LassoRegression_plus_LinearRegressionWithInteractions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "sb.set()\n",
    "color = sb.color_palette()\n",
    "import matplotlib as mpl\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import pickle\n",
    "\n",
    "from sklearn import preprocessing as pp \n",
    "#import the entire linear model\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from scipy.stats import pearsonr \n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LassoCV\n",
    "#from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "from scipy.stats import wilcoxon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_set = pd.read_pickle(\"./feature_set.pkl\")\n",
    "turnOverRate = pd.read_pickle(\"./turnOverRate.pkl\")\n",
    "LocationCodeComprehensive = pd.read_pickle(\"./LocationCodeComprehensive.pkl\")\n",
    "location_pay = pd.read_pickle(\"./location_pay.pkl\")\n",
    "location_sd = pd.read_pickle(\"./location_sdS_df.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try running the cross validation Lasso for different train test split and get the\n",
    "#mean r-squared, alpha, and weights. Then we can go look at the individual features\n",
    "#and their relationships.\n",
    "\n",
    "all_features = pd.concat([feature_set,location_pay,location_sd], axis = 1)\n",
    "\n",
    "xCV = all_features\n",
    "yCV = turnOverRate #Turn over\n",
    "\n",
    "train_number = 1000\n",
    "#Run this part for 1000 times and get the values for r-squared, alpha, and the weights.\n",
    "#reg.alpha_, reg.coef_, r2score.\n",
    "\n",
    "#initialize\n",
    "test_r_squared = np.zeros((train_number,1))\n",
    "lasso_alpha = np.zeros((train_number,1))\n",
    "lasso_coef = np.zeros((train_number,all_features.shape[1]))\n",
    "\n",
    "for trialNo in range(train_number):\n",
    "    \n",
    "    #split 80-20\n",
    "    XCV_train, XCV_test, YCV_train, YCV_test = train_test_split(xCV, yCV, test_size=0.2)\n",
    "\n",
    "    reg = LassoCV(cv=5).fit(XCV_train, YCV_train)\n",
    "\n",
    "    YpredLassoCV = reg.predict(XCV_test)\n",
    "    test_r_squared[trialNo] = r2_score(YCV_test, YpredLassoCV)\n",
    "    lasso_alpha[trialNo] = reg.alpha_\n",
    "    lasso_coef[trialNo,:] = reg.coef_ \n",
    "    #print(\"r^2 on test data : %f\" % r2ScoreLassoCV)\n",
    "\n",
    "    #ActualPredictionCV = pd.DataFrame({'Actual': YCV_test, 'Predicted': YpredLassoCV})\n",
    "\n",
    "    #sb.lmplot(x='Predicted', y='Actual', data=ActualPredictionCV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('lasso_output.pickle', 'wb') as f:\n",
    "    pickle.dump([test_r_squared, lasso_alpha, lasso_coef], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting back the objects:\n",
    "with open('lasso_output.pickle', 'rb') as f:  \n",
    "    test_r_squared, lasso_alpha, lasso_coef = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "test_r_squared_df=pd.DataFrame(data=test_r_squared)\n",
    "test_r_squared_df.rename(columns = {0:'coefficient of determination'}, inplace = True) \n",
    "\n",
    "sb.distplot(test_r_squared_df)\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.52570745e-57]\n"
     ]
    }
   ],
   "source": [
    "#One sample t-test to show the significance.\n",
    "from scipy.stats import ttest_1samp\n",
    "\n",
    "tset, pval = ttest_1samp(test_r_squared, 0)\n",
    "print(pval)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.51059490731279e-61\n"
     ]
    }
   ],
   "source": [
