Imbalanced_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://machinelearningmastery.com/cross-validation-for-imbalanced-classification/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> Class=0 : 990/1000 (99.0%)\n",
      "> Class=1 : 10/1000 (1.0%)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "# generate 2 class dataset\n",
    "X, y = make_classification(n_samples=1000, n_classes=2, weights=[0.99, 0.01], flip_y=0, random_state=1)\n",
    "# summarize dataset\n",
    "classes = np.unique(y)\n",
    "total = len(y)\n",
    "for c in classes:\n",
    "    n_examples = len(y[y==c])\n",
    "    percent = n_examples / total * 100\n",
    "    print('> Class=%d : %d/%d (%.1f%%)' % (c, n_examples, total, percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Fold "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-folds "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">Train: 0=791, 1=9, Test: 0=199, 1=1\n",
      ">Train: 0=793, 1=7, Test: 0=197, 1=3\n",
      ">Train: 0=794, 1=6, Test: 0=196, 1=4\n",
      ">Train: 0=790, 1=10, Test: 0=200, 1=0\n",
      ">Train: 0=792, 1=8, Test: 0=198, 1=2\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "kfold = KFold(n_splits=5, shuffle=True, random_state=1)\n",
    "# enumerate the splits and summarize the distributions\n",
    "for train_ix, test_ix in kfold.split(X):\n",
    "    # select rows\n",
    "    train_X, test_X = X[train_ix], X[test_ix]\n",
    "    train_y, test_y = y[train_ix], y[test_ix]\n",
    "    # summarize train and test composition\n",
    "    train_0, train_1 = len(train_y[train_y==0]), len(train_y[train_y==1])\n",
    "    test_0, test_1 = len(test_y[test_y==0]), len(test_y[test_y==1])\n",
    "    print('>Train: 0=%d, 1=%d, Test: 0=%d, 1=%d' % (train_0, train_1, test_0, test_1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">Train: 0=497, 1=3, Test: 0=493, 1=7\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# split into train/test sets with same class ratio\n",
    "trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2)\n",
    "# summarize\n",
    "train_0, train_1 = len(trainy[trainy==0]), len(trainy[trainy==1])\n",
    "test_0, test_1 = len(testy[testy==0]), len(testy[testy==1])\n",
    "print('>Train: 0=%d, 1=%d, Test: 0=%d, 1=%d' % (train_0, train_1, test_0, test_1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting (Stratified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">Train: 0=792, 1=8, Test: 0=198, 1=2\n",
      ">Train: 0=792, 1=8, Test: 0=198, 1=2\n",
      ">Train: 0=792, 1=8, Test: 0=198, 1=2\n",
      ">Train: 0=792, 1=8, Test: 0=198, 1=2\n",
      ">Train: 0=792, 1=8, Test: 0=198, 1=2\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)\n",
    "# enumerate the splits and summarize the distributions\n",
    "for train_ix, test_ix in kfold.split(X, y):\n",
    "    # select rows\n",
    "    train_X, test_X = X[train_ix], X[test_ix]\n",
    "    train_y, test_y = y[train_ix], y[test_ix]\n",
    "    # summarize train and test composition\n",
    "    train_0, train_1 = len(train_y[train_y==0]), len(train_y[train_y==1])\n",
    "    test_0, test_1 = len(test_y[test_y==0]), len(test_y[test_y==1])\n",
    "    print('>Train: 0=%d, 1=%d, Test: 0=%d, 1=%d' % (train_0, train_1, test_0, test_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">Train: 0=495, 1=5, Test: 0=495, 1=5\n"
     ]
    }
   ],
   "source": [
    "# split into train/test sets with same class ratio\n",
    "trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2, stratify=y)\n",
    "# summarize\n",
    "train_0, train_1 = len(trainy[trainy==0]), len(trainy[trainy==1])\n",
    "test_0, test_1 = len(testy[testy==0]), len(testy[testy==1])\n",
    "print('>Train: 0=%d, 1=%d, Test: 0=%d, 1=%d' % (train_0, train_1, test_0, test_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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