{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4f580f07",
   "metadata": {},
   "source": [
    "# Constraint Satisfaction Matcher\n",
    "\n",
    "The ConstraintSatisfactionMatcher can be used to optimize any linear function of the baseline covariates. We support constraints on the size of the subset populations and the allowed mismatch.\n",
    "\n",
    "Here, we demonstrate the optimization of balance subject to size constraints only. Namely, we solve:\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{aligned}\n",
    "& \\underset{\\hat{P}}{\\text{minimize}}\n",
    "& & \\sum_k |\\mu_{\\hat{P}k} - \\mu_{Tk}| \\\\\n",
    "& \\text{subject to}\n",
    "& & |\\hat{P}| = P^* \\\\\n",
    "& & & |\\hat{T}| = T^* \\\\\n",
    "\\end{aligned}\n",
    "\\end{equation}\n",
    "\n",
    "where $P$ and $T$ refer to two populations we are trying to match, $\\hat{P}$ and $\\hat{T}$ are the subsets of $P$ and $T$ we are seeking, $P^*$ and $T^*$ are fixed integers, and  $k$ indexes the covariates of $P$ and $T$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0f723264-db60-46d9-846d-b8dc17998db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging \n",
    "logging.basicConfig(\n",
    "    format=\"%(levelname)-4s [%(filename)s:%(lineno)d] %(message)s\",\n",
    "    level='INFO',\n",
    ")\n",
    "from pybalance.utils import (\n",
    "    BetaBalance, \n",
    "    BetaXBalance, \n",
    "    GammaBalance, \n",
    "    GammaXBalance,\n",
    "    GammaXTreeBalance\n",
    ")\n",
    "from pybalance.sim import generate_toy_dataset\n",
    "from pybalance.lp import ConstraintSatisfactionMatcher\n",
    "from pybalance.visualization import (\n",
    "    plot_numeric_features, \n",
    "    plot_categoric_features, \n",
    "    plot_binary_features,\n",
    "    plot_per_feature_loss,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6bd8a3d5-3c19-466f-8994-91b394935bb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_limit = 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d42b61e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
       "        <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>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>binary_0</th>\n",
       "      <th>binary_1</th>\n",
       "      <th>binary_2</th>\n",
       "      <th>binary_3</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62.511573</td>\n",
       "      <td>190.229250</td>\n",
       "      <td>105.165097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.505065</td>\n",
       "      <td>161.121236</td>\n",
       "      <td>95.001474</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.071384</td>\n",
       "      <td>162.325356</td>\n",
       "      <td>84.290576</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44.423692</td>\n",
       "      <td>150.948096</td>\n",
       "      <td>82.031381</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.695052</td>\n",
       "      <td>132.952651</td>\n",
       "      <td>54.857540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>21.474205</td>\n",
       "      <td>168.602546</td>\n",
       "      <td>70.342128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>40.643320</td>\n",
       "      <td>188.188724</td>\n",
       "      <td>61.611744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>29.472765</td>\n",
       "      <td>161.408162</td>\n",
       "      <td>57.214095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>41.291949</td>\n",
       "      <td>150.968833</td>\n",
       "      <td>91.270798</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>67.530294</td>\n",
       "      <td>155.124741</td>\n",
       "      <td>56.196505</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x156813950>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = generate_toy_dataset()\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd5ea80-904c-42b9-8611-0553dec48b05",
   "metadata": {},
   "source": [
    "## Optimize Beta (Mean Absolute SMD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6172737a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:66] Scaling features by factor 240.00 in order to use integer solver with <= 0.2841% loss.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'objective': 'beta',\n",
       " 'pool_size': 1000,\n",
       " 'target_size': 1000,\n",
       " 'max_mismatch': None,\n",
       " 'time_limit': 60,\n",
       " 'num_workers': 4,\n",
       " 'ps_hinting': False,\n",
       " 'verbose': True}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective = beta = BetaBalance(m)\n",
    "matcher = matcher_beta = ConstraintSatisfactionMatcher(\n",
    "    m, \n",
    "    time_limit=time_limit,\n",
    "    objective=objective,\n",
    "    ps_hinting=False,\n",
    "    num_workers=4)\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "271c5183",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:499] Solving for match population with pool size = 1000 and target size = 1000, optimizing balance (no max_mismatch cap).\n",
      "INFO [matcher.py:503] Matching on 15 dimensions ...\n",
      "INFO [matcher.py:510] Building model variables and constraints ...\n",
      "INFO [matcher.py:519] Calculating bounds on feature variables ...\n",
      "INFO [matcher.py:609] Applying size constraints on pool and target ...\n",
      "INFO [matcher.py:239] Solving with 4 workers ...\n",
      "INFO [matcher.py:91] Initial balance score: 0.2328\n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 1, time = 0.02 m\n",
      "INFO [matcher.py:102] Objective:\t452948000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2298\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 2, time = 0.03 m\n",
      "INFO [matcher.py:102] Objective:\t452876000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2297\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 3, time = 0.04 m\n",
      "INFO [matcher.py:102] Objective:\t452777000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2297\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 4, time = 0.04 m\n",
      "INFO [matcher.py:102] Objective:\t452736000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2296\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 5, time = 0.04 m\n",
      "INFO [matcher.py:102] Objective:\t452730000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2296\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 6, time = 0.05 m\n",