    "sum_ranks, pval_w = wilcoxon(test_r_squared[:,0])\n",
    "print(pval_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso_alpha_df = pd.DataFrame(data=lasso_alpha)\n",
    "lasso_coef_df = pd.DataFrame(data=lasso_coef)\n",
    "\n",
    "sb.distplot(lasso_alpha_df)\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_coef=lasso_coef_df.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6c094c5a58>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(mean_coef,alpha=0.7,linestyle='none',marker='*',markersize=5,color='red',label=r'Lasso; $\\alpha = 0.05$',zorder=7) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_set.to_pickle(\"./feature_set.pkl\")\n",
    "\n",
    "lasso_r_squared_1000 = pd.DataFrame(data=test_r_squared)\n",
    "lasso_alpha_1000 = pd.DataFrame(data=lasso_alpha)\n",
    "lasso_coef_1000 = pd.DataFrame(data=lasso_coef)\n",
    "\n",
    "lasso_r_squared_1000.to_pickle(\"./lasso_r_squared_1000.pkl\")\n",
    "lasso_alpha_1000.to_pickle(\"./lasso_alpha_1000.pkl\")\n",
    "lasso_coef_1000.to_pickle(\"./lasso_coef_1000.pkl\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_set = pd.read_pickle(\"./feature_set.pkl\")\n",
    "\n",
    "lasso_r_squared_df = pd.read_pickle(\"./lasso_r_squared_1000.pkl\")\n",
    "lasso_alpha_df = pd.read_pickle(\"./lasso_alpha_1000.pkl\")\n",
    "lasso_coef_df = pd.read_pickle(\"./lasso_coef_1000.pkl\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "abs_mean_coef=abs(mean_coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55    0.157820\n",
       "62    0.127956\n",
       "21    0.086186\n",
       "64    0.075024\n",
       "24    0.042282\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_coef=abs_mean_coef.sort_values(ascending=False)\n",
    "top_coef.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_features = pd.concat([feature_set,location_pay,location_sd], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Choose the features with top n coefficients \n",
    "\n",
    "features_number = 3\n",
    "#get the top n features and construct a new feature.\n",
    "top_coef=abs_mean_coef.sort_values(ascending=False)\n",
    "\n",
    "selected_features = all_features.iloc[:,top_coef.index[0]]\n",
    "\n",
    "for features in range(features_number-1):\n",
    "    selected_features = pd.concat([selected_features, all_features.iloc[:,top_coef.index[features+1]]], axis = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>55</th>\n",
       "      <th>1</th>\n",
       "      <th>21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.467229</td>\n",
       "      <td>-1.180298</td>\n",
       "      <td>1.103600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.504403</td>\n",
       "      <td>-1.045167</td>\n",
       "      <td>-0.092121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.888779</td>\n",
       "      <td>0.775737</td>\n",
       "      <td>-0.562840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.026882</td>\n",
       "      <td>-0.108571</td>\n",
       "      <td>-0.092121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.679443</td>\n",
       "      <td>-0.224316</td>\n",
       "      <td>-1.280074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         55        1         21\n",
       "0  0.467229 -1.180298  1.103600\n",
       "1 -1.504403 -1.045167 -0.092121\n",
       "2  0.888779  0.775737 -0.562840\n",
       "3 -0.026882 -0.108571 -0.092121\n",
       "4  0.679443 -0.224316 -1.280074"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.467229</td>\n",
