iris_lda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from sklearn import metrics\n",
    "import pandas as pd\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold(n_splits=5, random_state=3, shuffle=True)\n"
     ]
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "# Let us do cross validation on the dataset.\n",
    "\n",
    "X = iris.data\n",
    "# wonder the difference between X and df?\n",
    "df = pd.DataFrame(iris.data)\n",
    "y = iris.target\n",
    "#split the data into 5\n",
    "kf = KFold(n_splits=5, random_state=3, shuffle=True) \n",
    "kf.get_n_splits(X) # returns the number of splitting iterations in the cross-validator\n",
    "print(kf) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 2 2 2]\n",
      "[0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         1.         0.96666667 0.93333333 1.        ]\n"
     ]
    }
   ],
   "source": [
    "#read carefully what the following does. It is indexing.\n",
    "for train_index, test_index in kf.split(X):\n",
    "   #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "model = lda.fit(X_train,y_train)\n",
    "predictions = lda.predict(X_test)\n",
    "print(predictions)\n",
    "print(y_test)\n",
    "\n",
    "plt.plot(y_test, predictions)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "scores = cross_val_score(model, df, y, cv=5)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}