      "INFO [matcher.py:102] Objective:\t452596000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2295\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 7, time = 0.05 m\n",
      "INFO [matcher.py:102] Objective:\t452537000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2294\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 8, time = 0.06 m\n",
      "INFO [matcher.py:102] Objective:\t452040000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2291\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 9, time = 0.08 m\n",
      "INFO [matcher.py:102] Objective:\t451825000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2289\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 10, time = 0.08 m\n",
      "INFO [matcher.py:102] Objective:\t451808000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2289\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 11, time = 0.08 m\n",
      "INFO [matcher.py:102] Objective:\t451755000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2289\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 12, time = 0.08 m\n",
      "INFO [matcher.py:102] Objective:\t22699000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0104\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 13, time = 0.10 m\n",
      "INFO [matcher.py:102] Objective:\t22422000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0124\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 14, time = 0.12 m\n",
      "INFO [matcher.py:102] Objective:\t22306000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0124\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 15, time = 0.13 m\n",
      "INFO [matcher.py:102] Objective:\t22291000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0124\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 16, time = 0.14 m\n",
      "INFO [matcher.py:102] Objective:\t22121000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0122\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 17, time = 0.16 m\n",
      "INFO [matcher.py:102] Objective:\t22119000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0102\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 18, time = 0.21 m\n",
      "INFO [matcher.py:102] Objective:\t22103000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0121\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 19, time = 0.23 m\n",
      "INFO [matcher.py:102] Objective:\t22101000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0122\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 20, time = 0.25 m\n",
      "INFO [matcher.py:102] Objective:\t22099000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0122\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 21, time = 0.27 m\n",
      "INFO [matcher.py:102] Objective:\t22088000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0122\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 22, time = 0.60 m\n",
      "INFO [matcher.py:102] Objective:\t22084000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0122\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:252] Status = FEASIBLE\n",
      "INFO [matcher.py:253] Number of solutions found: 22\n"
     ]
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      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x1577c9880>"
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     },
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     "metadata": {},
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  },
  {
   "cell_type": "markdown",
   "id": "87a0dbdf",
   "metadata": {},
   "source": [
    "Note that it is possible for the \"balance\" value to go up (hopefully only slightly) as the solver progresses. This behavior is due to rounding errors incurred when casting a continuous problem into the discrete space understood by the solver. The \"balance\" reported here is the actual balance computed to full accuracy on the original dataset; the \"objective\" value here is the actual quantity being optimized and should never increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2e79558",
   "metadata": {},
   "outputs": [
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       "<pybalance.utils.matching_data.MatchingData at 0x1577c9880>"
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  {
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   "id": "e7a9b589",
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    {
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",
      "text/plain": [
       "<Figure size 1200x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "match = matcher.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "# fig = plot_per_feature_loss(match, beta, 'target', debin=False)\n",
    "fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])\n",
    "fig = plot_categoric_features(match,  hue_order=['pool (prematch)', 'pool', 'target'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "310a104c-9639-41bf-b5f6-929dca8436b5",
   "metadata": {},
   "source": [
    "## Optimize Beta With Cross Terms Added"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f0112e",
   "metadata": {},
   "source": [
    "Sometimes it helps to add a known (non-optimal) solution as a hint to the solver. A natural choice for hinting the solver is to take a solution from PS matching. We can choose to use the PS as a hint to the solver by passing ps_hinting=True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b928facc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [preprocess.py:548] Added cross term height * weight to matching features.\n",
      "INFO [preprocess.py:548] Added cross term gender * age to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_0 * binary_3 to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_0 * age to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_3 * weight to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_2 * weight to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_1 * binary_3 to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_2 * binary_3 to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_2 * height to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_0 * binary_2 to matching features.\n",
      "INFO [matcher.py:66] Scaling features by factor 240.00 in order to use integer solver with <= 0.3751% loss.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'objective': 'beta_x',\n",
       " 'pool_size': 1000,\n",
       " 'target_size': 1000,\n",
