MaltsReadMe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyMALTS is a learning-to-matching interpretable causal inference method. PyMALTS implements MALTS algorithm proposed by Harsh Parikh, Cynthia Rudin and Alexander Volfovsky in their 2019 paper titled \"MALTS: Matching After Learning to Stretch\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyMALTS is a Python3 library and it requires Numpy, Pandas, Scipy, Scikit-Learn, Matplotlib and Seaborn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pymalts2\n",
      "  Downloading pymalts2-0.0.2-py3-none-any.whl (10 kB)\n",
      "Installing collected packages: pymalts2\n",
      "Successfully installed pymalts2-0.0.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pymalts2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymalts2 as pymalts\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "np.random.seed(0)\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2500, 20)\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>X11</th>\n",
       "      <th>X12</th>\n",
       "      <th>X13</th>\n",
       "      <th>X14</th>\n",
       "      <th>X15</th>\n",
       "      <th>X16</th>\n",
       "      <th>X17</th>\n",
       "      <th>X18</th>\n",
       "      <th>outcome</th>\n",
       "      <th>treated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.566765</td>\n",
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       "      <td>0.938379</td>\n",
       "      <td>-2.055124</td>\n",
       "      <td>1.942873</td>\n",
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       "      <td>3.329552</td>\n",
       "      <td>-1.822938</td>\n",
       "      <td>3.240945</td>\n",
       "      <td>2.106121</td>\n",
       "      <td>0.857190</td>\n",
       "      <td>0.577264</td>\n",
       "      <td>-2.370578</td>\n",
       "      <td>-5.133200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>1.378660</td>\n",
       "      <td>1.794625</td>\n",
       "      <td>0.701158</td>\n",
       "      <td>1.815518</td>\n",
       "      <td>1.129920</td>\n",
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       "      <td>0.845063</td>\n",
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       "      <td>2.239003</td>\n",
       "      <td>-0.632832</td>\n",
       "      <td>39.684984</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "            X1        X2        X3        X4        X5        X6        X7  \\\n",
       "1355  1.881335  1.684164  0.532332  2.002254  1.435032  1.450196  1.974763   \n",
       "1320  0.666476  1.263065  0.657558  0.498780  1.096135  1.002569  0.881916   \n",
       "1233 -0.193200  0.961823  1.652723  1.117316  0.590318  0.566765  0.775715   \n",
       "706   1.378660  1.794625  0.701158  1.815518  1.129920  1.188477  0.845063   \n",
       "438   0.434297  0.296656  0.545785  0.110366  0.151758 -0.257326  0.601965   \n",
       "\n",
       "            X8        X9       X10       X11       X12       X13       X14  \\\n",
       "1355  1.321659  0.709443 -1.141244  0.883130  0.956721  2.498229  2.251677   \n",
       "1320  0.740392  2.780857 -0.765889  1.230980 -1.214324 -0.040029  1.554477   \n",
       "1233  0.938379 -2.055124  1.942873 -0.606074  3.329552 -1.822938  3.240945   \n",
       "706   1.217270  5.847379  0.566517 -0.045607  0.736230  0.941677  0.835420   \n",
       "438   0.499884 -0.973684 -0.552586 -0.778477  0.936956  0.831105  2.060040   \n",
       "\n",
       "           X15       X16       X17       X18    outcome  treated  \n",
       "1355  0.375271 -0.545129  3.334220  0.081259 -15.679894        0  \n",
       "1320  4.235513  3.596213  0.959022  0.513409  -7.068587        0  \n",