       "      <td>-1.180298</td>\n",
       "      <td>1.103600</td>\n",
       "      <td>-0.551469</td>\n",
       "      <td>0.515634</td>\n",
       "      <td>-1.302577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.504403</td>\n",
       "      <td>-1.045167</td>\n",
       "      <td>-0.092121</td>\n",
       "      <td>1.572352</td>\n",
       "      <td>0.138588</td>\n",
       "      <td>0.096282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.888779</td>\n",
       "      <td>0.775737</td>\n",
       "      <td>-0.562840</td>\n",
       "      <td>0.689458</td>\n",
       "      <td>-0.500240</td>\n",
       "      <td>-0.436616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.026882</td>\n",
       "      <td>-0.108571</td>\n",
       "      <td>-0.092121</td>\n",
       "      <td>0.002919</td>\n",
       "      <td>0.002476</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.679443</td>\n",
       "      <td>-0.224316</td>\n",
       "      <td>-1.280074</td>\n",
       "      <td>-0.152410</td>\n",
       "      <td>-0.869737</td>\n",
       "      <td>0.287141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5\n",
       "0  0.467229 -1.180298  1.103600 -0.551469  0.515634 -1.302577\n",
       "1 -1.504403 -1.045167 -0.092121  1.572352  0.138588  0.096282\n",
       "2  0.888779  0.775737 -0.562840  0.689458 -0.500240 -0.436616\n",
       "3 -0.026882 -0.108571 -0.092121  0.002919  0.002476  0.010002\n",
       "4  0.679443 -0.224316 -1.280074 -0.152410 -0.869737  0.287141"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = pp.PolynomialFeatures(interaction_only=True,include_bias = False)\n",
    "selected_features_interaction=poly.fit_transform(selected_features)\n",
    "selected_features_interaction=pd.DataFrame(data=selected_features_interaction)\n",
    "selected_features_interaction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x0', 'x1', 'x2', 'x0 x1', 'x0 x2', 'x1 x2']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly.get_feature_names()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a model with interactions.\n",
    "#Run N times and take the average.\n",
    "#Try to use the shuffle split function => later\n",
    "x_linear = selected_features_interaction\n",
    "y_linear = turnOverRate #Turn over\n",
    "\n",
    "train_number = 1000\n",
    "#Run this part for 1000 times and get the values for r-squared, alpha, and the weights.\n",
    "#reg.alpha_, reg.coef_, r2score.\n",
    "\n",
    "#initialize\n",
    "linear_r_squared = np.zeros((train_number,1))\n",
    "linear_coef = np.zeros((train_number,x_linear.shape[1]))\n",
    "\n",
    "for trialNo in range(train_number):\n",
    "    \n",
    "    #split 80-20\n",
    "    Xl_train, Xl_test, Yl_train, Yl_test = train_test_split(x_linear, y_linear, test_size=0.2)\n",
    "    \n",
    "\n",
    "    linear_reg = linear_model.LinearRegression()\n",
    "    linear_reg.fit(Xl_train,Yl_train)\n",
    "    \n",
    "    \n",
    "    Ypredl = linear_reg.predict(Xl_test)\n",
    "    \n",
    "    linear_r_squared[trialNo] = r2_score(Yl_test, Ypredl)\n",
    "    linear_coef[trialNo,:] = linear_reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_r_squared_df=pd.DataFrame(data=linear_r_squared)\n",
    "#linear_r_squared_df.hist()\n",
    "sb.distplot(linear_r_squared_df)\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5198293402326091e-92\n"
     ]
    }
   ],
   "source": [
    "sum_ranks, pval_w = wilcoxon(linear_r_squared[:,0])\n",
