track-based-tagging-SS.ipynb

{
 "cells": [
  {
   "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\n",
    "figsize(8, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "import numpy\n",
    "from folding_group import FoldingGroupClassifier\n",
    "from rep.data import LabeledDataStorage\n",
    "from rep.report import ClassificationReport\n",
    "from rep.report.metrics import RocAuc\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils import get_N_B_events, get_events_number, get_events_statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading initial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import root_numpy\n",
    "data_nan = pandas.DataFrame(root_numpy.root2array('datasets/tracks.root', 'tracks'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>run</th>\n",
       "      <th>event</th>\n",
       "      <th>Bmass</th>\n",
       "      <th>i</th>\n",
       "      <th>mult</th>\n",
       "      <th>partP</th>\n",
       "      <th>partPt</th>\n",
       "      <th>ptB</th>\n",
       "      <th>IPs</th>\n",
       "      <th>...</th>\n",
       "      <th>proj</th>\n",
       "      <th>ID</th>\n",
       "      <th>veloch</th>\n",
       "      <th>signB</th>\n",
       "      <th>signTrack</th>\n",
       "      <th>Dist_phi</th>\n",
       "      <th>N_sig_sw</th>\n",
       "      <th>mu_cut</th>\n",
       "      <th>e_cut</th>\n",
       "      <th>K_cut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>115839</td>\n",
       "      <td>204997902</td>\n",
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       "      <td>1.103876</td>\n",
       "      <td>4.004197</td>\n",
       "      <td>1.375382</td>\n",
       "      <td>...</td>\n",
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       "      <td>-211</td>\n",
       "      <td>0.796731</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>115839</td>\n",
       "      <td>204997902</td>\n",
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       "      <td>1.182519</td>\n",
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       "      <td>4.338812</td>\n",
       "      <td>...</td>\n",
       "      <td>10.585135</td>\n",
       "      <td>-211</td>\n",
       "      <td>0.946629</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.856516</td>\n",
       "      <td>0.59521</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>115839</td>\n",
       "      <td>204997902</td>\n",
       "      <td>5.309576</td>\n",
       "      <td>3</td>\n",
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       "      <td>2.287509</td>\n",
       "      <td>...</td>\n",
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       "      <td>204997902</td>\n",
       "      <td>5.309576</td>\n",
       "      <td>4</td>\n",
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       "      <td>0.516123</td>\n",
       "      <td>4.004197</td>\n",
       "      <td>0.562424</td>\n",
       "      <td>...</td>\n",
       "      <td>5.617354</td>\n",
       "      <td>211</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index     run      event     Bmass  i  mult     partP    partPt       ptB  \\\n",
       "0      0  115839  204997902  5.309576  0    13   3.67156  0.300418  4.004197   \n",
       "1      1  115839  204997902  5.309576  1    13   8.33952  1.103876  4.004197   \n",
       "2      2  115839  204997902  5.309576  2    13   8.37654  1.182519  4.004197   \n",
       "3      3  115839  204997902  5.309576  3    13  25.72961  0.905010  4.004197   \n",
       "4      4  115839  204997902  5.309576  4    13   3.70597  0.516123  4.004197   \n",
       "\n",
       "        IPs  ...         proj   ID    veloch  signB  signTrack  Dist_phi  \\\n",
       "0  0.816143  ...     1.058442 -211  0.911645      1         -1  0.114615   \n",
       "1  1.375382  ...     3.121358 -211  0.796731      1         -1  0.051334   \n",
       "2  4.338812  ...    10.585135 -211  0.946629      1         -1  1.856516   \n",
       "3  2.287509  ...     7.485243  211  1.058989      1          1  0.577419   \n",
       "4  0.562424  ...     5.617354  211  1.042135      1          1  1.314513   \n",
       "\n",
       "   N_sig_sw  mu_cut  e_cut  K_cut  \n",
       "0   0.59521       0      0      0  \n",
       "1   0.59521       0      0      0  \n",
       "2   0.59521       0      0      0  \n",
       "3   0.59521       0      0      0  \n",
       "4   0.59521       0      0      0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "event_id_column = 'event_id'\n",
    "event_id = data_nan.run.apply(str) + '_' + data_nan.event.apply(str)\n",
    "data_nan[event_id_column] = numpy.unique(event_id, return_inverse=True)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Events': 1005757, 'tracks': 27156193}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_events_statistics(data_nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "742867.7142562866"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_N_B_events()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove rows with NAN from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27156193, 27156190, {'Events': 1005757, 'tracks': 27156190})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data_nan.dropna()\n",
