train_and_evaluate_classifiers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Script for training ready-for-discharge (RFD) classifiers using multiple source cross-validation. \n",
    "\n",
    "#### TODO: implement multiple folds per source when using both MIMIC and GICU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import bisect\n",
    "import pickle\n",
    "import yaml\n",
    "from pathlib import Path\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_curve, precision_recall_curve, roc_auc_score, brier_score_loss\n",
    "from sklearn.metrics import auc as auc_metric\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import fbeta_score, make_scorer, f1_score, accuracy_score\n",
    "from sklearn.model_selection import PredefinedSplit\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "original_features = [\n",
    "    'airway', 'fio2', 'spo2_min', \n",
    "    'hco3', 'resp_min', 'resp_max', \n",
    "    'bp_min', 'hr_min', 'hr_max', 'pain', \n",
    "    'gcs_min', 'temp_min', 'temp_max', \n",
    "    'haemoglobin', 'k','na','creatinine','bun'\n",
    "] \n",
    "\n",
    "config = {\n",
    "    'data_sources': ['mimic', 'gicu'],  # 'gicu' can be included or omitted to train on MIMIC only\n",
    "    'feature_set': 'extended',  # 'extended' includes demographic features\n",
    "    'features': {\n",
    "        'original': original_features,\n",
    "        'extended': original_features + ['bmi', 'los', 'age', 'sex']\n",
    "    },\n",
    "    'fit_score': None,  # optimise hyperparameters according to this score (e.g. 'f1')\n",
    "    'n_splits': 1,  # number of repeated train:test splits to evaluate for peformance statistics\n",
    "    'random_seed': 42,\n",
    "    'missing_data_treatment': 'imputed',  # or 'completecase'\n",
    "    'data_directory': Path('./fms/'),\n",
    "    'results_directory': Path('./results'),\n",
    "    'fpr_range': np.linspace(0.1, 1, 10),  # flase positive rates at which to evaluate relevant performance metrics\n",
    "    'cv_folds': 5,  # number of cross-validation folds to use (currently only used when GICU is omitted)\n",
    "    'test_size': 0.4  # fraction to hold out for test\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "features = config['features'][config['feature_set']]\n",
    "\n",
    "with open(config['results_directory'] / 'config.yaml', 'w') as out_file:\n",
    "    yaml.dump(config, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def fm_path(data_source, missing_data, feature_set, standardised=True):  \n",
    "    \n",
    "    file_name = \"fm_%s_%s_%s\" % (data_source, missing_data, feature_set) \n",
    "    \n",
    "    if standardised:\n",
    "        file_name = file_name + '_standardised'\n",
    "    \n",
    "    return (config['data_directory'] / file_name).with_suffix('.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fm_mimic = pd.read_csv(fm_path('mimic', config['missing_data_treatment'], config['feature_set']))\n",
    "\n",
    "fm_gicu = (\n",
    "    pd.read_csv(fm_path('gicu', config['missing_data_treatment'], config['feature_set']))\n",
    "    if 'gicu' in config['data_sources']\n",
    "    else None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/expert/Documents/work/smartt/algorithms/old_rfd_model/venv/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'airway'}>,\n",
       "        <AxesSubplot:title={'center':'fio2'}>,\n",
       "        <AxesSubplot:title={'center':'spo2_min'}>,\n",
       "        <AxesSubplot:title={'center':'hco3'}>,\n",
       "        <AxesSubplot:title={'center':'resp_min'}>],\n",
       "       [<AxesSubplot:title={'center':'resp_max'}>,\n",