    "print(pval_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4333812cc0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_coef_df = pd.DataFrame(data=linear_coef)\n",
    "\n",
    "mean_linear_coef=linear_coef_df.mean()\n",
    "\n",
    "plt.plot(mean_linear_coef,alpha=0.7,linestyle='none',marker='*',markersize=5,color='red',label=r'Lasso; $\\alpha = 0.05$',zorder=7) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(linear_coef_df.iloc[:,1])\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(linear_coef_df.iloc[:,0])\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(linear_coef_df.iloc[:,2])\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(linear_coef_df.iloc[:,3])\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(linear_coef_df.iloc[:,4])\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'linear_r_squared_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-b30328520178>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'linear_output.pickle'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlinear_r_squared_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinear_coef_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselected_features_interaction\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'linear_r_squared_df' is not defined"
     ]
    }
   ],
   "source": [
    "with open('linear_output.pickle', 'wb') as f:\n",
    "    pickle.dump([linear_r_squared_df, linear_coef_df], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting back the objects:\n",
    "with open('linear_output.pickle', 'rb') as f:  \n",
    "    linear_r_squared_df, linear_coef_df = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.467229</td>\n",
       "      <td>-1.180298</td>\n",
       "      <td>1.103600</td>\n",
       "      <td>-0.551469</td>\n",
       "      <td>0.515634</td>\n",
       "      <td>-1.302577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.504403</td>\n",
       "      <td>-1.045167</td>\n",
       "      <td>-0.092121</td>\n",
       "      <td>1.572352</td>\n",
       "      <td>0.138588</td>\n",
       "      <td>0.096282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.888779</td>\n",
       "      <td>0.775737</td>\n",
       "      <td>-0.562840</td>\n",
       "      <td>0.689458</td>\n",
       "      <td>-0.500240</td>\n",
       "      <td>-0.436616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.026882</td>\n",
       "      <td>-0.108571</td>\n",
       "      <td>-0.092121</td>\n",
       "      <td>0.002919</td>\n",
       "      <td>0.002476</td>\n",
       "      <td>0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.679443</td>\n",
       "      <td>-0.224316</td>\n",
       "      <td>-1.280074</td>\n",
       "      <td>-0.152410</td>\n",
       "      <td>-0.869737</td>\n",
       "      <td>0.287141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5\n",
       "0  0.467229 -1.180298  1.103600 -0.551469  0.515634 -1.302577\n",
       "1 -1.504403 -1.045167 -0.092121  1.572352  0.138588  0.096282\n",
       "2  0.888779  0.775737 -0.562840  0.689458 -0.500240 -0.436616\n",
       "3 -0.026882 -0.108571 -0.092121  0.002919  0.002476  0.010002\n",
       "4  0.679443 -0.224316 -1.280074 -0.152410 -0.869737  0.287141"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features_interaction=pd.DataFrame(data=selected_features_interaction)\n",