    "len(data_nan), len(data), get_events_statistics(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add diff_pt and cos(diff_phi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# add different between max pt in event and pt for each track\n",
    "def add_diff_pt(data):\n",
    "    max_pt = group_max(data[event_id_column].values.astype(str), data.partPt.values)\n",
    "    data.loc[:, 'diff_pt'] = max_pt - data['partPt'].values\n",
    "\n",
    "# max is computing max over tracks in the same event for saome data\n",
    "def group_max(groups, data):\n",
    "    # computing unique integer id for each group\n",
    "    assert len(groups) == len(data)\n",
    "    _, event_id = numpy.unique(groups, return_inverse=True)\n",
    "    max_over_event = numpy.zeros(max(event_id) + 1) - numpy.inf\n",
    "    numpy.maximum.at(max_over_event, event_id, data)\n",
    "    return max_over_event[event_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/moosefs/ipython_env/local/lib/python2.7/site-packages/pandas/core/indexing.py:260: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/moosefs/ipython_env/local/lib/python2.7/site-packages/pandas/core/indexing.py:420: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "# add diff pt\n",
    "add_diff_pt(data)\n",
    "# add cos(diff_phi)\n",
    "data.loc[:, 'cos_diff_phi'] = numpy.cos(data.diff_phi.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add max, sum among PIDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from itertools import combinations\n",
    "PIDs = {'k': data.PIDNNk.values,\n",
    "        'e': data.PIDNNe.values,\n",
    "        'mu': data.PIDNNm.values,\n",
    "       }\n",
    "for (pid_name1, pid_values1), (pid_name2, pid_values2) in combinations(PIDs.items(), 2):\n",
    "    data.loc[:, 'max_PID_{}_{}'.format(pid_name1, pid_name2)] = numpy.maximum(pid_values1, pid_values2)\n",
    "    data.loc[:, 'sum_PID_{}_{}'.format(pid_name1, pid_name2)] = pid_values1 + pid_values2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define `label` = `signB` * `signTrack`\n",
    "\n",
    "* if > 0 (same sign) - label **1**\n",
    "* if < 0 (different sign) - label **0**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.loc[:, 'label'] = (data.signB.values * data.signTrack.values > 0) * 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'index,run,event,Bmass,i,mult,partP,partPt,ptB,IPs,IP,IPerr,partlcs,EOverP,ghostProb,IPPU,nnkrec,PIDNNk,PIDNNpi,PIDNNp,PIDNNm,PIDNNe,diff_eta,diff_phi,phi,eta,proj,ID,veloch,signB,signTrack,Dist_phi,N_sig_sw,mu_cut,e_cut,K_cut,event_id,diff_pt,cos_diff_phi,max_PID_mu_k,sum_PID_mu_k,max_PID_mu_e,sum_PID_mu_e,max_PID_k_e,sum_PID_k_e,label'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "','.join(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply ghost prob cut, leave  Same Side (SS) tracks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "initial_cut = '(ghostProb < 0.4)'\n",
    "data = data.query(initial_cut)\n",
    "ss_selection = (data.IPs.values < 3) * (abs(data.diff_eta.values) < 0.6) * (abs(data.diff_phi.values) < 0.825)\n",
    "data = data[ss_selection]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Events': 655033, 'tracks': 1585243}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_events_statistics(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Leave not muons, kaons, electrons, pions, protons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "threshold_kaon = 0.\n",
    "threshold_muon = 0.\n",
    "threshold_electron = 0.\n",
    "threshold_pion = 0.\n",
    "threshold_proton = 0.\n",
    "cut_pid = \" ( (PIDNNk > {trk}) | (PIDNNm > {trm}) | (PIDNNe > {tre}) | (PIDNNpi > {trpi}) | (PIDNNp > {trp})) \"\n",
    "cut_pid = cut_pid.format(trk=threshold_kaon, trm=threshold_muon, tre=threshold_electron, trpi=threshold_pion,\n",
    "                         trp=threshold_proton)\n",
    "    \n",
    "data = data.query(cut_pid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Events': 655033, 'tracks': 1585242}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_events_statistics(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating tagging efficiency ($\\epsilon_{tag}$)\n",
    "\n",
    "$$N (\\text{passed selection}) = \\sum_{\\text{passed selection}} sw_i$$\n",
    "\n",
    "$$N (\\text{all events}) = \\sum_{\\text{all events}} sw_i,$$\n",
    "\n",
    "where $sw_i$ - sPLot weight (sWeight for signal)\n",
    "\n",
    "$$\\epsilon_{tag} = \\frac{N (\\text{passed selection})} {N (\\text{all events})}$$\n",
    "\n",
    "$$\\Delta\\epsilon_{tag} = \\frac{\\sqrt{N (\\text{passed selection})}} {N (\\text{all events})}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7239764091362766, 0.00098720299778957249)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_B_passed = float(get_events_number(data))\n",
