02-LDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#**************************#\n",
    "#import packages\n",
    "#**************************#\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, PowerTransformer\n",
    "\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n",
    "\n",
    "from collections import Counter\n",
    "from imblearn.combine import SMOTEENN , SMOTETomek\n",
    "from imblearn.over_sampling import BorderlineSMOTE , SMOTE, ADASYN\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "#from sklearn.impute import SimpleImputer \n",
    "\n",
    "#from sklearn.feature_selection import SelectKBest , chi2, f_classif\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix , classification_report, roc_auc_score, f1_score, accuracy_score, cohen_kappa_score, log_loss\n",
    "#from sklearn import tree\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "#from sklearn.svm import SVC\n",
    "\n",
    "import pandas_profiling\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      count         mean           std           min  \\\n",
      "Data Year - Fiscal  92872.0  2004.105888      9.875926  1.979000e+03   \n",
      "Tobin's Q           92620.0    25.369008    902.818813  9.283364e-03   \n",
      "EPS                 92867.0   -14.461355   2195.467288 -3.840000e+05   \n",
      "Liquidity           92625.0    -2.631237    121.610921 -2.596852e+04   \n",
      "Profitability       92625.0   -29.537274    677.230667 -7.968200e+04   \n",
      "Productivity        92625.0    -1.222870     35.885556 -5.093000e+03   \n",
      "Leverage Ratio      92846.0     1.345316    253.038093 -7.811000e+03   \n",
      "Asset Turnover      92625.0     1.053613      2.115945 -3.159000e+01   \n",
      "Operational Margin  87315.0    -7.915485    214.460079 -3.017570e+04   \n",
      "Return on Equity    92864.0    -2.112382    352.596902 -8.887514e+04   \n",
      "Market Book Ratio   92815.0   358.506265  26063.637981 -3.151500e+06   \n",
      "Assets Growth       86171.0     1.294075     73.769522 -1.000000e+00   \n",
      "Sales Growth        86171.0     1.900108    177.632638 -2.743103e+01   \n",
      "Employee Growth     85862.0     0.343322     14.074156 -1.000000e+00   \n",
      "BK                  92872.0     0.006008      0.077280  0.000000e+00   \n",
      "\n",
      "                            25%          50%          75%         max  \n",
      "Data Year - Fiscal  1998.000000  2006.000000  2012.000000     2017.00  \n",
      "Tobin's Q              1.120000     1.540000     2.590000   194537.67  \n",
      "EPS                   -0.140000     0.330000     1.530000    55339.00  \n",
      "Liquidity              0.020000     0.190000     0.400000        1.00  \n",
      "Profitability         -0.640000     0.070000     0.310000      140.58  \n",
      "Productivity          -0.060000     0.060000     0.110000     1102.00  \n",
      "Leverage Ratio         0.000000     0.280000     0.820000    75970.38  \n",
      "Asset Turnover         0.390000     0.830000     1.390000      276.38  \n",
      "Operational Margin    -0.030000     0.060000     0.140000      394.47  \n",
      "Return on Equity      -0.080000     0.030000     0.070000    39500.00  \n",
      "Market Book Ratio     11.200000    58.280000   240.140000  3455419.33  \n",
      "Assets Growth         -0.052596     0.051844     0.192062    14231.00  \n",
      "Sales Growth          -0.033522     0.059733     0.204231    39850.00  \n",
      "Employee Growth       -0.048107     0.017241     0.130944     2699.00  \n",
      "BK                     0.000000     0.000000     0.000000        1.00  \n",
      "Data Year - Fiscal       0\n",
      "Tobin's Q              252\n",
      "EPS                      5\n",
      "Liquidity              247\n",
      "Profitability          247\n",
      "Productivity           247\n",
      "Leverage Ratio          26\n",
      "Asset Turnover         247\n",
      "Operational Margin    5557\n",
      "Return on Equity         8\n",
      "Market Book Ratio       57\n",
      "Assets Growth         6701\n",
      "Sales Growth          6701\n",
      "Employee Growth       7010\n",
      "BK                       0\n",
      "dtype: int64\n",
      "0    92314\n",
      "1      558\n",
      "Name: BK, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x936 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x936 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#**************************#\n",
