04 - Grid Searches for Hyper Parameters.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid Searches\n",
    "================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid-Search with build-in cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.cross_validation import train_test_split\n",
    "digits = load_digits()\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define parameter grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "param_grid = {'C': 10. ** np.arange(-3, 3),\n",
    "              'gamma' : 10. ** np.arange(-5, 0)}\n",
    "              \n",
    "\n",
    "np.set_printoptions(suppress=True)\n",
    "print(param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(SVC(), param_grid, verbose=3, cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A GridSearchCV object behaves just like a normal classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
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   "outputs": [],
   "source": [
    "grid_search.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid_search.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# We extract just the scores\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "scores = [x[1] for x in grid_search.grid_scores_]\n",
    "scores = np.array(scores).reshape(6, 5)\n",
    "\n",
    "plt.matshow(scores)\n",
    "plt.xlabel('gamma')\n",
    "plt.ylabel('C')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(5), param_grid['gamma'])\n",
    "plt.yticks(np.arange(6), param_grid['C']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Exercises\n",
    "Use GridSearchCV to adjust n_neighbors of KNeighborsClassifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %load solutions/grid_search_k_neighbors.py"
   ]
  }
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