1. Adaline Perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use(r'C:\\Users\\Will\\.matplotlib\\stylelib\\test.mplstyle')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class AdalineGD(object):\n",
    "    \n",
    "    def __init__(self, eta=0.01, n_epochs=10, printing=False):\n",
    "        \n",
    "        \"\"\"Initializes Adaline Perceptron class with learning rate and number of iterations\n",
    "        \"\"\"\n",
    "        self.eta = eta  #Initialize learning rate\n",
    "        self.n_epochs = n_epochs #Initialize number of epochs\n",
    "        self.printing = printing #Initialize printing options\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "    \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 and n_features is the number of features.\n",
    "        \n",
    "        y : array-like, shape = [n_samples]\n",
    "          :Target values\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "        \"\"\"\n",
    "        \n",
    "        self.w_ = np.zeros(1 + X.shape[1])  #initialize the weights ('w_') to zero\n",
    "        self.cost_ = []  #for storing cost of each epoch\n",
    "        self.score_ = [] #for storing score of each epoch\n",
    "        \n",
    "        #For each epoch...\n",
    "        for i in range(1,n_epochs+1):\n",
    "            output = self.net_input(X) #Output is net input of X and w_\n",
    "            errors = (y - output) #Error is the difference between actual classes y and output (w.T * x)  #NOTE ORDER?\n",
    "\n",
    "            cost = (errors**2).sum() / 2  #Objective cost function J(x): Sum-Squared Error \n",
    "            self.cost_.append(cost) #append epoch cost to cost_\n",
    "\n",
    "            #Update weights with Gradient Descent\n",
    "            gradient = self.gradient(X,errors) #Calculate gradient vector; vector of partial derivative of J(w) with respect to each weight\n",
    "            self.w_[1:] += self.eta * gradient #Update feature weights\n",
    "            self.w_[0] += self.eta * errors.sum() #Update model bias\n",
    "            \n",
    "            #Score model after epoch\n",
    "            score = self.score(X, y)\n",
    "            self.score_.append(score) #record epoch score\n",
    "\n",
    "            #If printing=True, print results for each epoch\n",
    "            if self.printing:\n",
    "                print 'Epoch', i, score, self.w_, cost\n",
    "\n",
    "        return self\n",
    "    \n",
    "    def net_input(self, X):\n",
    "        \"\"\"Calculate net input\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0] #Dot product of the training set and fitted weights, plus fitted weight-zero\n",
    "    \n",
    "    def gradient(self, X, errors):\n",
    "        \"\"\"Compute the gradient vector; vector of partial derivative of J(w) with respect to each weight\"\"\"\n",
    "        return X.T.dot(errors)\n",
    "    \n",
    "    def activation(self, X):\n",
    "        \"\"\"Compute linear activation\"\"\"\n",
    "        return self.net_input(X)\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Return class label after unit step\"\"\"\n",
    "        return np.where(self.activation(X) >= 0.0, 1, -1)\n",
    "        \n",
    "    def score(self, X, y):\n",
    "        \"\"\"Score the model --> Actual vs Predicted Classes\"\"\"\n",
    "        predicts = self.predict(X)\n",
    "        results = np.equal(predicts,y)\n",
    "        correct = np.sum(results)\n",
    "        score = float(correct) / float(len(results))\n",
    "        return score          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AdalineSGD(object):\n",
    "    \n",
    "    def __init__(self, eta=0.01, n_epochs=10, _shuffle=True, printing=False):\n",
    "        \n",
    "        \"\"\"Initializes Adaline Perceptron class with learning rate and number of iterations\n",
    "        \"\"\"\n",
    "        self.eta = eta  #Initialize learning rate\n",
    "        self.n_epochs = n_epochs #Initialize number of epochs\n",
    "        self.shuffle = _shuffle #Initialize shuffle\n",
    "        self.printing = printing #Initialize printing options\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "    \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 and n_features is the number of features.\n",
    "        \n",
    "        y : array-like, shape = [n_samples]\n",
    "          :Target values\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "        \"\"\"\n",
    "        \n",
    "        self.w_ = np.zeros(1 + X.shape[1])  #initialize the weights ('w_') to zero\n",
    "        self.cost_ = []  #for storing cost of each epoch\n",
    "        self.score_ = [] #for storing score of each epoch\n",
    "        \n",
    "        #For each epoch...\n",
    "        for i in range(1,n_epochs+1):\n",
    "            \n",
    "            sample_cost = [] #for storing cost of sample instance\n",
    "            \n",
    "            #SGD requires that the training data is shuffled\n",
    "            if self._shuffle:\n",
    "                X, y = self._shuffle(X, y)            \n",
    "            \n",
    "            #For each sample instance xi...    Stochastic Gradient Descent runs model/updates weights for every sample instance xi\n",
