Day_2_training_a_perceptron_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "964aa3d9",
   "metadata": {},
   "source": [
    "# Day 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b15a656",
   "metadata": {},
   "source": [
    "## Training a perceptron model on the Iris dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0bfdb95",
   "metadata": {},
   "source": [
    "### Import the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f56e5c8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From URL: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n",
      "       0    1    2    3                4\n",
      "90   5.5  2.6  4.4  1.2  Iris-versicolor\n",
      "91   6.1  3.0  4.6  1.4  Iris-versicolor\n",
      "92   5.8  2.6  4.0  1.2  Iris-versicolor\n",
      "93   5.0  2.3  3.3  1.0  Iris-versicolor\n",
      "94   5.6  2.7  4.2  1.3  Iris-versicolor\n",
      "95   5.7  3.0  4.2  1.2  Iris-versicolor\n",
      "96   5.7  2.9  4.2  1.3  Iris-versicolor\n",
      "97   6.2  2.9  4.3  1.3  Iris-versicolor\n",
      "98   5.1  2.5  3.0  1.1  Iris-versicolor\n",
      "99   5.7  2.8  4.1  1.3  Iris-versicolor\n",
      "100  6.3  3.3  6.0  2.5   Iris-virginica\n",
      "101  5.8  2.7  5.1  1.9   Iris-virginica\n",
      "102  7.1  3.0  5.9  2.1   Iris-virginica\n",
      "103  6.3  2.9  5.6  1.8   Iris-virginica\n",
      "104  6.5  3.0  5.8  2.2   Iris-virginica\n",
      "105  7.6  3.0  6.6  2.1   Iris-virginica\n",
      "106  4.9  2.5  4.5  1.7   Iris-virginica\n",
      "107  7.3  2.9  6.3  1.8   Iris-virginica\n",
      "108  6.7  2.5  5.8  1.8   Iris-virginica\n",
      "109  7.2  3.6  6.1  2.5   Iris-virginica\n",
      "110  6.5  3.2  5.1  2.0   Iris-virginica\n",
      "111  6.4  2.7  5.3  1.9   Iris-virginica\n",
      "112  6.8  3.0  5.5  2.1   Iris-virginica\n",
      "113  5.7  2.5  5.0  2.0   Iris-virginica\n",
      "114  5.8  2.8  5.1  2.4   Iris-virginica\n",
      "115  6.4  3.2  5.3  2.3   Iris-virginica\n",
      "116  6.5  3.0  5.5  1.8   Iris-virginica\n",
      "117  7.7  3.8  6.7  2.2   Iris-virginica\n",
      "118  7.7  2.6  6.9  2.3   Iris-virginica\n",
      "119  6.0  2.2  5.0  1.5   Iris-virginica\n",
      "120  6.9  3.2  5.7  2.3   Iris-virginica\n",
      "121  5.6  2.8  4.9  2.0   Iris-virginica\n",
      "122  7.7  2.8  6.7  2.0   Iris-virginica\n",
      "123  6.3  2.7  4.9  1.8   Iris-virginica\n",
      "124  6.7  3.3  5.7  2.1   Iris-virginica\n",
      "125  7.2  3.2  6.0  1.8   Iris-virginica\n",
      "126  6.2  2.8  4.8  1.8   Iris-virginica\n",
      "127  6.1  3.0  4.9  1.8   Iris-virginica\n",
      "128  6.4  2.8  5.6  2.1   Iris-virginica\n",
      "129  7.2  3.0  5.8  1.6   Iris-virginica\n",
      "130  7.4  2.8  6.1  1.9   Iris-virginica\n",
      "131  7.9  3.8  6.4  2.0   Iris-virginica\n",
      "132  6.4  2.8  5.6  2.2   Iris-virginica\n",
      "133  6.3  2.8  5.1  1.5   Iris-virginica\n",
      "134  6.1  2.6  5.6  1.4   Iris-virginica\n",
      "135  7.7  3.0  6.1  2.3   Iris-virginica\n",
      "136  6.3  3.4  5.6  2.4   Iris-virginica\n",
      "137  6.4  3.1  5.5  1.8   Iris-virginica\n",
      "138  6.0  3.0  4.8  1.8   Iris-virginica\n",
      "139  6.9  3.1  5.4  2.1   Iris-virginica\n",
      "140  6.7  3.1  5.6  2.4   Iris-virginica\n",
      "141  6.9  3.1  5.1  2.3   Iris-virginica\n",
      "142  5.8  2.7  5.1  1.9   Iris-virginica\n",
      "143  6.8  3.2  5.9  2.3   Iris-virginica\n",
      "144  6.7  3.3  5.7  2.5   Iris-virginica\n",
      "145  6.7  3.0  5.2  2.3   Iris-virginica\n",
      "146  6.3  2.5  5.0  1.9   Iris-virginica\n",
      "147  6.5  3.0  5.2  2.0   Iris-virginica\n",
      "148  6.2  3.4  5.4  2.3   Iris-virginica\n",
      "149  5.9  3.0  5.1  1.8   Iris-virginica\n",
      "      0    1    2    3                4\n",
      "0   5.1  3.5  1.4  0.2      Iris-setosa\n",
      "1   4.9  3.0  1.4  0.2      Iris-setosa\n",
      "2   4.7  3.2  1.3  0.2      Iris-setosa\n",
      "3   4.6  3.1  1.5  0.2      Iris-setosa\n",
