K_Nearest_Neighbor_Deployment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Nearest neighbors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest neighbor on Terrain Data\n",
    "A continuation of [previous](https://denispastory.com/wp-content/uploads/2018/01/IntroductiontoML.html) work.\n",
    "\n",
    "This one is a more detailed where Terrain Data is created. For training and testing \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets start by creating data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/python\n",
    "import random\n",
    "import pandas as pd\n",
    "\n",
    "#!/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",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The visualizations of the data\n",
    "This function will help up visualize the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify(features_train, labels_train):\n",
    "    ### import the sklearn module for KNeighborsRegressor\n",
    "    ### create classifier\n",
    "    ### fit the classifier on the training features and labels\n",
    "    ### return the fit classifier\n",
    "    \n",
    "    from sklearn.neighbors import KNeighborsRegressor\n",
    "    neigh = KNeighborsClassifier()\n",
    "    return neigh.fit(features_train,labels_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from class_vis import prettyPicture\n",
    "from prep_terrain_data import makeTerrainData\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "import numpy as np\n",
    "import pylab as pl\n",
    "\n",
    "\n",
    "features_train, labels_train, features_test, labels_test = makeTerrainData()\n",
    "\n",
    "\n",
    "########################## SVM #################################\n",
    "### we handle the import statement and SVC creation for you here\n",
    "def classify_KNN(features_train, labels_train):\n",
    "    from sklearn.neighbors import KNeighborsClassifier\n",
    "    from sklearn.metrics import accuracy_score\n",
    "\n",
    "    knn = KNeighborsClassifier(n_neighbors=1)\n",
    "    \n",
    "    fit_ = knn.fit(features_train,labels_train)\n",
    "    pred = knn.predict(features_test)\n",
    "    accuracy = accuracy_score(pred, labels_test)\n",
    "    \n",
    "    print \"The model accuracy is:\",(accuracy*100),\"%\"\n",
    "    return fit_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying the above functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model accuracy is: 94.0 %\n"
     ]
    },
    {
     "data": {
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82a/ZEE1wrjsIq9NUdE6Fhp/aUeVorCR+oyTmxG77+tRftkLhjxqvH2+8NlcN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\nAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119205190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instantiate the classifier\n",
    "clf = classify_KNN(features_train, labels_train)\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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def KNNccuracy(features_train, labels_train, features_test, labels_test):\n",
    "    \"\"\" compute the accuracy of your \n",
    "        KNN classifier \n",
    "        \"\"\"\n",
    "    from sklearn.neighbors import KNeighborsClassifier\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    \n",
    "    ### create classifier\n",
    "    neigh = KNeighborsClassifier(n_neighbors=1)#TODO\n",
    "\n",
    "    ### fit the classifier on the training features and labels\n",
    "    fit = neigh.fit(features_train,labels_train)\n",
    "    \n",
    "    ### use the trained classifier to predict labels for the test features\n",
    "    pred = neigh.predict(features_test)\n",
    "\n",
    "    accuracy = accuracy_score(pred, labels_test)\n",
    "    print \"The model accuracy is:\",(accuracy*100),\"%\"\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model accuracy is: 94.0 %\n"
     ]
    }
   ],
   "source": [
    "KNNccuracy(features_train, labels_train, features_test, labels_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
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