    "            for xi, yi in zip(X,y):\n",
    "                \n",
    "                #Compute output and error for xi\n",
    "                output = self.net_input(xi) #Output is net input of xi and w_\n",
    "                errors = (yi - output) #Error is the difference between actual class yi and predicted class of xi (w.T * x)  #NOTE ORDER?\n",
    "\n",
    "                #Compute cost of sample xi\n",
    "                cost = (errors**2).sum() / 2  #Objective cost function J(x): Sum-Squared Error \n",
    "                sample_cost.append(cost) #append sample cost to sample_cost\n",
    "\n",
    "                #Update weights with Gradient Descent\n",
    "                gradient = self.gradient(xi,errors) #Calculate gradient vector; vector of partial derivative of J(w) with respect to each weight\n",
    "                self.w_[1:] += self.eta * gradient #Update feature weights\n",
    "                self.w_[0] += self.eta * errors.sum() #Update model bias\n",
    "\n",
    "            #Calculate average cost of epoch\n",
    "            avg_cost = sum(sample_cost)/len(sample_cost)\n",
    "            self.cost_.append(avg_cost)\n",
    "            \n",
    "            #Score model after epoch\n",
    "            score = self.score(X, y)\n",
    "            self.score_.append(score) #record epoch score\n",
    "\n",
    "            #If printing=True, print results for each epoch\n",
    "            if self.printing:\n",
    "                print 'Epoch', i, score, self.w_, cost\n",
    "\n",
    "        return self\n",
    "    \n",
    "    def net_input(self, X):\n",
    "        \"\"\"Calculate net input\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0] #Dot product of the training set and fitted weights, plus fitted weight-zero\n",
    "    \n",
    "    def gradient(self, X, errors):\n",
    "        \"\"\"Compute the gradient vector; vector of partial derivative of J(w) with respect to each weight\"\"\"\n",
    "        return X.T.dot(errors)\n",
    "    \n",
    "    def activation(self, X):\n",
    "        \"\"\"Compute linear activation\"\"\"\n",
    "        return self.net_input(X)\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Return class label after unit step\"\"\"\n",
    "        return np.where(self.activation(X) >= 0.0, 1, -1)\n",
    "        \n",
    "    def score(self, X, y):\n",
    "        \"\"\"Score the model --> Actual vs Predicted Classes\"\"\"\n",
    "        predicts = self.predict(X)\n",
    "        results = np.equal(predicts,y)\n",
    "        correct = np.sum(results)\n",
    "        score = float(correct) / float(len(results))\n",
    "        return score          \n",
    "    \n",
    "    def _shuffle(self,X,y):\n",
    "        \"Shuffles the data\"\n",
    "        r = np.random.permutation(len(y))\n",
    "        return X[r], y[r]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and Preview the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Iris dataset\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)\n",
    "\n",
    "#Reduce dataset to two classes: Iris-setosa and Iris-versicolor\n",
    "data = df[(df[4] == 'Iris-setosa') |(df[4] == 'Iris-versicolor')]\n",
    "\n",
    "#Reduce dataset to two features and one target: sepal/petal length measurements and species\n",
    "data = data[[0,2,4]]\n",
    "\n",
    "#Define feature set\n",
    "X = data[[0,2]].values\n",
    "\n",
    "#Define target set\n",
    "y = data[4].values\n",
    "\n",
    "#Replace species name with numerical variable\n",
    "y = np.where(y == 'Iris-setosa',-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_epochs = 15\n",
    "\n",
    "ada = AdalineGD(n_epochs=n_epochs, eta=0.0001, printing=False).fit(X,y)\n",
    "\n",
    "#n_epochs = np.argmax(ada.score_)  #Epoch number that produced the max score\n",
    "\n",
    "ada1 = AdalineSGD(n_epochs=n_epochs, eta=0.01, _shuffle=True, printing=False).fit(X,y) #Run again, stopping at 'optimal' n_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Gjmab2bNm1ip522ZWz8weMbOFZrbCzD40s+1S1Et5TqaZ1TWzW83sp2i43jwze8zMmiTV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\nZmZmZpPGSaaZmZmZmZlNmv8PSwbl+TnwkMYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8a86d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2,figsize=(15,10))\n",
    "\n",
    "ax1.plot(range(1, len(ada.score_) + 1), ada.score_)\n",
    "ax1.set_xlabel('Epochs')\n",
    "ax1.set_ylabel('Gradient Descent\\neta=0.0001\\nAccuracy ')\n",
    "ax1.set_title('')\n",
    "\n",
    "ax2.plot(range(1, len(ada.cost_) + 1), ada.cost_)\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax2.set_ylabel('Sum-squared-error')\n",
    "\n",
    "ax3.plot(range(1, len(ada1.score_) + 1), ada1.score_)\n",
    "ax3.set_xlabel('Epochs')\n",
    "ax3.set_ylabel('Stochastic Gradient Descent\\neta=0.001\\nAccuracy')\n",
    "\n",
    "ax4.plot(range(1, len(ada1.cost_) + 1), ada1.cost_)\n",
    "ax4.set_xlabel('Epochs')\n",
    "ax4.set_ylabel('Sum-squared-error')\n",
    "\n",
    "sup = fig.suptitle('Adaptive Linear Perceptron\\nGradient Descent // Stochastic Gradient Descent', fontsize=\"xx-large\")\n",
    "\n",
    "fig.savefig(r'C:\\Users\\Will\\Desktop\\GD v SDG.png')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}