    "#import and Explore data\n",
    "#**************************#\n",
    "\n",
    "#df = pd.read_excel(\"data/Bankruptcy_data_Final.xlsx\")\n",
    "#df.head()\n",
    "\n",
    "data = df.copy()\n",
    "\n",
    "#Data Statistics\n",
    "print(df.describe().transpose())\n",
    "\n",
    "# Check missing value\n",
    "print(df.isnull().sum())\n",
    "\n",
    "\n",
    "#pandas_profiling.ProfileReport(df)\n",
    "\n",
    "# target distribution -- Class0: 99.4%, Class1 : 0.6%\n",
    "print(df[\"BK\"].value_counts())\n",
    "\n",
    "#****************************#\n",
    "#Data Cleaning and Engineering \n",
    "#****************************#\n",
    "#Drop rows with more than 3 missing values\n",
    "df = df[df.isnull().sum(axis=1) < 3]\n",
    "\n",
    "#Fill remaining missing value with 0\n",
    "df = df.fillna(0)\n",
    "\n",
    "#Feature Scaling\n",
    "df.drop(['Data Year - Fiscal' ], axis = 1 , inplace = True)\n",
    "\n",
    "df.hist(figsize = (13,13), bins = 20 , color = 'gray')\n",
    "plt.show()\n",
    "\n",
    "num_features = [col for col in df.columns if col != 'BK']\n",
    "\n",
    "scaler = PowerTransformer(method='yeo-johnson')\n",
    "df[num_features] = scaler.fit_transform(df[num_features])\n",
    "\n",
    "#Histogram\n",
    "df.hist(figsize = (13,13), bins = 20)\n",
    "plt.show()\n",
    "\n",
    "#Export the cleaned data\n",
    "df.to_csv('data/cleaned_data.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('data/cleaned_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\")\n",
    "\n",
    "corr_matrix = df.corr()\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr_matrix, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr_matrix, mask=mask, cmap=cmap, vmax=.6, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, annot = True, fmt=\".0\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = \"BK\"\n",
    "features = list(df.columns)\n",
    "\n",
    "features = [f for f in features if f!=target]\n",
    "\n",
    "X = df[features].reset_index(drop=True).values\n",
    "y = df[target].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train , y_test = train_test_split(X, y, random_state = 42 , test_size = 0.2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GridSearch_func(X,y, estimator, param_grid, cv=10 , scoring = 'roc_auc'):\n",
    "\n",
    "    grid_search = GridSearchCV(estimator, param_grid, cv=cv,\n",
    "                           scoring=scoring)\n",
    "    grid_search.fit(X, y)\n",
    "    print (\"best parameters: \", grid_search.best_params_)\n",
    "    #print(f\"best {scoring} score during training: \", grid_search.best_score_)\n",
    "    print(\"-------\"*10)\n",
    "    means = grid_search.cv_results_['mean_test_score']\n",
    "    stds = grid_search.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, grid_search.cv_results_['params']):\n",
    "        print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "              % (mean, std * 2, params))\n",
    "    print()\n",
    "    print(f\"mean of {scoring} scores during validation: {sum(means)/len(means):0.3f}\")\n",
    "    print(\"-------\"*10)\n",
    "    best_model = grid_search.best_estimator_\n",
    "    print (\"best_model: \", best_model)\n",
    "    \n",
    "    return best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics( clf, X_test , y_test = y_test):\n",
    "    \n",
    "    y_pred = clf.predict(X_test)\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    print(\"Confusion Matrix:\")\n",
    "    print()\n",
    "    print(cm)\n",
    "    print(\"-----\"*5)\n",
    "    \n",
    "    print(classification_report(y_test, y_pred))\n",
    "    print(\"-----\"*5)\n",
    "    \n",
    "    y_proba = clf.predict_proba(X_test)\n",
    "    y_scores = y_proba[:, 1]\n",
    "    auc = roc_auc_score (y_test, y_scores)\n",
    "    \n",
    "    print()\n",
    "    print(f\"ROC AUC score: {auc:.3f}\")\n",
    "\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    print(f\"f1 score: {f1:.3f}\")\n",
    "    \n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    print(f\"Accuracy: {acc:.3f}\")\n",
    "    \n",
    "    kappa = cohen_kappa_score(y_test, y_pred)\n",
    "    print(f\"Kappa: {kappa:.3f}\".format())\n",
    "    \n",
    "    logLoss = log_loss(y_test, y_scores)\n",
    "    print(f\"Log loss: {logLoss:.3f}\".format())\n",
    "    \n",