       " 'max_mismatch': None,\n",
       " 'time_limit': 60,\n",
       " 'num_workers': 4,\n",
       " 'ps_hinting': True,\n",
       " 'verbose': True}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective = beta_x = BetaXBalance(m)\n",
    "matcher = matcher_betax = ConstraintSatisfactionMatcher(\n",
    "    m, \n",
    "    time_limit=time_limit,\n",
    "    objective=objective,\n",
    "    ps_hinting=True,\n",
    "    num_workers=4)\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c4bc59e4-2153-4a3c-aac6-386eee1de375",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:499] Solving for match population with pool size = 1000 and target size = 1000, optimizing balance (no max_mismatch cap).\n",
      "INFO [matcher.py:503] Matching on 25 dimensions ...\n",
      "INFO [matcher.py:510] Building model variables and constraints ...\n",
      "INFO [matcher.py:519] Calculating bounds on feature variables ...\n",
      "INFO [matcher.py:609] Applying size constraints on pool and target ...\n",
      "INFO [matcher.py:615] Applying hint ...\n",
      "INFO [matcher.py:622] Training PS model as guide for solver ...\n",
      "INFO [preprocess.py:548] Added cross term height * weight to matching features.\n",
      "INFO [preprocess.py:548] Added cross term gender * age to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_0 * binary_3 to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_0 * age to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_3 * weight to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_2 * weight to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_1 * binary_3 to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_2 * binary_3 to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_2 * height to matching features.\n",
      "INFO [preprocess.py:548] Added cross term binary_0 * binary_2 to matching features.\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 1/50, 0.001 min) ...\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: SGDClassifier\n",
      "INFO [matcher.py:142] \t* alpha: 0.1045355473186929\n",
      "INFO [matcher.py:142] \t* class_weight: None\n",
      "INFO [matcher.py:142] \t* early_stopping: False\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* loss: modified_huber\n",
      "INFO [matcher.py:142] \t* max_iter: 1500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:143] \tScore (beta_x): 0.1723\n",
      "INFO [matcher.py:144] \tSolution time: 0.002 min\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 2/50, 0.003 min) ...\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: SGDClassifier\n",
      "INFO [matcher.py:142] \t* alpha: 0.0354658577371722\n",
      "INFO [matcher.py:142] \t* class_weight: None\n",
      "INFO [matcher.py:142] \t* early_stopping: False\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* loss: modified_huber\n",
      "INFO [matcher.py:142] \t* max_iter: 1500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:143] \tScore (beta_x): 0.0887\n",
      "INFO [matcher.py:144] \tSolution time: 0.004 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 3/50, 0.004 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.7627616953429366\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta_x): 0.0670\n",
      "INFO [matcher.py:144] \tSolution time: 0.008 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 4/50, 0.008 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.015804928600429955\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta_x): 0.0615\n",
      "INFO [matcher.py:144] \tSolution time: 0.010 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 5/50, 0.010 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 6/50, 0.029 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 7/50, 0.054 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 8/50, 0.077 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 9/50, 0.079 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 10/50, 0.100 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 11/50, 0.122 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 12/50, 0.124 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 13/50, 0.139 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 14/50, 0.164 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 15/50, 0.166 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 16/50, 0.168 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 17/50, 0.194 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 18/50, 0.196 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 19/50, 0.197 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 20/50, 0.200 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 21/50, 0.202 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 22/50, 0.227 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 23/50, 0.229 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 24/50, 0.231 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 25/50, 0.233 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 26/50, 0.235 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 27/50, 0.243 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 28/50, 0.245 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 29/50, 0.264 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 30/50, 0.266 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 31/50, 0.284 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 32/50, 0.309 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 33/50, 0.311 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 34/50, 0.314 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 35/50, 0.316 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 36/50, 0.317 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 2.1909794891956405\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta_x): 0.0610\n",
      "INFO [matcher.py:144] \tSolution time: 0.341 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 37/50, 0.341 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 38/50, 0.344 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 39/50, 0.346 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 40/50, 0.370 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 41/50, 0.388 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 42/50, 0.398 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 43/50, 0.401 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 44/50, 0.403 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 45/50, 0.417 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 46/50, 0.424 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 47/50, 0.426 min) ...\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: SGDClassifier\n",
