3. K Nearest Neighbors.ipynb

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  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "class kNN_Classifier(object):\n",
    "    \n",
    "    \"\"\"k-Nearest Neighbors Classifier\"\"\"\n",
    "    \n",
    "    def __init__(self, _k=5, printing=False):\n",
    "        self.k = _k\n",
    "        self.printing = printing\n",
    "        \n",
    "    def fit(self, trainingSet, testSet):\n",
    "        predictions = []\n",
    "        \n",
    "        #for each test instance...\n",
    "        for x in range(len(testSet)):\n",
    "            testInstance = testSet[x]\n",
    "            \n",
    "            #find k nearest neighbors\n",
    "            neighbors = self.k_neighbors(trainingSet, testInstance)\n",
    "            \n",
    "            #vote on class\n",
    "            result = self.predict(neighbors)\n",
    "            predictions.append(result)\n",
    "            \n",
    "            #printing options\n",
    "            if self.printing:\n",
    "                print('> predicted=' + repr(result) + ', actual=' + repr(testInstance[-1]))\n",
    "        \n",
    "        #assess accuracy\n",
    "        accuracy = self.score(testSet, predictions)\n",
    "        print('Accuracy: ' + repr(accuracy) + '%')\n",
    "        \n",
    "    def euclidean_distance(self,instance1,instance2):\n",
    "        #euclidean_distance = np.sqrt(np.sum((np.array(instance1) - np.array(instance2)**2)))\n",
    "        euclidean_distance = np.linalg.norm(np.array(instance1) - np.array(instance2))\n",
    "        return euclidean_distance\n",
    "    \n",
    "    def k_neighbors(self, trainingSet, testInstance):\n",
    "        distances = []\n",
    "        \n",
    "        #for each training sample...\n",
    "        for x in range(len(trainingSet)):\n",
    "            #calculate the distance to the testInstance\n",
    "            dist = self.euclidean_distance(trainingSet[x], testInstance)\n",
    "            distances.append((trainingSet[x], dist))\n",
    "        \n",
    "        #sort distances\n",
    "        distances.sort(key=operator.itemgetter(1))\n",
    "        \n",
    "        #create neighborhood\n",
    "        neighborhood = []\n",
    "        \n",
    "        #for each of the top k nearsest neighbors...\n",
    "        for x in range(self.k):\n",
    "            #add to neighborhood\n",
    "            neighborhood.append(distances[x][0])\n",
    "        \n",
    "        return neighborhood\n",
    "    \n",
    "    def predict(self, neighbors):\n",
    "        classVotes = {}\n",
    "        \n",
    "        #for each of the top k nearest neighbors...\n",
    "        for x in range(len(neighbors)):\n",
    "            #record neighbor class\n",
    "            response = neighbors[x][-1]\n",
    "            \n",
    "            #update class vote count\n",
    "            if response in classVotes:\n",
    "                classVotes[response] += 1\n",
    "            else:\n",
    "                classVotes[response] = 1\n",
    "        \n",
    "        #return class with most votes\n",
    "        vote = max(classVotes.items(), key=operator.itemgetter(1))[0]\n",
    "        return vote\n",
    "\n",
    "    def score(self, testSet, predictions):\n",
    "        correct = 0\n",
    "        \n",
    "        #for each testInstance...\n",
    "        for x in range(len(testSet)):\n",
    "            #add point if actual class == predicted class...\n",
    "            if testSet[x,-1] == predictions[x]:\n",
    "                correct += 1\n",
    "        return (correct/float(len(testSet))) * 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clump_thickness</th>\n",
       "      <th>uni_cell_size</th>\n",
       "      <th>uni_cell_shape</th>\n",
       "      <th>marg_adhes</th>\n",
       "      <th>epith_cell_size</th>\n",
       "      <th>bare_nuclei</th>\n",
       "      <th>bland_chrom</th>\n",
       "      <th>norm_nucleoli</th>\n",
       "      <th>mitoses</th>\n",
       "      <th>class</th>\n",
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      "text/plain": [
       "   clump_thickness  uni_cell_size  uni_cell_shape  marg_adhes  \\\n",
       "0              4.0            2.0             3.0         5.0   \n",
       "1              5.0            4.0             6.0        10.0   \n",
       "2              1.0            1.0             1.0         1.0   \n",
       "3              5.0            1.0             1.0         1.0   \n",
       "4              7.0            5.0             6.0        10.0   \n",
       "\n",
       "   epith_cell_size  bare_nuclei  bland_chrom  norm_nucleoli  mitoses  class  \n",
       "0              3.0          8.0          7.0            6.0      1.0    4.0  \n",
       "1              2.0         10.0          4.0            1.0      1.0    4.0  \n",
       "2              2.0          1.0          2.0            1.0      1.0    2.0  \n",
       "3              2.0          1.0          3.0            2.0      1.0    2.0  \n",
       "4              5.0         10.0          7.0            9.0      4.0    4.0  "
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     "execution_count": 145,
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   ],
   "source": [
    "#load data\n",
    "df = pd.read_csv('../data/breast-cancer-wisconsin.data.txt')\n",
    "\n",
    "#preprocess data\n",
    "df = df.replace('?',-99999) #not vulnerable to outliers\n",
    "df = df.astype(float)\n",
    "df = df.drop(['id'],1)\n",
    "\n",
    "#Shuffle the Data\n",
    "df = df.reindex(np.random.permutation(df.index))\n",
    "df = df.reset_index(drop=True)\n",
    "\n",
    "#preview data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create copy\n",
    "tf = df.copy()\n",
    "#tf = tf.drop('class',axis=1) #drop class\n",
    "\n",
    "#Define the percentage of the data that you want to use for testing\n",
    "test_size = 0.2\n",
    "\n",
    "#Grabs the first (1-test_size) of the data\n",
    "train_data = tf[:-int(test_size*len(tf))]\n",
    "\n",
    "#Grabs the last (test_size) of the data\n",
    "test_data = tf[-int(test_size*len(tf)):]\n",
    "\n",
    "#save as value arrays\n",
    "train_data = train_data.values\n",
    "test_data = test_data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.56115107913669%\n"
     ]
    }
   ],
   "source": [
    "#run model\n",
    "knn = kNN_Classifier(printing=False).fit(train_data,test_data)"
   ]
  }
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