roc_auc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score\n",
    "from sklearn.metrics import f1_score, make_scorer, plot_roc_curve\n",
    "from sklearn.model_selection import cross_validate, StratifiedKFold, train_test_split\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_33192/464768573.py:7: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
      "  set_matplotlib_formats('svg')\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import set_matplotlib_formats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# STYLE\n",
    "plt.style.use('seaborn-ticks')\n",
    "# SVG\n",
    "set_matplotlib_formats('svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.load('data/data_2/X.npy').astype(np.int8)\n",
    "y = np.load('data/data_2/y.npy').astype(np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2], dtype=int8), array([305, 923]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = LogisticRegression(penalty='l2',\n",
    "                         C=0.01,\n",
    "                         class_weight='balanced',\n",
    "                         n_jobs=-1)\n",
    "\n",
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "roc_curves = []\n",
    "for train_index, val_index in skf.split(X, y):\n",
    "    X_train, X_val = X[train_index], X[val_index]\n",
    "    y_train, y_val = y[train_index], y[val_index]\n",
    "\n",
    "    clf.fit(X_train, y_train)\n",
    "    ax = plt.gca()\n",
    "    roc_curves.append(plot_roc_curve(clf, X_val, y_val, ax=ax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for roc_curve in roc_curves:\n",
    "    ax = plt.gca()\n",
    "    roc_curve.plot(ax=ax)\n",
    "plt.plot(np.linspace(0, 1), np.linspace(0, 1), label='Chance', linestyle='--', c='black')\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.where(y==1, 0, 1)\n",
    "np.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[05:03:57] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[05:06:27] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[05:09:00] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[05:11:33] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[05:14:07] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "                    colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
    "                    learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
    "                    min_child_weight=10, missing=None, n_estimators=100, n_jobs=1,\n",
    "                    nthread=None, objective='binary:logistic',\n",
    "                    predictor='gpu_predictor', random_state=0, reg_alpha=0,\n",
    "                    reg_lambda=1, scale_pos_weight=0.3, seed=None, silent=None,\n",
    "                    subsample=1.0, use_label_encoder=False, verbosity=1)\n",
    "\n",
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "roc_curves = []\n",
    "for train_index, val_index in skf.split(X, y):\n",
    "    X_train, X_val = X[train_index], X[val_index]\n",
    "    y_train, y_val = y[train_index], y[val_index]\n",
    "\n",
    "    clf.fit(X_train, y_train)\n",
    "    ax = plt.gca()\n",
    "    roc_curves.append(plot_roc_curve(clf, X_val, y_val, ax=ax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for roc_curve in roc_curves:\n",
    "    ax = plt.gca()\n",
    "    roc_curve.plot(ax=ax)\n",
    "plt.plot(np.linspace(0, 1), np.linspace(0, 1), label='Chance', linestyle='--', c='black')\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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