      "4   5.0  3.6  1.4  0.2      Iris-setosa\n",
      "5   5.4  3.9  1.7  0.4      Iris-setosa\n",
      "6   4.6  3.4  1.4  0.3      Iris-setosa\n",
      "7   5.0  3.4  1.5  0.2      Iris-setosa\n",
      "8   4.4  2.9  1.4  0.2      Iris-setosa\n",
      "9   4.9  3.1  1.5  0.1      Iris-setosa\n",
      "10  5.4  3.7  1.5  0.2      Iris-setosa\n",
      "11  4.8  3.4  1.6  0.2      Iris-setosa\n",
      "12  4.8  3.0  1.4  0.1      Iris-setosa\n",
      "13  4.3  3.0  1.1  0.1      Iris-setosa\n",
      "14  5.8  4.0  1.2  0.2      Iris-setosa\n",
      "15  5.7  4.4  1.5  0.4      Iris-setosa\n",
      "16  5.4  3.9  1.3  0.4      Iris-setosa\n",
      "17  5.1  3.5  1.4  0.3      Iris-setosa\n",
      "18  5.7  3.8  1.7  0.3      Iris-setosa\n",
      "19  5.1  3.8  1.5  0.3      Iris-setosa\n",
      "20  5.4  3.4  1.7  0.2      Iris-setosa\n",
      "21  5.1  3.7  1.5  0.4      Iris-setosa\n",
      "22  4.6  3.6  1.0  0.2      Iris-setosa\n",
      "23  5.1  3.3  1.7  0.5      Iris-setosa\n",
      "24  4.8  3.4  1.9  0.2      Iris-setosa\n",
      "25  5.0  3.0  1.6  0.2      Iris-setosa\n",
      "26  5.0  3.4  1.6  0.4      Iris-setosa\n",
      "27  5.2  3.5  1.5  0.2      Iris-setosa\n",
      "28  5.2  3.4  1.4  0.2      Iris-setosa\n",
      "29  4.7  3.2  1.6  0.2      Iris-setosa\n",
      "30  4.8  3.1  1.6  0.2      Iris-setosa\n",
      "31  5.4  3.4  1.5  0.4      Iris-setosa\n",
      "32  5.2  4.1  1.5  0.1      Iris-setosa\n",
      "33  5.5  4.2  1.4  0.2      Iris-setosa\n",
      "34  4.9  3.1  1.5  0.1      Iris-setosa\n",
      "35  5.0  3.2  1.2  0.2      Iris-setosa\n",
      "36  5.5  3.5  1.3  0.2      Iris-setosa\n",
      "37  4.9  3.1  1.5  0.1      Iris-setosa\n",
      "38  4.4  3.0  1.3  0.2      Iris-setosa\n",
      "39  5.1  3.4  1.5  0.2      Iris-setosa\n",
      "40  5.0  3.5  1.3  0.3      Iris-setosa\n",
      "41  4.5  2.3  1.3  0.3      Iris-setosa\n",
      "42  4.4  3.2  1.3  0.2      Iris-setosa\n",
      "43  5.0  3.5  1.6  0.6      Iris-setosa\n",
      "44  5.1  3.8  1.9  0.4      Iris-setosa\n",
      "45  4.8  3.0  1.4  0.3      Iris-setosa\n",
      "46  5.1  3.8  1.6  0.2      Iris-setosa\n",
      "47  4.6  3.2  1.4  0.2      Iris-setosa\n",
      "48  5.3  3.7  1.5  0.2      Iris-setosa\n",
      "49  5.0  3.3  1.4  0.2      Iris-setosa\n",
      "50  7.0  3.2  4.7  1.4  Iris-versicolor\n",
      "51  6.4  3.2  4.5  1.5  Iris-versicolor\n",
      "52  6.9  3.1  4.9  1.5  Iris-versicolor\n",
      "53  5.5  2.3  4.0  1.3  Iris-versicolor\n",
      "54  6.5  2.8  4.6  1.5  Iris-versicolor\n",
      "55  5.7  2.8  4.5  1.3  Iris-versicolor\n",
      "56  6.3  3.3  4.7  1.6  Iris-versicolor\n",
      "57  4.9  2.4  3.3  1.0  Iris-versicolor\n",
      "58  6.6  2.9  4.6  1.3  Iris-versicolor\n",
      "59  5.2  2.7  3.9  1.4  Iris-versicolor\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "s = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"''\n",
    "print('From URL:',s)\n",
    "df = pd.read_csv(s, header=None, encoding='utf-8')\n",
    "\n",
    "print(df.tail(60))\n",
    "print(df.head(60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5abf257d",
   "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# we want to select setosa and versicolor in the target\n",
    "y = df.iloc[0:100,4].values\n",
    "y = np.where(y == 'Iris-setosa', 0, 1)\n",
    "\n",
    "# only using sepal length and petal length features\n",
    "X = df.iloc[0:100,[0,2]].values\n",
    "\n",
    "# plotting data\n",
    "\n",
    "plt.scatter(X[:50,0], X[:50,1], color='red', marker='s', label='setosa')\n",
    "plt.scatter(X[50:100,0], X[50:100,1], color='green', marker='o', label='versicolor')\n",
    "\n",
    "plt.xlabel('Sepal length [cm]')\n",
    "plt.ylabel('Petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34434054",
   "metadata": {},
   "source": [
    "### Training the perceptron model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "367db015",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "class Perceptron:\n",
