{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Randomized Searching\n",
      "======================"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%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)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.grid_search import RandomizedSearchCV"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import expon"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist([expon.rvs(scale=0.001) for x in xrange(10000)], bins=100, normed=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f150e06aa90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rs.fit(X_train, y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "RandomizedSearchCV(cv=None, error_score='raise',\n",
        "          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n",
        "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False),\n",
        "          fit_params={}, iid=True, n_iter=50, n_jobs=1,\n",
        "          param_distributions={'C': <scipy.stats.distributions.rv_frozen object at 0x7f150dfc7510>, 'gamma': <scipy.stats.distributions.rv_frozen object at 0x7f150df92c10>},\n",
        "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
        "          scoring=None, verbose=0)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rs.best_params_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "{'C': 1.6214755609400757, 'gamma': 0.24979204736154573}"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rs.best_score_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "0.9732142857142857"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scores, Cs, gammas = zip(*[(score.mean_validation_score, score.parameters['C'], score.parameters['gamma']) for score in rs.grid_scores_])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(Cs, gammas, s=50, c=scores, linewidths=0)\n",
      "plt.xlabel(\"C\")\n",
      "plt.ylabel(\"gamma\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.text.Text at 0x7f150dfa8090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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goER74KRJbJkzh9yUlDJz/K7/qlUE9upl12ul8uVlZODh7V3u3/BXDhnC6UWL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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f150dd54c90>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(np.log(Cs), np.log(gammas), s=50, c=scores, linewidths=0)\n",
      "plt.xlabel(\"C\")\n",
      "plt.ylabel(\"gamma\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x7f150df1ef90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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miRq0LMdvv/3Grl27UKlU3H777fTv398Y2RpMziSEsAw5q1eTomedLs9Jk+j1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\nIiGEUMSJLVt0tl8+fZoLR46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       "text": [
        "<matplotlib.figure.Figure at 0x7f150df60a90>"
       ]
      }
     ],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}