       "        <AxesSubplot:title={'center':'bp_min'}>,\n",
       "        <AxesSubplot:title={'center':'hr_min'}>,\n",
       "        <AxesSubplot:title={'center':'hr_max'}>,\n",
       "        <AxesSubplot:title={'center':'pain'}>],\n",
       "       [<AxesSubplot:title={'center':'gcs_min'}>,\n",
       "        <AxesSubplot:title={'center':'temp_min'}>,\n",
       "        <AxesSubplot:title={'center':'temp_max'}>,\n",
       "        <AxesSubplot:title={'center':'haemoglobin'}>,\n",
       "        <AxesSubplot:title={'center':'k'}>],\n",
       "       [<AxesSubplot:title={'center':'na'}>,\n",
       "        <AxesSubplot:title={'center':'creatinine'}>,\n",
       "        <AxesSubplot:title={'center':'bun'}>,\n",
       "        <AxesSubplot:title={'center':'bmi'}>,\n",
       "        <AxesSubplot:title={'center':'los'}>],\n",
       "       [<AxesSubplot:title={'center':'age'}>,\n",
       "        <AxesSubplot:title={'center':'sex'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AAAAAAAAAAAAAAAAAAAoS2AYAAAAAAAAAAAAAAAAAAChIYBsAAAAAAAAAAAAAAAAAAKAggW0AAAAAAAAAAAAAAAAAAICCBLYBAAAAAAAAAAAAAAAAAAAKEtgGAAAAAAAAAAAAAAAAAAAoSGAbAAAAAAAAAAAAAAAAAACgIIFtAAAAAAAAAAAAAAAAAACAggS2AQAAAAAAAAAAAAAAAAAACvr/5doGNlaz/JEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 5000x5000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(10,50))\n",
    "# # axes = plt.subplots(len(features), 1)\n",
    "# for ai, feature in enumerate(features):\n",
    "#     ax=plt.subplot(len(features), 1, ai+1)\n",
    "#     fm_mimic[feature].hist(ax=ax)#, xlabel=feature)\n",
    "fig=plt.figure(figsize=(50,50))\n",
    "fm_mimic[features].hist(ax=fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MIMIC cohort contains:  13243  patients.\n"
     ]
    }
   ],
   "source": [
    "print(\"MIMIC cohort contains: \", len(fm_mimic), \" patients.\")\n",
    "if 'gicu' in config['data_sources']:\n",
    "    print(\"GICU cohort contains: \", len(fm_gicu), \" patients.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def split_data(mimic, gicu, test_size=config['test_size'], seed=config['random_seed'], VERBOSE=True):\n",
    "    \n",
    "    if gicu is not None:\n",
    "        # first we split the smaller GICU data according to test_size\n",
    "        g_train, g_test = train_test_split(fm_gicu, test_size=test_size, random_state=seed)\n",
    "\n",
    "        # then split MIMIC such that size of m_test equals that of g_test\n",
    "        mfrac = len(g_test) / len(fm_mimic)\n",
    "        \n",
    "    else:\n",
    "        g_train = None\n",
    "        g_test = None\n",
    "        mfrac = test_size\n",
    "        \n",
    "    m_train, m_test = train_test_split(fm_mimic, test_size=mfrac, random_state=seed)\n",
    "    \n",
    "    if VERBOSE:\n",
    "        print(\"Using %.4f of MIMIC as training data\" %(1-mfrac))\n",
    "        print(\"giving test sets gicu_test:%d and mimic_test:%d\" %(len(g_test) if g_test is not None else 0,len(m_test)))\n",
    "    \n",
    "    return m_train, m_test, g_train, g_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 0.6000 of MIMIC as training data\n",
      "giving test sets gicu_test:0 and mimic_test:5298\n"
     ]
    }
   ],
   "source": [
    "_,_,_,_ = split_data(fm_mimic, fm_gicu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_fpr_tpr_for_threshold(fpr, tpr, threshold, prob_thresholds):\n",
    "    p = bisect.bisect_left(fpr, threshold)\n",
    "    fpr = fpr.copy()\n",
    "    fpr[p] = threshold\n",
    "    return fpr[: p + 1], tpr[: p + 1], prob_thresholds[: p + 1]\n",
    "\n",
    "def partial_auc_score(y, y_pred, threshold=0.5):\n",
    "    fpr, tpr, prob_thresholds = roc_curve(y, y_pred, pos_label=1) \n",
