notebooks/05_fit_interpretable_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit interpretable models to the training set and test on validation sets. Uses imodels package as of 10/25/2020 (v0.2.5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'seaborn'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [2], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m      5\u001b[0m sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../src\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mviz\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpickle\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpkl\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
      "File \u001b[0;32m~/rules/iai-clinical-decision-rule/notebooks/../src/viz.py:10\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mos\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mos\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpath\u001b[39;00m \u001b[39mimport\u001b[39;00m join \u001b[39mas\u001b[39;00m oj\n\u001b[0;32m---> 10\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mseaborn\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msns\u001b[39;00m\n\u001b[1;32m     12\u001b[0m DIR_FILE \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mdirname(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mrealpath(\u001b[39m__file__\u001b[39m)) \u001b[39m# directory of this file\u001b[39;00m\n\u001b[1;32m     13\u001b[0m DIR_FIGS \u001b[39m=\u001b[39m oj(DIR_FILE, \u001b[39m'\u001b[39m\u001b[39m../reports/figs\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'seaborn'"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import sys\n",
    "sys.path.append('../src')\n",
    "import viz\n",
    "import pickle as pkl\n",
    "import matplotlib.pyplot as plt\n",
    "from os.path import join as oj\n",
    "import os\n",
    "import numpy as np\n",
    "from dtreeviz.trees import *\n",
    "import imodels\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from tqdm import tqdm\n",
    "from sklearn import metrics\n",
    "from sklearn.inspection import permutation_importance\n",
    "import data\n",
    "import validate\n",
    "outcome_def = 'iai_intervention' # output\n",
    "MODELS_DIR = '../models/simple_3_splits'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(MODELS_DIR, exist_ok=True)\n",
    "\n",
    "\n",
    "# load the data\n",
    "df_pecarn, df_psrc, common_feats, filtered_feats_pecarn, filtered_feats_psrc = data.load_it_all(dummy=True)\n",
    "df = df_pecarn[common_feats].append(df_psrc[common_feats])\n",
    "processed_feats = data.select_final_feats(common_feats)\n",
    "\n",
    "# split the idxs\n",
    "train_idxs = df.cv_fold.isin(data.pecarn_train_idxs)\n",
    "test_idxs1 = df.cv_fold.isin(data.pecarn_test_idxs)\n",
    "test_idxs2 = df.cv_fold.isin(data.psrc_train_idxs + data.psrc_test_idxs)\n",
    "\n",
    "# split the data\n",
    "X, y = df[processed_feats], df[outcome_def]\n",
    "half = train_idxs.sum() // 2\n",
    "split_to_plot = '_test2'\n",
    "\n",
    "# 3-split\n",
    "X_train, y_train = X[train_idxs], y[train_idxs]\n",
    "X_cv, y_cv = X_train, y_train\n",
    "X_test1, y_test1 = X[test_idxs1], y[test_idxs1]\n",
    "\n",
    "# try loading psrc data\n",
    "if df_psrc.shape[0] > 0:\n",
    "    X_test2, y_test2 = X[test_idxs2], y[test_idxs2]\n",
    "else:\n",
    "    X_test2, y_test2 = X_test1, y_test1\n",
    "    \n",
    "data_sizes = {\n",
    "    'train': (y_train.sum(), X_train.shape[0]),\n",
    "    'cv': (y_cv.sum(), X_cv.shape[0]),\n",
    "    'test1': (y_test1.sum(), X_test1.shape[0]),\n",
    "    'test2': (y_test2.sum(), X_test2.shape[0]),\n",
    "}\n",
    "\n",
    "def predict_and_save(model, model_name='decision_tree', split_to_plot='_cv'):\n",
    "    '''Plots cv and returns cv, saves all stats\n",
    "    '''\n",
    "    results = {'model': model}\n",
    "    for x, y, suffix in zip([X_train, X_test1, X_test2, X_cv],\n",
    "                            [y_train, y_test1, y_test2, y_cv], \n",
    "                            ['_train', '_test1', '_test2', '_cv']):\n",
    "        stats, threshes = validate.all_stats_curve(y, model.predict_proba(x)[:, 1],\n",
    "                                                  plot=suffix == split_to_plot)\n",
    "        plt.title(split_to_plot[1:])\n",
    "        for stat in stats.keys():\n",
    "            results[stat + suffix] = stats[stat]\n",
    "#         results['sens' + suffix] = sens\n",
    "#         results['spec' + suffix] = spec\n",
    "        results['threshes' + suffix] = threshes\n",
    "    pkl.dump(results, open(oj(MODELS_DIR, model_name + '.pkl'), 'wb'))        \n",
    "    return stats, threshes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit simple models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**decision tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit decition tree\n",
    "dt = DecisionTreeClassifier(max_depth=4, class_weight={0:1, 1:1e3})\n",
    "dt.fit(X_train, y_train)\n",
    "stats, threshes = predict_and_save(dt, model_name='decision_tree', split_to_plot=split_to_plot)\n",
    "# plt.xlim((0.8, 1.0))\n",
    "# plt.ylim((0.5, 1.0))\n",
    "plt.show()\n",
    "\n",
    "# fig = plt.figure(figsize=(50,40))\n",