    "selected_features_interaction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features_interaction.to_pickle(\"./selected_features_interaction.pkl\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#From Fabian Pedregosa-Izquierdo, f@bianp.net\n",
    "import numpy as np\n",
    "from scipy import stats, linalg\n",
    "\n",
    "def partial_corr(C):\n",
    "    \"\"\"\n",
    "    Returns the sample linear partial correlation coefficients between pairs of variables in C, controlling \n",
    "    for the remaining variables in C.\n",
    "    Parameters\n",
    "    ----------\n",
    "    C : array-like, shape (n, p)\n",
    "        Array with the different variables. Each column of C is taken as a variable\n",
    "    Returns\n",
    "    -------\n",
    "    P : array-like, shape (p, p)\n",
    "        P[i, j] contains the partial correlation of C[:, i] and C[:, j] controlling\n",
    "        for the remaining variables in C.\n",
    "    \"\"\"\n",
    "    \n",
    "    C = np.asarray(C)\n",
    "    p = C.shape[1]\n",
    "    P_corr = np.zeros((p, p), dtype=np.float)\n",
    "    for i in range(p):\n",
    "        P_corr[i, i] = 1\n",
    "        for j in range(i+1, p):\n",
    "            idx = np.ones(p, dtype=np.bool)\n",
    "            idx[i] = False\n",
    "            idx[j] = False\n",
    "            beta_i = linalg.lstsq(C[:, idx], C[:, j])[0]\n",
    "            beta_j = linalg.lstsq(C[:, idx], C[:, i])[0]\n",
    "\n",
    "            res_j = C[:, j] - C[:, idx].dot( beta_i)\n",
    "            res_i = C[:, i] - C[:, idx].dot(beta_j)\n",
    "            \n",
    "            corr = stats.pearsonr(res_i, res_j)[0]\n",
    "            P_corr[i, j] = corr\n",
    "            P_corr[j, i] = corr\n",
    "        \n",
    "    return P_corr\n",
    "\n",
    "turnOver_features = pd.concat([turnOverRate, selected_features], axis = 1)\n",
    "partial_corr_array = turnOver_features.to_numpy()\n",
    "\n",
    "# Calculate the partial correlation coefficients\n",
    "partial_corr_results = partial_corr(partial_corr_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.285774</td>\n",
       "      <td>-0.180665</td>\n",
       "      <td>0.209384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.285774</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116342</td>\n",
       "      <td>0.644236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.180665</td>\n",
       "      <td>0.116342</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.175639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.209384</td>\n",
       "      <td>0.644236</td>\n",
       "      <td>-0.175639</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  1.000000 -0.285774 -0.180665  0.209384\n",
       "1 -0.285774  1.000000  0.116342  0.644236\n",
       "2 -0.180665  0.116342  1.000000 -0.175639\n",
       "3  0.209384  0.644236 -0.175639  1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partial_corr_results_df = pd.DataFrame(data=partial_corr_results)\n",
    "partial_corr_results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb125a8b080>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mxbaDaHTtpVRtkJxnTObmHbwzMIXVsz7Ps/3A3t9558HxvNRuMBP7jqLT0N6UqlAmmuUXLp+PEp37sW/S0+x96QHiG1+Gr2re18JXuQbFW3Zh76tD2PvSQ/w+9408+0tc2QP/+u+iWPTJC/gDYV9c4FTId+58NZPfnAHAf5cuJ6FiAjVqVMszZu/efSxa7P0wHzhwgOUr1pCcnATAueeexccffwrAwkWfcW3ndlGsPnqaN21EQoXysS4jKmo1bUDGhq3s2rSN7APZrHn/C85td0GeMZmpO9j6wyYCgbxTv4x16WSsTwdg97ZMfsvIomzlClGrvbDF1WqAPyOdwK5tkJ1N9prPKXZuizxjijW/koP/nQf79ngb9mQdPr5mXXzlEsheuyqaZZ88fyFcHOBUyCfXrEHqps05t9NSt5Bcs8YxxyckVOCaTm35eKEX7KtXf0fX6zsC0KVLBypUKE