       "1233  2.106121  0.857190  0.577264 -2.370578  -5.133200        0  \n",
       "706  -0.560388  0.427255  2.239003 -0.632832  39.684984        1  \n",
       "438   3.153799  0.027665  0.376857 -1.221457  -2.954324        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('example/example_data.csv',index_col=0)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using MALTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance Metric Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up the model for learning the distance metric.\n",
    "1. Variable name for the outcome variable: 'outcome'.\n",
    "2. Variable name for the treatment variable: 'treated'\n",
    "3. Data is assigned to python variable df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2349.312366962433\n"
     ]
    }
   ],
   "source": [
    "t1 = time.time()\n",
    "m = pymalts.malts_mf( outcome='outcome', treatment='treated', data=df) # running MALTS with default setting\n",
    "t2 = time.time()\n",
    "print(t2-t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matched Groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matched Group matrix (MG_matrix) is NxN matrix with each row corresponding to each query unit and each column corresponds to matched units. Cell (i,j) in the matrix corresponds to the weight of unit j in the matched group of unit i. The weight corresponds to the numbers of times a unit is included in a matched group across M-folds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2500 rows × 2500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      1355  1320  1233  706   438   184   1108  1612  816   131   ...  1181  \\\n",
       "1355   4.0   0.0   0.0   2.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "1320   0.0   4.0   0.0   0.0   0.0   0.0   0.0   1.0   4.0   0.0  ...   0.0   \n",
       "1233   0.0   0.0   4.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "706    0.0   0.0   0.0   4.0   0.0   0.0   0.0   1.0   0.0   0.0  ...   0.0   \n",
       "438    0.0   0.0   0.0   0.0   4.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "1520   0.0   0.0   0.0   0.0   2.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "1408   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   3.0   0.0  ...   0.0   \n",
       "909    0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "603    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "2285   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "\n",
       "      1698  916   59    2267  1520  1408  909   603   2285  \n",
       "1355   0.0   0.0   0.0   0.0   0.0   0.0   3.0   0.0   3.0  \n",
       "1320   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "1233   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "706    0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0  \n",
       "438    0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0  \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "1520   0.0   0.0   0.0   0.0   4.0   0.0   0.0   0.0   0.0  \n",
       "1408   0.0   0.0   0.0   0.0   0.0   4.0   0.0   0.0   0.0  \n",
       "909    0.0   0.0   0.0   0.0   0.0   0.0   4.0   0.0   0.0  \n",
       "603    2.0   0.0   0.0   0.0   0.0   0.0   0.0   4.0   0.0  \n",
       "2285   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   4.0  \n",
       "\n",
       "[2500 rows x 2500 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.MG_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing matched group matrix as heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24d487d3348>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "sns.heatmap(m.MG_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing the matched group for an example unit with index equal to \"1\" and visualizing the weights as bar-chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24d5af78908>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MG1 = m.MG_matrix.loc[1] #matched group for unit \"1\"\n",
