{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About\n",
    "\n",
    "This notebook demonstrates stacking machine learning algorithm - folding, which physics use in their analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy, pandas\n",
    "from rep.utils import train_test_split\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "sig_data = pandas.read_csv('toy_datasets/toyMC_sig_mass.csv', sep='\\t')\n",
    "bck_data = pandas.read_csv('toy_datasets/toyMC_bck_mass.csv', sep='\\t')\n",
    "\n",
    "labels = numpy.array([1] * len(sig_data) + [0] * len(bck_data))\n",
    "data = pandas.concat([sig_data, bck_data])\n",
    "\n",
    "train_data, test_data, train_labels, test_labels = train_test_split(data, labels, train_size=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "variables = [\"FlightDistance\", \"FlightDistanceError\", \"IP\", \"VertexChi2\", \"pt\", \"p0_pt\", \"p1_pt\", \"p2_pt\", 'LifeTime', 'dira']\n",
    "data = data[variables]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Folding strategy - stacking algorithm\n",
    "\n",
    "It implements the same interface as all classifiers, but with some difference:\n",
    "\n",
    "* all prediction methods have additional parameter \"vote\\_function\" (example folder.predict(X, __vote\\_function=None)__), which is used to combine all classifiers' predictions. By default \"mean\" is used as \"vote_function\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.estimators import SklearnClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define folding model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rep.metaml import FoldingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FoldingClassifier(base_estimator=GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
       "              max_depth=3, max_features=None, max_leaf_nodes=None,\n",
       "              min_samples_leaf=1, min_samples_split=2, n_estimators=100,\n",
       "              random_state=None, subsample=1.0, verbose=0,\n",
       "              warm_start=False),\n",
       "         features=['FlightDistance', 'FlightDistanceError', 'IP', 'VertexChi2', 'pt', 'p0_pt', 'p1_pt', 'p2_pt', 'LifeTime', 'dira'],\n",
       "         ipc_profile=None, n_folds=4, random_state=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_folds = 4\n",
    "folder = FoldingClassifier(GradientBoostingClassifier(), n_folds=n_folds, features=variables)\n",
    "folder.fit(train_data, train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default prediction (predict i_th_ fold by i_th_ classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold prediction using folds column\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.2383179 ,  0.7616821 ],\n",
       "       [ 0.27117691,  0.72882309],\n",
       "       [ 0.02837497,  0.97162503],\n",
       "       ..., \n",
       "       [ 0.03500956,  0.96499044],\n",
       "       [ 0.35562003,  0.64437997],\n",
       "       [ 0.91160588,  0.08839412]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder.predict_proba(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voting prediction (predict i-fold by all classifiers and take value, which is calculated by `vote_function`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# definition of mean function, which combines all predictions\n",
    "def mean_vote(x):\n",
    "    return numpy.mean(x, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using voting KFold prediction\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.01176629,  0.98823371],\n",
       "       [ 0.04199359,  0.95800641],\n",
       "       [ 0.01372648,  0.98627352],\n",
       "       ..., \n",
       "       [ 0.06014284,  0.93985716],\n",
       "       [ 0.85454863,  0.14545137],\n",
       "       [ 0.71465495,  0.28534505]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder.predict_proba(test_data, vote_function=mean_vote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of folds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again use `ClassificationReport` class to compare different results. For folding classifier this report uses only __default prediction__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Report training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold prediction using folds column\n"
     ]
    }
   ],
   "source": [
    "from rep.data.storage import LabeledDataStorage\n",
    "from rep.report import ClassificationReport\n",
    "# add folds_column to dataset to use mask\n",
    "train_data[\"FOLDS\"] = folder._get_folds_column(len(train_data))\n",
    "lds = LabeledDataStorage(train_data, train_labels)\n",
    "\n",
    "report = ClassificationReport({'folding': folder}, lds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Signal distribution for each fold\n",
    "\n",
    "Use `mask` parameter to plot distribution for the specific fold "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "RRpO3VbSrCLg/7Bv6HfF9y+m1HhEOc/5J7A38DVswH0gdtbxi1SZz2MV/HVYpf59bPpu9rXT+/84\n",
       "NrB9Pjbw/jjwvfixJ7Apw1+P47sU+ETOPr4Y/z8Puwjxb8ABWOrU9Otm5W0TEZEystNlRaSG1G0l\n",
       "zSrddSQiNabGQ5pVtttHRERERERERERERERERERERERERERERCR0/w8O6e9nepMobQAAAABJRU5E\n",
       "rkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12043dbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for fold_num in range(n_folds):\n",
    "    report.prediction_pdf(mask=\"FOLDS == %d\" % fold_num, labels_dict={1: 'sig fold %d' % fold_num}).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background distribution for each fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "ePw1cDySHtZ/3TCmfYqiKEoMwi64iqKkjKq2lF7Hr15SFKUNaEei9Dph1ZCiKIqiKIqiKIqiKIqi\n",
       "KIqiKIqiKIqiKIqiKIqiKIqiKIqijFf+P4wlYw4SGnI4AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12043dd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for fold_num in range(n_folds):\n",
    "    report.prediction_pdf(mask=\"FOLDS == %d\" % fold_num, labels_dict={0: 'bck fold %d' % fold_num}).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROCs (each fold used as test dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "lFJKKaWUUkoppZRSqhj8f7cCEpBwK+SQAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117307590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for fold_num in range(n_folds):\n",
    "    report.roc(mask=\"FOLDS == %d\" % fold_num).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Report for test dataset \n",
    "\n",
    "__NOTE__: Here vote function is None, so default prediction is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold prediction using folds column\n"
     ]
    }
   ],
   "source": [
    "lds = LabeledDataStorage(test_data, test_labels)\n",
    "\n",
    "report = ClassificationReport({'folding': folder}, lds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "ERERERERERERERERERERkWr6/4E8dQ2+HWc8AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117111f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report.prediction_pdf().plot(new_plot=True, figsize = (9, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f0f7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report.roc().plot(xlim=(0.5, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.8"
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 "nbformat": 4,
 "nbformat_minor": 0
}