Run_LassoRegression_and_Average.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load all features and run lasso regression\n",
    "\n",
    "### Load all features and combine them¶\n",
    "\n",
    "- Answers to 60 questions\n",
    "\n",
    "- Tenure of key employees in the care center\n",
    "\n",
    "- Median household income at each location\n",
    "\n",
    "- Median salary at each location\n",
    "\n",
    "- Measure of variability in the answers\n",
    "\n",
    "### Run lasso regression 1000 times\n",
    "\n",
    "- Split the data to test and train 1000 times and run lasso regression (with cross validation for the best alpha to use)\n",
    "\n",
    "- Find the distribution of coefficient of determination and weight.\n",
    "\n",
    "### Save the output data (as .pkl file)\n",
    "\n",
    "- Save the combined features and the results from the lasso regression."
   ]
  },
  {
   "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",
    "\n",
    "from scipy.stats import wilcoxon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load all the features"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "### Run Lasso regression 1000 times (with cross validation)"
   ]
  },
  {
   "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. \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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save features\n",
    "feature_set.to_pickle(\"./feature_set.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the distribution of the coefficient of determination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "#Distribution of coefficient of determination\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.830100814603451e-56\n"
     ]
    }
   ],
   "source": [
    "#Check the statistical significance\n",
    "sum_ranks, pval_w = wilcoxon(test_r_squared[:,0])\n",
    "print(pval_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "#Plot the alpha value for the 1000 lasso regression\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "### Plot the mean coefficient for each feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f99b102d1d0>]"
      ]
     },
     "execution_count": 12,
     "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": [
    "mean_coef=lasso_coef_df.mean()\n",
    "\n",
    "plt.plot(mean_coef,alpha=0.7,linestyle='none',marker='*',markersize=8,color='red') "
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
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