    "MG1[MG1>1].sort_values(ascending=False).plot(kind='bar',figsize=(20,5)) #Visualizing all the units matched to unit 1 more than once"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ATE and CATE Estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>avg.CATE</th>\n",
       "      <th>std.CATE</th>\n",
       "      <th>outcome</th>\n",
       "      <th>treated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47.232061</td>\n",
       "      <td>21.808950</td>\n",
       "      <td>-15.313091</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.600643</td>\n",
       "      <td>21.958906</td>\n",
       "      <td>-16.963202</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.877320</td>\n",
       "      <td>22.204570</td>\n",
       "      <td>9.527929</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.768578</td>\n",
       "      <td>19.740320</td>\n",
       "      <td>-3.940218</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39.920257</td>\n",
       "      <td>21.744433</td>\n",
       "      <td>-8.011915</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2495</th>\n",
       "      <td>49.227788</td>\n",
       "      <td>21.581176</td>\n",
       "      <td>-14.529871</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496</th>\n",
       "      <td>42.352355</td>\n",
       "      <td>21.385861</td>\n",
       "      <td>19.570055</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2497</th>\n",
       "      <td>43.737763</td>\n",
       "      <td>19.859275</td>\n",
       "      <td>-16.342666</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2498</th>\n",
       "      <td>41.189297</td>\n",
       "      <td>20.346711</td>\n",
       "      <td>-9.165242</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>45.427037</td>\n",
       "      <td>23.762884</td>\n",
       "      <td>-17.604829</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2500 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       avg.CATE   std.CATE    outcome  treated\n",
       "0     47.232061  21.808950 -15.313091      0.0\n",
       "1     40.600643  21.958906 -16.963202      0.0\n",
       "2     40.877320  22.204570   9.527929      1.0\n",
       "3     37.768578  19.740320  -3.940218      0.0\n",
       "4     39.920257  21.744433  -8.011915      0.0\n",
       "...         ...        ...        ...      ...\n",
       "2495  49.227788  21.581176 -14.529871      0.0\n",
       "2496  42.352355  21.385861  19.570055      1.0\n",
       "2497  43.737763  19.859275 -16.342666      0.0\n",
       "2498  41.189297  20.346711  -9.165242      0.0\n",
       "2499  45.427037  23.762884 -17.604829      0.0\n",
       "\n",
       "[2500 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.CATE_df #each row is a cate estimate for a corresponding unit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate Average Treatment Effect (ATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.29673993471417"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATE = m.CATE_df['avg.CATE'].mean()\n",
