Naive_Bayes_Deployment_with_terrain_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Guassian NB Deployment on Train Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets start by creating data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/python\n",
    "import random\n",
    "\n",
    "\n",
    "def makeTerrainData(n_points=1000):\n",
    "###############################################################################\n",
    "### make the toy dataset\n",
    "    random.seed(42)\n",
    "    grade = [random.random() for ii in range(0,n_points)]\n",
    "    bumpy = [random.random() for ii in range(0,n_points)]\n",
    "    error = [random.random() for ii in range(0,n_points)]\n",
    "    y = [round(grade[ii]*bumpy[ii]+0.3+0.1*error[ii]) for ii in range(0,n_points)]\n",
    "    for ii in range(0, len(y)):\n",
    "        if grade[ii]>0.8 or bumpy[ii]>0.8:\n",
    "            y[ii] = 1.0\n",
    "\n",
    "### split into train/test sets\n",
    "    X = [[gg, ss] for gg, ss in zip(grade, bumpy)]\n",
    "    split = int(0.75*n_points)\n",
    "    X_train = X[0:split]\n",
    "    X_test  = X[split:]\n",
    "    y_train = y[0:split]\n",
    "    y_test  = y[split:]\n",
    "\n",
    "    grade_sig = [X_train[ii][0] for ii in range(0, len(X_train)) if y_train[ii]==0]\n",
    "    bumpy_sig = [X_train[ii][1] for ii in range(0, len(X_train)) if y_train[ii]==0]\n",
    "    grade_bkg = [X_train[ii][0] for ii in range(0, len(X_train)) if y_train[ii]==1]\n",
    "    bumpy_bkg = [X_train[ii][1] for ii in range(0, len(X_train)) if y_train[ii]==1]\n",
    "\n",
    "#    training_data = {\"fast\":{\"grade\":grade_sig, \"bumpiness\":bumpy_sig}\n",
    "#            , \"slow\":{\"grade\":grade_bkg, \"bumpiness\":bumpy_bkg}}\n",
    "\n",
    "\n",
    "    grade_sig = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==0]\n",
    "    bumpy_sig = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==0]\n",
    "    grade_bkg = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==1]\n",
    "    bumpy_bkg = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==1]\n",
    "\n",
    "    test_data = {\"fast\":{\"grade\":grade_sig, \"bumpiness\":bumpy_sig}\n",
    "            , \"slow\":{\"grade\":grade_bkg, \"bumpiness\":bumpy_bkg}}\n",
    "\n",
    "    return X_train, y_train, X_test, y_test\n",
    "#    return training_data, test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The visualizations of the data\n",
    "This function will help up visualize the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/python\n",
    "\n",
    "#from udacityplots import *\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import matplotlib \n",
    "%matplotlib inline \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "\n",
    "#import numpy as np\n",
    "#import matplotlib.pyplot as plt\n",
    "#plt.ioff()\n",
    "\n",
    "def prettyPicture(clf, X_test, y_test):\n",
    "    x_min = 0.0; x_max = 1.0\n",
    "    y_min = 0.0; y_max = 1.0\n",
    "\n",
    "    # Plot the decision boundary. For that, we will assign a color to each\n",
    "    # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
    "    h = .01  # step size in the mesh\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "\n",
    "    plt.pcolormesh(xx, yy, Z, cmap=pl.cm.seismic)\n",
    "\n",
    "    # Plot also the test points\n",
    "    grade_sig = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==0]\n",
    "    bumpy_sig = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==0]\n",
    "    grade_bkg = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==1]\n",
    "    bumpy_bkg = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==1]\n",
    "\n",
    "    plt.scatter(grade_sig, bumpy_sig, color = \"b\", label=\"fast\")\n",
    "    plt.scatter(grade_bkg, bumpy_bkg, color = \"r\", label=\"slow\")\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"bumpiness\")\n",
    "    plt.ylabel(\"grade\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using sklearn models, creat a classifier function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify(features_train, labels_train):   \n",
    "    ### import the sklearn module for GaussianNB\n",
    "    ### create classifier\n",
    "    ### fit the classifier on the training features and labels\n",
    "    ### return the fit classifier\n",
    "    \n",
    "    ### your code goes here!\n",
    "    from sklearn.naive_bayes import GaussianNB\n",
    "    clf = GaussianNB()\n",
    "    return clf.fit(features_train,labels_train)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying the above functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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sekAonANcBRzaeH1s4zUkFQydPAA8oIlzCbCxjRep1lfFtp7HglLl/AQXZq+iI+xc3G8N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\nxpjz7O6QxHySZgEte3GNw9f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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba68550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!/usr/bin/python\n",
    "\n",
    "\n",
    "#from prep_terrain_data import makeTerrainData\n",
    "#from class_vis import prettyPicture, output_image\n",
    "#from ClassifyNB import classify\n",
    "\n",
    "import numpy as np\n",
    "import pylab as pl\n",
    "\n",
    "\n",
    "features_train, labels_train, features_test, labels_test = makeTerrainData()\n",
    "\n",
    "### the training data (features_train, labels_train) have both \"fast\" and \"slow\" points mixed\n",
    "### in together--separate them so we can give them different colors in the scatterplot,\n",
    "### and visually identify them\n",
    "grade_fast = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==0]\n",
    "bumpy_fast = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==0]\n",
    "grade_slow = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==1]\n",
    "bumpy_slow = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==1]\n",
    "\n",
    "\n",
    "clf = classify(features_train, labels_train)\n",
    "\n",
    "### draw the decision boundary with the text points overlaid\n",
    "prettyPicture(clf, features_test, labels_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating NB Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NBAccuracy(features_train, labels_train, features_test, labels_test):\n",
    "    \"\"\" compute the accuracy of your Naive Bayes classifier \"\"\"\n",
    "    ### import the sklearn module for GaussianNB\n",
    "    from sklearn.naive_bayes import GaussianNB\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    \n",
    "    ### create classifier\n",
    "    clf = GaussianNB()#TODO\n",
    "\n",
    "    ### fit the classifier on the training features and labels\n",
    "    fit = clf.fit(features_train,labels_train)\n",
    "\n",
    "    ### use the trained classifier to predict labels for the test features\n",
    "    pred = clf.predict(features_test)\n",
    "\n",
    "    accuracy = accuracy_score(pred, labels_test)\n",
    "    print(accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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