    "# plot_tree(dt, feature_names=list(X_train), filled=True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rename(x):\n",
    "    RENAME_DICT = {}\n",
    "    x = (\n",
    "        RENAME_DICT\n",
    "        .get(x, x.replace('_yes', '')\n",
    "        .replace('_', ' '))\n",
    "        .replace('ThoracicTrauma', 'Thoracic Trauma')\n",
    "        .replace('VomitWretch', 'Vomit/wretch')\n",
    "        .replace('AbdDistention or AbdomenPain', 'Abdominal distention / Abdomen pain')\n",
    "        .replace('DecrBreathSounds', 'Decreased breath sounds')\n",
    "        .replace('AbdTenderDegree None', 'No Abdominal Tenderness')\n",
    "    )\n",
    "    return x\n",
    "feature_names = [rename(x) for x in list(X_train)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/accounts/projects/vision/.local/lib/python3.8/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass classes=[0 1], y=[0 0 0 ... 0 0 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
     ]
    },
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      "text/plain": [
       "<dtreeviz.trees.DTreeViz at 0x7f9471440f70>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dviz = dtreeviz(dt,\n",
    "               X_train,\n",
    "               y_train.values,\n",
    "               feature_names=feature_names,\n",
    "               class_names=[\"Healty\", \"IAI-I\"],\n",
    "               fancy=False)\n",
    "dviz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**bayesian rule list**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(13)\n",
    "# train classifier (allow more iterations for better accuracy; use BigDataRuleListClassifier for large datasets)\n",
    "print('training bayesian_rule_list...')\n",
    "brl = imodels.BayesianRuleListClassifier(listlengthprior=2, max_iter=10000, class1label=\"IwI\", verbose=False)\n",
    "brl.fit(X_train, y_train, feature_labels=processed_feats)\n",
    "stats, threshes = predict_and_save(brl, model_name='bayesian_rule_list', split_to_plot=split_to_plot)\n",
    "print(brl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trained RuleListClassifier for detecting IwI\n",
      "=============================================\n",
      "IF GCSScore_Full_yes_0.5_to_inf AND AbdTenderDegree_None_0.5_to_inf THEN probability of IwI: 0.3% (0.2%-0.5%)\n",
      "ELSE IF DecrBreathSound_yes_0.5_to_inf AND ThoracicTrauma_yes_0.5_to_inf THEN probability of IwI: 23.3% (13.6%-34.7%)\n",
      "ELSE IF AbdTrauma_or_SeatBeltSign_yes_-inf_to_0.5 AND Hypotension_yes_-inf_to_0.5 THEN probability of IwI: 1.6% (1.1%-2.1%)\n",
      "ELSE IF GCSScore_Full_yes_0.5_to_inf THEN probability of IwI: 5.3% (3.7%-7.2%)\n",
      "ELSE probability of IwI: 20.7% (15.0%-27.0%)\n",
      "============================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(brl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**rulefit**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit a rulefit model\n",
    "np.random.seed(13)\n",
    "rulefit = imodels.RuleFitRegressor(max_rules=4)\n",
    "rulefit.fit(X_train, y_train, feature_names=processed_feats)\n",
    "\n",
    "# preds = rulefit.predict(X_test)\n",
    "stats, threshes = predict_and_save(rulefit, model_name='rulefit', split_to_plot=split_to_plot)\n",
    "'''\n",
    "def print_best(sens, spec):\n",
    "    idxs = np.array(sens) > 0.9\n",
    "    print(np.array(sens)[idxs], np.array(spec)[idxs])\n",
    "print_best(sens, spec)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rule</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AbdTenderDegree_None</td>\n",
       "      <td>-0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AbdTrauma_or_SeatBeltSign_yes</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CostalTender_yes</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GCSScore_Full_yes</td>\n",
       "      <td>-0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MOI_Bike_collision/fall</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MOI_Motor_vehicle_collision</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MOI_Object_struck_abdomen</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ThoracicTrauma_yes</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>VomitWretch_yes</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GCSScore_Full_yes &gt; 0.5</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AbdTenderDegree_None &gt; 0.5</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AbdTenderDegree_None &lt;= 0.5</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GCSScore_Full_yes &lt;= 0.5</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             rule   coef\n",
       "1   AbdTenderDegree_None          -0.014\n",
       "2   AbdTrauma_or_SeatBeltSign_yes  0.040\n",
       "4   CostalTender_yes               0.010\n",
       "6   GCSScore_Full_yes             -0.049\n",
       "8   MOI_Bike_collision/fall        0.000\n",
       "10  MOI_Motor_vehicle_collision    0.009\n",
       "12  MOI_Object_struck_abdomen     -0.001\n",
       "14  ThoracicTrauma_yes             0.008\n",
       "15  VomitWretch_yes                0.014\n",
       "17  GCSScore_Full_yes > 0.5       -0.000\n",
       "16  AbdTenderDegree_None > 0.5    -0.000\n",
       "18  AbdTenderDegree_None <= 0.5    0.000\n",
