Perceptron Learning Algorithsm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load PerceptronLearning.py\n",
    "# define a class named Perceptron, which has a \"fit\" method, and \"predict\" method.\n",
    "import numpy as np\n",
    "\n",
    "class Perceptron(object):\n",
    "    \"\"\"Perceptron classifier.\n",
    "\n",
    "    Parameters\n",
    "    -------------\n",
    "    eta : float\n",
    "      Learning rate (between 0 and 1)\n",
    "    n_iter : int\n",
    "      Passes over the training dataset\n",
    "    random_state : int\n",
    "      Random number generator seed for random weight in the beginning\n",
    "\n",
    "    Attributes\n",
    "    -------------\n",
    "    w_ : 1d-array\n",
    "      Weights after fitting\n",
    "    errors_ : list\n",
    "      Number of misclassifications(updates) in each epoch\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, eta = 0.01, 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_samples, n_features]\n",
    "            Training vectors, where n_samples is the number of samples(rows) and\n",
    "            n_features is the number of features(columns)\n",
    "        y : array-like, shape = [n_samples]\n",
    "            Traget values\n",
    "\n",
    "        Returns\n",
    "        ______________\n",
    "        self : object\n",
    "        \"\"\"\n",
    "        rgen = np.random.RandomState(self.random_state)\n",
    "        self.w_ = rgen.normal(loc = 0.0, scale = 0.01, size = 1 + x.shape[1])\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_[1:] += update*xi\n",
    "                self.w_[0] += update\n",
    "                errors += int(update != 0.0)\n",
    "            self.errors_.append(errors)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, x):\n",
    "        return np.dot(x, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def predict(self, x):\n",
    "        return np.where(self.net_input(x) >= 0.0, 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3               4\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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header = None)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extract the first 100 class labels (50 setosa and 50 versicolor), convert the class label into the two integer class labels 1(versicolor) and -1(setosa), and assign to a vector y.\n",
    "Similarly, extrac the first feature column(sepal length) and the third feature column(petal length) of those 100 training samples and assign them to a feature matrix X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select setosa (column 1) and versicolor (column 3)\n",
    "y = df.iloc[0:100, 4].values\n",
    "y = np.where(y == 'Iris-setosa', -1, 1)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1 1.4]\n",
      " [4.9 1.4]\n",
      " [4.7 1.3]]\n"
     ]
    }
   ],
   "source": [
    "# extract sepal length and petal length\n",
    "X = df.iloc[0:100, [0, 2]].values\n",
    "print(X[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot data\n",
    "plt.scatter(X[:50, 0], X[:50, 1], color = 'red', marker = 'o', label = 'setosa')\n",
    "\n",
    "plt.scatter(X[50:100, 0], X[50:100, 1], color = 'blue', marker = 'x', label = 'versicolor')\n",
    "\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.ylabel('sepal lenght [cm]')\n",
    "plt.legend(loc = 'upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's train our perceptron algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
    "plt.plot(range(1, len(ppn.errors_)+1),\n",
    "        ppn.errors_, marker = 'o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel(\"Number of updates\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization for decision boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "def plot_decision_region(X, y, classifier, resolution = 0.02):\n",
    "    # setup marker generator and color map\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "    \n",
    "    # plot the decision surface\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",
    "    z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    z = z.reshape(xx1.shape)\n",
    "    \n",
    "    plt.contourf(xx1, xx2, z, alpha = 0.3, cmap = cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "    \n",
    "    # plot class samples\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 = cl,\n",
    "                    edgecolor = 'black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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_region(X, y, classifier = ppn)\n",
    "plt.xlabel('Sepal Length')\n",
    "plt.ylabel('Petal Length')\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}