Part 08 - Randomized Parameter Search.ipynb

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   "source": [
    "Randomized Searching\n",
    "======================"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "\n",
    "iris = load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "from sklearn.grid_search import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.stats import expon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.hist([expon.rvs(scale=0.001) for x in xrange(10000)], bins=100, normed=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "\n",
    "param_distributions = {'C': expon(), 'gamma': expon()}\n",
    "rs = RandomizedSearchCV(SVC(), param_distributions=param_distributions, n_iter=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "rs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "rs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "rs.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scores, Cs, gammas = zip(*[(score.mean_validation_score, score.parameters['C'], score.parameters['gamma']) for score in rs.grid_scores_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "plt.scatter(Cs, gammas, s=50, c=scores, linewidths=0)\n",
    "plt.xlabel(\"C\")\n",
    "plt.ylabel(\"gamma\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.scatter(np.log(Cs), np.log(gammas), s=50, c=scores, linewidths=0)\n",
    "plt.xlabel(\"C\")\n",
    "plt.ylabel(\"gamma\")"
   ]
  }
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