       "19  GCSScore_Full_yes <= 0.5       0.000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.reset_option('display.max_colwidth')\n",
    "rulefit.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**slim - sparse linear integer model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fewest_feats = data.fewest_feats\n",
    "CLASS_WEIGHT = 100\n",
    "n = X_train.shape[0]\n",
    "lambda_reg = 10\n",
    "\n",
    "# optionally upweight positive samples\n",
    "sample_weight = np.ones((n))\n",
    "sample_weight[y_train[:n] == 1] *= CLASS_WEIGHT\n",
    "sample_weight /= sample_weight.max()\n",
    "\n",
    "# fit\n",
    "X_train_small = X_train[fewest_feats]\n",
    "slim = imodels.SLIMRegressor()\n",
    "slim.fit(X_train_small[:n], y_train[:n] * 100, lambda_reg=lambda_reg,\n",
    "         sample_weight=sample_weight)\n",
    "\n",
    "# calculate mse on the training data\n",
    "# sens, spec, threshes = predict_and_save(slim, model_name='slim')\n",
    "\n",
    "X_test_small = X_test1[fewest_feats]\n",
    "# preds = slim.predict(X_test_small)\n",
    "# preds_proba = [1 / (1 + np.exp(-y)) for y in preds]\n",
    "preds_proba = slim.predict_proba(X_test_small)\n",
    "print('considered ', len(fewest_feats), 'feats')\n",
    "coefs = slim.model.coef_.astype(int)\n",
    "intercept = slim.model.intercept_\n",
    "for i in range(len(fewest_feats)):\n",
    "    if coefs[i] != 0:\n",
    "        print(fewest_feats[i], coefs[i])\n",
    "print('intercept', intercept)\n",
    "stats, threshes = validate.all_stats_curve(y_test1, preds_proba[:, 1], plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**skope rules**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # feature_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n",
    "# clf = imodels.SkopeRulesClassifier(max_depth_duplication=100,\n",
    "#                  n_estimators=100,\n",
    "#                  precision_min=0.01, # per rule precision min\n",
    "#                  recall_min=0.1,     # per rule recall min\n",
    "#                  feature_names=processed_feats)\n",
    "# clf.fit(X_train, y_train)\n",
    "# # print the rules\n",
    "# rules = clf.rules_\n",
    "# print(\"Rules for iai\")\n",
    "# for rule in rules:\n",
    "#     print(rule)\n",
    "\n",
    "# # evaluate\n",
    "# sens, spec, threshes = predict_and_save(clf, model_name='skope', split_to_plot=split_to_plot)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**greedy (CART) rule list**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/accounts/projects/vision/.local/lib/python3.7/site-packages/numpy/lib/function_base.py:2530: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/accounts/projects/vision/.local/lib/python3.7/site-packages/numpy/lib/function_base.py:2531: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n",
      "  0%|          | 0/9 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.67 -> Hypotension_yes -> 1.45 (7869) 16.38 (116)\n",
      "1.45 -> AbdTrauma_or_SeatBeltSign_yes -> 0.78 (6657) 5.12 (1212)\n",
      "0.78 -> DecrBreathSound_yes -> 0.66 (6529) 7.03 (128)\n",
      "0.66 -> ThoracicTrauma_yes -> 0.58 (5709) 1.22 (820)\n",
      "0.58 -> MOI_Motor vehicle collision -> 0.47 (4011) 0.82 (1698)\n",
      "0.47 -> MOI_Bike collision/fall -> 0.43 (3740) 1.11 (271)\n",
      "0.43 -> MOI_Motorcycle/ATV/Scooter collision -> 0.40 (3481) 0.77 (259)\n",
      "0.40 -> MOI_Fall from an elevation -> 0.35 (2305) 0.51 (1176)\n",
      "0.35 -> VomitWretch_yes -> 0.33 (2116) 0.53 (189)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9/9 [00:00<00:00, 33.37it/s]\n",
      "100%|██████████| 9/9 [00:00<00:00, 64.72it/s]\n",
      "100%|██████████| 9/9 [00:00<00:00, 117.47it/s]\n",
      "100%|██████████| 9/9 [00:00<00:00, 33.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t                                    => \u001b[96m1.67\u001b[00m% risk (7985 pts)\n",
      "                         if Hypotension_yes ===> \u001b[91m16.4\u001b[00m% risk (116 pts)\n",
      "\t                                    => \u001b[96m1.45\u001b[00m% risk (7869 pts)\n",
      "           if AbdTrauma_or_SeatBeltSign_yes ===> \u001b[91m5.1\u001b[00m% risk (1212 pts)\n",
      "\t                                    => \u001b[96m0.78\u001b[00m% risk (6657 pts)\n",
      "                     if DecrBreathSound_yes ===> \u001b[91m7.0\u001b[00m% risk (128 pts)\n",
      "\t                                    => \u001b[96m0.66\u001b[00m% risk (6529 pts)\n",
      "                      if ThoracicTrauma_yes ===> \u001b[91m1.2\u001b[00m% risk (820 pts)\n",
      "\t                                    => \u001b[96m0.58\u001b[00m% risk (5709 pts)\n",
      "             if MOI_Motor vehicle collision ===> \u001b[91m0.8\u001b[00m% risk (1698 pts)\n",
      "\t                                    => \u001b[96m0.47\u001b[00m% risk (4011 pts)\n",
      "                 if MOI_Bike collision/fall ===> \u001b[91m1.1\u001b[00m% risk (271 pts)\n",
      "\t                                    => \u001b[96m0.43\u001b[00m% risk (3740 pts)\n",
      "    if MOI_Motorcycle/ATV/Scooter collision ===> \u001b[91m0.8\u001b[00m% risk (259 pts)\n",
      "\t                                    => \u001b[96m0.4\u001b[00m% risk (3481 pts)\n",
