Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_dataset(filePath,delimiter=','):\n",
    "    return pd.read_csv(filePath)\n",
    "\n",
    "def append_bias_reshape(features,labels):\n",
    "    n_training_samples = features.shape[0]\n",
    "    n_dim = features.shape[1]\n",
    "    f = np.reshape(np.c_[np.ones(n_training_samples),features],[n_training_samples,n_dim + 1])\n",
    "    l = np.asarray(labels)\n",
    "    return f, l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = read_dataset('iris.csv')\n",
    "features = data.ix[:,1:-1]\n",
    "labels = pd.get_dummies(data.ix[:,-1])\n",
    "\n",
    "f, l = append_bias_reshape(features,labels)\n",
    "n_dim = f.shape[1]\n",
    "\n",
    "rnd_indices = np.random.rand(len(f)) < 0.75\n",
    "\n",
    "train_x = f[rnd_indices]\n",
    "train_y = l[rnd_indices]\n",
    "test_x = f[~rnd_indices]\n",
    "test_y = l[~rnd_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "learning_rate = 0.01\n",
    "training_epochs = 1000\n",
    "\n",
    "X = tf.placeholder(tf.float32,[None,n_dim])\n",
    "Y = tf.placeholder(tf.float32,[None,3])\n",
    "W = tf.Variable(tf.ones([n_dim,3]))\n",
    "\n",
    "init = tf.initialize_all_variables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_ = tf.nn.softmax(tf.matmul(X,W))\n",
    "cost_function = tf.reduce_mean(tf.reduce_sum((-Y * tf.log(y_)) - ((1 - Y) * tf.log(1 - y_)), reduction_indices=[1]))\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.948718\n",
      "[ 0.01        1.88266325  1.86100256 ...,  0.50961167  0.50936365\n",
      "  0.50911564]\n"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5df422c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cost_history = np.empty(shape=[1],dtype=float)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    for epoch in range(training_epochs):\n",
    "        sess.run(optimizer,feed_dict={X:train_x,Y:train_y})\n",
    "        cost_history = np.append(cost_history,sess.run(cost_function,feed_dict={X: train_x,Y: train_y}))\n",
    "    \n",
    "    y_pred = sess.run(y_ , feed_dict={X: test_x})\n",
    "    correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    print(sess.run(accuracy, feed_dict={X: test_x, Y: test_y}))\n",
    "    \n",
    "    #plot cost\n",
    "    plt.plot(cost_history)\n",
    "    plt.axis([0,training_epochs,0,np.max(cost_history)])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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