      "INFO [matcher.py:142] \t* alpha: 0.08294085369181486\n",
      "INFO [matcher.py:142] \t* class_weight: balanced\n",
      "INFO [matcher.py:142] \t* early_stopping: True\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* loss: log_loss\n",
      "INFO [matcher.py:142] \t* max_iter: 1500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:143] \tScore (beta_x): 0.0594\n",
      "INFO [matcher.py:144] \tSolution time: 0.427 min\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 48/50, 0.428 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 49/50, 0.430 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 50/50, 0.431 min) ...\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: SGDClassifier\n",
      "INFO [matcher.py:142] \t* alpha: 0.08294085369181486\n",
      "INFO [matcher.py:142] \t* class_weight: balanced\n",
      "INFO [matcher.py:142] \t* early_stopping: True\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* loss: log_loss\n",
      "INFO [matcher.py:142] \t* max_iter: 1500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:143] \tScore (beta_x): 0.0594\n",
      "INFO [matcher.py:144] \tSolution time: 0.427 min\n",
      "INFO [matcher.py:659] Hint achieves objective value = 150510.\n",
      "INFO [matcher.py:661] Applying hints ...\n",
      "INFO [matcher.py:239] Solving with 4 workers ...\n",
      "INFO [matcher.py:91] Initial balance score: 0.2324\n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 1, time = 0.10 m\n",
      "INFO [matcher.py:102] Objective:\t150510000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0594\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 2, time = 0.10 m\n",
      "INFO [matcher.py:102] Objective:\t150275000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0592\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 3, time = 0.11 m\n",
      "INFO [matcher.py:102] Objective:\t150126000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0593\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 4, time = 0.11 m\n",
      "INFO [matcher.py:102] Objective:\t149549000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0590\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 5, time = 0.13 m\n",
      "INFO [matcher.py:102] Objective:\t149538000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0591\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 6, time = 0.13 m\n",
      "INFO [matcher.py:102] Objective:\t149451000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0592\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 7, time = 0.14 m\n",
      "INFO [matcher.py:102] Objective:\t149266000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0591\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 8, time = 0.14 m\n",
      "INFO [matcher.py:102] Objective:\t149098000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0590\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 9, time = 0.15 m\n",
      "INFO [matcher.py:102] Objective:\t149054000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0590\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 10, time = 0.17 m\n",
      "INFO [matcher.py:102] Objective:\t148612000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0589\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 11, time = 0.17 m\n",
      "INFO [matcher.py:102] Objective:\t148570000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0590\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 12, time = 0.18 m\n",
      "INFO [matcher.py:102] Objective:\t148457000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0589\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 13, time = 0.18 m\n",
      "INFO [matcher.py:102] Objective:\t148402000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0588\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 14, time = 0.21 m\n",
      "INFO [matcher.py:102] Objective:\t148292000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0588\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 15, time = 0.24 m\n",
      "INFO [matcher.py:102] Objective:\t148241000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0587\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 16, time = 0.27 m\n",
      "INFO [matcher.py:102] Objective:\t148137000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0588\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 17, time = 0.28 m\n",
      "INFO [matcher.py:102] Objective:\t148062000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0587\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 18, time = 0.30 m\n",
      "INFO [matcher.py:102] Objective:\t148015000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0588\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 19, time = 0.32 m\n",
      "INFO [matcher.py:102] Objective:\t147905000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0587\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 20, time = 0.35 m\n",
      "INFO [matcher.py:102] Objective:\t147665000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0587\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 21, time = 0.38 m\n",
      "INFO [matcher.py:102] Objective:\t147445000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0587\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 22, time = 0.43 m\n",
      "INFO [matcher.py:102] Objective:\t147387000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0587\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 23, time = 0.44 m\n",
      "INFO [matcher.py:102] Objective:\t147292000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0587\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 24, time = 0.50 m\n",
      "INFO [matcher.py:102] Objective:\t146776000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0586\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 25, time = 0.50 m\n",
      "INFO [matcher.py:102] Objective:\t146598000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0585\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 26, time = 0.56 m\n",
      "INFO [matcher.py:102] Objective:\t146233000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0584\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 27, time = 0.58 m\n",
