1_Sepal_Cassification_SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "iris[\"feature_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  petal length (cm)  target\n",
       "0                5.1                1.4       0\n",
       "1                4.9                1.4       0\n",
       "2                4.7                1.3       0\n",
       "3                4.6                1.5       0\n",
       "4                5.0                1.4       0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 整理合併資料 ，只拿 sepal length (cm) sepal width (cm) 做分類\n",
    "iris = datasets.load_iris()\n",
    "x = pd.DataFrame(iris[\"data\"], columns =iris['feature_names'])\n",
    "y = pd.DataFrame(iris[\"target\"],columns =[\"target\"])\n",
    "iris_data = pd.concat([x,y],axis =1)\n",
    "iris_data\n",
    "iris_data = iris_data[[\"sepal length (cm)\",\"petal length (cm)\",\"target\"]]\n",
    "iris_data = iris_data[iris_data[\"target\"].isin([0,1])]\n",
    "iris_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 依7:3將資料 分成 train test \n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(\n",
    "    iris_data[[\"sepal length (cm)\",\"petal length (cm)\"]], iris_data[[\"target\"]], test_size = 0.3, random_state= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "sc.fit(X_train)\n",
    "X_train_std = sc.transform(X_train)\n",
    "X_test_std = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC(kernel='rbf', probability=True)\n",
    "# svm.fit(X_train, Y_train[\"target\"].values)\n",
    "svm.fit(X_train_std, Y_train[\"target\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.predict(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "error = 0\n",
    "for i, v in enumerate(svm.predict(X_test_std)):\n",
    "    if v!= Y_test['target'].values[i]:\n",
    "        error+=1\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.96582297, 0.03417703],\n",
       "       [0.02817663, 0.97182337],\n",
       "       [0.97503944, 0.02496056],\n",
       "       [0.01401723, 0.98598277],\n",
       "       [0.02949512, 0.97050488],\n",
       "       [0.02735387, 0.97264613],\n",
       "       [0.98013952, 0.01986048],\n",
       "       [0.02535103, 0.97464897],\n",
       "       [0.02844355, 0.97155645],\n",
       "       [0.0131566 , 0.9868434 ],\n",
       "       [0.00984927, 0.99015073],\n",
       "       [0.01773799, 0.98226201],\n",
       "       [0.02160928, 0.97839072],\n",
       "       [0.9487707 , 0.0512293 ],\n",
       "       [0.97311108, 0.02688892],\n",
       "       [0.9611448 , 0.0388552 ],\n",
       "       [0.97549597, 0.02450403],\n",
       "       [0.91162266, 0.08837734],\n",
       "       [0.97470197, 0.02529803],\n",
       "       [0.95205982, 0.04794018],\n",
       "       [0.96582297, 0.03417703],\n",
       "       [0.02556098, 0.97443902],\n",
       "       [0.96124226, 0.03875774],\n",
       "       [0.02945518, 0.97054482],\n",
       "       [0.97431902, 0.02568098],\n",
       "       [0.97444315, 0.02555685],\n",
       "       [0.96708181, 0.03291819],\n",
       "       [0.01358815, 0.98641185],\n",
       "       [0.01955338, 0.98044662],\n",
       "       [0.02872077, 0.97127923]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.predict_proba(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n",
    "\n",
    "    # setup marker generator and color map\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "\n",
    "    # plot the decision surface\n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "                           np.arange(x2_min, x2_max, resolution))\n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], \n",
    "                    y=X[y == cl, 1],\n",
    "                    alpha=0.6, \n",
    "                    c=cmap(idx),\n",
    "                    edgecolor='black',\n",
    "                    marker=markers[idx], \n",
    "                    label=cl)\n",
    "\n",
    "    # highlight test samples\n",
    "    if test_idx:\n",
    "        # plot all samples\n",
    "        if not versiontuple(np.__version__) >= versiontuple('1.9.0'):\n",
    "            X_test, y_test = X[list(test_idx), :], y[list(test_idx)]\n",
    "            warnings.warn('Please update to NumPy 1.9.0 or newer')\n",
    "        else:\n",
    "            X_test, y_test = X[test_idx, :], y[test_idx]\n",
    "\n",
    "        plt.scatter(X_test[:, 0],\n",
    "                    X_test[:, 1],\n",
    "                    c='',\n",
    "                    alpha=1.0,\n",
    "                    edgecolor='black',\n",
    "                    linewidths=1,\n",
    "                    marker='o',\n",
    "                    s=55, label='test set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116324ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(X_train_std, Y_train['target'].values, classifier=svm)\n",
    "plt.xlabel('sepal length [standardized]')\n",
    "plt.ylabel('petal width [standardized]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}