-6.78261288e-01, -1.09662143e+00, -3.89198171e-01,\n",
       "         1.50575249e+00, -1.26088395e+00,  2.52063708e-01],\n",
       "       [-6.39225662e-01,  1.18674111e+00, -1.14054824e+00,\n",
       "        -1.00634985e+00,  6.86260190e-01,  1.42299196e-13],\n",
       "       [-7.47179884e-01, -1.03371682e+00, -4.89499351e-01,\n",
       "         1.46830827e+00, -6.53329233e-01,  3.52196910e-03],\n",
       "       [ 3.24237337e-01,  2.23245441e+00, -4.09257060e-01,\n",
       "        -2.58590856e+00,  1.02330610e-02,  3.18019858e-06],\n",
       "       [-1.24868955e+00,  2.35790248e+00, -2.24181979e+00,\n",
       "        -2.00918545e+00, -1.07774478e+00,  7.94812610e-02],\n",
       "       [ 4.60937578e-01,  1.89691056e+00, -1.36855905e-01,\n",
       "        -2.28138610e+00, -1.23781550e+00,  2.28602013e-03],\n",
       "       [-7.34265238e-01,  1.72251306e+00, -1.43534875e+00,\n",
       "        -1.54851014e+00,  5.04987279e-01,  3.18019858e-06],\n",
       "       [-1.05463759e+00, -5.56580559e-01, -1.00544254e+00,\n",
       "         1.08659413e+00, -2.52568151e-01,  1.90431799e-11],\n",
       "       [ 7.35947717e-01,  1.20316590e+00,  4.17672896e-01,\n",
       "        -1.64832073e+00, -1.51785095e-01,  1.08707055e-01],\n",
       "       [ 8.46092076e-01, -1.14052601e+00,  1.35970566e+00,\n",
       "         8.61991467e-01,  6.29628842e-01,  3.18019858e-06],\n",
       "       [-8.44677131e-01,  1.64319490e+00, -1.53327490e+00,\n",
       "        -1.41175860e+00, -1.61271587e+00,  1.15504682e-06],\n",
       "       [-1.28274360e+00,  1.20370482e-01, -1.50077611e+00,\n",
       "         4.50590196e-01,  2.12215620e+00,  1.29198893e+00],\n",
       "       [-7.22168462e-01, -1.12868571e+00, -4.27958242e-01,\n",
       "         1.56070438e+00, -1.02438764e+00,  3.29338194e-78]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Specifying the columns using strings is only supported for pandas DataFrames",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[79], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m y_log_pred \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_combined\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/sklearn/pipeline.py:457\u001b[0m, in \u001b[0;36mPipeline.predict\u001b[0;34m(self, X, **predict_params)\u001b[0m\n\u001b[1;32m    455\u001b[0m Xt \u001b[38;5;241m=\u001b[39m X\n\u001b[1;32m    456\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _, name, transform \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter(with_final\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m--> 457\u001b[0m     Xt \u001b[38;5;241m=\u001b[39m \u001b[43mtransform\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mpredict(Xt, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpredict_params)\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/sklearn/compose/_column_transformer.py:763\u001b[0m, in \u001b[0;36mColumnTransformer.transform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    758\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    759\u001b[0m     \u001b[38;5;66;03m# ndarray was used for fitting or transforming, thus we only\u001b[39;00m\n\u001b[1;32m    760\u001b[0m     \u001b[38;5;66;03m# check that n_features_in_ is consistent\u001b[39;00m\n\u001b[1;32m    761\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_n_features(X, reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 763\u001b[0m Xs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    764\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    765\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    766\u001b[0m \u001b[43m    \u001b[49m\u001b[43m_transform_one\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    767\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfitted\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    768\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumn_as_strings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfit_dataframe_and_transform_dataframe\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    769\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    770\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_output(Xs)\n\u001b[1;32m    772\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m Xs:\n\u001b[1;32m    773\u001b[0m     \u001b[38;5;66;03m# All transformers are None\u001b[39;00m\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/sklearn/compose/_column_transformer.py:621\u001b[0m, in \u001b[0;36mColumnTransformer._fit_transform\u001b[0;34m(self, X, y, func, fitted, column_as_strings)\u001b[0m\n\u001b[1;32m    615\u001b[0m transformers \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[1;32m    616\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter(\n\u001b[1;32m    617\u001b[0m         fitted\u001b[38;5;241m=\u001b[39mfitted, replace_strings\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, column_as_strings\u001b[38;5;241m=\u001b[39mcolumn_as_strings\n\u001b[1;32m    618\u001b[0m     )\n\u001b[1;32m    619\u001b[0m )\n\u001b[1;32m    620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    623\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtransformer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrans\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfitted\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrans\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    624\u001b[0m \u001b[43m            \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_safe_indexing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    625\u001b[0m \u001b[43m            \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    626\u001b[0m \u001b[43m            \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    627\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmessage_clsname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mColumnTransformer\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    628\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtransformers\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    