    "tagging_efficiency = N_B_passed / get_N_B_events()\n",
    "tagging_efficiency_delta = sqrt(N_B_passed) / get_N_B_events()\n",
    "tagging_efficiency, tagging_efficiency_delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb4476590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(data.diff_pt.values, bins=100)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose most probable B-events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+0rdO/2N9CnjpIrZDkiRJksbWYo+xW11VG9v0RmB1m94LuLlvuZuBvWcov6WV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\nSZI6zmAnSZIkSR1nsJMkSZKkjvs/DIVgC3x9THEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6abc210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, take_indices = numpy.unique(data[event_id_column], return_index=True)\n",
    "\n",
    "figure(figsize=[15, 5])\n",
    "\n",
    "subplot(1, 2, 1)\n",
    "hist(data.Bmass.values[take_indices], bins=100)\n",
    "title('B mass hist')\n",
    "xlabel('mass')\n",
    "\n",
    "subplot(1, 2, 2)\n",
    "hist(data.N_sig_sw.values[take_indices], bins=100, normed=True)\n",
    "title('sWeights hist')\n",
    "xlabel('signal sWeights')\n",
    "plt.savefig('img/Bmass_SS.png' , format='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define B-like events for training \n",
    "\n",
    "Events with low sWeight still will be used only to test quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Events': 436099, 'tracks': 1046087}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweight_threshold = 1.\n",
    "data_sw_passed = data[data.N_sig_sw > sweight_threshold]\n",
    "data_sw_not_passed = data[data.N_sig_sw <= sweight_threshold]\n",
    "get_events_statistics(data_sw_passed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o/ShJK2vwXBRGe+6w1DrSe/IkSVpmSc4GPg4ckeT6JL8xbZHtZw1VtQk4B9gE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\nkN/NZidkWdJe1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3f09510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, take_indices = numpy.unique(data_sw_passed[event_id_column], return_index=True)\n",
    "\n",
    "figure(figsize=[15, 5])\n",
    "subplot(1, 2, 1)\n",
    "hist(data_sw_passed.Bmass.values[take_indices], bins=100)\n",
    "title('B mass hist for sWeight > 1 selection')\n",
    "xlabel('mass')\n",
    "\n",
    "subplot(1, 2, 2)\n",
    "hist(data_sw_passed.N_sig_sw.values[take_indices], bins=100, normed=True)\n",
    "title('sWeights hist for sWeight > 1 selection')\n",
    "xlabel('signal sWeights')\n",
    "plt.savefig('img/Bmass_selected_SS.png' , format='png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hkiStEGMx5F5Vx5L8G+BbwGnADsNckqThjUUPXZIknZpxmeX+lrzhzNJLsibJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3197e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(data_sw_passed.diff_pt.values, bins=100)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main idea: \n",
    "## find tracks, which can help reconstruct the sign of B if you know track sign.\n",
    "\n",
    "`label` = `signB` * `signTrack`\n",
    "* the highest output means that this is same sign B as track\n",
    "* the lowest output means that this is opposite sign B than track"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cos_diff_phi',\n",
       " 'diff_pt',\n",
       " 'partPt',\n",
       " 'partP',\n",
       " 'nnkrec',\n",
       " 'diff_eta',\n",
       " 'EOverP',\n",
       " 'ptB',\n",
       " 'sum_PID_mu_k',\n",
       " 'proj',\n",
       " 'PIDNNe',\n",
       " 'sum_PID_k_e',\n",
       " 'PIDNNk',\n",
       " 'sum_PID_mu_e',\n",
       " 'PIDNNm',\n",
       " 'phi',\n",
       " 'IP',\n",
       " 'IPerr',\n",
       " 'IPs',\n",
       " 'veloch',\n",
       " 'max_PID_k_e',\n",
       " 'ghostProb',\n",
       " 'IPPU',\n",
       " 'eta',\n",
       " 'max_PID_mu_e',\n",
       " 'max_PID_mu_k',\n",
       " 'partlcs']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = list(set(data.columns) - {'index', 'run', 'event', 'i', 'signB', 'signTrack', 'N_sig_sw', 'Bmass', 'mult', \n",
    "                                     'PIDNNp', 'PIDNNpi', 'label', 'thetaMin', 'Dist_phi', event_id_column, \n",
    "                                     'mu_cut', 'e_cut', 'K_cut', 'ID', 'diff_phi'})\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PID pairs scatters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/moosefs/ipython_env/local/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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PmOwea/ldrvf3v2WjiLTuY9PCUlcrVzah66Dmt7j9aUzv4JE0jza+J83HzS2l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\n9cwTMUmSJEnqmSdikiRJktSz/x+jmhhzXSpJngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3667f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=[15, 16])\n",
    "bins = 60\n",
    "step = 3\n",
    "for i, (feature1, feature2) in enumerate(combinations(['PIDNNk', 'PIDNNm', 'PIDNNe', 'PIDNNp', 'PIDNNpi'], 2)):\n",