    "    fpr_t, tpr_t, _ = get_fpr_tpr_for_threshold(fpr, tpr, threshold, prob_thresholds)\n",
    "    \n",
    "    return auc_metric(fpr_t,tpr_t) \n",
    "\n",
    "def calculate_all_metrics(ytrue, ypred, fpr_thresh):\n",
    "    \n",
    "    results = {}\n",
    "    fpr, tpr, prob_thresholds = roc_curve(ytrue, ypred, pos_label=1) \n",
    "    fpr_t, tpr_t, cutoff = get_fpr_tpr_for_threshold(fpr, tpr, fpr_thresh, prob_thresholds)\n",
    "    \n",
    "    ypred_class = ypred>=cutoff[-1]\n",
    "    \n",
    "    results['f1'] = f1_score(ytrue, ypred_class)\n",
    "    results['accuracy'] = accuracy_score(ytrue, ypred_class)\n",
    "    results['sensitivity'] = tpr_t[-1]\n",
    "    results['specificity'] = 1 - fpr_t[-1]\n",
    "    results['pauc'] = partial_auc_score(ytrue, ypred, threshold=fpr_thresh)\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def store_performance_results(ytrue, ypred, results_dict, sps, dbsource=None):\n",
    "    \n",
    "    auc = roc_auc_score(ytrue, ypred)\n",
    "    brier = brier_score_loss(ytrue, ypred)\n",
    "    \n",
    "    results_dict['performance_%s' %dbsource]['auc'].append(auc)\n",
    "    results_dict['performance_%s' %dbsource]['brier'].append(brier)\n",
    "    \n",
    "    for ff in config['fpr_range']:\n",
    "        \n",
    "        results = calculate_all_metrics(ytrue, ypred, ff)\n",
    "        for metric, result in results.items():\n",
    "            results_dict['performance_%s' %dbsource][metric]['%.2f' %ff].append(result)\n",
    "    \n",
    "    return results_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def optimise_return_test_scores(\n",
    "    parameters_to_optimise, classifier, \n",
    "    fm_mimic, fm_gicu, config, \n",
    "    verbose=False, savename=None\n",
    "):\n",
    "    \n",
    "    fpr_range = config['fpr_range'] \n",
    "    number_of_splits=config['n_splits']\n",
    "    features = config['features'][config['feature_set']]\n",
    "    use_gicu = 'gicu' in config['data_sources']\n",
    "    \n",
    "    # initialise results dictionary\n",
    "    results_dict = dict()\n",
    "    for dbs in ['gicu', 'mimic', 'train', 'combined']:\n",
    "        \n",
    "        results_dict['performance_%s' %dbs] = {\n",
    "            'auc': [],\n",
    "            'brier': []\n",
    "        }\n",
    "    \n",
    "        # the following metrics are evaulated at a range of FPR values:\n",
    "        performance_metrics = ['f1', 'accuracy', 'sensitivity', 'specificity', 'pauc']\n",
    "        for metric in performance_metrics:\n",
    "            results_dict['performance_%s' %dbs][metric] = {\n",
    "                '%.2f' %ff: []\n",
    "                for ff in fpr_range\n",
    "            }\n",
    "\n",
    "    params = []\n",
    "    this_seed = []\n",
    "    opt_clfs = []\n",
    "    \n",
    "    for sp in range(number_of_splits):\n",
    "        \n",
    "        print(\"Split %d\" %sp)\n",
    "        m_train, m_test, g_train, g_test = split_data(fm_mimic, fm_gicu, seed=sp)\n",
    "        \n",
    "        train = m_train.append(g_train)\n",
    "        combined = m_test.append(g_test)\n",
    "        \n",
    "        if use_gicu:\n",
    "            test_fold = [0 for i in m_train]  # 0th test fold is MIMC data\n",
    "            test_fold.extend([1 for i in g_train])  # 1st test fold is GICU data\n",
    "            cv_folds = PredefinedSplit(test_fold)\n",
    "        else:\n",
    "            cv_folds = config['cv_folds']\n",
    "    \n",
    "        print(\"Creating gridsearch.\")\n",
    "        clf = GridSearchCV(\n",
    "            classifier, parameters_to_optimise, cv=cv_folds, \n",