    "ATE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing ATE and probability density function of CATE (using KDE plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(38.29673993471417, 0.04, '$\\\\hat{ATE}$')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mLedsERFRWnUbtqZNm4adO3fC4/EgGAxi27ZtmD59enJ7RUUFtFotdu/eDQCoqanB9OnTUVBQAK1Wi127dgEAdu/eDaPRiMLCwjR9FMp38TUR9Sm5EjHBZtLAzbBFRERp1O1pxNLSUixduhSLFy9GJBLBggULMGnSJCxZsgQPP/wwJk6ciJUrV+Kpp56Cz+fDhAkTsHjxYgiCgJdffhnPPfccQqEQjEYjXnrppUx8JspD/lAErb4wik8vIJ0qNpMWewMehCMxaNTKlL42ERER0IOwBQBVVVWoqqrqdN9rr72W/HncuHHYsGHDec+bNGkS3njjjX6WSAQ0NsfnWhWY+7dMz7nOXJEYwmC7MaWvTUREBLCDPOWIxLwqs16d0tc902ur+0n2REREfcGwRTkhEbYsBk03j+ydxJEth4dhi4iI0oNhi3KC2xuC1aiBUtGztiE9pdeqoNMo4WjhJHkiIkoPhi3KCS5vEEVWXUrbPiRYjRq2fyAiorRh2KKc4G4NotCiS2nbhwSbSQuXN5T6FyYiIgLDFuWAaEyEp60jOb8q1WwmLTxtoZxYrYCIiHIPwxZlvea2ECQAVmNqr0RMsJk0iIkSWto70vL6REQ0sDFsUdZLtn0wpvZKxITEETNXK+dtERFR6jFsUdZzn55PZdanKWydbpTqaGH7ByIiSj2GLcp6Lm8QKqUAo7ZHCx70mlmvhkIhwOHhkS0iIko9hi3Keq7WEAotOiC1LbaSFAoBVqMGTvbaIiKiNGDYoqzn8gZRZNGl9WpBm4m9toiIKD0Ytijrub1BFFjS0/YhId5rKwgpHY28iIhoQGPYoqwWCEXhD0VhS9OViAk2kxahcAyBjmha34eIiAYehi3Kau7W9LZ9SLCZ4q/PTvJERJRqDFuU1RLzqCyG9DQ0TbCeDnNu9toiIqIUY9iirOZKc4+tBEsibHGSPBERpRjDFmU1V2sQBq0KWo0yre+jVSuhUSngbuNpRCIiSi2GLcpqbm8IRdb0tn0AAEEQYDFq4Gnj+ohERJRaDFuU1RI9tjLRkcFsUMPDI1tERJRiDFuUtURJgrs1lFy7MN3MBh7ZIiKi1GPYoqzV6gsjGhOTbRnSzWLQwBeMIBKNZeT9iIhoYGDYoqyVaPtgSnPbhwSLMf4+nvZwRt6PiIgGBoYtylrJHltpbvuQYDHE36eF87aIiCiFGLYoa7lbQxAQn7ieCYleW6429toiIqLUYdiirOXyBmEza6EQhIy8n0kfD3XNXLKHiIhSiGGLspbbG0ShRYtYmntsJaiUChh1KjTzNCIREaUQwxZlLVdrCEUWXUbf02LUoJntH4iIKIUYtigrRaIxeNs7YDVlpsdWgsWgYWNTIiJKKYYtykru1hAkADZjZq5ETDAb1Ghp74CUiZb1REQ0IDBsUVZqbo0fXUpMWs8Ui1GDSFSEPxTJ6PsSEVH+YtiirOQ+HbbMhswe2Ur22mJjUyIiShGGLcpK7tYQlAoBBq0qo++b6LXlbmWvLSIiSg2GLcpK7tZ42wdkpsVWUqKBauI0JhERUX8xbFFWam4NodCsg5ihHlsJBq0KSoUAF8MWERGlCMMWZSV3awgFlsy2fQAAQRBgMWrgYdgiIqIUYdiirBOOxNDqD8Oa4bYPCRaDGs3tDFtERJQaDFuUdRLL5VgyfCVigsWgQUs7u8gTEVFqMGxR1kn22DJktsdWgtmoQZsvjJgoyvL+RESUXxi2KOske2zp5TuyJQHwstcWERGlAMMWZR25emwlWE4fUfNw3hYREaUAwxZlHbl6bCWYk41NGbaIiKj/GLYo68jVYyshMTHfxS7yRESUAgxblHXk6rGVoFYpoNeq4GnlFYlERNR/DFuUVeTusZVgMaiTLSiIiIj6g2GLsorcPbYSzAYNPAxbRESUAj0KW5s3b8bs2bMxc+ZMrFmz5rztBw4cQHV1NSorK7F8+XJEo1EAgNPpxP3334/58+dj4cKFOHXqVGqrp7wjd4+tBIuRjU2JiCg1ug1bDocDq1atwtq1a7Fp0yasW7cOhw8f7vSYZcuW4emnn8bWrVshSRLWr18PAHjsscdwww03YNOmTZg3bx5WrlyZnk9BeUPuHlsJFoMaoXAMwY6orHUQEVHu6zZs7dixA1OnToXNZoPBYEBlZSVqa2uT2+vr6xEKhTB58mQAQHV1NWpra+HxeHDw4EEsXLgQAPDNb34Tjz76aJo+BuULuXtsJVhOzxlrbuPRLSIi6p9u/0ZzOp2w2+3J2yUlJdizZ0+X2+12OxwOB06ePIlBgwbhhRdewK5du2C32/HDH/6wV8UVFZl69Xg52e1muUvIC75QFMU2Paw2PaQedn6w2QzdPkalUvT4sQBQ0REDAETA3+3FcGzkw7GXB8ddPrk89t2GLVEUIQhnuktKktTpdlfbo9Eo9u/fj4ceeghPPPEE3njjDTz++OP4/e9/3+Pimpt9svVa6g273QyXq13uMvJCvbMdNqMGLS2BHj3eZjPA6+3+sdFofJ3DnjwWABSn10U8cqIFI+zGHj1noOF+Lx+OvTw47vLJ9rFXKISLHiDq9jRiWVkZXC5X8rbL5UJJSUmX291uN0pKSmC322E0GnHDDTcAAObOndvpiBjRhcjdYyvBoFNBpRTg9LKxKRER9U+3YWvatGnYuXMnPB4PgsEgtm3bhunTpye3V1RUQKvVYvfu3QCAmpoaTJ8+HUOHDkVZWRk++OADAMBf/vIXTJgwIU0fg/JBtvTYAgBBEGA1auFi2CIion7qNmyVlpZi6dKlWLx4MebPn4+5c+di0qRJWLJkCfbu3QsAWLlyJVasWIFZs2YhEAhg8eLFAICXXnoJv/nNbzB37lz87ne/w09+8pP0fhrKadnSYyvBatKwsSkREfVbjy75qqqqQlVVVaf7XnvtteTP48aNw4YNG8573siRI3s1R4sGtmzpsZVgNWpwsK5F7jKIiCjHsYM8ZY1s6bGVYDVqEOyIIdgRkbsUIiLKYQxblDWypcdWQmLumJsLUhMRUT8wbFHWcLcGUWjRAkL3j80Eqyl+VaS7lZPkiYio7xi2KGs0t4ZQaNFlTW+1xJEtZwvDFhER9R3DFmUNd2sIBWb5e2wl6DRKaNQKtn8gIqJ+YdiirJBNPbYSzvTaYvsHIiLqO4YtygrJHltZFLaA+KlE9toiIqL+YNiirJA4emTSZ0ePrQSrUYPm1hCknq6KTUREdA6GLcoKiXlR2dI9PsFq1KAjEoM/xF5bRETUNwxblBVc3iDUKkXW9NhKsJri4a+5jb22iIiobxi2KCu4vEEUW3XItpN1iQn7LrZ/ICKiPmLYoqzg8oZQlEU9thISjU2dbP9ARER9xLBFspMkCa7WIAos2dNjK0GrVkKnUbLXFhER9RnDFsmuPRhBRzgGqzH7whYQP5WYWCSbiIiotxi2SHbJKxGzrMdWgtWkhZtHtoiIqI8Ytkh2ibBlzrIeWwnxxqYd7LVFRER9wrBFsks0NM2mpXrOZjVqEI2JaA+E5S6FiIhyEMMWyc7lDcJi1ECpEOQu5YISIdDNXltERNQHDFskO/fpHltilp6mSzQ2dbUEZK6EiIhyEcMWyc7lDaLQokOWZq3kVZIOTpInIqI+YNgiWUVjIjxtHSgwZ2fbBwDxZYR0Kri9bP9ARES9x7BFsmpuDUFC9k6OT7AaNWz/QEREfcKwRbJKtn0wZGfbh4R4+wce2SIiot5j2CJZJRuaGrL9yJYWnraOrFu7kYiIsh/DFsnK5Q3F50RpVXKXclFWkwYxUUKrn+0fiIiodxi2SFYubxBFFh2y/XhRstcW10gkIqJeYtgiWbm8QRRZdVl/es5mil8t6WzhJHkiIuodhi2SjSRJcLUGUdiHtg/vbt2Uhoq6ZjFqIAiAw8PGpkRE1