    "    subplot(4, 3, i + 1)\n",
    "    Z, (x, y) = numpy.histogramdd(data_sw_passed[[feature1, feature2]].values, bins=bins, range=([0, 1], [0, 1]))\n",
    "    pcolor(log(Z), vmin=0)\n",
    "    xlabel(feature1)\n",
    "    ylabel(feature2)\n",
    "    xticks(arange(bins, step), x[::step]), yticks(arange(bins, step), y[::step])\n",
    "plt.savefig('img/PID_selected_SS.png' , format='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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akqTGGdaSJDXOsJYkqXGGtSRJjTOsJUlqnGEtSVLjDGtJkhpnWEuS1DjDWpKkxvnULe1I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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3836e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(data_sw_passed.diff_pt.values, bins=60, normed=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## count of tracks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VrwKuT7Ie2ArsEvKSJGlqA43oZxJH9Ht/nlbOcaDOI0mzwXRH9N4CV5Kkhhn0\nkiQ1zKCXJKlhBr0kSQ0z6CVJaphBL0lSwwx6SZIaZtBLktQwg16SpIYZ9JIkNcyglySpYQa9JEkN\nM+glSWqYQS9JUsMG/Xv0UnOSXf/qo38KV9JsZ9BLffpjfa//2LMkzWBeupckqWEGvSRJDTPoJUlq\nmEEvSVLDDHpJkhpm0EuS1DCDXpKkhhn0kiQ1zKCXJKlhBr0kSQ3zFrjSQeT99CUdbAa9dJB5P31J\nB5OX7iVJaphBL0lSwwx6SZIaZtBLktQwJ+NJjXFmv6R+M35En2RpknuTrE/yO8NujzQbVN9D0qFt\nRgd9kmcAfwosBRYDr0/youG2av8bG3YD9pOxYTdgPxgbdgP2k7FhN2A/GRsbG3YT9lkLfYA2+tFC\nHwYxo4MeOB24v6rGq+oJ4K+Ac4bcpv1ubNgN2E/Ght2A/WBs2A3YT8YOwjmS7PLY31r4xdxCH6CN\nfrTQh0HM9KA/Dniw7/nGriZpBvAtAmnmm+mT8Yb++2PL977HLzznOTufP7pt2xBbI7VtsqsCK1eu\nPODncLKiWpaZ/B88ycuBlVW1tHt+MfBkVV3at83M7YAkSQdAVe31e2UzPejnAP8InAk8BKwFXl9V\n3xhqwyRJmiVm9KX7qtqe5NeBvwWeAVxlyEuStPdm9IhekiTtm5k+6363WriRTpKFST6b5OtJ/iHJ\nhcNu06CSPCPJXUk+Mey2DCrJvCQfTfKNJOu6OSKzSpKLu/9P9yS5Icnhw27T3khydZItSe7pq81P\nsibJfUluTzJvmG3cG7vpxx92/6e+luRjSY4aZhv3ZLI+9K17V5Ink8wfRtumY3f9SPL27vvxD0ku\n3d3+M8Vu/k+dnmRt9zv3i0l+fKpjzMqgb+hGOk8A76yqFwMvB35tlvYD4B3AOmbAJyX2weXArVX1\nIuAlwKx6myjJicDbgFOr6hR6b3ctG2abpuEaej/P/S4C1lTVycCnu+cz3WT9uB14cVX9GHAfcPFB\nb9X0TNYHkiwEfhb45kFv0WB26UeSnwHOBl5SVUuAPxpGw6Zpsu/HB4Hfr6qXAu/unu/WrAx6GrmR\nTlVtrqqvdsvfpRcsxw63VdOXZAHwWuDPgf1/15SDoBtl/VRVXQ29+SFV9fiQmzVd36b34vGIbiLr\nEcCm4TZp71TV54BHJ5TPBlZ1y6uAcw9qowYwWT+qak1VPdk9vRNYcNAbNg27+V4A/DHw2we5OQPb\nTT8uAN6PWQ5TAAADDElEQVTf5QZV9a2D3rBp2k0//hnYcWVoHnv4OZ+tQd/cjXS60dhL6f0imG0u\nA34LeHJPG85gJwHfSnJNkq8k+UiSI4bdqOmoqkeADwEP0PuUymNV9anhtmqfjFTVlm55CzAyzMbs\nJ28Fbh12I6YryTnAxqq6e9ht2UeLgJ9OckeSsSQvG3aDBnQR8KEkDwB/yB6uEs3WoJ/Nl4d3keTZ\nwEeBd3Qj+1kjyc8DD1fVXczS0XxnDnAqcEVVnQpsY3ZcKt4pyQuA3wBOpHdl6NlJ3jDURu0n1Zs1\nPKt/7pP8HvD9qrph2G2Zju4F7+8Cl/SXh9ScfTUHeG5VvZze4GT1kNszqKuAC6vqeOCdwNVTbTxb\ng34TsLDv+UJ6o/pZJ8kzgZuBv6iqW4bdngG8Ajg7yQbgRuBVSa4bcpsGsZHeiOWL3fOP0gv+2eRl\nwOeramtVbQc+Ru/7M1ttSfJ8gCTHAA8PuT0DS/IWem9vzcYXXi+g9+Lxa93P+QLgy0l+ZKitGsxG\nej8XdD/rTyY5erhNGsjpVfW/u+WP0ns7e7dma9B/CViU5MQkhwHnAR8fcpumLb17cV4FrKuqPxl2\newZRVb9bVQur6iR6E78+U1VvHna7pquqNgMPJjm5K70a+PoQmzSIe4GXJ5nb/d96Nb0JkrPVx4Hz\nu+Xzgdn4QpgkS+mNHs+pqn8bdnumq6ruqaqRqjqp+znfSG/C52x84XUL8CqA7mf9sKraOtwmDeT+\nJK/sll9Fb5Lnbs3oG+bsTkM30vlJ4I3A3Unu6moXV9VtQ2zTvprNl1ffDvxl9+Lxn4BfHnJ7pqWq\nvtZdTfkSvfkSXwH+bLit2jtJbgReCTwvyYP0ZhJ/AFidZDkwDrxueC3cO5P04xJ6758eBqzp7rP/\nhapaMbxWTq2vD0fv+F5U1TV9m8yKn/HJ+kHvEvfV3UfVvg/M+EHJbn42fhX4793HZ/+1e777Y3jD\nHEmS2jVbL91LkqS9YNBLktQwg16SpIYZ9JIkNcyglySpYQa9JEkNM+glSWqYQS9JUsP+P9Qc/I74\nRE+pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8dceefd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, n_tracks = numpy.unique(data_sw_passed[event_id_column], return_counts=True)\n",
    "hist(n_tracks, bins=100)    \n",
    "title('Number of tracks')\n",
    "plt.savefig('img/tracks_number_SS.png' , format='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PIDs histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HAw90Hf4AcMwo8c0tTlveD1BVO4DHkhy5h3NJ0qjGnJxdkiRJz1ZVTwOvSHIY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\n78Rx5tgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8db26890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=[15, 4])\n",
    "for i, column in enumerate(['PIDNNm', 'PIDNNe', 'PIDNNk']):\n",
    "    subplot(1, 3, i + 1)\n",
    "    hist(data_sw_passed[column].values, bins=60, range=(0, 1), label=column)\n",
    "    legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train to distinguish same sign vs opposite sign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from hep_ml.decisiontrain import DecisionTrainClassifier\n",
    "from hep_ml.losses import LogLossFunction\n",
    "from rep.estimators import SklearnClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_sw_passed_lds = LabeledDataStorage(data_sw_passed, data_sw_passed.label, data_sw_passed.N_sig_sw.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23min 33s, sys: 16 ms, total: 23min 34s\n",
      "Wall time: 23min 33s\n"
     ]
    }
   ],
   "source": [
    "tt_base = DecisionTrainClassifier(learning_rate=0.02, n_estimators=3000, depth=6,\n",
    "                                  max_features=15, loss=LogLossFunction(regularization=100))\n",
