{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "467bd2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import sklearn\n",
    "from sklearn.datasets import make_classification\n",
    "from matplotlib import pyplot\n",
    "from numpy import where"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c48d903",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define dataset\n",
    "X, y = sklearn.datasets.make_classification(n_samples=10000, n_features=2, n_redundant=0,\n",
    " n_clusters_per_class=1, weights=[0.99], flip_y=0, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd33cae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 9900, 1: 100})\n"
     ]
    }
   ],
   "source": [
    "# summarize class distribution\n",
    "counter = Counter(y)\n",
    "print(counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abbb183d",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "for label, _ in counter.items():\n",
    " row_ix = where(y == label)[0]\n",
    " pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(label))\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be5b198d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fcce207d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the dataset\n",
    "oversample = SMOTE()\n",
    "X, y = oversample.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "11e0f081",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 9900, 1: 9900})\n"
     ]
    }
   ],
   "source": [
    "# summarize the new class distribution\n",
    "counter = Counter(y)\n",
    "print(counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "32ea9ffd",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# scatter plot of examples by class label\n",
    "for label, _ in counter.items():\n",
    " row_ix = where(y == label)[0]\n",
    " pyplot.scatter(X[row_ix, 0], X[row_ix, 1], label=str(label))\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df761cd0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