    "            scoring=config['fit_score'], return_train_score=False, refit=True, verbose=True, n_jobs=-1\n",
    "        )\n",
    "        \n",
    "        print(\"Running gridsearch...\")\n",
    "        clf.fit(train[features].to_numpy(), list(train['outcome']))\n",
    "        best_params = clf.best_params_\n",
    "        print(\"Gridsearch complete.\")\n",
    "        \n",
    "        # evaluate performance metrics on test sets, training set and combined test sets:\n",
    "        test_frames = (\n",
    "            {'gicu': g_test, 'mimic': m_test, 'train': train, 'combined': combined}\n",
    "            if use_gicu\n",
    "            else {'mimic': m_test, 'train': train}\n",
    "        )\n",
    "        for dbs in test_frames.keys():\n",
    "            \n",
    "            ytrue = list(test_frames[dbs]['outcome'])\n",
    "            ypred = clf.predict_proba(test_frames[dbs][features].to_numpy())[:,1]\n",
    "            results_dict = store_performance_results(ytrue, ypred, results_dict, sp, dbsource=dbs)\n",
    "\n",
    "        params.append(best_params)    \n",
    "        this_seed.append(sp)\n",
    "        opt_clfs.append(clf)\n",
    "        \n",
    "        if sp%20==0:\n",
    "            with open(savename, 'wb') as fh:\n",
    "                pickle.dump(results_dict, fh)\n",
    "    \n",
    "    ## add additional information to results dictionary\n",
    "    results_dict['optimal_classifiers'] = opt_clfs\n",
    "    results_dict['split_seeds'] = this_seed\n",
    "    results_dict['optimal_params'] = params\n",
    "    \n",
    "    with open(savename, 'wb') as fh:\n",
    "            pickle.dump(results_dict, fh)\n",
    "        \n",
    "    return results_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LR classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hyper_parameters = [{'C': np.logspace(-3,3,20)}]\n",
    "\n",
    "classifier = linear_model.LogisticRegression(\n",
    "    penalty='l1', random_state=config['random_seed'], \n",
    "    fit_intercept='True', solver='liblinear'\n",
    ")\n",
    "\n",
    "results_fname = config['results_directory'] / 'lr_results.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Split 0\n",
      "Using 0.6000 of MIMIC as training data\n",
      "giving test sets gicu_test:0 and mimic_test:5298\n",
      "Creating gridsearch.\n",
      "Running gridsearch...\n",
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "Gridsearch complete.\n"
     ]
    }
   ],
   "source": [
    "lr_results = optimise_return_test_scores(\n",
    "    hyper_parameters, classifier, fm_mimic, fm_gicu, config, verbose=False, savename=results_fname\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8755 (+- 0.0000)\n",
      "0.8765 (+- 0.0000)\n"
     ]
    }
   ],
   "source": [
    "metric = 'auc'\n",
    "print(\"%.4f (+- %.4f)\" %(np.mean(lr_results['performance_mimic'][metric]),np.std(lr_results['performance_mimic'][metric])))\n",
    "# print(\"%.4f (+- %.4f)\" %(np.mean(lr_results['performance_gicu'][metric]),np.std(lr_results['performance_gicu'][metric])) )\n",
    "# print(\"%.4f (+- %.4f)\" %(np.mean(lr_results['performance_combined'][metric]),np.std(lr_results['performance_combined'][metric])) )\n",
    "print(\"%.4f (+- %.4f)\" %(np.mean(lr_results['performance_train'][metric]),np.std(lr_results['performance_train'][metric])) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hyper_parameters = [{'n_estimators': [20,50,100],'max_features': [0.3,0.5,0.7], 'max_depth':[4,5,6,7]}]\n",
    "classifier = RandomForestClassifier(random_state=config['random_seed'], n_jobs=-1)\n",