      "              if MOI_Fall from an elevation ===> \u001b[91m0.5\u001b[00m% risk (1176 pts)\n",
      "\t                                    => \u001b[96m0.35\u001b[00m% risk (2305 pts)\n",
      "                         if VomitWretch_yes ===> \u001b[91m0.5\u001b[00m% risk (189 pts)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_weight = {0: 1, 1: 100}\n",
    "d = imodels.GreedyRuleListClassifier(max_depth=9, class_weight=class_weight, criterion='neg_corr')\n",
    "d.fit(X_train, y_train, verbose=True)\n",
    "stats, threshes = predict_and_save(d, model_name='grl', split_to_plot=split_to_plot)\n",
    "d.print_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**irf - iterative Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 269/269 [00:07<00:00, 34.72it/s]\n",
      "100%|██████████| 227/227 [00:03<00:00, 67.05it/s]\n",
      "100%|██████████| 165/165 [00:01<00:00, 120.50it/s]\n",
      "100%|██████████| 269/269 [00:07<00:00, 34.49it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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OHNakN5JZmT5ZLBI4y3e8F7P8u1fW8/yynRQVdKeooDuDC3pQ1K87RQU9KCrozsBeeYSyrIVXEzlxCgKRdnbp6EG8GjXpzYdGDqBP91y2VlTx8urd7D5YHdM+J2Sc0jccEkV9ezA4KiSKCnowsFc3shQUcgIUBCLtrPlJ5MblRkdq69m2/zBbKw6ztaIq8v0wW/ZV8fdVu9hTGRsUuaEsTumbR1FB85AIPz4pX0EhiSkIRDIgeojr5vJyQpx2Uj6nnZQf9/nDNY1BERUSkccvrHiXPZU1Me1zQ1mcGhUM0SExuKA7AxQUgacgEOlkuueGOH1gPqcPTBQUVWyJhMTWfVVNexd/276TvYdig6JbdmNQxIZE4+OT8rthpqDoyhQEIl1MOCh6cfrAXnGfr6qpY1uzPYnGvYtl2w6wL05QxIZDbGAMyM/FzPjp7JU8v3wnZ/aqoQPdNCspUBCIBEyP3GxGFPZiRGH8oDhUXce2/eFzEs3PU7y9dT8VVbGXu+blZNEtJ4sDVeGRVjfuhZ/OXqmZ2zoRBYGIxOjZLZuRhb0Y2UJQVFaH9yjCQREOiQf/uTGmzYw3NisIOhEFgYgcl/xu2bzv5F687+SjQfHogs0crD46XqS7Z6I0aSVNVSkiJ+z60uKEy9KxKQhE5IRNmziKHjkh8ruFmDg0W4eFOhkdGhKRNtG7ew4fHnkSlw7Yl+lS5Dhpj0BEJOAUBCIiAacgEBEJOAWBiEjA6WSxiLSJQ9V1lK/ZRfcqpyzTxchx0R6BiJywGfM3c7C6jnffq+bBFTXMmL850yXJcVAQiMgJe27ZjoTL0rEpCETkhB1sNu9y82Xp2BQEInLC1u2uTLgsHVtag8DMJpjZajNbZ2bT4jw/xMxeNrO3zOxtM5uYznpEJD0GF/SIWe6dl62B5zqRtAWBmYWAe4BLgTOB68zszGbNvg885u7nAZOAe9NVj4ikz4+uOjvmj8n2A9Vcc99cFm/Zz6JNFdzz8joWbarIWH2SWDovHx0LrHP39QBmNhO4AlgR1caB3pHHfYDtaaxHRNKkpLiAv3z5A8xbv5ec/RvpUzSSX8xZw5X3vE6WhX/Rc0NZzLiplJKh/QBYtKmCeev3Mm54f0qKCzL7AQLO0rX7ZmbXABPc/abI8g1AqbtPjWozCPgbUAD0BD7m7ovivNYUYApAYWFhycyZM1tVU2VlJfn58ed5lTD1UWLqn+Qa++hwnXPXoiOsqmhoei47C4b2zqJXjvH2nnoaHLIMrjg9h8G9sggZZGcZ2VnhttmR5b+srmZNRQPnDAhx87l5Gfx0bSMTP0dlZWUtTjyd6RvKrgMedPdfmtl44CEzG+3uDdGN3H06MB1gzJgxXtbKCVHLy8tp7bZBoT5KTP2TXHQfDRxRwfW/m0dNfQNZZnxsVCH7qmpYsmU/9ZH/Qesdnlib2lVGc3fWU7izD3dOOi9N1bePjvZzlM4g2AYMjlouiqyL9kVgAoC7zzWzPGAAsCuNdYlIOykpLuDhL4075hDQoo37mHz/fGrrG8gJZfGzq8/h9IH51NQ3UFvXEP5e30BNnfPvjy3mUM3R2c+eWrydFTveY0i/nhT378GQfj0YEvleVNCdbtkhZszfzHPLdnDp6EFMLh2SqY/faaQzCBYAI8xsGOEAmARMbtZmM3Ax8KCZjQLygN1prElE2llJccEx5wBKhvZjRpyAiOf5ZYU8tfjo6cPhA3owpF9Ptuyr4rV1uzlSe/QAgln4iqUDh+sAeHXtHgCFQRJpCwJ3rzOzqcAcIAQ84O7LzewOYKG7zwL+HfidmX2D8PmkG13XnIkEQryAiKfxMFD5mt2UjTwp5rCQu7O7sprNe6vYtLeKzfuqeOSNzUBdU5vnlu1QECSR1nME7j4bmN1s3W1Rj1cAH0xnDSLS+bV0TsDMGNgrj4G98hgTuRqpsHcetz65tKnNpaMHtUuNnZnuLBaRLmVy6RAKemSTnWVcee4p2htIgYJARLqUGfM3U1FVR12D89Ti7RoJNQUKAhHpUjQS6vFTEIhIl9L8nED08oz5m7nh9/O1l9BMpm8oExFpU+87uVfTYwP+tnwnizZVsH53JW9t2Q+ELyvdU3mEG8YNpXf3HH7x/CqeX76TCWedzLSJozJTeAYpCESkS5m3fi9G+Hp0B97cXEGvvBz2VFbHtPvVC2v51QtrY9bd98p6gMCFgQ4NiUiXMm54f7rlhMctysvJ4g+fH8vr0z7K7Z88K6bdtWNO5bZPnEleTuyfwccWbW3PcjsE7RGISJdSUlzAwzcde9dy42WkzYeeuPfltTF3J2e1ODRb16UgEJEup6W7lieXDjnmvoJzivrw0uo9TcvnDQnekNgpHRoys7PTXYiISCYU9+8Zs3zagJ4ttOy6Uj1HcK+ZvWFmXzGzPmmtSESkHa3aeTBmefmO9zJUSeakFATufhFwPeFhpReZ2QwzuyStlYmItIOPjSqMWQ7i2EQpXzXk7msJzzH8XeDDwN1mtsrMPpWu4kRE0m3EwKP3HWRnxd6HEBSpniM4x8x+DawEPgp80t1HRR7/Oo31iYik1VtbKpoe1zXA719bz8Ej4RnTFm2q4J6X17FoU0VLm3cJqV419BvgfuBWdz/cuNLdt5vZ99NSmYhIO7hwxEncW/4ONXXhS0hnL93Jiyt3cd7gvizaVEF9g5OTncUjXxqX0vwJnVGqQfCkuz8UvcLM/s3d72q+XkSkMykpLjg6W9qwfmDG00u2M3PBZuoawvNk1dQ18MSbWwMfBJ8F7my27kbgrrYsRkQkE5rfd1BSXEBNXT0z3tjStK4rT52YMAjM7DrC8wwPM7NZUU/1AvalszARkUy6umQwjy/aSk19OALGRmZA64qS7RH8E9gBDAB+GbX+IPB2uooSEcm0kuICHpkynhdXvMuMNzZz19/X8pEzBtKne06mS2tzCYPA3TcBm4Dx7VOOiEjH0XjI6KOjBnLd9Hl849HF3P/ZMWR1sQGJEl4+amavRb4fNLP3or4Omlnwbr8TkUC6YGg/bv/kmby0ahd3vrgm0+W0uWR7BBdGvgfvDgsRkSifGVfM0m0HuPuldWzeV8UN44d2mauIUr2hbJyZ9Ypa7mVmpSlsN8HMVpvZOjObFuf5X5vZ4sjXGjPbf1zVi4i0EzPjqvNOxQyeWrydf73vn3zn8SX8Y81uDhyu7dTTYKZ6+ej/AOdHLR+Ksy6GmYWAe4BLgK3AAjOb5e4rGtu4+zei2n8NOC/10kVE2tebm/c3zX7W4PDYwq08tjB2IptX14aHtG4+3HVHlupYQ+buTZfRunsDyUNkLLDO3de7ew0wE7giQfvrgEdSrEdEpN2NG96f3Oyjs5899MWxPHxTKf16xl5J9MDrGzJUYetY1N/3lhuZPQGUE94LAPgK8BF3vzLBNtcAE9z9psjyDUCpu0+N07YYmAcUuXt9nOenAFMACgsLS2bOnJm05ngqKyvJz89v1bZBoT5KTP2TXFfvo3UV9azaV88Z/UKcXhAC4NZXDrG96mibU3rAjz/U8rwGmeijsrKyFi91SvXQ0C3A3YRHH3Xg70T+MLeRScDj8UIAwN2nA9MBxowZ42VlZa16k/Lyclq7bVCojxJT/yTX1fuoLM66qd03c+uTS48uf/xsyhIcGupofZRSELj7LsJ/rI/HNsLzFzQqiqyLZxLw1eN8fRGRDmFy6RBeXLmTl1bt5sdXnd2pzg9A8iEmvuPuPzez3xBnqA13/z8JNl8AjDCzYYQDYBLh4Sqav8cZQAEw93gKFxHpSEaf2peXVu3udCEAyfcIGq/wWXi8L+zudWY2FZgDhIAH3H25md0BLHT3xrGLJgEzPZWTFSIi0uaSBcGngWeAvu5+3CONuvtsYHazdbc1W/7B8b6uiIi0nWSXj5aY2SnAF8yswMz6RX+1R4EiIpJeyfYI7iN8hdBwYBEQffmRR9aLiEgnlnCPwN3vjsxN/IC7D3f3YVFfCgERkS4g2VVDvd39PeA/4h0KcndNTiMi0sklOzQ0A/gE4cNCjg4NiYh0OcmGof5E5Puw9ilHRETaW6rDUF9lZn2ilvua2ZVpq0pERNpNqqOP3u7uBxoX3H0/cHtaKhIRkXaVahDEa5fqgHUiIl3enOU7APj6zLcyXMnxSzUIFprZr8zstMjXrwifQBYRCbyvz3yL1TsrgfDsZZ0tDFINgq8BNcCjhCeYOYJGCxURAWDO8ncTLnd0qQ5DfQiYZmY9I49FRCQiKyvxckeXUhCY2QeA+4F8YIiZvR+42d2/ks7iREQ6g+65IQ5VH51X63B1PR//9St0zw3RPSdEj9wQebkheuSE6J4bYs/OGhbXraFH5Pm8nBA9crPpnpvF71/bwPJt71H2vpO4c1L7TOOe6gnfXwMfB2YBuPsSM/tQ2qoSEelE+ublsOdgTdNyz24hhg7oQVVNPUdq63n3YG34cU09VbX1HDpSy+wNaxO+5lOLtwO0SxikfOWPu28xi5nyMu60kiIiQfOFC4fHTFX5vYlnJpygpry8nIs+9GGO1NY3hcXh2nquvvefHKyuO9puze601t0o1SDYEjk85GaWA/wbsDJ9ZYmIdB6Nf/SfW7aDS0cPSmmWslCW0bNbNj27Hf0zfPGogU17AgBlI09q+2LjOJ7J6+8CTgW2E551TFcNiYhETC4dcsLTVN456TyeXrKd7FAWl44+uWOdI3D3PcD1aa5FRCTwuudm8+kLBvN/P3Fmu71nqmMNDTezp81st5ntMrO/mplGHhUR6QJSvdp1BvAYMAg4BfgL8Ei6ihIRkfaTahD0cPeH3L0u8vVnIC+dhYmISPtINQieM7NpZjbUzIrN7DvA7GST2JvZBDNbbWbrzGxaC22uNbMVZrbczGa05kOIiEjrpXrV0LWR7zcTnpkMwrOVTaKFmcrMLATcA1wCbAUWmNksd18R1WYE8D3gg+5eYWYDW/UpRESk1VLdI/gu8P7ITGV/AJYAVyeZxH4ssM7d17t7DeHB6q5o1uZLwD3uXgHg7ruO+xOIiMgJMXdP3sjsbXc/x8wuBH4I/Ddwm7uXJtjmGmCCu98UWb4BKHX3qVFtngLWAB8EQsAP3P35OK81BZgCUFhYWDJz5szUP2GUyspK8vPzW7VtUKiPElP/JKc+Si5RH93ywiE+XJTNdaO6tel7lpWVWUvPpXpoqHE4icuA37n7s2b2oxOuLPz+I4AyoAh4xczOjsyA1sTdpwPTAcaMGeNlZWWterPy8nJau21QqI8SU/8kpz5KLlEfZb88h6LBgykr62D3EQDbzOx/gU8TPkncLYVttwGDo5aLIuuibQVmuXutu28gvHcwIsWaRESkDaQaBNcSHlbi45H/1vsB306yzQJghJkNM7NcwieWZzVr8xThvQHMbAAwElifYk0iItIGUh1iogp4Imp5B7AjyTZ1ZjaVcICEgAfcfbmZ3QEsdPdZkef+xcxWED789G1339u6jyIiIq2R1gno3X02MLvZutuiHjvwzciXiIhkQCebUE1ERNqagkBEJOAUBCIiAacgEBEJOAWBiEjAKQhERAJOQSAiEnAKAhGRgFMQiIgEnIJARCTgFAQiIh1IfYOzZMt+Fm2qaLf3VBCIiHQQizZVcLi2noWbKrjud/PaLQwUBCIiHcQTb25telxT1xCznE4KAhGRDqL5xMHJJxJuGwoCEZEO4urzi5oe54YsZjmd0jofgYiIpK6kuIDuOSHOOqU335s4ipLignZ5X+0RiIh0IKEs4/2D+7ZbCICCQEQk8BQEIiIBpyAQEQk4BYGISMApCEREAi6tQWBmE8xstZmtM7NpcZ6/0cx2m9niyNdN6axHRESOlbb7CMwsBNwDXAJsBRaY2Sx3X9Gs6aPuPjVddYiISGLp3CMYC6xz9/XuXgPMBK5I4/uJiEgrmHt6RrMws2uACe5+U2T5BqA0+r9/M7sR+AmwG1gDfMPdt8R5rSnAFIDCwsKSmTNntqqmyspK8vPzW7VtUKiPElP/JKc+Si5RH93ywiE+XJTNdaO6tel7lpWVWUvPZXqIiaeBR9y92sxuBv4IfLR5I3efDkwHGDNmjJeVlbXqzcrLy2nttkGhPkpM/ZOc+ii5RH2U/fIcigYPpqzszHarJ52HhrYBg6OWiyLrmrj7XnevjizeD5SksR4REYkjnUGwABhhZsPMLBeYBMyKbmBmg6IWLwdWprEeERGJI22Hhty9zsymAnOAEPCAuy83szuAhe4+C/g/ZnY5UAfsA25MVz0iIhJfWs8RuPtsYHazdbdFPf4e8L101iAiIonpzmIRkYBTEIiIBJyCQEQk4BQEIiIBpyAQEQk4BYGISMApCEREAk5BICIScAoCEZGAUxCIiAScgkBEJOAUBCIiAacgEBEJOAWBiEjAKQhERAJOQSAiEnAKAhGRgFMQiIgEnIJARCTgFAQiIgGnIBARCbi0BoGZTTCz1Wa2zsymJWh3tZm5mY1JZz0iInKstAWBmYWAe4BLgTOB68zszDjtegH/BsxPVy0iItKydO4RjAXWuft6d68BZgJXxGn3Q+BnwJE01iIiIi1IZxCcCmyJWt4aWdfEzM4HBrv7s2msQ0REEsjO1BubWRbwK+DGFNpOAaYAFBYWUl5e3qr3rKysbPW2QaE+Skz9k5z6KLlEfVRXV8fWLVsoL9/Vpu9ZVlbW4nPpDIJtwOCo5aLIuka9gNFAuZkBnAzMMrPL3X1h9Au5+3RgOsCYMWM80QdKpLy8PGFniPooGfVPcuqj5BL1UfbLcygaPJiysmNOqaZNOg8NLQBGmNkwM8sFJgGzGp909wPuPsDdh7r7UGAecEwIiIhIeqUtCNy9DpgKzAFWAo+5+3Izu8PMLk/X+4qIyPFJ6zkCd58NzG627rYW2palsxYREYlPdxaLiAScgkBEJOAUBCIiAacgEBEJOAWBiEjAKQhERDqQ+gZnyZb9LNpU0W7vqSAQEekgFm2q4HBtPQs3VXDd7+bFhMGM+Zu54ffzmTF/c5u/b8bGGhIRkVhPvLm16XFNXQPf+stixg7tz8a9h5i/YR8Ar67dA8Dk0iFt9r7aIxAR6SC82fK771VTvmZXUwg0euD1DW36vgoCEZEO4urzi8gNGQbkhoyHvljK/Fs/xvABPWIbevPIODE6NCQi0kGUFBfwyJTxzFu/l3HD+1NSXADATRedxq1PLm1q94ULh7fp+yoIREQ6kJLigqYAaNR4PuC5ZTu4dPSgNj0/AAoCEZFOYXLpkDYPgEY6RyAiEnAKAhGRgFMQiIgEnIJARCTgFAQiIgGnIBARCTjzNr5DLd3MbDewqZWbDwD2tGE5XZH6KDH1T3Lqo+Qy0Ud73H1CvCc6XRCcCDNb6O5jMl1HR6Y+Skz9k5z6KLmO1kc6NCQiEnAKAhGRgAtaEEzPdAGdgPooMfVPcuqj5DpUHwXqHIGIiBwraHsEIiLSjIJARCTgumQQmNkEM1ttZuvMbFqc57uZ2aOR5+eb2dAMlJlRKfTRN81shZm9bWZ/N7PiTNSZKcn6J6rd1WbmZtZhLgVsL6n0kZldG/k5Wm5mM9q7xkxK4XdsiJm9bGZvRX7PJmaiTgDcvUt9ASHgHWA4kAssAc5s1uYrwH2Rx5OARzNddwfso48APSKPvxykPkqlfyLtegGvAPOAMZmuu6P1ETACeAsoiCwPzHTdHax/pgNfjjw+E9iYqXq74h7BWGCdu6939xpgJnBFszZXAH+MPH4cuNjMrB1rzLSkfeTuL7t7VWRxHlDUzjVmUio/QwA/BH4GHGnP4jqIVProS8A97l4B4O672rnGTEqlfxzoHXncB9jejvXF6IpBcCqwJWp5a2Rd3DbuXgccAPq3S3UdQyp9FO2LwHNprahjSdo/ZnY+MNjdn23PwjqQVH6GRgIjzex1M5tnZnGHN+iiUumfHwCfMbOtwGzga+1T2rE0VaUkZGafAcYAH850LR2FmWUBvwJuzHApHV024cNDZYT3KF8xs7PdfX8mi+pArgMedPdfmtl44CEzG+3uDe1dSFfcI9gGDI5aLoqsi9vGzLIJ75btbZfqOoZU+ggz+xjwH8Dl7l7dTrV1BMn6pxcwGig3s43AOGBWwE4Yp/IztBWY5e617r4BWEM4GIIglf75IvAYgLvPBfIID0bX7rpiECwARpjZMDPLJXwyeFazNrOAz0UeXwO85JEzNgGRtI/M7DzgfwmHQJCO7UKS/nH3A+4+wN2HuvtQwudQLnf3hZkpNyNS+T17ivDeAGY2gPChovXtWGMmpdI/m4GLAcxsFOEg2N2uVUZ0uSCIHPOfCswBVgKPuftyM7vDzC6PNPs90N/M1gHfBFq8PLArSrGPfgHkA38xs8Vm1vyHuMtKsX8CLcU+mgPsNbMVwMvAt909EHveKfbPvwNfMrMlwCPAjZn6h1RDTIiIBFyX2yMQEZHjoyAQEQk4BYGISMApCEREAk5BICIScAoCkTZgZv+MfB9qZpOj1o8xs7uTbHuLmX028vhGMzslvdWKxNLloyJtyMzKgG+5+ydauX15ZPsg3ZwmGaY9AgkMM+tpZs+a2RIzW2ZmnzazEjP7h5ktMrM5ZjYo0rbczH5mZm+Y2Rozuyiy/qzIusWRMeRHRNZXRt7mp8BFkee/YWZlZvaMmWWZ2UYz6xtVz1ozKzSzH5jZt8zsGsLjOj0c2f4yM3sqqv0lZvZk+/SWBImCQIJkArDd3d/v7qOB54HfANe4ewnwAPBfUe2z3X0s8HXg9si6W4C73P1cwn+0tzZ7j2nAq+5+rrv/unFlZCCxvwJXAZhZKbDJ3d+NavM4sBC4PvL6s4EzzOykSJPPR2oUaVMKAgmSpcAlkf/0LyI8KNho4AUzWwx8n9h5F56IfF8EDI08ngvcambfBYrd/fBxvP+jwKcjjydFllsUGW7gIcJDFfcFxhOs4cClnWgYagkMd18TmUdgIvAj4CVgubuPb2GTxhFX64n8rrj7DDObD1wGzDazm939pRRLmAucHvkP/8pIDcn8AXia8OQ3f4mMYSPSprRHIIERuRqnyt3/THhQvVLgpMhY8JhZjpmdleQ1hgPr3f1uwod6zmnW5CDhYaqPEfkP/0nCcxmsbGEAtpjt3X074Zmrvk84FETanPYIJEjOBn5hZg1ALeG5mOuAu82sD+HfhzuB5Qle41rgBjOrBXYCP272/NtAfWREyQcJz9kb7VHCQxTf2MLrPwjcZ2aHgfGRQ08PAye5+8rkH1Hk+OnyUZEOzsx+C7zl7r/PdC3SNSkIRDowM1sEHAIuCdgscdKOFAQiIgGnk8UiIgGnIBARCTgFgYhIwCkIREQCTkEgIhJw/x9M/IJHrZGhbwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# specify a decision tree with a maximum depth\n",
    "from irf.ensemble.wrf import RandomForestClassifierWithWeights as IRFClassifier\n",
    "irf = IRFClassifier()\n",