    "    metrics = pd.DataFrame({\"Accuracy\" :[acc], \"AUC\" : [auc], \"f1\": [f1], \"Kappa\": [kappa], \"Log Loss\": [logLoss]}).round(3)\n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best parameters:  {'C': 0.1, 'penalty': 'l2'}\n",
      "----------------------------------------------------------------------\n",
      "nan (+/-nan) for {'C': 0.1, 'penalty': 'l1'}\n",
      "0.775 (+/-0.024) for {'C': 0.1, 'penalty': 'l2'}\n",
      "nan (+/-nan) for {'C': 1, 'penalty': 'l1'}\n",
      "0.775 (+/-0.025) for {'C': 1, 'penalty': 'l2'}\n",
      "\n",
      "mean of roc_auc scores during validation: nan\n",
      "----------------------------------------------------------------------\n",
      "best_model:  LogisticRegression(C=0.1, class_weight={0: 0.01, 1: 0.99}, dual=False,\n",
      "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
      "                   max_iter=100, multi_class='auto', n_jobs=-1, penalty='l2',\n",
      "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "# #Logistic Regression\n",
    "# lg_clf = LogisticRegression(n_jobs = -1  , class_weight= {0 : 0.01, 1: 0.99})\n",
    "# param_grid_lg = [{\"penalty\" :['l1', 'l2'] , \"C\" :[ 0.1, 1 ]}]\n",
    "# lg_model = GridSearch_func(X = X_train, y= np.ravel(y_train),\n",
    "#                            estimator = lg_clf, param_grid = param_grid_lg, cv= 5  , scoring = \"roc_auc\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Classifier\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      "[[15367  1717]\n",
      " [   46    65]]\n",
      "-------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.90      0.95     17084\n",
      "           1       0.04      0.59      0.07       111\n",
      "\n",
      "    accuracy                           0.90     17195\n",
      "   macro avg       0.52      0.74      0.51     17195\n",
      "weighted avg       0.99      0.90      0.94     17195\n",
      "\n",
      "-------------------------\n",
      "\n",
      "ROC AUC score: 0.785\n",
      "f1 score: 0.069\n",
      "Accuracy: 0.897\n",
      "Kappa: 0.057\n",
      "Log loss: 0.416\n"
     ]
    }
   ],
   "source": [
    "# print(\"Logistic Regression Classifier\" )\n",
    "# print()\n",
    "# log_reg_metrics = metrics(lg_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "\n",
      "[[17065    19]\n",
      " [  108     3]]\n",
      "-------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00     17084\n",
      "           1       0.14      0.03      0.05       111\n",
      "\n",
      "    accuracy                           0.99     17195\n",
      "   macro avg       0.57      0.51      0.52     17195\n",
      "weighted avg       0.99      0.99      0.99     17195\n",
      "\n",
      "-------------------------\n",
      "\n",
      "ROC AUC score: 0.777\n",
      "f1 score: 0.045\n",
      "Accuracy: 0.993\n",
      "Kappa: 0.043\n",
      "Log loss: 0.041\n"
     ]
    }
   ],
   "source": [
    "#Baseline\n",
    "clf = LDA()\n",
    "clf.fit(X_train , y_train)\n",
    "base_metrics = metrics(clf, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled Using <class 'imblearn.combine._smote_enn.SMOTEENN'>\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      "[[11198  5886]\n",
      " [   29    82]]\n",
      "-------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.66      0.79     17084\n",
      "           1       0.01      0.74      0.03       111\n",
      "\n",
      "    accuracy                           0.66     17195\n",
      "   macro avg       0.51      0.70      0.41     17195\n",
      "weighted avg       0.99      0.66      0.79     17195\n",
      "\n",
      "-------------------------\n",
      "\n",
      "ROC AUC score: 0.787\n",
      "f1 score: 0.027\n",
      "Accuracy: 0.656\n",
      "Kappa: 0.014\n",
      "Log loss: 0.604\n",
      "**************************\n",
      "Resampled Using <class 'imblearn.combine._smote_tomek.SMOTETomek'>\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      "[[12023  5061]\n",
      " [   29    82]]\n",
      "-------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.70      0.83     17084\n",
      "           1       0.02      0.74      0.03       111\n",
      "\n",
      "    accuracy                           0.70     17195\n",
      "   macro avg       0.51      0.72      0.43     17195\n",
      "weighted avg       0.99      0.70      0.82     17195\n",
      "\n",
      "-------------------------\n",
      "\n",
      "ROC AUC score: 0.785\n",
      "f1 score: 0.031\n",
      "Accuracy: 0.704\n",
      "Kappa: 0.019\n",