      "INFO [matcher.py:102] Objective:\t145754000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0584\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 28, time = 0.60 m\n",
      "INFO [matcher.py:102] Objective:\t145603000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0583\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 29, time = 0.62 m\n",
      "INFO [matcher.py:102] Objective:\t145161000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0581\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 30, time = 0.64 m\n",
      "INFO [matcher.py:102] Objective:\t144785000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0580\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 31, time = 0.67 m\n",
      "INFO [matcher.py:102] Objective:\t144740000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0580\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 32, time = 0.70 m\n",
      "INFO [matcher.py:102] Objective:\t144439000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0579\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 33, time = 0.70 m\n",
      "INFO [matcher.py:102] Objective:\t144288000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0578\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 34, time = 0.71 m\n",
      "INFO [matcher.py:102] Objective:\t144198000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0578\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 35, time = 0.73 m\n",
      "INFO [matcher.py:102] Objective:\t144077000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0578\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 36, time = 0.77 m\n",
      "INFO [matcher.py:102] Objective:\t144032000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0579\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 37, time = 0.81 m\n",
      "INFO [matcher.py:102] Objective:\t143921000.0\n",
      "INFO [matcher.py:123] Balance (beta_x):\t0.0578\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:252] Status = FEASIBLE\n",
      "INFO [matcher.py:253] Number of solutions found: 37\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
       "        <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>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>binary_0</th>\n",
       "      <th>binary_1</th>\n",
       "      <th>binary_2</th>\n",
       "      <th>binary_3</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.261578</td>\n",
       "      <td>139.396134</td>\n",
       "      <td>94.438359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.113091</td>\n",
       "      <td>165.563337</td>\n",
       "      <td>67.433016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.232216</td>\n",
       "      <td>160.859857</td>\n",
       "      <td>71.915385</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58.996941</td>\n",
       "      <td>140.357415</td>\n",
       "      <td>115.606615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.850195</td>\n",
       "      <td>189.983706</td>\n",
       "      <td>53.000581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9933</th>\n",
       "      <td>68.194783</td>\n",
       "      <td>127.495418</td>\n",
       "      <td>69.177329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9947</th>\n",
       "      <td>64.290077</td>\n",
       "      <td>168.091011</td>\n",
       "      <td>63.511962</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>51.242281</td>\n",
       "      <td>130.812647</td>\n",
       "      <td>87.967028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>68.616093</td>\n",
       "      <td>167.546870</td>\n",
       "      <td>58.683367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>66.480954</td>\n",
       "      <td>169.018678</td>\n",
       "      <td>76.221503</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x1581c7320>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matcher.match()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c104c51f-a3be-4244-90df-843d1975327a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1250x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "match = matcher.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "fig = plot_per_feature_loss(match, objective, 'target', debin=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ca2b67-933c-4cfd-b433-315ed781edf4",
   "metadata": {},
   "source": [
    "## Optimize Gamma (Area Between CDFs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "12f7223f-06bf-4550-8952-3da49698130a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [preprocess.py:340] Discretized age with bins [18.05, 27.54, 37.04, 46.53, 56.02, 65.51, 75.0].\n",
      "INFO [preprocess.py:340] Discretized height with bins [125.01, 136.68, 148.34, 160.01, 171.67, 183.34, 195.0].\n",
      "INFO [preprocess.py:340] Discretized weight with bins [50.0, 61.67, 73.33, 85.0, 96.66, 108.33, 120.0].\n",
      "INFO [matcher.py:66] Scaling features by factor 200.00 in order to use integer solver with <= 0.0000% loss.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'objective': 'gamma',\n",
       " 'pool_size': 1000,\n",
       " 'target_size': 1000,\n",
       " 'max_mismatch': None,\n",
       " 'time_limit': 60,\n",
       " 'num_workers': 4,\n",
       " 'ps_hinting': True,\n",
       " 'verbose': True}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective = gamma = GammaBalance(m)\n",
    "matcher = matcher_gamma = ConstraintSatisfactionMatcher(\n",
    "    m, \n",
    "    time_limit=time_limit,\n",
    "    objective=objective,\n",
    "    ps_hinting=True,\n",
    "    num_workers=4)\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5895e06a-2aff-4afd-b771-71d2dd25e7a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:499] Solving for match population with pool size = 1000 and target size = 1000, optimizing balance (no max_mismatch cap).\n",
      "INFO [matcher.py:503] Matching on 27 dimensions ...\n",
      "INFO [matcher.py:510] Building model variables and constraints ...\n",
      "INFO [matcher.py:519] Calculating bounds on feature variables ...\n",
      "INFO [matcher.py:609] Applying size constraints on pool and target ...\n",
      "INFO [matcher.py:615] Applying hint ...\n",
      "INFO [matcher.py:622] Training PS model as guide for solver ...\n",
      "INFO [preprocess.py:340] Discretized age with bins [18.05, 27.54, 37.04, 46.53, 56.02, 65.51, 75.0].\n",