    "irf.fit(X_train, y_train)\n",
    "stats, threshes = predict_and_save(irf, model_name='irf', split_to_plot=split_to_plot)\n",
    "# sens, spec, threshes = validate.sensitivity_specificity_curve(y_test, preds_proba[:, 1], plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imps = irf.model.feature_importances_.round(3)\n",
    "args = np.argsort(imps)[::-1]\n",
    "imps = imps[args]\n",
    "feats = np.array(processed_feats)[args]\n",
    "for imp, feat in zip(imps, feats):\n",
    "    print(feat, imp)\n",
    "\n",
    "# # visualize one tree\n",
    "# dviz = dtreeviz(irf.model.estimators_[-1],\n",
    "#                X_train,\n",
    "#                y_train.values,\n",
    "#                feature_names=list(X_train),\n",
    "#                class_names=[\"healty\", \"iai-i\"],\n",
    "#                fancy=True)\n",
    "# dviz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**rf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # specify a decision tree with a maximum depth\n",
    "# rf = RandomForestClassifier()\n",
    "# rf.fit(X_train, y_train)\n",
    "# sens, spec, threshes = predict_and_save(rf, model_name='rf')\n",
    "# # sens, spec, threshes = validate.sensitivity_specificity_curve(y_test, preds_proba[:, 1], plot=True)\n",
    "# imps = rf.feature_importances_.round(3)\n",
    "# args = np.argsort(imps)\n",
    "# imps = imps[args]\n",
    "# feats = np.array(processed_feats)[args]\n",
    "# for imp, feat in zip(imps, feats):\n",
    "#     print(feat, imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# look at all the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator DummyRegressor from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator DecisionTreeRegressor from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator GradientBoostingRegressor from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator LassoCV from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator DummyRegressor from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator DecisionTreeRegressor from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator GradientBoostingRegressor from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator LassoCV from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator DummyRegressor from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator DecisionTreeRegressor from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator GradientBoostingRegressor from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n",
      "/system/linux/anaconda3.8/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator LassoCV from version 0.23.2 when using version 0.23.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bayesian_rule_list.pkl 95.16 60.16\n",
      "rulefit.pkl 95.16 56.44\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"x\\nplt.figure(dpi=200, figsize=(C * 6, R * 6))\\nfor i, suffix in enumerate(suffixes):\\n    plt.subplot(R, C, i + 1)\\n    plot_metrics(suffix, title=titles[i])\\n    plt.xlim((80, 101))\\n    plt.ylim((25, 70))\\nplt.subplot(R, C, 1)    \\nplt.legend()\\nplt.tight_layout()\\nviz.savefig('metrics_3_splits_zoom')\\nplt.show()\\nplt.legend(bbox_to_anchor=(1.05, 1.05))\\n\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_metrics(suffix, title=None, fs=15):\n",
    "    for fname in sorted(os.listdir(MODELS_DIR)):\n",
    "        if 'pkl' in fname:\n",
    "            if not fname[:-4] == 'rf':\n",
    "                r = pkl.load(open(oj(MODELS_DIR, fname), 'rb'))\n",
    "        #         print(r)\n",
    "#                 print(r.keys())\n",
    "\n",
    "                threshes = np.array(r['threshes' + suffix])\n",
    "                sens = np.array(r['sens' + suffix])\n",
    "                spec = np.array(r['spec' + suffix])\n",
    "                plt.plot(100 * sens, 100 * spec, 'o-', label=viz.rename(fname[:-4]), alpha=0.6, markersize=3)\n",
    "                plt.xlabel('Sensitivity (%)', fontsize=fs)\n",
    "                plt.ylabel('Specificity (%)', fontsize=fs)\n",
    "                s = suffix[1:]\n",
    "                if title is None:\n",
    "                    plt.title(f'{s}\\n{data_sizes[s][0]} IAI-I / {data_sizes[s][1]}')\n",
    "                else:\n",
    "                    plt.title(title, fontsize=fs)\n",
    "                \n",
    "                # print best results\n",
    "                if suffix == '_test2': \n",
    "                    idxs = (sens > 0.95) & (spec > 0.43)\n",
    "                    if np.sum(idxs) > 0:\n",
    "                        idx_max = np.argmax(spec[idxs])\n",
    "                        print(fname, f'{100 * sens[idxs][idx_max]:0.2f} {100 * spec[idxs][idx_max]:0.2f}')\n",
    "                    \n",
    "    if suffix == '_test2':\n",
    "        plt.plot(96.77, 43.98, 'o', color='black', label='Original CDR', ms=4)\n",
    "    else:\n",
    "        plt.plot(97.0, 42.5, 'o', color='black', label='Original CDR', ms=4)\n",
    "    plt.grid()\n",
    "\n",
    "\n",
    "\n",
    "suffixes = ['_train', '_test1', '_test2',] # _train, _test1, _test2, _cv    \n",
    "titles = ['Training (PECARN)', 'Validation (PECARN)', 'Test (PSRC)']\n",
    "R, C = 1, len(suffixes)\n",
    "plt.figure(dpi=200, figsize=(C * 2.5, R * 3), facecolor='w')\n",