    "results_fname = config['results_directory'] / 'rf_results.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Split 0\n",
      "Using 0.6000 of MIMIC as training data\n",
      "giving test sets gicu_test:0 and mimic_test:5298\n",
      "Creating gridsearch.\n",
      "Running gridsearch...\n",
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
      "Gridsearch complete.\n"
     ]
    }
   ],
   "source": [
    "rf_results = optimise_return_test_scores(\n",
    "    hyper_parameters, classifier, fm_mimic, fm_gicu, \n",
    "    config, verbose=False, savename=results_fname\n",
    ")                                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8923 (+- 0.0000)\n",
      "0.9285 (+- 0.0000)\n"
     ]
    }
   ],
   "source": [
    "metric = 'auc'\n",
    "print(\"%.4f (+- %.4f)\" %(np.mean(rf_results['performance_mimic'][metric]),np.std(rf_results['performance_mimic'][metric])))\n",
    "# print(\"%.4f (+- %.4f)\" %(np.mean(rf_results['performance_gicu'][metric]),np.std(rf_results['performance_gicu'][metric])) )\n",
    "# print(\"%.4f (+- %.4f)\" %(np.mean(rf_results['performance_combined'][metric]),np.std(rf_results['performance_combined'][metric])) )\n",
    "print(\"%.4f (+- %.4f)\" %(np.mean(rf_results['performance_train'][metric]),np.std(rf_results['performance_train'][metric])) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Now we save one of each trained classifier along with the train and test data for that split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf = rf_results['optimal_classifiers'][0]\n",
    "lr = lr_results['optimal_classifiers'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "seed = rf_results['split_seeds'][0]\n",
    "lr_seed = lr_results['split_seeds'][0]\n",
    "assert lr_seed == seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 0.6000 of MIMIC as training data\n",
      "giving test sets gicu_test:0 and mimic_test:5298\n"
     ]
    }
   ],
   "source": [
    "m_train, m_test, _, _ = split_data(fm_mimic, fm_gicu, seed=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open(config['results_directory'] / 'rf.pickle', 'wb') as out_file:\n",
    "    pickle.dump(rf, out_file)\n",
    "    \n",
    "with open(config['results_directory'] / 'lr.pickle', 'wb') as out_file:\n",
    "    pickle.dump(lr, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "m_train.to_csv(config['results_directory'] / 'training_data.csv')\n",
    "m_test.to_csv(config['results_directory'] / 'test_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Quick sanity check of classifier performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def roc(df, clf):\n",
    "    ytrue = df['outcome']\n",
    "    ypred = clf.predict_proba(df[features].to_numpy())[:,1]\n",
    "    fpr, tpr, _ = roc_curve(ytrue, ypred)\n",
    "    auc = auc_metric(fpr, tpr)\n",
    "    return fpr, tpr, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr_train, tpr_train, auc_train = roc(m_train, lr)\n",
    "fpr_test, tpr_test, auc_test = roc(m_test, lr)\n",
    "\n",
    "plt.plot(fpr_train, tpr_train, label='train (auc: %.3f)' % auc_train)\n",
    "plt.plot(fpr_test, tpr_test, label='test (auc: %.3f)' % auc_test)\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.title('Logistic Regression')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr_train, tpr_train, auc_train = roc(m_train, rf)\n",
    "fpr_test, tpr_test, auc_test = roc(m_test, rf)\n",
    "\n",
    "plt.plot(fpr_train, tpr_train, label='train (auc: %.3f)' % auc_train)\n",
    "plt.plot(fpr_test, tpr_test, label='test (auc: %.3f)' % auc_test)\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.title('Random Forest')\n",
    "plt.legend();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}