/lypUiW7REVIXqlfh1c0bO7awtO6lQvXKB7ye5SX3iixdj54athVleVPkqVCbw6+HXIvBrBr7yeV+LuCo18SUmUeq24ZS6fQTxDZoED/ZRon0f9n8wJZolSyE46ZA3xqwpzEIKg8/nO2rbsb44ID4+njcnj+PlcRNYt24jAIMffoqWLS/mq6XzaPnHi0lN3cLBgwcjWrNEWAH+ThxLuaoV6TbmTmYOeuUU/yKKo18LOOL5xMURl5jEvglP8vvbf6dElzugVBmKXdiO7B9XEMjKCHEfRVPAH/7FBcddeDXG/OE4uxMLuZaTcucdfenX72YAli1bSa0zaubsS66VxOYtoWdeKeNH89Padbz40ms527Zs2cqN3fsDULZsGbpe34msrN0RrF4iLSt9Jwk1D/9VrZBUmd3bduX7+JLlStPn9UF8+Px0UlesjUSJURPIysCXcPi18CUkEtid97UI/LoTf+qP4M8mkLmdwI7NxCUmEX/G2cTVPpdiF7bDV6IUxBcjsH8fBxa8Fe2nkX9RDmljzNnARLxszAD6WGt/OmLME8BdwKGWw2fW2ruD+8oArwMXAAeBgdba2eHWdaKza74B1hN6ClAl3AcvDONTJjI+ZSIAHTtcyV133sK0abO46MJmZP2aRXr6tqOOGf7kYBISynP7Xwbm2Z6YWImdOzMJBAL89eEBvDHx31F5DhI5aat+JrFODSrVqkrW1p006nwJ0+99OV/HxheP56ZXHmDFzCV8O/e/Ea408vxpPxOXmISvYlUCu3cS3+jSvGfOANnfLyW+8eWwYjGUKY+vShL+nVv5fcZLOWOKnd+KuJr1i3bAE5OZeAowzlo7xRjTC3gFuCLEuEnW2oEhtg8EdltrGxhjzgKWGGMaWGt/C6eoE4X8euCP1tq0I3cYYzaF88CRMPc/H9G+/RXY7z/jf3v3ctttD+bsW/bVfJq3aEdychKPPnIf3//wE18tnQfAP/7xOhNen0qrVpcy8qlHCBBgyZIvGXDvY7F6KhE1aNgovlqxmszMLK7s0ou7+vXmhs5Xx7qsiPBn+5k99A36Tvqrdwrl24vY9lMaVz7QjbQ1v/DDh8tJblyPm155gNIJZTnnymZc8UA3Xmo3mIadLqbOhedQplI5mnVrCcA7A18h/bsNMX5WJ8nvZ//sCZTq+xjExXFw+UIC21IpfkV3/Jt/JvuHr8leu4r4Bk0oPWAMBPzsnzcF9oaVMbETxZA3xlQDmgFtg5umAi8bY6paa7fn8256AH0BrLU/GWOWAR2A6eHU5jtej9EY8yzwf9baz0Ps+7u19r6CPFixEsmnckOz0Bzcn8aBHb/EuowioXiVejxe56ZYl1EkjFj/FnuGdI91GUVC2afehtAdhHzbcXWrsPPmsvXplYCKIXZlWmszD90wxlyAN0M/L9e274Be1trlubY9AdwG7ATSgWHW2i+C+3YD9Q79UjDG/ANYa60dE85zOO7Cq7V2UKiAD+4rUMCLiERTIS283g+sC3G5/yTLSgHqWmsbA88Cs4wxEV3fdOoUShGRQwop5McCdUNcxh7xcJuAZGNMPEDwz5rB7TmstenW2gPB6wuC+xsGd28EaucafuaRx58M5z7WQEQECmfhNdiSyczHuG3GmJVAT2BK8M8VR/bjjTHJh9Y4jTFNgTqADe6eDvwFWBZceG0RvJ+wKORFRArHHcBEY8xQYBfQB8AYMxcYaq1dBjwd7N9nA/uB3tba9ODxzwJvGGPWBvffbq0N+xxuhbyIuCkQ1rptgVlrfwAuCrG9Y67rfY9z/B7gxsKuSyEvIk5y5R2r4VLIi4iTAv7ozuSLKoW8iDhJM3mPTqEUEXGYZvIi4qRAlBdeiyqFvIg4Se0aj0JeRJykhVePevIiIg7TTF5EnHRKf4lXIVLIi4iT1K7xKORFxEkKeY9CXkScpHaNRwuvIiIO00xeRJykdo1HIS8iTtI7Xj0KeRFxkt7x6lHIi4iT/JrJA1p4FRFxmmbyIuIk9eQ9CnkRcZLOrvEo5EXESXozlEc9eRERh2kmLyJOUrvGo5AXESfpFEqPQl5EnKSzazwKeRFxkhZePVp4FRFxmGbyIuIk9eQ9CnkRcZJ68h6FvIg4ST15jy8Q3VdCL7uI5FdYU/FltbqEnTfNU9895f85oIVXERGHRbVdUzuxcTQfrsjakLGax+vcFOsyioQR69/iwI5fYl1GkVC8Sj3qJjaJdRlFwrqMVWHfh3ryHvXkRcRJOrvGo5AXESdpAdCjnryIiMM0kxcRJ6ld41HIi4iTtPDqUciLiJP8sS6giFDIi4iTAuG9l8oZWngVEXGYZvIi4iS/zqEEFPIi4ii/2jWAQl5EHKWevEchLyJO0tk1Hi28iog4TDN5EXGS2jUehbyIOEntGo9CXkScpJD3qCcvIuIwzeRFxEnqyXsU8iLiJL8yHlDIi4ij9I5Xj0JeRJykj67xKORFRAqBMeZsYCKQCGQAfay1Px0xZgjwJ+Bg8PKotXZecN8bwFXAjuDw6dbakeHWpbNrRMRJ/kK4FFAKMM5aezYwDnglxJilQAtrbRPgVmCaMaZ0rv2jrLVNg5ewAx4U8iLiKL/PF/Ylv4wx1YBmwNTgpqlAM2NM1dzjrLXzrLX/C95cDfjwZv4Ro3aNiDipMHryxpiKQMUQuzKttZm5bp8BpFlrswGstdnGmM3B7duPcfd9gJ+ttam5tj1ojPkL8DPwiLX2+3Cfg2byIuKkQmrX3A+sC3G5P5zajDGtgKeAnrk2PwY0sNY2AmYCHxhj4sN5HFDIi4gcz1igbojL2CPGbQKSD4Vy8M+awe15GGMuAaYAXay19tB2a22atdYfvD4JKAfUCvcJqF0jIk4qjDdDBVsymfkYt80YsxJvZj4l+OcKa22eVo0xpgUwDehmrV1+xL5ka21a8PrVQDaQFu5zUMiLiJNi8GaoO4CJxpihwC68njvGmLnAUGvtMuAfQGngFWPMoeN6W2vXBI+tjtcpygKutdYeDLcohbyIOCnab4ay1v4AXBRie8dc11sc5/irIlGXevIiIg7TTF5EnKQPKPMo5EXESfrSEI9CXkScpA8o8zjXk3/imYdZ/NVsPvhkBg0bnxtyzKDHBvDF6vl8t+HLPNtrJtfg3+++xtyF0/jgkxm0ueryaJQcEWe1asx9Hz3HA4vG0PLOzkftr3PhOdw1eyRPrp3MeR0uzNle4w+1uX3mkwyYP5p7/jOKhtdcHM2yo+7xp8fQstOf6NLrjliXEjXDnnmYhV+9z38+mc55jc8JOWbgY/fw2ep5fLPhizzbaybX4K13X2P2wmn855PptC7CPyN+X/gXFzgV8m2uupy69WrTqsU1PPLgcEY893jIcR/OW8x1bW86avuAh25n9qz5dGzTgwH9B/PUs49FuuSI8MX56Dz8z0y6ZTQvth1Eo2svpWqD5DxjMjfv4J2BKaye9Xme7Qf2/s47D47npXaDmdh3FJ2G9qZUhTLRLD+qunRsS8qYEbEuI2paX3U5deqdSZsWnU/4M9Kl7c1