car_popularity_logistic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import scipy\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1302 326\n"
     ]
    }
   ],
   "source": [
    "data = np.loadtxt(\"C:/Downloads/car popularity/train.csv\",skiprows=1,delimiter=',')\n",
    "train,test = train_test_split(data,test_size=0.20, shuffle=True)\n",
    "train_x = train[:, :-1].T\n",
    "train_y = np.where(train[:, -1] == 4, 1, 0)\n",
    "train_records=train_y.shape[0]\n",
    "train_y= train_y.reshape(1,train_records)\n",
    "\n",
    "test_x = test[:, :-1].T\n",
    "test_y = np.where(test[:, -1] == 4, 1, 0)\n",
    "test_records=test_y.shape[0]\n",
    "test_y= test_y.reshape(1,test_records)\n",
    "\n",
    "\n",
    "m_train = np.shape(train_x)[1]\n",
    "m_test = np.shape(test_y)[1]\n",
    "\n",
    "print(m_train,m_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "  \n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    s = 1/(1+np.exp(-z))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def initialize_with_zeros(dim):\n",
    "  \n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    w = np.zeros((dim,1))\n",
    "    b = 0\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(w.shape == (dim, 1))\n",
    "    assert(isinstance(b, float) or isinstance(b, int))\n",
    "    \n",
    "    return w, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def propagate(w, b, X, Y):\n",
    "      \n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # FORWARD PROPAGATION (FROM X TO COST)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A = sigmoid((np.dot(w.T,X)) + b)                                                  # compute activation\n",
    "    cost = (-1/m)*np.sum(np.multiply(Y,np.log(A)) + np.multiply(1-Y,np.log(1-A)))     # compute cost\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # BACKWARD PROPAGATION (TO FIND GRAD)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    dw = (1/m)*np.dot(X,(A-Y).T)\n",
    "    db = (1/m)*np.sum(A-Y)\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(dw.shape == w.shape)\n",
    "    assert(db.dtype == float)\n",
    "    cost = np.squeeze(cost)\n",
    "    assert(cost.shape == ())\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return grads, cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n",
    "     \n",
    "    costs = []\n",
    "    \n",
    "    for i in range(num_iterations):\n",
    "        \n",
    "        \n",
    "        # Cost and gradient calculation (≈ 1-4 lines of code)\n",
    "        ### START CODE HERE ### \n",
    "        grads, cost = propagate(w, b, X, Y)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Retrieve derivatives from grads\n",
    "        dw = grads[\"dw\"]\n",
    "        db = grads[\"db\"]\n",
    "        \n",
    "        # update rule (≈ 2 lines of code)\n",
    "        ### START CODE HERE ###\n",
    "        w = w - learning_rate * dw\n",
    "        b = b - learning_rate * db\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Record the costs\n",
    "        if i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "        \n",
    "        # Print the cost every 100 training examples\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    \n",
    "    params = {\"w\": w,\n",
    "              \"b\": b}\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return params, grads, costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(w, b, X):\n",
    "      \n",
    "    m = X.shape[1]\n",
    "    Y_prediction = np.zeros((1,m))\n",
    "    w = w.reshape(X.shape[0], 1)\n",
    "    \n",
    "    # Compute vector \"A\" predicting the probabilities of a cat being present in the picture\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    A = sigmoid((np.dot(w.T,X)) + b)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    for i in range(A.shape[1]):\n",
    "        \n",
    "        # Convert probabilities A[0,i] to actual predictions p[0,i]\n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "        if A[0,i] <= 0.5:\n",
    "            Y_prediction[0,i] = 0\n",
    "        else:\n",
    "            Y_prediction[0,i] = 1\n",
    "        ### END CODE HERE ###\n",
    "    \n",
    "    assert(Y_prediction.shape == (1, m))\n",
    "    \n",
    "    return Y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):\n",
    "  \n",
    "    ### START CODE HERE ###\n",
    "    \n",
    "    # initialize parameters with zeros (≈ 1 line of code)\n",
    "    dim = np.shape(X_train)[0]\n",
    "    w, b = initialize_with_zeros(dim)\n",
    "\n",
    "    # Gradient descent (≈ 1 line of code)\n",
    "    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost = False)\n",
    "    \n",
    "    # Retrieve parameters w and b from dictionary \"parameters\"\n",
    "    w = parameters[\"w\"]\n",
    "    b = parameters[\"b\"]\n",
    "    \n",
    "    # Predict test/train set examples (≈ 2 lines of code)\n",
    "    Y_prediction_test = predict(w, b, X_test)\n",
    "    Y_prediction_train = predict(w, b, X_train)\n",
    "\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # Print train/test Errors\n",
    "    print(\"train accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))\n",
    "    print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))\n",
    "\n",
    "    \n",
    "    d = {\"costs\": costs,\n",
    "         \"Y_prediction_test\": Y_prediction_test, \n",
    "         \"Y_prediction_train\" : Y_prediction_train, \n",
    "         \"w\" : w, \n",
    "         \"b\" : b,\n",
    "         \"learning_rate\" : learning_rate,\n",
    "         \"num_iterations\": num_iterations}\n",
    "    \n",
    "    return d  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy: 97.6958525345622 %\n",
      "test accuracy: 96.93251533742331 %\n"
     ]
    }
   ],
   "source": [
    "d = model(train_x, train_y, test_x, test_y, num_iterations = 2000, learning_rate = 0.4, print_cost = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19472804dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "costs = np.squeeze(d['costs'])\n",
    "plt.plot(costs)\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations (per hundreds)')\n",
    "plt.title(\"Learning rate =\" + str(d[\"learning_rate\"]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}