PropensityScoreMatch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymatch.Matcher import Matcher\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Interesting... Lisa P. has provided a canned r...</td>\n",
       "      <td>1.485610e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>I was experiencing some urological issues and ...</td>\n",
       "      <td>1.481383e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>A family member went in last June with a badly...</td>\n",
       "      <td>1.407794e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>How on earth does it take 10 hours to be disch...</td>\n",
       "      <td>1.470444e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Everything was efficient and professional, and...</td>\n",
       "      <td>1.525963e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stars                                               text     timestamp  \\\n",
       "0    1.0  Interesting... Lisa P. has provided a canned r...  1.485610e+09   \n",
       "1    2.0  I was experiencing some urological issues and ...  1.481383e+09   \n",
       "2    5.0  A family member went in last June with a badly...  1.407794e+09   \n",
       "3    1.0  How on earth does it take 10 hours to be disch...  1.470444e+09   \n",
       "4    5.0  Everything was efficient and professional, and...  1.525963e+09   \n",
       "\n",
       "   gender  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "data_dir = Path.cwd() / Path('csv_files')\n",
    "fields = ['stars', 'text', 'timestamp', 'gender']\n",
    "disclosed_female_df = pd.read_csv(data_dir / 'disclosed_female_l_s_r.csv', sep='|', usecols=fields)\n",
    "disclosed_male_df = pd.read_csv(data_dir / 'disclosed_male_l_s_r.csv', sep='|', usecols=fields)\n",
    "disclosed_male_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dis_f_samples = disclosed_female_df.sample(2000)\n",
    "dis_m_samples = disclosed_male_df.sample(20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>878642</th>\n",
       "      <td>3.0</td>\n",
       "      <td>The only joint to eat 24 hrs at the Luxor. Bre...</td>\n",
       "      <td>1.295392e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951030</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Wow. You could dominate with a place like this...</td>\n",
       "      <td>1.417019e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22490</th>\n",
       "      <td>4.0</td>\n",
       "      <td>My wife and I dined here to celebrate our two ...</td>\n",
       "      <td>1.481727e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210205</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Sadly, the place closed a few months ago.</td>\n",
       "      <td>1.356464e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1648376</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Well, I don't usually write reviews for busine...</td>\n",
       "      <td>1.425339e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         stars                                               text  \\\n",
       "878642     3.0  The only joint to eat 24 hrs at the Luxor. Bre...   \n",
       "951030     2.0  Wow. You could dominate with a place like this...   \n",
       "22490      4.0  My wife and I dined here to celebrate our two ...   \n",
       "1210205    5.0          Sadly, the place closed a few months ago.   \n",
       "1648376    5.0  Well, I don't usually write reviews for busine...   \n",
       "\n",
       "            timestamp  gender  \n",
       "878642   1.295392e+09       0  \n",
       "951030   1.417019e+09       0  \n",
       "22490    1.481727e+09       0  \n",
       "1210205  1.356464e+09       0  \n",
       "1648376  1.425339e+09       0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis_m_samples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test: 2000 control: 20000\n"
     ]
    }
   ],
   "source": [
    "print('test:', len(dis_f_samples), 'control:', len(dis_m_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Formula:\n",
      "gender ~ stars+text+timestamp\n",
      "n majority: 20000\n",
      "n minority: 2000\n"
     ]
    }
   ],
   "source": [
    "m = Matcher(dis_f_samples, dis_m_samples, yvar='gender', exclude=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting Models on Balanced Samples: 1\\100Error: Perfect separation detected, results not available\n",
      "Fitting Models on Balanced Samples: 1\\100"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-af340bceef94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20170925\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[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbalance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnmodels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.conda/envs/ma/lib/python3.7/site-packages/pymatch/Matcher.py\u001b[0m in \u001b[0;36mfit_scores\u001b[0;34m(self, balance, nmodels)\u001b[0m\n\u001b[1;32m    107\u001b[0m                 \u001b[0mglm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGLM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_samp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_samp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfamilies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBinomial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m                     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_accuracy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_scores_to_accuracy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_samp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_samp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/ma/lib/python3.7/site-packages/statsmodels/genmod/generalized_linear_model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, start_params, maxiter, method, tol, scale, cov_type, cov_kwds, use_t, full_output, disp, max_start_irls, **kwargs)\u001b[0m\n\u001b[1;32m   1026\u001b[0m             return self._fit_irls(start_params=start_params, maxiter=maxiter,\n\u001b[1;32m   1027\u001b[0m                                   \u001b[0mtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcov_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcov_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1028\u001b[0;31m                                   cov_kwds=cov_kwds, use_t=use_t, **kwargs)\n\u001b[0m\u001b[1;32m   1029\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1030\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optim_hessian\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'optim_hessian'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/ma/lib/python3.7/site-packages/statsmodels/genmod/generalized_linear_model.py\u001b[0m in \u001b[0;36m_fit_irls\u001b[0;34m(self, start_params, maxiter, tol, scale, cov_type, cov_kwds, use_t, **kwargs)\u001b[0m\n\u001b[1;32m   1165\u001b[0m                                             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_endog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1166\u001b[0m                                             check_weights=True)\n\u001b[0;32m-> 1167\u001b[0;31m             \u001b[0mwls_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwls_mod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwls_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1168\u001b[0m             \u001b[0mlin_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwls_results\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1169\u001b[0m             \u001b[0mlin_pred\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_offset_exposure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/ma/lib/python3.7/site-packages/statsmodels/regression/_tools.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, method)\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m             params, _, _, _ = np.linalg.lstsq(self.wexog, self.wendog,\n\u001b[0;32m--> 102\u001b[0;31m                                               rcond=-1)\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m         \u001b[0mfitted_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/ma/lib/python3.7/site-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36mlstsq\u001b[0;34m(a, b, rcond)\u001b[0m\n\u001b[1;32m   2234\u001b[0m         \u001b[0;31m# lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2235\u001b[0m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_rhs\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2236\u001b[0;31m     \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrcond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2237\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2238\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "np.random.seed(20170925)\n",
    "m.fit_scores(balance=True, nmodels=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.predict_scores()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}