Hbb/nof7MmTWPa9r0YED/h3nq2UcjXbKEyal2TdsObXhn2vsArFi2mgoJ5alWvQrbtu7IM27FstUhjw8EApQrXxaA8uXLsS39WB85UbTVatqAjA1b2bVpGwBr3v+Cc9tdwPa1h99XkZnqvSaBQN7OZca69Jzru7dl8ltGFmUrV2Bf1v9wUfOmjUjbsjXWZURN2w5tmBn8GVm5bA0VEspTtXoVth/xM7Jy2ZqQxwcCUK58OcD7GdlahH9G1JP3HDfkjTGJwN+AM4FZ1tpxufa9Y629IcL1FUiNpGpsTjscUumbt1I9qdpRIX8sY0ePZ/KMV7il/02UKVOam7r2j1SpEVWheiV+3ZyRcztry05qNW1Q4PtJblKf+OLF2Lnh9AlB11VPqsaWtMP/P7ds3kqNpGpHhfyxjB09nkkzUujbvydlypSmV9fbI1Vq2BTynhO1a14BduJ9TnIXY8xMY8yhXwz1IlrZSfCF+GjQQCD/yy/Xdu3AjKmzuLhRW27pcRdjxz8d8j6LvDBfB4ByVSvSbcydzBz0SoGPlaIr1F/ngv6MvDP1PS5t1I4/97ibMeNHFtmfkYAv/IsLThTyDay1g621M4F2wBZgtjGmVORLy58+/Xowd9HbzF30NlvTt1MzuUbOvho1qxeo5dKj1/XMfnceAMuXraZkyZJUTqxU6DVHWlb6ThJqHv6I6gpJldm9bVe+jy9ZrjR9Xh/Eh89PJ3XF2kiUKFHUu18P5iyaxpxF09iWvp2k5Oo5+5JqVi9Qy6V7r+uZE/wZWVHEf0Zi8KUhRdKJQr7koSvW2oC19m5gDTAHKBJBP+lf0+jYujsdW3dn/tyPuaGHdybJ+c0bsztrd75bNQCbU9O5rJX3ruQGZ9elZKkSZOzYGZG6Iylt1c8k1qlBpVpViS8eT6POl/DDgq/zdWx88XhueuUBVsxcwrdz/xvhSiUaJv9rGp1a96BT6x7Mn7uQrsGfkabNG7E767d8t2oANqdu4dLgz0j9U/hn5HRyopD/xRjTMvcGa+0g4Evg7IhVdZI+XrCEjRtS+WTZHEa9MIzHBx3+9qy5i97Ouf7IsAf4cs0CSpcpxZdrFnD/4DsBGDH0OXr2voH/LJ7Oi//8Gw/dPSTqz6Ew+LP9zB76Bn0n/ZX7PnyOb2Z/ybaf0rjygW6cc1UzAJIb12PQFy/RsONFXPd0PwbMHw1Aw04XU+fCc2jWrSV3z32au+c+TY0/1I7l04moQcNGcfNfHmD9xlSu7NKLd96fF+uSImrhgiVs2pDKomWzeeaFYQzJ9TMyZ9G0nOt/HXY/n6+ZT+kypfh8zXzuG+ydYjpy6PP8qXdX5i5+mxf/OYpBdw+N+nPIL83kPb7j9eOMMZWBgLX2qH/rG2P+YK39riAPVjuxsZq7wIaM1Txe5+hTOE9HI9a/xYEdv8S6jCKheJV61E1sEusyioR1GauA8D5G8qUzeoWdNwM2TTnlO/PHPbvGWnvMf4cVNOBFRKLJlTczhcupN0OJiEheTr0ZSkTkEFd66uFSyIuIkxTyHoW8iDhJZ3l4FPIi4iQtvHq08Coi4jDN5EXESerJexTyIuIk9eQ9CnkRcZJfMQ+oJy8i4jTN5EXESerJexTyIuIkNWs8CnkRcZJm8h6FvIg4SW+G8mjhVUTEYZrJi4iTdAqlRyEvIk5SxHsU8iLiJC28ehTyIuIktWs8WngVEXGYZvIi4iTN4z0KeRFxknryHoW8iDhJPXmPevIiIg7TTF5EnKR5vEchLyJOUk/eo5AXEScFNJcHFPIi4ijN5D1aeBURcZhm8iLiJJ1C6VHIi4iTFPEehbyIOEkzeY9CXkScpIVXjxZeRUQcppm8iDhJ58l7fIFAVF8Iveoikl++cA6+tU63sPNmwvoZYdVQFGgmLyJO0kzeE9WQf7VWr2g+XJHVP3UKe4Z0j3UZRULZp96mbmKTWJdRJKzLWMWBHb/EuowioXiVerEuwRmayYuIk3R2jUchLyJO8kd3vbHIUsiLiJMU8R6FvIg4KdrveDXGnA1MBBKBDKCPtfanI8bEAy8C7fF+D42y1r52on3h0JuhREQKRwowzlp7NjAOeCXEmJuBBsBZwCXAE8aYOvnYd9IU8iLipIaVbp8AAAyLSURBVEAh/JdfxphqQDNganDTVKCZMabqEUN7AK9aa/3W2u3Au8CN+dh30tSuEREnFcbZNcaYikDFELsyrbWZuW6fAaRZa7MBrLXZxpjNwe3bc407E9iQ6/bG4JgT7TtpmsmLiJP8BMK+APcD60Jc7o/dMysYhbyIOKmQ2jVjgbohLmOPeLhNQHJw8fTQImrN4PbcNgK1c90+M9eY4+07aWrXiIgcQ7Alk5mPcduMMSuBnsCU4J8rgr313KYD/Y0xM/HOwukCtMzHvpOmkBcRJ8XgHa93ABONMUOBXUAfAGPMXGCotXYZMBm4CDh0auVwa+2hz7I43r6TppAXESdF+RN2sdb+gBfSR27vmOt6NnDnMY4/5r5wKORFxEn6+j+PFl5FRBymmbyIOEmfQulRyIuIk/SlIR6FvIg4ST15j0JeRJwU7bNriiotvIqIOEwzeRFxkhZePQp5EXGSFl49CnkRcZIWXj0KeRFxkhZePVp4FRFxmGbyIuIktWs8CnkRcZIWXj0KeRFxkl89eUA9eRERp2kmLyJO0jzeo5AXESdp4dWjkBcRJynkPQp5EXGS3gzl0cKriIjDNJMXESepXeNxKuRrtW7MJU/2xhcfh526iFXj3s+zv1H/DpierfFnZ7MvYzefPPRPfkvLAKD9lMFUO78+W7/6kXm3PB+L8gtVfIMmlOj0Z/DFcfDrjziwZNbRYxpeQok2NwIB/Okb+H36i4d3lixN6XtfIPu7peyfMyF6hUfIsGcepvVVl7Nv7z4G3jOEb1f/cNSYgY/dw/U9OpOQUIGGtS/J2V4zuQbPjRtBhYTyxMfH8bfhf2fRh59Gs/yoePzpMXzy2VIqV6rIu1NSYl1O2PRmKI8z7RpfnI/LRvTlg96jmdFmMPWvu5iKZ9XMM2bHt+v5v45DmNn2UdbNWcqFj/XM2bd6/BwW3Xfq/8UGwOejROd+7Jv0NHtfeoD4xpfhq5qcd0jlGhRv2YW9rw5h70sP8fvcN/LsL3FlD/zrv4ti0ZHT+qrLqVPvTNq06MwjDw5nxHOPhxz34bzFdGl781Hb73moP3NmzeOaNj0Y0P9hnnr20UiXHBNdOrYlZcyIWJdRaAKBQNgXFxQ45I0xlSJRSLiqNq1P1vqt7N64Hf+BbH6e9SW1212QZ8yWz78ne99+ALYtX0vZpMo5+zZ/9i0H9uyLas2RElerAf6MdAK7tkF2NtlrPqfYuS3yjCnW/EoO/nce7NvjbdiTdfj4mnXxlUsge+2qaJYdMW07tGHmNO9fdSuXraFCQnmqVq9y1LiVy9awfeuOo7YHAlCufDkAypcvx9b07ZEtOEaaN21EQoXysS6j0PgJhH1xwXHbNcaYJsAEIBvoCzwHtDHGZACdrbUrI19i/pRNqsRvW3bm3N6TvpNq59c/5njTsxWpC90IsSP5KlQm8GtGzu3ArxnE1Torz5i4KjXxA6VuGw5xcRz4eLoX6j4fJdr34fcZLxNfv2GUK4+M6knV2JK2Nef2ls1bqZFULWSghzJ29HgmzUihb/+elClTml5db49UqSKF7kQz+ReBJ4GXgQ+At6y1ZYC78AK/CPEdvekYv4gbdL2MKo3rsSplTmRLipkQr8WRL0ZcHHGJSeyb8CS/v/13SnS5A0qVodiF7cj+cQWBrIwQ93Fq8oX6q1GAf4pf27UD70x9j0sbtePPPe5mzPiR+ELdqRQpatd4TrTwWt5a+x6AMeYpa+2bANba940xwyNeXQHs2bKTcrnaL2VrVGZP+q6jxtW8/DyaDriW2d1G4t9/MJolRk0gKwNfQmLObV9CIoHdeV+LwK878af+CP5sApnbCezYTFxiEvFnnE1c7XMpdmE7fCVKQXwxAvv3cWDBW9F+GmHp3a8Hf+rdFYDVK74lKbl6zr6kmtUL1HLp3ut6brnxTgBWLFtNyZIlqZxYiYwdO09wpMSSK+2WcJ1oJp97ujK/gMdG1fZVv1Chbg3Kn1GVuOLx1L/uYjYuWJ5nTOJ5tfnjqFuZf+sY9mVkHeOeTn3+tJ+JS0zCV7EqxMcT3+hSDv6wLM+Y7O+XElc32I4pUx5flST8O7fy+4yX2Pv8Xewdcw/7503m4MpPTrmAB5j8r2l0at2DTq17MH/uQrr26AxA0+aN2J31W75bNQCbU7dwaauLAKh/dl1KliqhgD8FBArhPxecaCa/3hhT3lq721rb/9BGY0wt4H+RLa1gAtl+Ph8ykQ5vDsYXF4edtphdP6ZxwcAb2L5qHRsXLOeix3tSrGwprkq5F4Df0jKYf+sYADq/M4SEBkkUL1uKnl+9yJKBr5K6eE0sn9LJ8/vZP3sCpfo+BnFxHFy+kMC2VIpf0R3/5p/J/uFrsteuIr5BE0oPGAMBP/vnTYG9v8W68ohYuGAJbdpezqJls9m7dx+DBwzN2TdnkffLAOCvw+7n2m4dKV2mFJ+vmc+0yTP5++gURg59nmdeGEq/O3oRCAQYdPfQYz3UKW3QsFF8tWI1mZlZXNmlF3f1680Nna+OdVkSJt/J9J2MMWWBstbabQU57tVavdz41Rim/qlT2DOke6zLKBLKPvU2dRObxLqMImFdxioO7Pgl1mUUCcWr1IPQi0v51rD6xWHnzTdbvzzlF19O6s1Q1to9wJ5CrkVEpNC40m4Jl1PveBUROUTfDOVRyIuIkzST9xSpM2RERKRwaSYvIk5Su8ajkBcRJ6ld41HIi4iTNJP3KORFxEmayXu08Coi4jDN5EXESYGAP9YlFAkKeRFxkj6F0qOQFxEnufJ58OFST15ExGGayYuIk9Su8SjkRcRJatd4FPIi4iS9GcqjkBcRJ+nNUB4tvIqIOEwzeRFxknryHoW8iDhJZ9d4FPIi4iTN5D3qyYuIOEwzeRFxkk6h9CjkRcRJRaldY4wpA7wOXAAcBAZaa2eHGHcdMBQoCfiACdba54P7bgHGAuuDw9dZa68/0WMr5EXESUVs4XUgsNta28AYcxawxBjTwFr72xHj0oHO1trNxpgE4GtjzFJr7ZLg/g+ttd0K8sDqyYuIkwKBQNiXQtQDSAGw1v4ELAM6HDnIWvtfa+3m4PVfge+B2uE8sGbyIiLHYIypCFQMsSvTWptZgLs6E9iQ6/ZG4IwTPPY5wMXAX3JtbmWMWQlkAX+z1s450QMr5EXESYW08Ho/MCzE9ieBJw7dMMYsxwvyUKoX9EGNMUnALODuQzN7YDYwzVq71xhzPvCBMaa1tfb7492XQl5EnFRIn10zFngjxPY8s3hrbbPj3YkxZiNe22V7cNOZwMJjjK0GfAg8a619O9dj7Mh1fYUx5lPgQryWzjEp5EXESYUxkw+2ZArSljmW6Xhtl2XBhdcWQM8jBxljEoEFwMvW2teO2JdsrU0LXq+N18oZcaIHVsiLiJOK0imUwLPAG8aYtUA2cLu1djeAMWY4sNlamwL8FTgb+Isx5lAv/u/W2teBu4OnWB4Mbn/UWrviRA+skBcRiTBr7R7gxmPsG5rr+iBg0DHGPQo8WtDHVsiLiJP0efIehbyIOKmItWtiRiEvIk5SyHv0jlcREYf59NtORMRdmsmLiDhMIS8i4jCFvIiIwxTyIiIOU8iLiDhMIS8i4jCFvIiIwxTyIiIOU8iLiDjstPnsGmPM2cBEIBHIAPoEv1D3tGOMeQ64AagDNLLWfhPbimIj+AUNk4H6wO/AWuAv1trtxz3QUcaYd4G6gB/4DRhgrV0Z26okXKfTTD4FGGetPRsYB7wS43pi6V2gJXm/WPh0FABGW2uNtbYx8DMwKsY1xVJfa20Ta+35wHPAhFgXJOE7LUI++J2JzYCpwU1TgWbGmKqxqyp2rLWfWms3xbqOWLPW7rTWLsq16Uu87+E8LVlrf811MwFvRi+nuNOlXXMGkGatzQaw1mYbYzYHt5+W/zSXvIwxccCdwHuxriWWjDGvAe0AH9A+xuVIITgtZvIi+fASXh/65VgXEkvW2tustWfifc3cs7GuR8J3uoT8JiDZGBMPEPyzZnC7nOaCC9FnAT2stWpRANbayUCb4OK0nMJOi5C31m4DVgI9g5t6AitO17Mo5DBjzEjgAqCLtfb3WNcTK8aYcsaYM3Ld7gzsDF7kFHbafGmIMeYcvFMoKwG78E6htLGtKjaMMS8CXYEawA4gw1p7Xmyrij5jzHnAN8CPwN7g5nXW2utjV1VsGGOqA7OAskA2XrgPtNYuj2lhErbTJuRFRE5Hp0W7RkTkdKWQFxFxmEJeRMRhCnkREYcp5EVEHKaQFxFxmEJeRMRhCnkREYf9P6OLm3fyJRM0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(6, 6))\n",
    "sb.heatmap(partial_corr_results, annot=True, linewidths=.5, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb126523780>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Put together into a single data frame so we can plot easier\n",
    "frameMVP = { 'turnover rate': turnOverRate, 'Provide excellent service': all_features.iloc[:,55]} \n",
    "dataForPlotMVP = pd.DataFrame(frameMVP)\n",
    "# Recommended way\n",
    "sb.lmplot(x='Provide excellent service', y='turnover rate', data=dataForPlotMVP)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}