gistfile1.txt

{
 "metadata": {
  "name": "",
  "signature": "sha256:029e6d75e1a5bcd05ac659251fefd2b5420dbe5f5b1aa26b34bd5472d5097e80"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rep.estimators import TMVAClassifier"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pay attention to efficiency\n",
      "clf = TMVAClassifier('kCuts', sigmoid_function='sig_eff=0.4', FitMethod='GA')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import make_blobs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X, y = make_blobs(n_samples=1000, n_features=4, centers=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.fit(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "TMVAClassifier(FitMethod='GA',\n",
        "        factory_options='!V:!Silent:Color:Transformations=I;D;P;G,D:AnalysisType=Classification',\n",
        "        features=['Feature_0', 'Feature_1', 'Feature_2', 'Feature_3'],\n",
        "        method='kCuts')"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# will be zeros and ones\n",
      "p = clf.predict_proba(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}