      "INFO [preprocess.py:340] Discretized height with bins [125.01, 136.68, 148.34, 160.01, 171.67, 183.34, 195.0].\n",
      "INFO [preprocess.py:340] Discretized weight with bins [50.0, 61.67, 73.33, 85.0, 96.66, 108.33, 120.0].\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 1/50, 0.001 min) ...\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: SGDClassifier\n",
      "INFO [matcher.py:142] \t* alpha: 4.774501445415405\n",
      "INFO [matcher.py:142] \t* class_weight: None\n",
      "INFO [matcher.py:142] \t* early_stopping: True\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* loss: modified_huber\n",
      "INFO [matcher.py:142] \t* max_iter: 1500\n",
      "INFO [matcher.py:142] \t* penalty: elasticnet\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.2142\n",
      "INFO [matcher.py:144] \tSolution time: 0.002 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 2/50, 0.003 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.058541995189524146\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0472\n",
      "INFO [matcher.py:144] \tSolution time: 0.005 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 3/50, 0.005 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.0909955270741388\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0448\n",
      "INFO [matcher.py:144] \tSolution time: 0.007 min\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 4/50, 0.007 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 5/50, 0.009 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 6/50, 0.010 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.15085738749804528\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0347\n",
      "INFO [matcher.py:144] \tSolution time: 0.037 min\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 7/50, 0.038 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 8/50, 0.039 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 9/50, 0.041 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 10/50, 0.045 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 22.70303412073022\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0331\n",
      "INFO [matcher.py:144] \tSolution time: 0.072 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 11/50, 0.072 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 12/50, 0.084 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 13/50, 0.086 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 14/50, 0.111 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 15/50, 0.115 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 16/50, 0.116 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 17/50, 0.118 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 18/50, 0.121 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 19/50, 0.123 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.7964686611607528\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0308\n",
      "INFO [matcher.py:144] \tSolution time: 0.150 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 20/50, 0.150 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 21/50, 0.173 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 22/50, 0.175 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 23/50, 0.176 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 24/50, 0.201 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 25/50, 0.202 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 26/50, 0.204 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 27/50, 0.217 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 28/50, 0.219 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 29/50, 0.220 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 30/50, 0.226 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 31/50, 0.227 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 32/50, 0.228 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 33/50, 0.232 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 34/50, 0.255 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 35/50, 0.264 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 36/50, 0.265 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 37/50, 0.266 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 38/50, 0.268 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 39/50, 0.279 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 40/50, 0.280 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 41/50, 0.282 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 42/50, 0.289 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 43/50, 0.290 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 44/50, 0.292 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 45/50, 0.294 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 46/50, 0.297 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 47/50, 0.299 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 48/50, 0.301 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 49/50, 0.328 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 50/50, 0.330 min) ...\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.7964686611607528\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (gamma): 0.0308\n",
      "INFO [matcher.py:144] \tSolution time: 0.150 min\n",
      "INFO [matcher.py:659] Hint achieves objective value = 100400.\n",
      "INFO [matcher.py:661] Applying hints ...\n",
      "INFO [matcher.py:239] Solving with 4 workers ...\n",
      "INFO [matcher.py:91] Initial balance score: 0.2110\n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 1, time = 0.06 m\n",
      "INFO [matcher.py:102] Objective:\t115600000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0349\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 2, time = 0.15 m\n",
      "INFO [matcher.py:102] Objective:\t115400000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0349\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 3, time = 0.16 m\n",
      "INFO [matcher.py:102] Objective:\t114600000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0346\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 4, time = 0.17 m\n",
      "INFO [matcher.py:102] Objective:\t114400000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0346\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 5, time = 0.18 m\n",
      "INFO [matcher.py:102] Objective:\t113800000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0344\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 6, time = 0.20 m\n",
