{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets\n",
    "from matplotlib.pylab import rcParams\n",
    "\n",
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = 10, 5\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Search for SVM Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {\n",
    "    'kernel': ['linear', 'rbf'],\n",
    "    'C': [0.01, 0.1, 1, 10],\n",
    "    'gamma': [0.01, 0.1, 1, 10]\n",
    "}\n",
    "model = svm.SVC()\n",
    "model.fit(X, y)\n",
    "best_model = GridSearchCV(model,\n",
    "                          parameters,\n",
    "                          cv=5,\n",
    "                          scoring='accuracy',\n",
    "                          return_train_score='cv_results_')\n",
    "best_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('accuracy before Grid Search CV:%.4f' % model.score(X, y))\n",
    "print('accuracy after Grid Search CV:%.4f' % best_model.score(X, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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