      "Log loss: 0.570\n",
      "**************************\n",
      "Resampled Using <class 'imblearn.over_sampling._smote.BorderlineSMOTE'>\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      "[[15109  1975]\n",
      " [   44    67]]\n",
      "-------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.88      0.94     17084\n",
      "           1       0.03      0.60      0.06       111\n",
      "\n",
      "    accuracy                           0.88     17195\n",
      "   macro avg       0.51      0.74      0.50     17195\n",
      "weighted avg       0.99      0.88      0.93     17195\n",
      "\n",
      "-------------------------\n",
      "\n",
      "ROC AUC score: 0.791\n",
      "f1 score: 0.062\n",
      "Accuracy: 0.883\n",
      "Kappa: 0.051\n",
      "Log loss: 0.399\n",
      "**************************\n",
      "Resampled Using <class 'imblearn.over_sampling._adasyn.ADASYN'>\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      "[[11808  5276]\n",
      " [   29    82]]\n",
      "-------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.69      0.82     17084\n",
      "           1       0.02      0.74      0.03       111\n",
      "\n",
      "    accuracy                           0.69     17195\n",
      "   macro avg       0.51      0.71      0.42     17195\n",
      "weighted avg       0.99      0.69      0.81     17195\n",
      "\n",
      "-------------------------\n",
      "\n",
      "ROC AUC score: 0.785\n",
      "f1 score: 0.030\n",
      "Accuracy: 0.691\n",
      "Kappa: 0.018\n",
      "Log loss: 0.578\n",
      "**************************\n"
     ]
    }
   ],
   "source": [
    "x_clfs = []\n",
    "for resampling in [SMOTEENN, SMOTETomek, BorderlineSMOTE, ADASYN]:\n",
    "    method = resampling(random_state=42)\n",
    "    X_resampled, y_resampled = method.fit_resample(X_train, y_train)\n",
    "    clf = LDA()\n",
    "    #print(sorted(Counter(y_resampled).items()))\n",
    "    x_clf = clf.fit_transform(X_resampled, y_resampled)\n",
    "    x_clfs.append(x_clf)\n",
    "    print('Resampled Using ' + str(resampling))\n",
    "    print('')\n",
    "    clf_metrics = metrics(clf, X_test, y_test)\n",
    "    \n",
    "    print('**************************')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('LD1')\n",
    "plt.scatter(\n",
    "    x_clfs[3],\n",
    "    np.zeros(len(x_clfs[3])),\n",
    "    c=y_resampled,\n",
    "    cmap='rainbow',\n",
    "    alpha=0.7,\n",
    "    edgecolors='b'\n",
    ")\n",
    "#plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2349a6f7438>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 443), (1, 443)]\n",
      "Confusion Matrix:\n",
      "\n",
      "[[11787  5297]\n",
      " [   29    82]]\n",
      "-------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.69      0.82     17084\n",
      "           1       0.02      0.74      0.03       111\n",
      "\n",
      "    accuracy                           0.69     17195\n",
      "   macro avg       0.51      0.71      0.42     17195\n",
      "weighted avg       0.99      0.69      0.81     17195\n",
      "\n",
      "-------------------------\n",
      "\n",
      "ROC AUC score: 0.783\n",
      "f1 score: 0.030\n",
      "Accuracy: 0.690\n",
      "Kappa: 0.017\n",
      "Log loss: 0.589\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "X_resampled, y_resampled = rus.fit_resample(X_train, y_train)\n",
    "print(sorted(Counter(y_resampled).items()))\n",
    "\n",
    "\n",
    "clf = LDA()\n",
    "clf.fit(X_resampled, y_resampled)\n",
    "lda_reg_metrics = metrics(clf, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "\n",
      "[[ 1996 15088]\n",
      " [   12    99]]\n",
      "-------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.12      0.21     17084\n",
      "           1       0.01      0.89      0.01       111\n",
      "\n",
      "    accuracy                           0.12     17195\n",
      "   macro avg       0.50      0.50      0.11     17195\n",
      "weighted avg       0.99      0.12      0.21     17195\n",
      "\n",
      "-------------------------\n",
      "\n",
      "ROC AUC score: 0.724\n",
      "f1 score: 0.013\n",
      "Accuracy: 0.122\n",
      "Kappa: 0.000\n",
      "Log loss: 10.121\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n",
    "\n",
    "clf = QDA()\n",
    "clf.fit(X_resampled, y_resampled)\n",
    "qda_reg_metrics = metrics(clf, X_test, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}