2018/day7_Breast_Cancer_KNN_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Breast Cancer \n",
    "## Part 2 : KNN Classification\n",
    "### 1. Load Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import sys, os\n",
    "import pandas as pd\n",
    "import helper_functions as hf\n",
    "from helper_functions import training_set, class_set, test_set, test_class_set\n",
    "from sklearn.neighbors import KNeighborsClassifier # Kth Nearest Neighbor\n",
    "from sklearn.model_selection import KFold, cross_val_score # Cross validation\n",
    "from sklearn.model_selection import KFold, GridSearchCV\n",
    "from sklearn.metrics import roc_curve # ROC Curves\n",
    "from sklearn.metrics import auc # Calculating Area Under Curve for ROC's!\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "pd.set_option('display.max_columns', 500) \n",
    "# Included to show all the columns \n",
    "# since it is a fairly large data set\n",
    "\n",
    "plt.style.use('ggplot') # Using ggplot2 style visuals "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "breast_cancer = hf.breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dimensions of our data frame\n",
    "breast_cancer.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis                    int64\n",
       "radius_mean                float64\n",
       "texture_mean               float64\n",
       "perimeter_mean             float64\n",
       "area_mean                  float64\n",
       "smoothness_mean            float64\n",
       "compactness_mean           float64\n",
       "concavity_mean             float64\n",
       "concave_points_mean        float64\n",
       "symmetry_mean              float64\n",
       "fractal_dimension_mean     float64\n",
       "radius_se                  float64\n",
       "texture_se                 float64\n",
       "perimeter_se               float64\n",
       "area_se                    float64\n",
       "smoothness_se              float64\n",
       "compactness_se             float64\n",
       "concavity_se               float64\n",
       "concave_points_se          float64\n",
       "symmetry_se                float64\n",
       "fractal_dimension_se       float64\n",
       "radius_worst               float64\n",
       "texture_worst              float64\n",
       "perimeter_worst            float64\n",
       "area_worst                 float64\n",
       "smoothness_worst           float64\n",
       "compactness_worst          float64\n",
       "concavity_worst            float64\n",
       "concave_points_worst       float64\n",
       "symmetry_worst             float64\n",
       "fractal_dimension_worst    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data types of our columns \n",
    "breast_cancer.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Fitting model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_knn = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# Training model\n",
    "fit_knn.fit(training_set, class_set['diagnosis'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since KNN was first algorithm I included training set metrics\n",
    "# to give context  \n",
    "# We predict the class for our training set\n",
    "\n",
    "predictionsTrain = fit_knn.predict(training_set) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Measure the accuracy based on the training set\n",
    "accuracy_train = fit_knn.score(training_set, \n",
    "                               class_set['diagnosis'])\n",
    "\n",
    "train_error_rate = 1 - accuracy_train  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we predict the Dx for the test set and call it predictions\n",
    "predictions = fit_knn.predict(test_set)\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's get the accuracy of our test set\n",
    "accuracy = fit_knn.score(test_set, \n",
    "                         test_class_set['diagnosis'])\n",
    "\n",
    "test_error_rate = 1 - accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ROC Curve and AUC Calculations\n",
    "fpr, tpr, _ = roc_curve(predictions, test_class_set)\n",
    "\n",
    "auc_knn = auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FITTING MODEL KNN USING k=3\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
      "           weights='uniform')\n",
      "Optimal K\n",
      "Optimal K is 3\n",
      "TRAINING SET CALCULATIONS\n",
      "Actual Values       0    1\n",
      "Predicted Values          \n",
      "0                 281   18\n",
      "1                   5  151\n",
      "Here is our accuracy for our training set:\n",
      "  0.949\n",
      "The train error rate for our model is:\n",
      "  0.051\n",
      "CROSS VALIDATION\n",
      "Accuracy:  0.925 (+/-  0.025)\n",
      "TEST SET CALCULATIONS\n",
      "Actual Values      0   1\n",
      "Predicted Values        \n",
      "0                 68   5\n",
      "1                  3  38\n",
      "Here is our accuracy for our test set:\n",
      "  0.930\n",
      "The test error rate for our model is:\n",
      "  0.070\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    print('FITTING MODEL KNN USING k=3')\n",
    "    print(fit_knn)\n",
    "    \n",
    "    print('Optimal K')\n",
    "    myKs = []\n",
    "    for i in range(0, 50):\n",
    "        if (i % 2 != 0):\n",
    "            myKs.append(i)\n",
    "\n",
    "    cross_vals = []\n",
    "    for k in myKs:\n",
    "        knn = KNeighborsClassifier(n_neighbors=k)\n",
    "        scores = cross_val_score(knn,\n",
    "                                 training_set, \n",
    "                                 class_set['diagnosis'], \n",
    "                                 cv = 10, \n",
    "                                 scoring='accuracy')\n",
    "        cross_vals.append(scores.mean())\n",
    "        \n",
    "    MSE = [1 - x for x in cross_vals]\n",
    "    optimal_k = myKs[MSE.index(min(MSE))]\n",
    "    print(\"Optimal K is {0}\".format(optimal_k))\n",
    "\n",
    "    print('TRAINING SET CALCULATIONS')\n",
    "    # Here we create a matrix comparing the actual values \n",
    "    # vs. the predicted values\n",
    "    \n",
    "    print(pd.crosstab(predictionsTrain, \n",
    "                      class_set['diagnosis'], \n",
    "                      rownames=['Predicted Values'], \n",
    "                      colnames=['Actual Values']))\n",
    "\n",
    "    print(\"Here is our accuracy for our training set:\\n {0: .3f}\"\n",
    "          .format(accuracy_train))\n",
    "    \n",
    "    print(\"The train error rate for our model is:\\n {0: .3f}\"\n",
    "          .format(train_error_rate))\n",
    "\n",
    "    print('CROSS VALIDATION')\n",
    "    hf.cross_val_metrics(fit_knn, training_set, \n",
    "                         class_set['diagnosis'], \n",
    "                         print_results = True)\n",
    "\n",
    "    print('TEST SET CALCULATIONS')\n",
    "    # Let's compare the predictions vs. the actual values\n",
    "    print(pd.crosstab(predictions, \n",
    "                      test_class_set['diagnosis'], \n",
    "                      rownames=['Predicted Values'], \n",
    "                      colnames=['Actual Values']))\n",
    "\n",
    "    # TEST ERROR RATE!!\n",
    "    print(\"Here is our accuracy for our test set:\\n {0: .3f}\"\n",
    "          .format(accuracy))\n",
    "\n",
    "    # Here we calculate the test error rate!\n",
    "    print(\"The test error rate for our model is:\\n {0: .3f}\"\n",
    "          .format(test_error_rate))\n",
    "\n",
    "    # ROC Curve\n",
    "    hf.plot_roc_curve(fpr, tpr, auc_knn, 'knn')\n",
    "\n",
    "    # Zoomed in ROC Curve\n",
    "    hf.plot_roc_curve(fpr, tpr, auc_knn, 'knn',\n",
    "                      (-0.001, 0.2), (0.7, 1.05))\n",
    "else:\n",
    "    def return_knn():\n",
    "        '''\n",
    "        Function to output values created in script \n",
    "        '''\n",
    "        return fpr, tpr, auc_knn, predictions, test_error_rate\n",
    "\n",
    "    mean_cv_knn, std_error_knn = hf.cross_val_metrics(fit_knn, \n",
    "                                                      training_set, \n",
    "                                                      class_set['diagnosis'],\n",
    "                                                      print_results = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}