DsMWyQbfyiKYEcMVlPvw9Yf1q/udHv9mlc73Q4FUxuKlAoBFoMGTrZ/ICKiXmLYItkkr0TsQ48t6ZxZXh99sLXT7WUP3dH3wrpgNWmSi2YTERH1FMMWyeZM24fe99gScM6i1ees9ZOOdRZtJm2yZiIiop5i2CLZJBua6lPQ0FToHL7OC2MpYDNp4AtG0BGJpvy1iYgof2V3cyPKay5vEBaDGiqlArFeXo3Y0RHEow8swNBhI2AvHYxYNIK644cxaPBwqFTp2a0TVyS6vB0YbOcfHSIi6hn+jUGycXlDKLLq+3TK78VXN6LuxBG4nXU4dPAA7KWD8K9PPwRBIWBQxXBEIuGU13smbAUw2G5M+esTEVF+Ytgi2bi8QQwvN5873eo8fn87ajb8Fxbd82DyPo1Wh3HjL4dt2jW4zhu/8jAajaD+5HHUnTiMkyeOpLxemyk+kZ/tH4iIqDcYtkgW0ZiI5rYQJo8pvujj9n7+MTZt+C2qb/9up/v/+X99C7/49cZO96lUagwbMQbDRoxJeb0AoNOooNMo4WBjUyIi6gVOkCdZeNpCkKQzy+BcjCQBCkXnXTUWk2eSus2k4RWJRETUKzyyRbJINAc1dxO2Jk6+CiNGjUPNm/+FSydckbw/HVcb9oTVqOX6iERE1CsMWyQLV0uix1b3R7ZMZgvu/PZDne4Lh0P45aofYfQlY1FSOhRDho1Csb0sLbWezWbS4nB9K0RRgkIhT+AjIqLcwrBFsnB6g1CrFDBqVb1u+wAASqUKQ4ePRt3xI3j/nT/B7XJArzdg8NARGDJsFIYMG4Xrrp+V8rptJg1iogSvrwOFFl3KX5+IiPIPwxbJwtkSRLFV1+2ViF1RKlWouvUu2GwGeL0BhIIBnKw7ipMnDqPuxBG8/87baQpbp9s/tIYYtoiIqEcYtkgWTm8QxX3ssQWcvzaiTm/AmLGXYczYy1JRXpeS7R9aAhg7xJbW9yIiovzAqxEp4yRJgqsliEKLts+vce4crkwxGzRQCOy1RUREPcewRRnn9YURjorJU3J9Me1rN3W5zdPswp7P/9bn174YhUKA1aSFk722iIioh3gakTIu0afK0oMeWxcTDnfg8BcncGD/fpyqO4qTdUdRf/IYAn4fdHoDXnptUyrKPY/VqIHby/YPRETUMz0KW5s3b8Yrr7yCaDSKe+65B3feeWen7QcOHMDy5cvh9/sxZcoUPPvss50WA96/fz++9a1vYd++famtnnKSoyV+Cs5sUPf5NZ76l3vhcjZAoVCipHQQBg0ejksnXIGTJ47giWd/gZGjxqWq3PPYTFp8ecqbttcnIqL80m3YcjgcWLVqFTZu3AiNRoOFCxfi6quvxujRo5OPWbZsGZ5//nlMnjwZTz75JNavX49FixYBAILBIJ577jlEIpH0fQrKKS5vEAohPv9J6kPbBwCAAOj1Riz5/v/GhEnTkne//85mFBeXpqjSC7OZNPCHogh2RKHX8uAwERFdXLdztnbs2IGpU6fCZrPBYDCgsrIStbW1ye319fUIhUKYPHkyAKC6urrT9hdeeAH33HNPGkqnXOVsCaLIqutXD/gf//Q1VFXfjddfWYUVP3oEXxzcm7L6upNs/8BTiURE1APd/rPc6XTCbrcnb5eUlGDPnj1dbrfb7XA4HACAd999F6FQCLNm9a3fUVGRqU/Pk4Pdbpa7hJzh8YVRWmSExaLv1+t88/ZFmDX3Fmz4f6ux6oXHMemKKxGNRGC1GmC1GTo9VqWK/7vCds79fTE4KgIAQjFxwP/eB/rnlxPHXh4cd/nk8th3G7ZEUYQgnDkGIUlSp9tdbXe5XHjllVfw29/+ts/FNTf7IPb1NFMG2e1muFztcpeRMxpdPkweUwyvt//tE2w2E+Yt+C6mTZ+NN9b+GoGAD//3t7/GgjuWQKs903Q0ejogpeI9lad7g31Z58Elgyz9fr1cxf1ePhx7eXDc5ZPtY69QCBc9QNTtacSysjK4XK7kbZfLhZKSki63u91ulJSU4P3334fX68Wdd96JefPmAQDmzZsHn8/Xpw9C+cEXjMAfisJm7nvbhwuxl5Tjfz36DB774c9w/OgXeOYHS1L6+mfTqpXQa1Vs/0BERD3SbdiaNm0adu7cCY/Hg2AwiG3btmH69OnJ7RUVFdBqtdi9ezcAoKamBtOnT8dtt92Gd955BzU1NaipqUluM5ly59QgpV6i7YO1n0kE1TAAABytSURBVG0fujJm7GVY/uOXMP+2b6fl9RNsJg3nbBERUY90G7ZKS0uxdOlSLF68GPPnz8fcuXMxadIkLFmyBHv3xiclr1y5EitWrMCsWbMQCASwePHitBdOuSnR9sFiSE/YSph67TfS+vo2kzYZHImIiC6mR9etV1VVoaqqqtN9r732WvLncePGYcOGDRd9jUOHDvWhPMo3rtOn3tIdttLNZtLgUF0LYqIIpYILMRARUdf4twRllNMbhNWkgVLRn8YP8rOZtBAlwNMWlrsUIiLKcgxblFHOliDsVj1EKfuvMr2YRK8tZwqubiQiovzGsEUZ5fTGG5rmeNZCoSUetupdfpkrISKibMewRRnTEY6h1RdGgSm1bR/kYNCqoNMo0eBmKxMiIro4hi3KmGTbB1NuT44HAEEQUGTVocHN04hERHRxDFuUMc7TYctsUMtcSWoUWXRo8jBsERHRxTFsUcY486TtQ0KRRRfviB+MyF0KERFlMYYtyhinNwijTgWtRil3KSmRmCTPo1tERHQxDFuUMa6WAIqt+pxYXLwniizxha7r3bwikYiIusawRRnjaAmi2Jb7bR8SrEYNVEqB7R+IiOiiGLYoI6IxEc1tIRSYc7/tQ4IgCCi06NDQzLBFRERdY9iijGhuDUGS4keD8kmhWYtGhi0iIroIhi3KiMQk8nwLW0VWHVraOhCOxuQuhYiIshTDFmVEMmzlQff4sxVZdJAAODxBuUshIqIsxbBFGdHYHIBJr4YuT9o+JJy5IpHL9hAR0YUxbFFGNHkCKCnIn7YPCQVmLQQBaOAViURE1AWGLcqIpmY/Sgr0edP2IUGlVMBm0qKhmY1NiYjowhi2KO38oQjaAhEUmnVyl5IWhRZekUhERF1j2KK0azp91Cefemydrciig7MliJgoyl0KERFlIYYtSrt8bfuQUGTRISZKcLd2yF0KERFlIYYtSrvG5gCUCgHmPA5bQHxeGhER0bkYtijtmjwB2G16CHIXkiaJsHWK7R+IiOgCGLYo7RJhK9/aPiRoNUqY9GouSE1ERBfEsEVpFRNFODwBFNv0cpeSVvErEtn+gYiIzsewRWnlbg0hJkooNOfnfK2EIosODk8AUr41EiMion5j2KK0ShztseXZmojnKrLoEArH0OoPy10KERFlGYYtSqtEjy2rKb+PbBXb4pPk6xycJE9ERJ0xbFFaNXn8MBvU0KrzawHqcw0qMkKlFLDvWLPcpRARUZZh2KK0amrOzwWoz6VSKlBRbMT+4y1yl0JERFmGYYvSqtETQInNkHcLUF/IsFIzGtx+tHHeFhERnYVhi9LGF4ygPRBBgSW/J8cnDCszAwAO1vHoFhERncGwRWmTWBMxXxegPldpgQFatRL7jnnkLoWIiLIIwxalTfJKxDxdE/FcCoWAoaUmHOC8LSIiOgvDFqVNo8cPpUKAxTAwwhYADC01o7ktBHdrUO5SiIgoSzBsUdo0NcfXRBxIhpWaAIBXJRIRURLDFqVNk2dgtH04W5FFB5NejX9w3hYREZ3GsEVpEY2JcLYEUWwdWEe2BCE+b+tgXQvXSSQiIgAMW5QmiQWoCywDZ75WwrBSM9oDEdS7/XKXQkREWYBhi9KiztEOACg062SuJPOGlcb7bfGqRCIiAhi2KE2ONbZBpVSgyDLwwpbFqEGBWct1EomICADDFqXJ8cZ2DC4xDth5S8NKzfjiZCuiMVHuUoiISGYMW5RyoiThuKMdg+2mAbEm4oUMKzOjIxLDkYY2uUshIiKZMWxRyjU1B9ARjqGscGBdiXi2EeVmqFUK/M8/muQuhYiIZMawRSl3vCl+NGegtX04m0alxCWDrfj4gBORKE8lEhENZAxblHLHG9uhUStgMw2MBai7Mn54IYIdUew9yonyREQDWY/C1ubNmzF79mzMnDkTa9asOW/7gQMHUF1djcrKSixfvhzRaBQAsHv3bixYsADz5s3DPffcg/r6+tRWT1npWFMbhpSYBuzk+IRhpWYYdSps38dTiUREA1m3YcvhcGDVqlVYu3YtNm3ahHXr1uHw4cOdHrNs2TI8/fTT2Lp1KyRJwvr165P3P//886ipqUFVVRWef/759HwKyhoxUUSdwzegJ8cnKBQCxg0rwJ4jbvhDEbnLISIimXQbtnbs2IGpU6fCZrPBYDCgsrIStbW1ye319fUIhUKYPHkyAKC6uhq1tbUIh8N45JFHMG7cOADA2LFj0djYmKaPQdmi3uVHJCqitNAgdylZYcLwQsRiEnYddMpdChERyUTV3QOcTifsdnvydklJCfbs2dPldrvdDofDAY1Gg3nz5gEARFHEyy+/jBkzZvSquKIiU68eLye73Sx3CVnhs6PxBZiHV1hhM2WmoanN1n2wU6kUPX5sKlmtetgL9PjbQScW3DQuo++dCdzv5cOxlwfHXT65PPbdhi1RFCEIQvK2JEmdbne3PRwO4/HHH0c0GsX3vve9XhXX3OyDKGb/uSi73QyXq13uMrLC3i9d0GtVUIoSvN5A2t/PZjP06H2ip68IzERN5xo3xIYP9zTiwJdOFNvy5wpN7vfy4djLg+Mun2wfe4VCuOgBom5PI5aVlcHlciVvu1wulJSUdLnd7XYnt/v9ftx3332IRqN45ZVXoFar+/QhKHcca4xPjhcH+oSts4wfVgAA+J8DDpkrISIiOXQbtqZNm4adO3fC4/EgGAxi27ZtmD59enJ7RUUFtFotdu/eDQCoqalJbl+2bBmGDRuGn//859BoNGn6CJQtIlERp5w+DLYbB/zk+LNZTVoMtpuwY2/TgL9Ck4hoIOo2bJWWlmLp0qVYvHgx5s+fj7lz52LSpElYsmQJ9u7dCwBYuXIlVqxYgVmzZiEQCGDx4sXYv38/3n33XXz66ae49dZbMW/ePCxZsiTtH4jkc8rlQ0yUODn+AsYPL0CTJ4A6p0/uUoiIKMO6nbMFAFVVVaiqqup032uvvZb8edy4cdiwYUOn7ePHj8ehQ4dSUCLliuON8c7xdmtmJsbnknFDbXh39yns2NeEYaW5O8mTiIh6jx3kKWWONbXDpFfDqOtRhh9QdBoVRg2y4G/7HYiJXL6HiGggYdiilDne2IahpSbEmCUuaPzwQrT5wzh4wit3KURElEEMW5QSHZEY6t1+DCrOnd5omTZykAU6jRLb97G5LxHRQMKwRSlxtL4VkgSUFeZPH6lUUykVuGSIDZ9+4UJHOCZ3OURElCEMW5QSnx9uhkqpwKAio9ylZLUJwwsRjoj47LBb7lKIiChDGLao3yRJwt8Pu3HJEBvOWjyALmCw3QiLUYPte3kqkYhooGDYon5r8gTg9AYxdqiNzUy7IQgCxg8rwP7jHrT6w3KXQ0REGcCwRf3298PNAIChJTyF2BPjhxdAkoCPuXwPEdGAwLBF/fb5YTcG240waLn2ZU8UW/UoK9Rjx94muUshIqIMYNiifvEFIzh8qhWXDi9ETOQ5xJ66dFghTjja0eDm8j1ERPmOYYv6Zd/RZoiShOFlFrlLySkThhdAqRDw512n5C6FiIjSjGGL+uXzw26YDWoUW7Vyl5JTDDo1LhtRiB17m9Ae4ER5IqJ8xrBFfRaNidh31IMJwwsRi/EUYm99dawdkZiI9z+vl7sUIiJKI4Yt6rPDp1oR6Ihi1GCr3KXkpGKrHiPLLXh31ylEolxQkogoXzFsUZ/9/YgbKqWACnaN77OvjrWjLRDBxweccpdCRERpwrBFffb54WaMGcyu8f0xvMyMYqsOWz8+AYkdYYmI8hLDFvVJvdsPhyeAsUML2DW+HwRBwJSxJTjl8uNgXYvc5RARURowbFGffPBZPZQKAaMGmeUuJeeNH14Ag06F2r+dlLsUIiJKA4Yt6rVQOIrt+xpxxSXFUCm5C/WXSqnAFaOLsfdoM+pdbHJKRJRv+Dcl9dr/7Hcg2BHDVy6x8xRiilxxiR0atQJ/+PCY3KUQEVGKMWxRr0iShPd212Ow3YhCMxuZpopBq8JV40rx6RcuHGlolbscIiJKIYYt6pXD9a045fJh6oQyiGwNlVJTxtph0Kmw4f0jvDKRiCiPMGxRr7z3aT30GiVGDeJaiKmmUStxzYQyHKrz4h/HPXKXQ0REKcKwRT3W6g9j10EnrhpfyrlaaXL5qCJYjRq88ZcjEDnIRER5gWGLeuyvf29ATJQwaVSR3KXkLZVSgesmluOk04ddB9lVnogoHzBsUY/ERBEffF6PsUNtMOrUcpeT1y4dVgC7TYeNHxxFNMaJcUREuY5hi3rk3V2n4GnrwFXjSyGKPL2VTgqFgOmTBsHpDeKPO0/IXQ4REfUTwxZ1y+EJ4M2/HsXEkYWoKDLIXc6AMKrCistGFGLz9mNsBUFElOMYtuiiREnC6i0HoFIqcNOVQ9nuIYO+8ZXBMBs0eO2t/QiFo3KXQ0REfcSwRRf13u5T+OJUK6quHQ6VQpC7nAFFq1Hi5qlD4fIGse69w3KXQ0REfcSwRV1yeoPY8MERjB9eiJHlXHBaDkNLzLjq0hJ88HkDPv/SLXc5RETUBwxbdEGiJOG3Ww5AIQiYdfUQnj6U0bUTy1FaoMfqPx1AY7Nf7nKIiKiXGLboPPGgdRAH67ynTx9yN5GTSqnAnGuGQRQlPPdfu3iEi4gox/BvUepEFOMT4j/a24hZVw/F6EFWuUsiAMVWPe6eORaFFi1+8eYevLX9GDvMExHlCIYtShJFCf+55QC2723CrKuH4orRxYixp1bWsBg1uP2GMZg4sgibPjyGl9/ci38c9yASjcldGhERXYRK7gIoO0RjIlZvOYCd/3AwaGUxtUqBWVcNQWmhHu9/Vo/PD7uhVilwyWArxgyxQaVUICZKEE//p1AIUCgEKBUC9BolvnKJHVaTVu6PQUQ0oDBsEdoCYfyfTftwsM6L2VOH4fJRRQxaWUwQBHxljB0TRxSioTmAOqcPxxra8I8Pj3X73LXvfIkrLrHjhisqMG6oDYLAdh5EROnGsDXA1Tna8dKbe9HqD2PhjDEYXmpm0MoRapUSw0rNGFZqxtcmliMmilAICigEQKEAIAiQJAmSKEGUBLQGOrD3qAd//9KNXQedGFRsxP1V4zG0lG09iIjSiXO2BrCPDzjwk9/vhihKuL9qPIaVmLjuYQ5TKhQQBEACEBOBWEyCKAISBAgCYDNq8bWJ5fjeLRNwy7XD4Q9GsGLNp9h7tFnu0omI8hrD1gDUEY7hd7UH8X9q/oEhpSbcO/dSmPRq8OK2gUGtUmDc0AIsmjEGBSYtXnxjD97/rF7usoiI8hZPIw4wx5va8Opb++H0BHDjVwfj6nF2xNiwdEAyGzS4/cbR+OPO4/jd1kNwtARwy7UjoNfya4GIKJX4rTpAxEQRtX+rw6YPj8Fi0OC7VeNRbNZxftYAp1UrMf+6kXjvs3ps/fgkPvi8AVMnlOHrkwcl53LFRBGtvjB8wQgMOhXMeg20GqXMlRMR5Q6GrQHgSH0rfrf1EE46fbjiEjtumjIYkMCmmAQAUCgEzPjqYEwYXoC9xzzYvqcR739Wj7JCAzoiMXh9HeedYtaoFCi06jDYbsLIcgtGVVgwrNQMjZohjIjoXAxbecwXjODND47gr583wGbS4u7KsagoMoAHs+hCyouMKC8yYvqkchyq86LO6YPZoIHZqIZFr4ZGrUIoHEUoHEWgI4pARwxHG1qx66ATACAAKLRoYbfpUWzTw2rUINgRhS8YQXsggkBHFLGYBEmSEBMlKJUCBhUZMbjEhCF2EyrsRhSYtVApOZWUiPILw1YeqnO0Y8e+JuzY14RAKIqvf6UCV44rOX00S+7qKNvpNCpcProYl48uvujjbDYDvN4AQuEomjxBuFqDaPWH0dLegb1HmtEWCMOgVcGoV8OkV8OoV0MpxK+MVCgERCIijja04ZPTYS3BbFDDZtLCZtKiwKxJ/lxs1WFUhZVzyogo5/ToW2vz5s145ZVXEI1Gcc899+DOO+/stP3AgQNYvnw5/H4/pkyZgmeffRYqlQoNDQ1YtmwZmpubMWLECKxcuRJGozEtH2SgicZEBDqi8AUiaA+E0eoPw90awsf7Hahz+qBUCLhsZCGum1QOk45XGlL66DQqDC8zY3jZmX5dghA/0iUIAqRuTllHYyKa20Jo8YURCMWPgvmCEXh9HahztKPNH0bi2UqFgJGDLBg/vBDjhxdgRLmlT0fCItEY3K0h+ILx9/IFIgiGzyx7JACIidLpI3kxBDqiiMZE6NRK6DQq6LRKmA0ajCg3Y7DdxKNxRHRR3YYth8OBVatWYePGjdBoNFi4cCGuvvpqjB49OvmYZcuW4fnnn8fkyZPx5JNPYv369Vi0aBGeffZZLFq0CHPmzMEvf/lL/OpXv8KyZcvS+oHSIRKNoaW9A15fGJGYCEiAFO9eBJNeDY1eA0mSUtaNOxCKwOUNweUNdvrP3RqCPxT/8o92cQnhsFIz5n9tJEYPMkOpVECSwKBFGSdJ8X5fPdn5VEoFSgsMKC0wnLdNEOIvEeyIwtPegZNOH443teGtj46h5qNj0GmUGDe0AOOHF2DMYBtMejV0WiV0GiUkCfC0d8B9+s+PsyWIxuYAGpr9cHmDPf5zoVUrodcqoVIq0BGJIRSOIRI98+dPo1JgeLkFIwdZMKjIiPJiAwYVGZNH4ERJQiQqItgRRUt7R/K/9kAYMVFKLq90wf+XztwWhPj6mFajBhWlFgiiCINWBb1WBYNOBbVKgUhURDgSQzgqIhbr/AEVCgF6rRL608/RqBRcQYAoQ7oNWzt27MDUqVNhs9kAAJWVlaitrcWDDz4IAKivr0coFMLkyZMBANXV1fjFL36B2267DZ988gl++ctfJu+/6667ehW2FIr0fhHsO9oMlzeImCghevoLLdQRhT8URSAUgb8jhjZ//F/b3VEqFbAa1TDrNdDrVDDpVNDr1FArFVAo4p9FKQgQJSTnrCT+EvGHogh2ROAPRuH1dSDQEe302gadGgVmLcaPKIROo4RGrYRaqYBarYBWo4JOFf/LRadVQqdWDqgrDNUqRY+ujCsvLwMAXkWXQj0d+1TQapSwmbUYOciC6zEI4aiIpmZ/fLkiRzve2X0K7+w+1ek5AoCz/yQoFQKKrDqMH1GIQrMOFqMaGpUSWo0CWrUKGqUCEM7kQ4UQD4KC4nR4lOL/wIo/RkIgGIWrNQinN4jG5iD+fsSNT79wJd9Pp1EhKoqIRrvurZJcu1KI/78i8f8KIb4SwFm3RVFCc2tb/PthX1O/x1SpEKDVqKDXxI/WqdWKeMiLSYhJYvJnUZQQkyRIADSnv3c0KiWUCgESzoRqUZQQPivsiTEJCqUAlTK+qoFSkfhZgFIZb8AbiYqIxuL/idLpxo+nP7dSKUCrVkKjUkCjjn/vaVQKaDRKaFVKqFQChORvOvG/8YDeiSAktyV+OOueXjEYmuEPdPTpuejj13K/vs0z/C/tvr6dhPg/SmIxCZGYiFhMRDgaQ0eHiGA4io5IDDEJCEdiiMXE+JxPQYjvC2oltOrTf4bV8e8krVoJlfL0nx0IEBQCxg8vRGmBPqWf92zd5ZVuw5bT6YTdbk/eLikpwZ49e7rcbrfb4XA40NLSApPJBJVK1en+3igoSO8px+uLTGl9fcoeW97+g9wlEBHRANXtRANRFDsdaj73dFlX2y90Wo2HrImIiGig6TZslZWVweU6c2jc5XKhpKSky+1utxslJSUoLCxEe3s7YrHYBZ9HRERENBB0G7amTZuGnTt3wuPxIBgMYtu2bZg+fXpye0VFBbRaLXbv3g0AqKmpwfTp06FWqzFlyhRs2bIFALBp06ZOzyMiIiIaCARJ6n5K2+bNm/Hqq68iEolgwYIFWLJkCZYsWYKHH34YEydOxMGDB/HUU0/B5/NhwoQJWLFiBTQaDerr6/H444+jubkZ5eXl+NnPfgar1ZqJz0VERESUFXoUtoiIiIiob9iJj4iIiCiNGLaIiIiI0ohhi4iIiCiNGLaIiIiI0ohhi4iIiCiNGLb64OWXX8acOXMwZ84c/Nu//RuA+BqSVVVVmDlzJlatWiVzhfntpz/9KR5//HEAwIEDB1BdXY3KykosX74c0Wi0m2dTX7z33nuorq7GzTffjOeffx4A9/lMqampSX7f/PSnPwXA/T6dfD4f5s6di1On4mttdrWf83eQeueO/bp16zB37lxUVVXhiSeeQDgcBpCjYy9Rr2zfvl26/fbbpY6ODikcDkuLFy+WNm/eLF1//fVSXV2dFIlEpHvvvVd6//335S41L+3YsUO6+uqrpR/84AeSJEnSnDlzpM8++0ySJEl64oknpDVr1shZXl6qq6uTrrvuOqmxsVEKh8PSHXfcIb3//vvc5zMgEAhIV155pdTc3CxFIhFpwYIF0vbt27nfp8nnn38uzZ07V5owYYJ08uRJKRgMdrmf83eQWueO/dGjR6WbbrpJam9vl0RRlB577DFp9erVkiTl5tjzyFYv2e12PP7449BoNFCr1Rg1ahSOHz+OYcOGYciQIVCpVKiqqkJtba3cpeYdr9eLVatW4YEHHgAA1NfXIxQKYfLkyQCA6upqjnsa/PnPf8bs2bNRVlYGtVqNVatWQa/Xc5/PgFgsBlEUEQwGEY1GEY1GoVKpuN+nyfr16/HMM88kl5bbs2fPBfdzfvek3rljr9Fo8Mwzz8BkMkEQBFxyySVoaGjI2bFXyV1ArhkzZkzy5+PHj+NPf/oT7rrrLtjt9uT9JSUlcDgccpSX155++mksXboUjY2NAACn09lp3O12O8c9DU6cOAG1Wo0HHngAjY2N+PrXv44xY8Zwn88Ak8mERx55BDfffDP0ej2uvPJKqNVq7vdp8q//+q+dbp/7HZPYz/ndk3rnjn1FRQUqKioAAB6PB2vWrMGKFStydux5ZKuPvvzyS9x777147LHHMGTIEAiCkNwmSVKn29R/b7zxBsrLy3HNNdck7xNFkeOeAbFYDDt37sRPfvITrFu3Dnv27MHJkyc59hlw8OBBvPnmm/jLX/6CDz/8EAqFAtu3b+fYZ0hX3zH87skch8OBe+65B9/85jdx9dVX5+zY88hWH+zevRsPP/wwnnzyScyZMwcff/wxXC5XcrvL5UoeCqXU2LJlC1wuF+bNm4fW1lYEAgEIgtBp3N1uN8c9DYqLi3HNNdegsLAQADBjxgzU1tZCqVQmH8N9Pj0++ugjXHPNNSgqKgIQP2Xy+uuvc7/PkLKysgt+t597P38H6XHkyBHcd999uPvuu3HvvfcCOP93kitjzyNbvdTY2Ijvf//7WLlyJebMmQMAuPzyy3Hs2DGcOHECsVgMb7/9NqZPny5zpfll9erVePvtt1FTU4OHH34YN954I1asWAGtVovdu3cDiF+1xXFPvRtuuAEfffQR2traEIvF8OGHH2LWrFnc5zNg3Lhx2LFjBwKBACRJwnvvvYerrrqK+32GdPXdXlFRwd9Bmvl8Pnz3u9/FI488kgxaAHJ27Hlkq5def/11dHR04IUXXkjet3DhQrzwwgt46KGH0NHRgeuvvx6zZs2SscqBY+XKlXjqqafg8/kwYcIELF68WO6S8s7ll1+O++67D4sWLUIkEsG1116LO+64AyNHjuQ+n2bXXXcd9u/fj+rqaqjVakycOBH3338/brrpJu73GaDVarv8bud3T3pt2LABbrcbq1evxurVqwEAN954Ix555JGcHHtBkiRJ7iKIiIiI8hVPIxIRERGlEcMWERERURoxbBERERGlEcMWERERURoxbBERERGlEcMWERERURoxbBERERGl0f8HctCGjxhPejoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "sns.kdeplot(m.CATE_df['avg.CATE'],shade=True)\n",
    "plt.axvline(ATE,c='black')\n",
    "plt.text(ATE-4,0.04,'$\\hat{ATE}$',rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MALTS Arguments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. outcome: Name of the outcome variable column in the data\n",
    "2. treatment: Name of the treatment variable column in the data\n",
    "3. data: Data in the pandas Dataframe format\n",
    "4. discrete: List of column names which are discrete (dummified); Default=[]\n",
    "5. C: Regularization constant; Default=1\n",
    "6. k_tr: Size of matched group in training step; Default=15\n",
    "7. k_est: Size of matched group in estimation step; Default=50\n",
    "8. estimator: CATE estimator inside a matched group; Default='linear'; Options: 'linear','mean' or 'RF'\n",
    "9. smooth_cate: Boolean to smoothen CATE estimates by fitting a regression model; Default=True\n",
    "10. reweight: Reweight treated and control groups as per their respective sample sizes in training step; Default=False,\n",
    "11. n_splits: Number of splits of the data for n_split-fold procedure; Default=5\n",
    "12. n_repeats: Number of repeats of the whole procedure; Default=1\n",
    "13. output_format: Output format of CATE dataframe; Default='brief'; Options: 'brief' or 'full'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking Inside a Matched-Group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the X1 and X2 marginal of matched-group of unit \"0\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Matched Group for Unit-0')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 405.725x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MG0 = m.MG_matrix.loc[0] #fetching the matched group\n",
    "matched_units_idx = MG0[MG0!=0].index #getting the indices of the matched units \n",
    "matched_units = df.loc[matched_units_idx] #fetching the data of matched units\n",
    "\n",
    "sns.lmplot(x='X1', y='X2', hue='treated', data=matched_units,palette=\"Set1\") #plotting the MG on (X1,X2)\n",
    "plt.scatter(x=[df.loc[0,'X1']],y=[df.loc[0,'X2']],c='black',s=100) #plotting the unit-0 on (X1,X2)\n",
    "plt.title('Matched Group for Unit-0') #setting title of the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting CATE versus covariate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting CATE v.s. X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24d613558c8>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_w_cate = df.join(m.CATE_df, rsuffix='_').drop(columns=['outcome_','treated_']) #joining cate dataframe with data\n",
    "\n",
    "sns.regplot( x='X1', y='avg.CATE', data=data_w_cate, scatter_kws={'alpha':0.5,'s':2}, line_kws={'color':'black'}, order=2 ) #fitting a degree 2 polynomial X1 on CATE"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}