    "fs = 10\n",
    "for i, suffix in enumerate(suffixes):\n",
    "    ax = plt.subplot(R, C, i + 1)\n",
    "    plot_metrics(suffix, title=titles[i], fs=fs)\n",
    "    if i > 0:\n",
    "        plt.ylabel('')\n",
    "        plt.yticks([0, 25, 50, 75, 100], labels=[''] * 5)\n",
    "#         ax.yaxis.set_visible(False) \n",
    "    plt.xlim((50, 101))\n",
    "    plt.ylim((0, 101))    \n",
    "plt.tight_layout()\n",
    "# plt.subplot(R, C, 1)\n",
    "# plt.legend(fontsize=20)\n",
    "plt.legend(bbox_to_anchor=(1.1, 1), fontsize=fs, frameon=False)\n",
    "viz.savefig('metrics_3_splits')\n",
    "plt.show()\n",
    "\n",
    "'''x\n",
    "plt.figure(dpi=200, figsize=(C * 6, R * 6))\n",
    "for i, suffix in enumerate(suffixes):\n",
    "    plt.subplot(R, C, i + 1)\n",
    "    plot_metrics(suffix, title=titles[i])\n",
    "    plt.xlim((80, 101))\n",
    "    plt.ylim((25, 70))\n",
    "plt.subplot(R, C, 1)    \n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "viz.savefig('metrics_3_splits_zoom')\n",
    "plt.show()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1.05))\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_metrics(suffix):\n",
    "    vals = {s: [] for s in ['sens', 'spec', 'ppv', 'npv', 'lr+', 'lr-', 'brier_score', 'f1']}\n",
    "    fnames = []\n",
    "    for fname in sorted(os.listdir(MODELS_DIR)):\n",
    "        if 'pkl' in fname:\n",
    "            if not fname[:-4] == 'rf':\n",
    "                r = pkl.load(open(oj(MODELS_DIR, fname), 'rb'))\n",
    "                threshes = np.array(r['threshes' + suffix])\n",
    "                m = r['model']\n",
    "                \n",
    "                # add more stats\n",
    "                for x, y, suff in zip([X_train, X_test1, X_test2, X_cv],\n",
    "                                    [y_train, y_test1, y_test2, y_cv], \n",
    "                                    ['_train', '_test1', '_test2', '_cv']):\n",
    "                    if suff == suffix:\n",
    "                        stats, threshes = validate.all_stats_curve(y, m.predict_proba(x)[:, 1],\n",
    "                                                  plot=suffix == split_to_plot)\n",
    "                        preds_proba = m.predict_proba(x)[:, 1]\n",
    "                        brier_score = metrics.brier_score_loss(y, preds_proba)\n",
    "#                         f1_score = \n",
    "                        \n",
    "                    plt.title(split_to_plot[1:])\n",
    "\n",
    "#                     for stat in stats.keys():\n",
    "#                         results[stat + suffix] = stats[stat]\n",
    "                \n",
    "                # pick best vals\n",
    "                sens = np.array(r['sens' + suffix])\n",
    "                spec = np.array(r['spec' + suffix])\n",
    "                best_idx = np.argmax(5 * sens + spec)\n",
    "                for k in vals.keys():\n",
    "                    if not k == 'brier_score':\n",
    "#                         print('k', k)\n",
    "                        vals[k].append(stats[k][best_idx])\n",
    "                vals['brier_score'].append(brier_score)\n",
    "                fnames.append(viz.rename(fname[:-4]))\n",
    "    stats = pd.DataFrame.from_dict(vals)\n",
    "    stats.index = fnames\n",
    "    return (stats).round(2).transpose()\n",
    "\n",
    "suffixes = ['_train', '_test1', '_test2',] # _train, _test1, _test2\n",
    "stats_list = {}\n",
    "for i, suffix in enumerate(suffixes):\n",
    "    stats_list[suffix] = print_metrics(suffix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bayesian rule list</th>\n",
       "      <th>CART decision tree</th>\n",
       "      <th>CART rule list</th>\n",
       "      <th>Iterative random forest</th>\n",
       "      <th>Rule fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sens</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spec</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ppv</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npv</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr+</th>\n",
       "      <td>2.18</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.33</td>\n",
       "      <td>3.33</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr-</th>\n",
       "      <td>0.19</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brier_score</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Bayesian rule list  CART decision tree  CART rule list  \\\n",
       "sens                       0.89                0.95            0.94   \n",
       "spec                       0.59                0.58            0.29   \n",
       "ppv                        0.04                0.04            0.02   \n",
       "npv                        1.00                1.00            1.00   \n",
       "lr+                        2.18                2.26            1.33   \n",
       "lr-                        0.19                0.09            0.21   \n",
       "brier_score                0.02                0.58            0.02   \n",
       "f1                         0.07                0.07            0.04   \n",
       "\n",
       "             Iterative random forest  Rule fit  \n",
       "sens                            0.98      0.95  \n",