    "    \"\"\"Perceptron classifier.\n",
    "\n",
    "    Parameters\n",
    "    ------------\n",
    "    eta : float\n",
    "      Learning rate (between 0.0 and 1.0)\n",
    "    n_iter : int\n",
    "      Passes over the training dataset.\n",
    "    random_state : int\n",
    "      Random number generator seed for random weight\n",
    "      initialization.\n",
    "\n",
    "    Attributes\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "      Weights after fitting.\n",
    "    b_ : Scalar\n",
    "      Bias unit after fitting.\n",
    "    errors_ : list\n",
    "      Number of misclassifications (updates) in each epoch.\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.05, n_iter=50, random_state=1):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "        self.random_state = random_state\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"Fit training data.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like}, shape = [n_examples, n_features]\n",
    "          Training vectors, where n_examples is the number of examples and\n",
    "          n_f\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        rgen = np.random.RandomState(self.random_state)\n",
    "        self.w_ = np.zeros(X.shape[1])\n",
    "        self.b_ = np.float_(0.)\n",
    "        \n",
    "        self.errors_ = []\n",
    "\n",
    "        for _ in range(self.n_iter):\n",
    "            errors = 0\n",
    "            for xi, target in zip(X, y):\n",
    "                update = self.eta * (target - self.predict(xi))\n",
    "                self.w_ += update * xi\n",
    "                self.b_ += update\n",
    "                errors += int(update != 0.0)\n",
    "            self.errors_.append(errors)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        \"\"\"Calculate net input\"\"\"\n",
    "        return np.dot(X, self.w_) + self.b_\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"Return class label after unit step\"\"\"\n",
    "        return np.where(self.net_input(X) >= 0.0, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a8dec1f",
   "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": [
    "ppn = Perceptron(eta=0.1, n_iter=10)\n",
    "ppn.fit(X,y)\n",
    "\n",
    "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Number of updates')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a47a9e4",
   "metadata": {},
   "source": [
    "### Function for plotting decision region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d853a65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_regions(X, y, classifier, resolution = 0.02):\n",
    "    \n",
    "    # setting up marker generator and color map\n",
    "    markers = ('o','s','^','v','<')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "    \n",
    "    # plotting the decision surface\n",
    "    \n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "                           np.arange(x2_min, x2_max, resolution))\n",
    "    lab = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    lab = lab.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, lab, alpha=0.3, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "\n",
    "    # plot class examples\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], \n",
    "                    y=X[y == cl, 1],\n",
    "                    alpha=0.8, \n",
    "                    c=colors[idx],\n",
    "                    marker=markers[idx], \n",
    "                    label=f'Class {cl}', \n",
    "                    edgecolor='black')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d2cbb3dd",
   "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": [
    "plot_decision_regions(X, y, classifier=ppn)\n",
    "plt.xlabel('Sepal length [cm]')\n",
    "plt.ylabel('Petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13a30106",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}