Machine Learning - Support Vector Machines vs Neural Networks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import roc_curve\n",
    "from keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "headers = ['name', 'hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator', 'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('zoo.data', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
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       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>aardvark</td>\n",
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       "      <td>bass</td>\n",
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       "      <th>4</th>\n",
       "      <td>boar</td>\n",
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      "text/plain": [
       "       name  hair  feathers  eggs  milk  airborne  aquatic  predator  toothed  \\\n",
       "0  aardvark     1         0     0     1         0        0         1        1   \n",
       "1  antelope     1         0     0     1         0        0         0        1   \n",
       "2      bass     0         0     1     0         0        1         1        1   \n",
       "3      bear     1         0     0     1         0        0         1        1   \n",
       "4      boar     1         0     0     1         0        0         1        1   \n",
       "\n",
       "   backbone  breathes  venomous  fins  legs  tail  domestic  catsize  type  \n",
       "0         1         1         0     0     4     0         0        1     1  \n",
       "1         1         1         0     0     4     1         0        1     1  \n",
       "2         1         0         0     1     0     1         0        0     4  \n",
       "3         1         1         0     0     4     0         0        1     1  \n",
       "4         1         1         0     0     4     1         0        1     1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_with_name = arr[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X_with_name[:, 1:].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = arr[:, -1].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [\n",
    "    SVC(kernel='linear'),\n",
    "    SVC(kernel='rbf'),\n",
    "    SVC(kernel='poly'),\n",
    "    SVC(kernel='sigmoid')\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross = KFold(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear - 1.0\n",
      "rbf - 0.9523809523809523\n",
      "poly - 1.0\n",
      "sigmoid - 0.8095238095238095\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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",
      "/Users/jatinkatyal/miniconda3/lib/python3.7/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 model in models:\n",
    "    best_score = -1\n",
    "    for train, test in cross.split(X, Y):\n",
    "        X_train = X[train]\n",
    "        Y_train = Y[train]\n",
    "        \n",
    "        X_test = X[test]\n",
    "        Y_test = Y[test]\n",
    "        \n",
    "        trained = model.fit(X_train, Y_train)\n",
    "        best_score = max(best_score, trained.score(X_test, Y_test))\n",
    "    print(\"{} - {}\".format(model.kernel, best_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.utils import to_categorical\n",
    "import keras_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=16, kernel_initializer='uniform', activation='relu'))\n",
    "model.add(Dense(10, kernel_initializer='uniform', activation='relu'))\n",
    "model.add(Dense(8, kernel_initializer='uniform', activation='relu'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21/21 [==============================] - 0s 1ms/step\n",
      "20/20 [==============================] - 0s 20us/step\n",
      "20/20 [==============================] - 0s 20us/step\n",
      "20/20 [==============================] - 0s 20us/step\n",
      "20/20 [==============================] - 0s 20us/step\n"
     ]
    }
   ],
   "source": [
    "best_score = -1\n",
    "for train, test in cross.split(X, Y):\n",
    "    train, test = list(train), list(test)\n",
    "    X_train = X[train]\n",
    "    Y_train = Y[train]\n",
    "\n",
    "    X_test = X[test]\n",
    "    Y_test = Y[test]\n",
    "    \n",
    "    model.fit(X_train, to_categorical(Y_train), epochs=75, verbose=False)\n",
    "    best_score = max(best_score, model.evaluate(X_test, to_categorical(Y_test, num_classes=8))[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9624999761581421"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test).ravel()"
   ]
  }
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