629\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    630\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtransformers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    631\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    632\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    633\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 2D array, got 1D array instead\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(e):\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/joblib/parallel.py:1918\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1916\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[1;32m   1917\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 1918\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(output)\n\u001b[1;32m   1920\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[1;32m   1921\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[1;32m   1922\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[1;32m   1923\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[1;32m   1924\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[1;32m   1925\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/joblib/parallel.py:1844\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1841\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1843\u001b[0m \u001b[38;5;66;03m# Sequentially call the tasks and yield the results.\u001b[39;00m\n\u001b[0;32m-> 1844\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43miterable\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m   1845\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_dispatched_batches\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n\u001b[1;32m   1846\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_dispatched_tasks\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/sklearn/compose/_column_transformer.py:624\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    615\u001b[0m transformers \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[1;32m    616\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter(\n\u001b[1;32m    617\u001b[0m         fitted\u001b[38;5;241m=\u001b[39mfitted, replace_strings\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, column_as_strings\u001b[38;5;241m=\u001b[39mcolumn_as_strings\n\u001b[1;32m    618\u001b[0m     )\n\u001b[1;32m    619\u001b[0m )\n\u001b[1;32m    620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Parallel(n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)(\n\u001b[1;32m    622\u001b[0m         delayed(func)(\n\u001b[1;32m    623\u001b[0m             transformer\u001b[38;5;241m=\u001b[39mclone(trans) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fitted \u001b[38;5;28;01melse\u001b[39;00m trans,\n\u001b[0;32m--> 624\u001b[0m             X\u001b[38;5;241m=\u001b[39m\u001b[43m_safe_indexing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    625\u001b[0m             y\u001b[38;5;241m=\u001b[39my,\n\u001b[1;32m    626\u001b[0m             weight\u001b[38;5;241m=\u001b[39mweight,\n\u001b[1;32m    627\u001b[0m             message_clsname\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumnTransformer\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    628\u001b[0m             message\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(name, idx, \u001b[38;5;28mlen\u001b[39m(transformers)),\n\u001b[1;32m    629\u001b[0m         )\n\u001b[1;32m    630\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m idx, (name, trans, column, weight) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(transformers, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    631\u001b[0m     )\n\u001b[1;32m    632\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    633\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 2D array, got 1D array instead\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(e):\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/sklearn/utils/__init__.py:353\u001b[0m, in \u001b[0;36m_safe_indexing\u001b[0;34m(X, indices, axis)\u001b[0m\n\u001b[1;32m    346\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    347\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m should be a 2D NumPy array, 2D sparse matrix or pandas \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    348\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdataframe when indexing the columns (i.e. \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxis=1\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m). \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    349\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m instead with \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m dimension(s).\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mtype\u001b[39m(X), X\u001b[38;5;241m.\u001b[39mndim)\n\u001b[1;32m    350\u001b[0m     )\n\u001b[1;32m    352\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m indices_dtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloc\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 353\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    354\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSpecifying the columns using strings is only supported for \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    355\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpandas DataFrames\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    356\u001b[0m     )\n\u001b[1;32m    358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miloc\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    359\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _pandas_indexing(X, indices, indices_dtype, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "\u001b[0;31mValueError\u001b[0m: Specifying the columns using strings is only supported for pandas DataFrames"
     ]
    }
   ],
   "source": [
