Split_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning for Engineers: [SplitData](https://www.apmonitor.com/pds/index.php/Main/SplitData)\n",
    "- [Split Data: Train, Validate, Test](https://www.apmonitor.com/pds/index.php/Main/SplitData)\n",
    " - Source Blocks: 5\n",
    " - Description: Splitting data ensures that there are independent sets for training, testing, and validation.\n",
    "- [Course Overview](https://apmonitor.com/pds)\n",
    "- [Course Schedule](https://apmonitor.com/pds/index.php/Main/CourseSchedule)\n",
    "\n",
    "<img align=left width=500px src='https://apmonitor.com/pds/uploads/Main/split_data.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  2880\n",
      "       Q1   Q2     T1     T2\n",
      "Time                        \n",
      "0.0   0.0  0.0  16.06  16.00\n",
      "1.0   0.0  0.0  16.06  15.97\n",
      "2.0   0.0  0.0  16.06  16.03\n",
      "3.0   0.0  0.0  16.03  16.00\n",
      "4.0   0.0  0.0  16.03  15.94\n",
      "Test:  721\n",
      "         Q1   Q2     T1     T2\n",
      "Time                          \n",
      "2880.0  0.0  0.0  59.25  27.02\n",
      "2881.0  0.0  0.0  59.22  27.02\n",
      "2882.0  0.0  0.0  58.99  27.02\n",
      "2883.0  0.0  0.0  58.93  27.02\n",
      "2884.0  0.0  0.0  58.93  27.02\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "data = pd.read_csv('http://apmonitor.com/pds/uploads/Main/tclab_data6.txt')\n",
    "data.set_index('Time',inplace=True)\n",
    "\n",
    "# Split into train and test subsets (20% for test)\n",
    "train, test = train_test_split(data, test_size=0.2, shuffle=False)\n",
    "\n",
    "print('Train: ', len(train))\n",
    "print(train.head())\n",
    "print('Test: ', len(test))\n",
    "print(test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000 4000 1000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "X, y = make_classification(n_samples=5000, n_features=20, n_informative=15)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, shuffle=True)\n",
    "\n",
    "print(len(X),len(X_train),len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 81.44%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.datasets import make_classification\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# define dataset\n",
    "X, y = make_classification(n_samples=5000, n_features=20, n_informative=15)\n",
    "\n",
    "# Set up K-fold cross validation\n",
    "kf = KFold(n_splits=5,shuffle=True)\n",
    "\n",
    "# Initialize model\n",
    "dtc = DecisionTreeClassifier()\n",
    "\n",
    "# Array to store accuracy scores\n",
    "scores = np.zeros(5)\n",
    "\n",
    "# Initialize plot\n",
    "plt.figure(figsize=(12,2))\n",
    "\n",
    "for i,(train_index, test_index) in enumerate(kf.split(X)):\n",
    "    Xtrain, Xtest = X[train_index], X[test_index]\n",
    "    ytrain, ytest = y[train_index], y[test_index]\n",
    "\n",
    "    dtc.fit(Xtrain,ytrain)\n",
    "    yp = dtc.predict(Xtest)\n",
    "    acc = accuracy_score(ytest,yp)\n",
    "    scores[i] = acc\n",
    "\n",
    "    plt.subplot(1,5,i+1)\n",
    "    cm = confusion_matrix(yp,ytest)\n",
    "    sns.heatmap(cm,annot=True)\n",
    "\n",
    "plt.show()\n",
    "print('Accuracy: %.2f%%' %(np.mean(scores*100)))"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}