xgboost-classification-cancer-data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "breast_cancer_data = load_breast_cancer()\n",
    "data = breast_cancer_data.data\n",
    "target = breast_cancer_data.target\n",
    "\n",
    "data = pd.DataFrame(data, columns=breast_cancer_data.feature_names)\n",
    "X = data[:]\n",
    "data['target'] = target\n",
    "y = data['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "xb_cls = xb.XGBClassifier(learning_rate=0.1, n_estimators=100, objective='binary:logistic', random_state=101)\n",
    "trainX, testX, trainY, testY = train_test_split(X,y)\n",
    "\n",
    "model = xb_cls.fit(trainX, trainY)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23ecc115390>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "\n",
    "feature_imp = model.feature_importances_\n",
    "sb.barplot(breast_cancer_data.feature_names, feature_imp, orient='v')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.965034965034965\n",
      "[[44  1]\n",
      " [ 4 94]]\n"
     ]
    }
   ],
   "source": [
    "predictedY = model.predict(testX)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "acs = accuracy_score(testY, predictedY)\n",
    "print (acs)\n",
    "\n",
    "\n",
    "cm = confusion_matrix(testY, predictedY)\n",
    "print (cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\MachineLearning\\anaconda\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n",
      "C:\\MachineLearning\\anaconda\\lib\\site-packages\\xgboost\\core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    }
   ],
   "source": [
    "dMatrix = xb.DMatrix(data=X, label=y)\n",
    "params = {\"objective\":\"binary:logistic\"}\n",
    "\n",
    "cv_results = xb.cv(params=params, dtrain=dMatrix, nfold=5, num_boost_round=20, metrics='error', as_pandas=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9805016826879857\n",
      "0.9805016826879857\n"
     ]
    }
   ],
   "source": [
    "accuracy = 1 - cv_results['test-error-std']\n",
    "print (accuracy.iloc[-1])\n",
    "print (accuracy[19])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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