       "spec                            0.70      0.47  \n",
       "ppv                             0.05      0.03  \n",
       "npv                             1.00      1.00  \n",
       "lr+                             3.33      1.80  \n",
       "lr-                             0.02      0.10  \n",
       "brier_score                     0.01      0.08  \n",
       "f1                              0.10      0.06  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_list['_train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bayesian rule list</th>\n",
       "      <th>CART decision tree</th>\n",
       "      <th>CART rule list</th>\n",
       "      <th>Iterative random forest</th>\n",
       "      <th>Rule fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sens</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spec</th>\n",
       "      <td>0.58</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ppv</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npv</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr+</th>\n",
       "      <td>2.13</td>\n",
       "      <td>1.93</td>\n",
       "      <td>1.26</td>\n",
       "      <td>2.29</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr-</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brier_score</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Bayesian rule list  CART decision tree  CART rule list  \\\n",
       "sens                       0.90                0.84            0.91   \n",
       "spec                       0.58                0.56            0.28   \n",
       "ppv                        0.04                0.03            0.02   \n",
       "npv                        1.00                1.00            0.99   \n",
       "lr+                        2.13                1.93            1.26   \n",
       "lr-                        0.17                0.28            0.31   \n",
       "brier_score                0.02                0.59            0.02   \n",
       "f1                         0.07                0.06            0.04   \n",
       "\n",
       "             Iterative random forest  Rule fit  \n",
       "sens                            0.73      0.97  \n",
       "spec                            0.68      0.26  \n",
       "ppv                             0.04      0.02  \n",
       "npv                             0.99      1.00  \n",
       "lr+                             2.29      1.31  \n",
       "lr-                             0.40      0.11  \n",
       "brier_score                     0.02      0.08  \n",
       "f1                              0.07      0.04  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_list['_test1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bayesian rule list</th>\n",
       "      <th>CART decision tree</th>\n",
       "      <th>CART rule list</th>\n",
       "      <th>Iterative random forest</th>\n",
       "      <th>Rule fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sens</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spec</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ppv</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npv</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr+</th>\n",
       "      <td>2.39</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1.21</td>\n",
       "      <td>2.26</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr-</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brier_score</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Bayesian rule list  CART decision tree  CART rule list  \\\n",
       "sens                       0.95                0.94            0.94   \n",
       "spec                       0.60                0.60            0.22   \n",
       "ppv                        0.07                0.06            0.03   \n",
       "npv                        1.00                1.00            0.99   \n",
       "lr+                        2.39                2.33            1.21   \n",
       "lr-                        0.08                0.11            0.29   \n",
       "brier_score                0.03                0.56            0.03   \n",
       "f1                         0.12                0.12            0.07   \n",
       "\n",
       "             Iterative random forest  Rule fit  \n",
       "sens                            0.82      0.97  \n",
       "spec                            0.64      0.56  \n",
       "ppv                             0.06      0.06  \n",
       "npv                             0.99      1.00  \n",
       "lr+                             2.26      2.21  \n",
       "lr-                             0.28      0.06  \n",
       "brier_score                     0.03      0.09  \n",
       "f1                              0.11      0.11  "
      ]
     },
     "execution_count": 21,
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     "output_type": "execute_result"
    }
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   "source": [
    "stats_list['_test2']"
   ]
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   "execution_count": null,
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   "outputs": [],
   "source": []
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