    "y_log_pred = model.predict(X_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "All intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' 'LogisticRegression()' (type <class 'sklearn.linear_model._logistic.LogisticRegression'>) doesn't",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 26\u001b[0m\n\u001b[1;32m     19\u001b[0m model \u001b[38;5;241m=\u001b[39m Pipeline([\n\u001b[1;32m     20\u001b[0m     (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscaler\u001b[39m\u001b[38;5;124m'\u001b[39m, StandardScaler()),  \u001b[38;5;66;03m# Optional: Apply feature scaling\u001b[39;00m\n\u001b[1;32m     21\u001b[0m     (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlogistic\u001b[39m\u001b[38;5;124m'\u001b[39m, logistic_model),\n\u001b[1;32m     22\u001b[0m     (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlinear\u001b[39m\u001b[38;5;124m'\u001b[39m, linear_model)\n\u001b[1;32m     23\u001b[0m ])\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# Fit the pipeline on the training data\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m# Predict probabilities using logistic regression\u001b[39;00m\n\u001b[1;32m     29\u001b[0m logistic_probs \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict_proba(X_test)\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/sklearn/pipeline.py:378\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    352\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model.\u001b[39;00m\n\u001b[1;32m    353\u001b[0m \n\u001b[1;32m    354\u001b[0m \u001b[38;5;124;03mFit all the transformers one after the other and transform the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    375\u001b[0m \u001b[38;5;124;03m    Pipeline with fitted steps.\u001b[39;00m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    377\u001b[0m fit_params_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_fit_params(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[0;32m--> 378\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params_steps\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)):\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/sklearn/pipeline.py:316\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[0;34m(self, X, y, **fit_params_steps)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_fit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params_steps):\n\u001b[1;32m    314\u001b[0m     \u001b[38;5;66;03m# shallow copy of steps - this should really be steps_\u001b[39;00m\n\u001b[1;32m    315\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps)\n\u001b[0;32m--> 316\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_steps\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    317\u001b[0m     \u001b[38;5;66;03m# Setup the memory\u001b[39;00m\n\u001b[1;32m    318\u001b[0m     memory \u001b[38;5;241m=\u001b[39m check_memory(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmemory)\n",
      "File \u001b[0;32m~/Documents/GitHub/Model_Interpretability/shap/lib/python3.11/site-packages/sklearn/pipeline.py:207\u001b[0m, in \u001b[0;36mPipeline._validate_steps\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mhasattr\u001b[39m(t, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(t, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_transform\u001b[39m\u001b[38;5;124m\"\u001b[39m)) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\n\u001b[1;32m    205\u001b[0m         t, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    206\u001b[0m     ):\n\u001b[0;32m--> 207\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    208\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll intermediate steps should be \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    209\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransformers and implement fit and transform \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    210\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor be the string \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    211\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m (type \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m) doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (t, \u001b[38;5;28mtype\u001b[39m(t))\n\u001b[1;32m    212\u001b[0m         )\n\u001b[1;32m    214\u001b[0m \u001b[38;5;66;03m# We allow last estimator to be None as an identity transformation\u001b[39;00m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    216\u001b[0m     estimator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    217\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(estimator, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    219\u001b[0m ):\n",
      "\u001b[0;31mTypeError\u001b[0m: All intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' 'LogisticRegression()' (type <class 'sklearn.linear_model._logistic.LogisticRegression'>) doesn't"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.compose import TransformedTargetRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "# Generate example data\n",
    "X_train = np.random.rand(100, 5)\n",
    "y_train = np.random.randint(0, 2, size=(100,))\n",
    "X_test = np.random.rand(20, 5)\n",
    "y_test = np.random.randint(0, 2, size=(20,))\n",
    "\n",
    "# Create the logistic regression model\n",
    "logistic_model = LogisticRegression()\n",
    "\n",
    "# Create the linear regression model\n",
    "linear_model = LinearRegression()\n",
    "\n",
    "# Create the pipeline\n",
    "model = Pipeline([\n",
    "    ('scaler', StandardScaler()),  # Optional: Apply feature scaling\n",
    "    ('logistic', logistic_model),\n",
    "    ('linear', linear_model)\n",
    "])\n",
    "\n",
    "# Fit the pipeline on the training data\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict probabilities using logistic regression\n",
    "logistic_probs = model.predict_proba(X_test)\n",
    "\n",
    "# Predict using linear regression\n",
    "linear_preds = model.predict(X_test)\n",
    "\n",
    "# Multiply probabilities with predictions\n",
    "combined_preds = logistic_probs[:, 1] * linear_preds\n",
    "\n",
    "# Add combined_preds as a step in the pipeline\n",
    "model.steps.append(('combined_preds', combined_preds))\n",
    "\n",
    "# Evaluate the combined model\n",
    "accuracy = model.score(X_test, y_test)\n"
   ]
  }
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