      "INFO [matcher.py:102] Objective:\t102000000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0310\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 7, time = 0.21 m\n",
      "INFO [matcher.py:102] Objective:\t101400000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0308\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 8, time = 0.23 m\n",
      "INFO [matcher.py:102] Objective:\t100800000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0306\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 9, time = 0.24 m\n",
      "INFO [matcher.py:102] Objective:\t100400000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0305\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 10, time = 0.31 m\n",
      "INFO [matcher.py:102] Objective:\t97800000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0300\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 11, time = 0.32 m\n",
      "INFO [matcher.py:102] Objective:\t97200000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0298\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 12, time = 0.39 m\n",
      "INFO [matcher.py:102] Objective:\t97000000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0298\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 13, time = 0.39 m\n",
      "INFO [matcher.py:102] Objective:\t96600000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0297\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 14, time = 0.45 m\n",
      "INFO [matcher.py:102] Objective:\t91000000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0282\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 15, time = 0.45 m\n",
      "INFO [matcher.py:102] Objective:\t90800000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0282\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 16, time = 0.47 m\n",
      "INFO [matcher.py:102] Objective:\t90600000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0281\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 17, time = 0.52 m\n",
      "INFO [matcher.py:102] Objective:\t80800000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0249\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 18, time = 0.53 m\n",
      "INFO [matcher.py:102] Objective:\t80600000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0248\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 19, time = 0.54 m\n",
      "INFO [matcher.py:102] Objective:\t80400000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0247\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 20, time = 0.60 m\n",
      "INFO [matcher.py:102] Objective:\t19000000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0070\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 21, time = 0.79 m\n",
      "INFO [matcher.py:102] Objective:\t18800000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0069\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 22, time = 0.80 m\n",
      "INFO [matcher.py:102] Objective:\t18400000.0\n",
      "INFO [matcher.py:123] Balance (gamma):\t0.0068\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:252] Status = OPTIMAL\n",
      "INFO [matcher.py:253] Number of solutions found: 22\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
       "        <div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
       "      <th>country</th>\n",
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       "      <th>binary_0</th>\n",
       "      <th>binary_1</th>\n",
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       "      <th>binary_3</th>\n",
       "      <th>patient_id</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>55.261578</td>\n",
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       "      <td>94.438359</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>10000</td>\n",
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       "      <th>1</th>\n",
       "      <td>63.113091</td>\n",
       "      <td>165.563337</td>\n",
       "      <td>67.433016</td>\n",
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       "      <th>2</th>\n",
       "      <td>58.232216</td>\n",
       "      <td>160.859857</td>\n",
       "      <td>71.915385</td>\n",
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       "      <td>58.996941</td>\n",
       "      <td>140.357415</td>\n",
       "      <td>115.606615</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>10003</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.850195</td>\n",
       "      <td>189.983706</td>\n",
       "      <td>53.000581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
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       "      <th>9932</th>\n",
       "      <td>72.514956</td>\n",
       "      <td>159.248205</td>\n",
       "      <td>118.505187</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>68.194783</td>\n",
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       "      <th>9957</th>\n",
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       "      <td>pool</td>\n",
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       "      <th>9981</th>\n",
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       "      <td>133.419182</td>\n",
       "      <td>71.135407</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
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       "      <th>9988</th>\n",
       "      <td>74.994562</td>\n",
       "      <td>142.069423</td>\n",
       "      <td>59.899521</td>\n",
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       "      <td>4</td>\n",
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       "      <td>9988</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x1581ce9f0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matcher.match()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84afee09-642c-40d0-8929-0afa6544660b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1350x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "match = matcher.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "fig = plot_per_feature_loss(match, objective, 'target', debin=False)\n",
    "fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])\n",
    "fig = plot_categoric_features(match,  hue_order=['pool (prematch)', 'pool', 'target'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pybal2",
   "language": "python",
   "name": "pybal2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}