svm-breast-cancer-diagnostic-wisconsin.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = \"/Users/tommykwan/Documents/MachineLearning/SupportVectorMachine/svm-breast-cancer-detection/breast-cancer-wisconsin-original.csv\"\n",
    "attributes = ['id','diagnosis','radius_mean','texture_mean','perimeter_mean','area_mean','smoothness_mean',\n",
    "              'compactness_mean','concavity_mean','concave_points_mean','symmetry_mean','fractal_dimension_mean',\n",
    "              'radius_se','texture_se','perimeter_se','area_se','smoothness_se','compactness_se','concavity_se',\n",
    "              'concave points_se','symmetry_se','fractal_dimension_se','radius_worst','texture_worst',\n",
    "              'perimeter_worst','area_worst','smoothness_worst','compactness_worst','concavity_worst','concave_points_worst',\n",
    "              'symmetry_worst','fractal_dimension_worst']\n",
    "df = pd.read_csv(filepath, names=attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the data\n",
    "# print('DataFrame - Original CSV')\n",
    "# print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 32)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['id'], 1, inplace=True)\n",
    "# print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(df['diagnosis'])\n",
    "X = np.array(df.drop(['diagnosis'], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "X_test_min = X_test.min()\n",
    "X_test_max = X_test.max()\n",
    "X_test_range = (X_test - X_test_min).max()\n",
    "X_test_scaled = (X_test-X_test_min)/X_test_range\n",
    "\n",
    "X_train_max = X_train.max()\n",
    "X_train_min = X_train.min()\n",
    "X_train_range = X_train_max-X_train_min\n",
    "X_train_scaled = (X_train - X_train_min)/(X_train_range)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring = 'accuracy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('SVM', SVC()))\n",
    "results = []\n",
    "names = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM: 0.624348 (0.087461)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "for attribute, model in models:\n",
    "    kfold = model_selection.KFold(n_splits=10, random_state = 42)\n",
    "    cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(attribute)\n",
    "    msg = \"%s: %f (%f)\" % (attribute, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM\n",
      "0.7982456140350878\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      1.00      0.86        73\n",
      "           1       1.00      0.44      0.61        41\n",
      "\n",
      "   micro avg       0.80      0.80      0.80       114\n",
      "   macro avg       0.88      0.72      0.74       114\n",
      "weighted avg       0.85      0.80      0.77       114\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "for attribute, model in models:\n",
    "    model.fit(X_train_scaled, y_train)\n",
    "    predictions = model.predict(X_test_scaled)\n",
    "    print(attribute)\n",
    "    print(accuracy_score(y_test, predictions))\n",
    "    print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clf = SVC(C = 1.0, kernel = 'rbf', degree = 3, gamma = 'auto_deprecated', coef0=0.0,\n",
    "#     shrinking = True, probability = False, tol = 0.001, cache_size = 200,\n",
    "#     class_weight = None, verbose = False, max_iter = -1, decision_function_shape = 'ovr',\n",
    "#     random_state = None)\n",
    "clf = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Accuracy\n",
      "0.7982456140350878\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "clf.fit(X_train_scaled, y_train)\n",
    "accuracy = clf.score(X_test_scaled, y_test)\n",
    "print(); print(); print()\n",
    "print('Accuracy')\n",
    "print(accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example_measures = np.array([[9,4,4,2,7,1,9,2,1]])\n",
    "# example_measures = example_measures.reshape(len(example_measures), -1)\n",
    "prediction_svm = clf.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      1.00      0.86        73\n",
      "           1       1.00      0.44      0.61        41\n",
      "\n",
      "   micro avg       0.80      0.80      0.80       114\n",
      "   macro avg       0.88      0.72      0.74       114\n",
      "weighted avg       0.85      0.80      0.77       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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