    "tt_folding = FoldingGroupClassifier(SklearnClassifier(tt_base), n_folds=2, random_state=11, \n",
    "                                    train_features=features, group_feature=event_id_column)\n",
    "%time tt_folding.fit_lds(data_sw_passed_lds)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cPickle\n",
    "with open('models/dt_SS.pkl', 'w') as f:\n",
    "    cPickle.dump(tt_folding, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold prediction using folds column\n"
     ]
    }
   ],
   "source": [
    "comparison_report = ClassificationReport({'tt': tt_folding}, data_sw_passed_lds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('tt', 0.53348900071000349)])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_report.compute_metric(RocAuc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<rep.plotting.FunctionsPlot at 0xb3dc8dd0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zyzlONQKS377+Gvr2hVdeCdvHHgs33ghrqcGnSCGKrY+AmW0I/Ce12RyYBmwO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\nMVxyl8BH7v5ppU5mNhmY5u4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heuDQQ+GCC8K0gWRUVtz2ly4z241Qp3Cyu09JOh4REYlQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8e074ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparison_report.roc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold prediction using folds column\n"
     ]
    }
   ],
   "source": [
    "lc = comparison_report.learning_curve(RocAuc(), steps=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<rep.plotting.FunctionsPlot at 0xb308e790>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8EW52t2RJuMTupJOUp5iUp/iUq3iUp1q4h3vdXHABvPceBo1/msbdlwBvAwdm\nPXUA4aqauMqBui65bU7dn63WMWbWmnB1zvR6xCZZ9D95lpkz4YgjQiFyxhlwUrjCXHmKR3mKT7mK\nR3mqhRn07Anjx8OIEQV9q7UKevTV3QyMMLM3CAXI6YTejLsAzOwaYFd33z/a7gMsAcYDK4DeQH/g\nwqoDmtkAQpPrxGhXd0Kfye0ZYy4h9IRMAloBvYATomNVjbkRGElolt0I+APQBnigAT+/NGXLloXL\n56ZOhT33DHftFBFJu+bN4YQT4MQTC/YWRb20190fA84BLgXeIVxa28vdp0RDOgGZ1zV6NPZN4A3g\nKOBkd781Y0wz4LroeG8SLhMeRCgmqvwI+DPwHjAa+BVworvfkzFmU+AvhLVGngQWAd0yYpM8ZM5f\nN3kXXwyjRoVL6B5/PCz3HlGe4lGe4lOu4lGe0qHYZ0Zw9z8TCoNcz52ctf0g8GAdx7sVuLWOMRcD\nF9cx5tjanhdZI48/Hi6Za9483G9mk02SjkhEJDV0b5o8qIFV6uWDD2C33WDBAhgyBM4+O+mIRETq\nrZDrjKgYyYOKEYlt3jzYdVeYODHcjfehh0JTmIhII1Mqi55JE9Sk52PdoW/fUIjssENYcrmGQqRJ\n56kelKf4lKt4lKd0UDEiUijXXQd/+xustx489RT86EdJRyQikkqapsmDpmmkTi++GK7PX7EC/vlP\nOPTQpCMSEVkjmqYRaUwmT4Zjjw2FyODBKkREROqgYkQKqsnNxy5eHFZYnT073J77sstivazJ5SlP\nylN8ylU8ylM6qBgRaSju0L9/9Z14H3oImul/MRGRuqhnJA/qGZGchg2D00+HNm3gtdega9e6XyMi\n0kioZ0Qk7caOhQEDwvd3361CRESkHlSMSEE1ifnY2bPhN7+BpUvhzDPDDaXqqUnkqQEoT/EpV/Eo\nT+mgYkRkTbjD738f7sS7xx5w001JRyQi0uioZyQP6hmRlar6RNZdF959F8rKko5IRKQgdG+alFEx\nIkC4Ad4uu8CiRfCXv8AxxyQdkYhIwaiBVRqtkp2PXbw4FB+LFoX7z6xhIVKyeWpgylN8ylU8ylM6\nqBgRycegQTBhAmy1Fdx2W9LRiIg0apqmyYOmaZq4Z54JS7y3aAFjxoSpGhGREqdpGpG0mD49TMsA\nXH21ChERkQagYkQKqqTmY1esgD59YNYs2H9/GDiwwQ5dUnkqIOUpPuUqHuUpHVSMiMR1883w4ovQ\noQM8+KBswcIGAAAgAElEQVTuOyMi0kDUM5IH9Yw0QW+/HRY1W7oU/vnP0DMiItKEqGdEJEnffw/H\nHlu93LsKERGRBqViRAqqJOZjzzoLPvkEdtgBbrihIG9REnkqAuUpPuUqHuUpHVSMiNTm0Ufh/vuh\ndeuwymrr1klHJCJSctQzkgf1jDQRX3wB5eUwdy4MHQr9+iUdkYhIYtQzIlJsy5bB8ceHQuSXvww3\nwxMRkYIoejFiZv3NbJKZLTKzt8xs71rGlpnZihyPAzPG9DCzMWY2y8wWmtmHZjYw6zhHRu81x8y+\nN7N3zOykNYlN4mm087FXXRVWV91kE7jnHrCC/DGwUqPNU5EpT/EpV/EoT+mwVjHfzMyOBoYA/YDR\nwBnAc2bWxd2n1PLSg4B3M7bnZHw/PzrmBGAhsDcwzMwWu/ud0ZhZwB+Bj4ClQG/gXjOb7e7PrGFs\nUmpeeQWuvDIUIA89BO3bJx2RiEhJK2rPiJmNBca7+2kZ+yYCT7j7xTnGlwGfA7u6+9v1eJ+ngEXu\nfnwtY94G/uXul9Q3NvWMlLA5c6BrV5gyBS66CP70p6QjEhFJhZLoGTGzlsBOwAtZT70A7FnHy58y\nsxlmNtrMjqjjfXYE9gBerOF5M7P9gJ8BLzVAbFIq3OHUU0MhsttucMUVSUckItIkFLNnpAPQHJiR\ntX8m0KmG18wHBgJHAgcTiodHzWy1Mx5mNtXMFgNvAcPcfXjW8+uZ2ffAD8CzwLnu/p81iE1iaFTz\nsffdB088AeusA488Eu7KWySNKk8JUp7iU67iUZ7Soag9I/Xl7rOBWzJ2jTOz9sCFwMNZw/cC1iac\nFbnBzGa6+9CM5+cBP4/G7A/cGo0ZWbAPII3H3LnVN74bOhR+8pNk4xERaUKKWYzMApYDHbP2dwSm\n1+M4bwK/zd7p7pOjb983s47ABcDQjOed0H8C8D8z2xY4FxiZT2x9+/alrKwMgHbt2lFeXk5FRQVQ\nXWlru4KKiopUxVPj9iOPUDF3LvToQeVmm0FlZdHjqZKKfKR0u9H8PGm70WxX7UtLPGnarqysZPjw\n4QArf98VSrEbWF8H3s3RJPp4VSNpjGPcAvR2961qGTMY+J27b1HLmPuAn7h7j/rGpgbWErNoEZSV\nwcyZ8PzzcOCBdb5ERKSpKYkG1sjNQF8z+52ZbWtmtxJ6Mu4CMLNrzOzfVYPNrI+ZHRuN3drMzgf6\nA7dnjBlgZoeY2U+jx+8IfSYjMsZcYmb7mVnn6FgDgRMyx9QVm+SnqspOtfvvD4XITjvBAQckEkKj\nyFMKKE/xKVfxKE/pUNSeEXd/LOr5uBTYmLA2SK+MdTw6AZ0zXxKN3YIwjfIxcLK7P5IxphlwHVAG\nLAM+BQYBwzLG/Aj4M7AZsAj4EDjR3R+tR2xSipYtq7753UUXFXxxMxERWZ3uTZMHTdOUkIceghNP\nhJ/9DD74AJo3TzoiEZFUKqVpGpH0WLECrr02fD9okAoREZGEqBiRgkr1fOzTT8P778Nmm8EJJyQa\nSqrzlCLKU3zKVTzKUzqoGJGmyb16qfeBA6Fly2TjERFpwtQzkgf1jJSAUaNg333DTfAmT4Yf/Sjp\niEREUk09IyIN7ZprwtezzlIhIiKSMBUjUlCpnI99+2148UVYe20488ykowFSmqcUUp7iU67iUZ7S\nQcWIND1VZ0VOOw022CDZWERERD0j+VDPSCP20UfQpUu4I++kSbDJJklHJCLSKKhnRKShXH99uJKm\nb18VIiIiKaFiRAoqVfOxU6bAiBHQrBlceGHS0awiVXlKMeUpPuUqHuUpHVSMSNNx003hXjRHHQU/\n+UnS0YiISEQ9I3lQz0gjNGsWbLEFLFwI48dD165JRyQi0qioZ0RkTd12WyhEevVSISIikjIqRqSg\nUjEfO38+3H57+P6ii5KNpQapyFMjoDzFp1zFozylg4oRKX3DhsF338Hee4eHiIikinpG8qCekUbk\nhx9gyy1h+nR45pkwTSMiIvWmnhGRfD3wQChEunaFgw9OOhoREclBxYgUVKLzscuWwXXXhe//3/8D\nK0hB3yA0bx2P8hSfchWP8pQOKkakdD3+OHz+eVhT5De/SToaERGpgXpG8qCekUbAHcrL4X//Cw2s\np56adEQiIo1aIXtGVIzkQcVII/DMM3DoobDxxuGGeK1aJR2RiEijpgZWabQSm4+95prwdeDARlGI\naN46HuUpPuUqHuUpHVSMSOl55RV49VVYf31Nz4iINAKapsmDpmlSrlcveO45GDwYrrgi6WhEREqC\nekZSRsVIio0fDzvuCG3bwpdfQvv2SUckIlIS1DMijVbR52Or1hU59dRGVYho3joe5Sk+5Soe5Skd\nil6MmFl/M5tkZovM7C0zq/FmIWZWZmYrcjwOzBjTw8zGmNksM1toZh+a2cCs45xiZq+Y2bdmNsfM\n/mNme2WNuTzH+0xr+AxIwXz5ZVhbZK214Lzzko5GRERiKuo0jZkdDYwA+gGjgTOAk4Eu7j4lx/gy\n4HPgIODdjKfmuPvSaMxOwFbABGAhsDcwDBjk7ndGYx4CxgCvAouAc4ETgXJ3/zQaczlwFFCR8T7L\n3X12jrg0TZNGgwbB9dfDscfCI48kHY2ISEkpmZ4RMxsLjHf30zL2TQSecPeLc4wvIxQju7r72/V4\nn6eARe5+fC1jpgNXZRQslwNHuPsOMY6vYiRtFiyAzTYLd+cdOxZ22y3piERESkpJ9IyYWUtgJ+CF\nrKdeAPas4+VPmdkMMxttZkfU8T47AnsAL9YyphXQGpiT9VRnM/vKzD43s7+Y2ZZ1xCV1KNp87IMP\nhkJkjz0aZSGieet4lKf4lKt4lKd0iFWMmNn2ZtY1x/6uZtYl5nt1AJoDM7L2zwQ61fCa+cBA4Ejg\nYOAl4FEzW+2Mh5lNNbPFwFvAMHcfXkssV0XHHpmx73WgD2FK6JQopjFmtkHtH0sSt2IF3Hpr+P6c\nc5KNRURE6i3WNI2ZjQFudfdHs/YfC5zh7jU2oWaM3QSYCnR399EZ+wcDx7n7NrECNrsD2Mfdu2bt\n3wJYm3BW5AbgEncfmuP1ZwN/BPZz97dqeZ+2wCTgWne/Jes5TdOkyXPPhbVFNt883BhvrbWSjkhE\npOQUcpom7r/aOwBv5tj/JvDzmMeYBSwHOmbt7whMj3mMqvf8bfZOd58cffu+mXUELgBWKUbM7BxC\nIdKztkIkOt5CM3uf0By7mr59+1JWVgZAu3btKC8vp6KiAqg+7aftIm0PHhy2zzwT1lor+Xi0rW1t\na7sEtisrKxk+fDjAyt93hRL3zMh3wP7Zv8DNbBdglLuvE+vNzF4H3s3RwPq4u18S8xi3AL3dPWeR\nEI0ZDPzO3bfI2HcecDnQK/PMTC3HaE04M3Knu1+V9ZzOjMRUWVm58oe8IN5/H7bfPixyNmUKbNA4\nZ9UKnqcSoTzFp1zFozzFl4YzIy8Dl5jZke6+LAqqBXBJ9FxcNwMjzOwNwqW2pxN6M+6KjnkN4cqZ\n/aPtPsASYDywAugN9AcurDqgmQ0gXHEzMdrVndBncnvGmAsIfSInAJ+aWVWPykJ3nxeNuZHQQzIF\n2Aj4A9AGeKAen0+K7bbbwtc+fRptISIi0tTFPTOyDWFdkPnRVyOs57E2oQfkg9hvaNaPUExsTFgb\n5NyqMxVmdj/Qw907R9snAYOALQhTPB8DQ9z9kYzjnU1oOC0DlgGfAvcQmlg9GjMJ+HEUd6bh7v7b\naMxfCIVMB+Ab4DXgD+7+UY7PoDMjaTB7dricd/Fi+PBD2CZW25GIiOQhFeuMRA2oZwA7Ag68Awx1\n9ya3SqmKkZS45hq4+GI4+GB49tmkoxERKWmpWGfE3ae5+yXu3svdD3H3S5tiISL1U9UM1eCWLoU7\n7gjfl8DlvAXLU4lRnuJTruJRntKhxp6RaJn1d919efR9jdx9XINHJlKbJ56AadNg223hgAOSjkZE\nRNZAjdM0ZrYC6OTuM6Pva+Lu3rwg0aWUpmkS5g677w5vvgnDhoU79IqISEEl0jMS3RfmS3dfEX1f\nI3f/ooHjSjUVIwl77TXYc09o3z5cztumTdIRiYiUvER6Rtz9i6gQaUG4nNajfas9ChGYlIaCzMcO\nGRK+nnZayRQimreOR3mKT7mKR3lKhzobWN19KaEYKUg1JFIvX34JTz4Zlnzv3z/paEREpAHEXWfk\nKeBpd7+v8CGln6ZpEjRoEFx/PRx3HDz8cNLRiIg0GYmvM2Jm/YHLgL8S7oq7IPN5d3+qEMGllYqR\nhCxYEBY5++47GDsWdtst6YhERJqMNKwzcgewITCAsDz6E1kPkZwadD72wQdDIbLnniVXiGjeOh7l\nKT7lKh7lKR1i3ZvG3WMvjiZSECtWVDeulsAiZyIiUi3uNM1JwKPu/kPW/pbAMe7+YIHiSyVN0yTg\n2WfhkENg883h889DA6uIiBRNGqZphgPr5di/bvScSGFVnRUZMECFiIhIiVnT6ZfNge8aIhApTQ0y\nH/v++/Dii9C2Lfz+92t+vBTSvHU8ylN8ylU8ylM61PonpplNyNj8r5kty9huDmwB6HapUli33hq+\n9u0L66+faCgiItLwau0ZMbPLo28HAzex6iW9S4BJwJPuvqRQAaaRekaKaNas0CeyeDF89BFsvXXS\nEYmINEmF7Bmp9cyIu18eBfAF8Fd3X1yIIERqdPfdoRDp1UuFiIhIiYrVM+LuwwEzsyPNbJCZrU/Y\nsZWZtS9kgNK4rdF87JIlcOed4fsSv5xX89bxKE/xKVfxKE/pEOuyBDPbCvg3sDbQDngcmAOcHm2X\nZlehJOuJJ2DaNOjSBfbfP+loRESkQOKuM/I0MJ1QfHwHdHX3z82sOzDc3TsXNsx0Uc9IEbiHVVbf\neitM1ZxyStIRiYg0aYn1jGTYE+jm7svNVoljCrBJg0cl8tproRBp3x5OOCHpaEREpIDqs85Iyxz7\nNgfmNlAsUoLyno+tWuTstNOgTZsGiyetNG8dj/IUn3IVj/KUDnGLkReA8zJ3mNl6wB+BZxo6KGni\nJk+GJ58MK6327590NCIiUmBxe0Y2BUZFm1sC44GtgBlAd3efWbAIU0g9IwU2aBBcfz0cfzw89FDS\n0YiICIXtGYlVjERBtAWOAXYGDBgHPOzuiwoRWJqpGCmgpUths81g5szQN9KtW9IRiYgI6bhRHoTL\neucC7wHvE3pITjYznUeXGtV7PvZf/wqFyLbbwu67FySmNNK8dTzKU3zKVTzKUzrEXWfkWOA+QvEy\nB8g+LTC0geOSpur++8PXvn3BClKAi4hIysTtGZkMPAD80d2X1TW+jmP1By4AOhHOsJzj7qNrGFsG\nfJ7jqZ7u/kI0pgdwDfAzoC0wGbjH3W/KOM4pwEnAdoQppneAP7j7q/nEpmmaAvnmG9hkE1ixAqZO\nhY03TjoiERGJpGGaZj3C4mZrWogcDQwBrgLKgTHAc2a2eR0vPYhQIFQ9RmU8Nz865j7AttGxrzCz\nMzLG9AD+AvwC2B34GHg+Wll2TWOThvLII7BsGfTsqUJERKQJiVuM/BU4pAHe7zzgfne/190/dvez\nCCu79qvjdd+6+8yMx9KqJ9x9nLs/5u4fuvtkd3+YcCnynhljTnD3oe7+rrtPdPd+hCKmZwPEJrWo\n13zs8OHh68knFyKUVNO8dTzKU3zKVTzKUzrEXYH1HOAfZrYfMAFYmvmku/+xrgOYWUtgJ+D6rKdW\nKRxq8JSZtQY+AW5x9ydreZ8dgT2Ai2oZ0wpoDXzbALFJQxg/Pjw22AB69046GhERKaK4xcipwAHA\nLML6IlUNExZ9X2cxAnQAmhPWJsk0kzD1kst8YCDwKrAMOBx41Mz6RGdAVjKzqdF7tACujO40XJOr\nomOPXIPYJIaKiop4A6vOihx3HLRqVahwUit2npo45Sk+5Soe5Skd4hYjlwLnu/vNhQwmm7vPBm7J\n2DXOzNoDFwIPZw3fi3D58R7ADWY2091Xu8rHzM4mFFf7ufv3hYlc6mXJEng4+s/Zt2+ioYiISPHF\nLUaaA/9Yw/eaBSwHOmbt70jozYjrTeC32TvdfXL07ftm1pFwVcwqxYiZnUM4i9PT3d9ak9j69u1L\nWVkZAO3ataO8vHxlhV01B6ntilXmY2scf911MGsWFTvsADvtlKr4i7U9fvx4zjnnnNTEk9btWD9P\n2qZKZs6Sjiet20OGDNG/3zVsV1ZWMjw6a131+65g3L3OB3ATMDjO2DqO8zowLGvfRODqehzjFuDT\nOsYMBiZn7TsPmAfsvaaxhbRJHKNGjap70GGHuYP7TTcVPJ60ipUnUZ7qQbmKR3mKL/rdt0Z1QE2P\nuOuMDAWOJ6y++j+qG1gtCu6sOIWPmR0FjAD6Ey6dPR04GdjO3aeY2TXAru6+fzS+D7CEcC+cFUBv\n4GrgQne/NRozgLAWycTobboDNwO3u/ul0ZgLCH0iJwCvZIS00N3nxYkt63N4nLxJDDNmwKabhgXO\nvvoKNtoo6YhERCSHQq4zEneapgthoTCAbTL2VzWwxuLuj0U9H5cCGxOuzOmV8cu+E9A58yXR2C0I\n0ygfAye7+yMZY5oB1wFlhCbXT4FBwLCMMf0Jn/XRrJCGE035xIhNCuHhh2H5cjjsMBUiIiJNVOwb\n5Uk1nRmJr7KycuVc5Grc4ec/h/feg7/9DX75y6LGlia15klWUp7iU67iUZ7iS8MKrCINb9y4UIh0\n6AC9eiUdjYiIJERnRvKgMyMN5Mwz4c474eyzYciQpKMREZFaFPLMiIqRPKgYaQA//BDuPzNnTlh5\ntWvXpCMSEZFaaJpGGq3MNQ9WMXJkKETKy1WIoPtjxKU8xadcxaM8pYOKEUlGE74pnoiIrErTNHnQ\nNM0amjYNNt8cmjcP33fokHREIiJSB03TSGl56CFYsSLcnVeFiIhIk6diRApqtflY9+opGt0UbyXN\nW8ejPMWnXMWjPKWDihEprjfegA8/hI4doWfPpKMREZEUUM9IHtQzsgb69YO77oKBA+HGG5OORkRE\nYtI6IymjYiRPixaFtUXmzoUJE2D77ZOOSEREYlIDqzRaq8zH/uMfoRDZZRcVIlk0bx2P8hSfchWP\n8pQOKkakeO6/P3xV46qIiGTQNE0eNE2Th6lT4cc/hhYtYPp02GCDpCMSEZF60DSNNH4PPhgu6z38\ncBUiIiKyChUjUlCVlZWrri2i5d9z0rx1PMpTfMpVPMpTOqgYkcIbMwY++SRcSXPAAUlHIyIiKaOe\nkTyoZ6SeTjkF7rkHBg2Ca69NOhoREcmD1hlJGRUj9bBgQTgjMn9+WHl1m22SjkhERPKgBlZptCr/\n9KdQiHTrpkKkFpq3jkd5ik+5ikd5SgcVI1JYzz0XvmptERERqYGmafKgaZqYJk+GsjJo3TqsLdKu\nXdIRiYhInjRNI43Tgw+Gr7/6lQoRERGpkYoRKYwVK2D4cCpBUzQxaN46HuUpPuUqHuUpHVSMSGGM\nHg2ffw4dOsB++yUdjYiIpJh6RvKgnpE6zJsHe+8NEybAxRfD1VcnHZGIiKyhkuoZMbP+ZjbJzBaZ\n2VtmtnctY8vMbEWOx4EZY3qY2Rgzm2VmC83sQzMbmHWc7czsCTP7LHr9ZTne6/Ic7zOtYT99E7B0\nKRx1VChEtt4azj8/6YhERCTlilqMmNnRwBDgKqAcGAM8Z2ab1/HSg4BOGY9RGc/Nj465D7BtdOwr\nzOyMjDFtgM+BS4FJQE2nNT7Kep8d4n42IdyDZsAAeP552HBDePZZKt99N+moGgXNW8ejPMWnXMWj\nPKXDWkV+v/OA+9393mj7LDPrCfQDLq7ldd+6+8xcT7j7OGBcxq7JZnYEsCdwZzTmLeAtADOr7X2W\n1/Q+EsONN8KwYeFS3pEjoXNn+PLLpKMSEZGUK1rPiJm1BBYAx7j7kxn77wC2d/eKHK8pI5zRmAK0\nBj4Bbsl8fY7X7Ag8C1zk7sNzPD8BeNzd/5i1/3LgAuA74AdgLHCxu0/KcQz1jGR74gk48sjw/WOP\nVX8vIiIloVR6RjoAzYEZWftnEqZEcpkPDASOBA4GXgIeNbPjswea2VQzW0w4AzIsVyFSh9eBPoQp\noVOimMaY2Qb1PE7TM3YsnHhi+P7aa1WIiIhIvaT60l53n+3ut7j7G+4+zt0vA+4CLswxfC9gZ+A0\n4Gwz61/P9/qXuz/h7u+5+0vAIYT89FnDj1HaJk2C3r1h8eJwd94LV/1Po/nYeJSneJSn+JSreJSn\ndChmz8gsYDnQMWt/R2B6PY7zJvDb7J3uPjn69n0z60iYchmaR5xVx1toZu8DW+V6vm/fvpSVlQHQ\nrl07ysvLqaioAKp/uEt+u2tX6NWLym++gV12oeLOO8EsPfE1ou3x48enKh5tN/7tKmmJJ63b48eP\nT1U8adqurKxk+PDhACt/3xVKUdcZMbPXgXfd/bSMfRMJPRyXxDzGLUBvd89ZJERjBgO/c/ctcjyX\ns2ckx7jWhCtv7nT3q7KeU8/IkiXQsyeMGgXbbx8WOVtvvaSjEhGRAilkz0ixr6a5GRhhZm8QLus9\nndCbcReAmV0D7Oru+0fbfYAlwHhgBdAb6E/GNI2ZDSA0uU6MdnUn9JncnjGmBbBdtNkG2NjMyoHv\n3f3TaMyNwEhCs+xGwB+isQ80aAZKgTucdlooRDp1gmeeUSEiIiJ5K2rPiLs/BpxDWO/jHcLlt73c\nfUo0pBPQOfMl0dg3gTeAo4CT3f3WjDHNgOui471JuEx4EKGYqLIp4fLfccCWhL6SccDdWWP+Qlhr\n5ElgEdAtIzapcvXVMHw4tG0LTz8NP/5xjUOzTxlLbspTPMpTfMpVPMpTOhT7zAju/mfgzzU8d3LW\n9oPAg3Uc71bg1jrGfEEdhZe7H1vb8xJ55BH4wx/ALHy/885JRyQiIo2c7k2ThybbMzJ6dLjp3ZIl\ncMstcM45SUckIiJFUsieERUjeWiSxcgnn0C3bvDtt3DmmXDbbeHsiIiINAmlsuiZNFazZ0OvXqEQ\nOeSQcFYkZiGi+dh4lKd4lKf4lKt4lKd0UDEitVu8GH75S/j0U9hxR/jrX2GtorcaiYhICdM0TR6a\nzDSNO5xwQmhU3XTTsOz7ppsmHZWIiCRA0zRSfKNHw557hkJk7bXDWiIqREREpABUjMiqPvooTMvs\nsw+8/jpstBH87W/QtWteh9N8bDzKUzzKU3zKVTzKUzqoGJHg66/h9NPD0u7/+EdY0Oyyy0KvyP77\nJx2diIiUMPWM5KGkeka+/x5uvDE8FiyA5s3h978PhcjGGycdnYiIpEQp3ZtG0mLpUrj3Xrj8cpgx\nI+w7/HC45hrYdttEQxMRkaZF0zRNjXvoAdl+e+jXLxQi3brBK6/A3//e4IWI5mPjUZ7iUZ7iU67i\nUZ7SQWdGmpIxY+CCC8JXgK22gmuvhV//WqupiohIYtQzkodG1zMycSJcdBE89VTY3nDD0BNy6qnQ\nokWysYmISKOgnhHJ36hRcNBBoUekbVs477xwdmTddZOOTEREBFDPSGmbNSusoLp0KRxzTLjZ3ZVX\nFrUQ0XxsPMpTPMpTfMpVPMpTOujMSKlyD5foTpsGe+0FI0bonjIiIpJK6hnJQ6PoGbnrrnC1zHrr\nwbvvwhZbJB2RiIg0YoXsGVExkofUFyMffAA77xzuuPvXv8LRRycdkYiINHK6UZ7Et3hx6A9ZvBhO\nPjnxQkTzsfEoT/EoT/EpV/EoT+mgYqTUDBoEEybAT38Kt92WdDQiIiJ10jRNHlI7TfPMM3DooaFR\n9bXXYJddko5IRERKhKZppG5ffx2mZQCuvlqFiIiINBoqRkrBihXQpw988w3suy+cf37SEa2k+dh4\nlKd4lKf4lKt4lKd0UDFSCm69FV54Adq3hwcfhGb6zyoiIo2HekbykKqekXfegd13D6us/v3vcPjh\nSUckIiIlSD0jktuCBXDccaEQ6ddPhYiIiDRKKkYas/POg48+gi5d4Kabko4mJ83HxqM8xaM8xadc\nxaM8pUPRixEz629mk8xskZm9ZWZ71zK2zMxW5HgcmDGmh5mNMbNZZrbQzD40s4FZx9nOzJ4ws8+i\n11+2prEl7qmn4O67oVUr+MtfoE2bpCMSERHJS1F7RszsaGAE0A8YDZwBnAx0cfcpOcaXAZ8DBwHv\nZjw1x92XRmN2ArYCJgALgb2BYcAgd78zGrMLcBTwDnAV8IC7/zHf2BLvGZkyBbp2hTlzwsJmAwYk\nF4uIiDQJJXNvGjMbC4x399My9k0EnnD3i3OMLyMUI7u6+9v1eJ+ngEXufnyO5yYAj+coRmLHlmgx\nsnw57Lcf/Pe/0KsXPP00WEF+NkRERFYqiQZWM2sJ7AS8kPXUC8Cedbz8KTObYWajzeyIOt5nR2AP\n4MUixVZc110XCpGOHeH++1NfiGg+Nh7lKR7lKT7lKh7lKR3WKuJ7dQCaAzOy9s8EOtXwmvnAQOBV\nYBlwOPComfVx94czB5rZ1Og9WgBXuvvwAsdWfGPHwuDB4fsHHoCNNko2HhERkQZQzGKk3tx9NnBL\nxq5xZtYeuBB4OGv4XsDahLMiN5jZTHcfWqjY+vbtS1lZGQDt2rWjvLyciooKoLrSbtDtBQuoOOss\nWL6cyiOPhFatqIhiKcj7NdB2RUVFquJJ83aVtMSTxm39PGm7ober9qUlnjRtV1ZWMnz4cICVv+8K\npWg9I9FUyALgGHd/MmP/nYQm0V/EPE4f4M/u3raWMZcAv3f3LXM8t1rPSH1jS6Rn5KSTYMQI2HHH\ncBO8Vq2K+/4iItKklUTPiLsvAd4GDsx66gBgTD0OVQ5Mq2NMc+rx2RowtsL43/9CIdK2bbiMtxEV\nIlVVttROeYpHeYpPuYpHeUqHYk/T3AyMMLM3CL/kTyf0ZNwFYGbXEK6c2T/a7gMsAcYDK4DeQH/C\nNA3RmAGEK24mRru6E/pMbs8Y0wLYLtpsA2xsZuXA9+7+aZzYEvXyy+HrEUfA1lsnG4uIiEgDK/q9\naaHTcGEAABPsSURBVMysH6GY2JiwNsi57j46eu5+oIe7d462TwIGAVsAy4GPgSHu/kjG8c4GTgHK\nCE2unwL3AMOq5lIyLhEGcKDqNFOlu+8bJ7asz1DcaZrjjgtnRO66C047re7xIiIiDaxk1hkpFUUv\nRsrKYPLkMF2zww7Fe18REZFISfSMSJ6++ioUIuuuG+5B08hoPjYe5Ske5Sk+5Soe5SkdVIyk3Zio\nf7ZbN2jePNlYRERECkDTNHko6jTNuefCkCFwxRXVC56JiIgUmaZpmrKqMyN7pmtVehERkYaiYiTN\nFi2CceOgWTPYbbeko8mL5mPjUZ7iUZ7iU67iUZ7SQcVImr31FixbFq6gWXfdpKMREREpCPWM5KFo\nPSPXXgsXXQT9+sHQgt1mR0REpE7qGWmq1C8iIiJNgIqRtHKvLkb22ivZWNaA5mPjUZ7iUZ7iU67i\nUZ7SQcVIWn3yCcyeDZ06hRVYRURESpR6RvJQlJ6R4cPh5JPh17+GJ58s7HuJiIjUQT0jTdGrr4av\n6hcREZESp2IkrUqkeVXzsfEoT/EoT/EpV/EoT+mgYiSN5syBDz6AVq1gp52SjkZERKSg1DOSh4L3\njDz3HPTqFa6iGT26cO8jIiISk3pGmpoSmaIRERGJQ8VIGpVQMaL52HiUp3iUp/iUq3iUp3RQMZI2\ny5bB2LHh+z32SDYWERGRIlDPSB4K2jMybhzsvDP85Cfw6aeFeQ8REZF6Us9IU1ICS8CLiIjUh4qR\ntCmhfhHQfGxcylM8ylN8ylU8ylM6qBhJmxIrRkREROqinpE8FKxn5KuvYLPNYN114dtvoXnzhn8P\nERGRPKhnpKmoOivSrZsKERERaTJUjKRJCTavaj42HuUpHuUpPuUqHuUpHVSMpIn6RUREpAkqes+I\nmfUHLgA6Ae8D57h7zhuwmFkZ8HmOp3q6+wvRmB7ANcDPgLbAZOAed78p61hHAFcCnYHPgEvc/e8Z\nz18ODM56n6/dfZMccTV8z8iiRaFXZMWKcKO8dddt2OOLiIisgUL2jKxViIPWxMyOBoYA/YDRwBnA\nc2bWxd2n1PLSg4B3M7bnZHw/PzrmBGAhsDcwzMwWu/ud0fvuAfyVUGw8BRwBPG5me7n7GxnH+gio\nyNheXu8Pma+33gqrr3btqkJERESalGJP05wH3O/u97r7x+5+FjCdUJzU5lt3n5nxWFr1hLuPc/fH\n3P1D9//f3v1H2zXeeRx/f8SPzGI06kfS+nWRGRkkgpHmikmpnyuqlDUYY4R2VDEGNWUpMpT6URli\nVIrpaBYlykK1VimzImMkodE0aBC6IiGIEOJH/Jbv/PHsm+wcN/fu3HvO2eee83mttVfu3vs5ez/7\nu57c+z37efazY0FE3AI8AOT7Ok4HpkTEpdl5LwGmZtvzPqs4z5LeXnBh06alf5usi8b9scU4TsU4\nTsU5VsU4To2hbsmIpHWBXUmJQl5l4tCZuyS9JumRrLulq/PsArQDD+Y2jyx43m0lvSxpnqTJkrbp\npl7V04SDV83MzIqo25gRSV8GFgKj82NEJI0Djo6IIZ18ZmPgWGAa8ClwCHAuMDa7A5IvuxDYBFgH\nuCgiLsjt+wj4dkT8IrftWOCGiOifrR8IbEDqqhkInAcMAXaMiDcrzlXdMSMRsOmmsGQJzJsH29Qv\nBzIzMyuiacaMrKmsm+Sq3KZZWYJyFnBLRfFRpGSiHbhC0uKImLgG57o/t/onSTOAF4CxFXWovuef\nT4nIoEHQ1lbTU5mZmTWaeiYjb5AGhA6s2D6QNG6kqJnAtyo3RsSC7Mc5kgaSntjpSEYWkZ7eqTzv\notWdJCLelzQHGNzZ/uOOO462LHEYMGAAw4cPZ6+99gJW9kEWXr/xxrS+xx4grfnnG3g93x/bCPVp\n1PXZs2dz+umnN0x9GnXd7an4ese2RqlPo65PmDChd7+/m3h96tSpTJo0CWDF37taqeujvZIeBZ6I\niBNz254D7oiIcwse4yrg4IjoNEnIyowjdctsna3fBmwUEQfkyjwAvB4R/7iaY/Qn3Rm5NiIurthX\n3W6aE06An/0Mxo+HM8+s3nEbwNSpU1c0cls9x6kYx6k4x6oYx6m4WnbT1DsZOQK4GTgZmA58Fzie\nNC7jJUmXArtHxL5Z+bHAx8BsYDlwMPAj4KyIuDorcyppLpLnstOMBq4EromI87Iy7cDDpHEg9wDf\nBC4ERkXEzKzMeODXwEvAZsD5pMeEh1Y+dlz1ZGTHHeHpp9Mg1vb26h3XzMysSppmzEhE3J6N+TgP\n+BJpbpAxuT/2g0iTkq34SFZ2a1IXz1zg+Ii4NVdmLeByoI00yPXPwNnA9bnzzpB0FHAx8MOszBEd\niUhmc2AyaRDs68AMYGQ385/03ltvpURkvfVg111reiozM7NG5Lf29kBV74zcdx+MGZMe6X2k04lo\n+zTfAi3GcSrGcSrOsSrGcSrOb+1tZn4fjZmZtTjfGemBqt4Z2WcfmDIF7r4bDj20Osc0MzOrsqYZ\nwNosqpaMfPopDBgAy5bBokUwsPKpZzMzs8bgbppm9eSTKREZPLhpE5H8nAe2eo5TMY5TcY5VMY5T\nY3AyUiaPFzEzM3M3TU9UrZvm6KNh8mS47jo48cTuy5uZmZXE3TTNyndGzMzMnIyU5uWXYcEC2HBD\n2GGHsmtTM+6PLcZxKsZxKs6xKsZxagxORsrScVdk5Ejo16/cupiZmZXIY0Z6oCpjRs44AyZMgAsv\nhHHjqlMxMzOzGvGYkWbk8SJmZmaAk5FyfPABzJoFa60FI0aUXZuacn9sMY5TMY5TcY5VMY5TY3Ay\nUobHH0+zrw4dmgawmpmZtTCPGemBXo8ZuewyOOccOOkkmDixehUzMzOrEY8ZaTYd40VGjSq3HmZm\nZg3AyUi9RbTU4FX3xxbjOBXjOBXnWBXjODUGJyP19vzzsGQJDBoEbW1l18bMzKx0HjPSA70aMzJp\nEhx/PBx2GNx5Z1XrZWZmViseM9JMWqiLxszMrAgnI/U2bVr6t0UGr7o/thjHqRjHqTjHqhjHqTE4\nGamnt96Cp5+G9daDXXYpuzZmZmYNwWNGeqDHY0buuw/GjEl3RR55pPoVMzMzqxGPGWkWHi9iZmb2\nOU5G6qkFkxH3xxbjOBXjOBXnWBXjODUGJyP18umn8Nhj6ef29nLrYmZm1kA8ZqQHejRmZO5cGDYM\nttoqTXxmZmbWhzTVmBFJJ0t6QdIHkh6XtGcXZdskLe9k2T9X5quSpkt6Q9L7kp6RdGYnxzpc0tOS\nPpQ0R9KhvanbGtt+e3j77TSI1czMzFaoazIi6UhgAnAxMByYDtwnactuPnoAMCi3PJTb9252zL8D\n/iY79oWSTsmdtx24DbgZ2Bm4BbhD0ogq1K24/v1h8OCqHa4vcH9sMY5TMY5TcY5VMY5TY6j3nZHv\nAT+PiP+OiLkR8a/Aq8BJ3XzuzYhYnFs+6dgREbMi4vaIeCYiFkTELcADQH6U6OnAlIi4NDvvJcDU\nbHtv62ZdmD17dtlV6BMcp2Icp+Icq2Icp8ZQt2RE0rrArqREIa8ycejMXZJek/SIpMO7Oc8uQDvw\nYG7zyK7O28u6WReWLl1adhX6BMepGMepOMeqGMepMaxdx3NtAvQDXqvYvpjU9dKZd4EzgWnAp8Ah\nwC8ljc3ugKwgaWF2jnWAiyJiUm73oE7O+1ruvD2pm5mZmVVBPZORNRYRS4CrcptmSdoYOIs07iNv\nFLAB6a7IFZIWR8TE+tTUVmf+/PllV6FPcJyKcZyKc6yKcZwaRETUZQHWBT4BDq/Yfi3w0BocZyzw\nfjdlzgVeyK0vAP6tosz3gfk9qRsQXrx48eLFS6sttcoR6nZnJCI+lvQHYH/gztyu/YA71uBQw4FX\nuinTj1XHw8zIzjO+4rzTelK3Wj1nbWZm1orq3U1zJXCzpN+THp39LmlMxnUAki4Fdo+IfbP1scDH\nwGxgOXAwcDKpm4aszKnAPOC5bNNo0jiTa3LnvRp4WNLZwD3AN4G9SF07hepmZmZmtVHXZCQibs/G\nfJwHfAl4ChgTES9lRQYB2+Y/kpXdGvgMmAscHxG35sqsBVwOtJEGuf4ZOBu4PnfeGZKOIs0h8sOs\nzBERMXMN6mZmZmY14OngzczMrFR+Ud4aqumU8Q1O0gWdTM3/SidlXs6m5n9I0g4V+9eTdI2k1yW9\nJ+keSZvX90qqT9JoSb+WtDCLy9hOyvQ6NpI2knSzpKXZcpOkL9T6+qqluzhJmtRJG5teUaYV4nSO\npJmS3pa0OIvZjp2Ua+k2VSROblOJpFMkPZHF6m2l16iMqShTWntyMrIGVI8p4xvfs6w6Nf/Qjh1K\nY3K+B/wLsDtpnpYHJW2Q+/wE4DDgKNIU/hsC90rq621xfeBJ4DTgA1IX4wpVjM2tpLZ3AHAgabK+\nm2twPbXSZZyy9QdZtY2NqSjTCnH6KvAT0lQFXyN1Qf+PpI06CrhNAQXihNtUh5dI4y13AXYDpgC/\nkrQzNEB7qtejvc2wAI8B11dsew64pOy61en6LwCeWs0+kabPPye3rT/wDvCdbP0LwEfAP+TKbEEa\nD7R/2ddXxTi9Cxxb7diQ3r20HGjPlRmVbfvrsq+7t3HKtk0CftPFZ1ouTln91yf9oT3Ibap4nNym\nuo3XEuCERmhPff3baN3IU8Z32Da7jTdP0mRJ22TbtwEGkotPRHwIPMzK+OxGmiE3X2Yh8AzNHcPe\nxqY929QOvBcRM3LHng4sy5Xp6wLYU+n1D3Ml3SBp09z+Vo3ThqQ72W9l625TnauME7hNfY6kfkoP\ndfQntZnS25OTkeI8ZTw8Spp07gBSNj0ImC7pi6yMQVfxGQR8Fmlm3bzXSP8RmlVvY5Mv83p+Z6Sv\nHs3UBu8H/ol0y/1MYAQwJfsyAK0bp6uBP5LmTAK3qdWpjBO4Ta0gaaik94APgRtIT5XOpQHaU0NP\nB2+NJSLuz63+SdIM4AVSgvJYVx+tacX6tu5i01IT7EXEL3Orc5QmI1wAHATc3cVHmzZOkq4kfTvd\nM/vF3p2WbFOri5Pb1CqeBYaRulz+HrhN0t7dfKYu7cl3Rop7g9Q3VvkNfiCpr63lRMT7wBxgMCtj\n0Fl8FmU/LwL6Kc3nkjcoV6YZdVxbT2JTWSZ/exlJAjajSeMXEa8CC0ltDFosTpKuAo4EvhYR83O7\n3KZyuojT57Rym4qITyJiXkT8MSJ+QLrbfQq9+/1dlTg5GSkoIj4GOqaMz9uP1CfWciT1Jw1YejUi\nXiA1tv0r9u/Jyvj8gfQOoHyZLYAhNHcMqxWbGcAGkvJ9r+2kQXtNGb+sb39zVv6ybJk4SbqalX9g\nn6vY7TaV6SZOnZVv2TbViX7AWlX8/d3zOJU9mrcvLcARpNHE3yb9Eb6aNNp4y7LrVqfrH0+abn8b\n4CvAvcDSjusnPTa2lDTd/k7AbaRvIOvnjjGR9IjZPqRHzB4CZpFNwNdXl+w/2/BsWQacn/1c1dgA\nvyU9Gjsy+0/+FHBP2ddfjThl+8Zn19ZGemXDDODFFozTtcDbwN6s+khqPg4t36a6i5Pb1CqxuoyU\nXLSRpmS4lHS3f79GaE+lB6ivLcBJpG8lHwIzSf2TpderTtc+GXiZlJAtJL1EcEhFmX8nvcjwg6yh\n7lCxf13gP0ndXstI7wravOxrq0Js9iI9vrY8+w/e8fON1YwNMID0zP7b2XITsGHZ11+NOJFG9t9P\nGhD3ETA/214Zg1aIU2V8OpZxFeVauk11Fye3qVXq//Ps+j/M4vEAWSLSCO3J08GbmZlZqTxmxMzM\nzErlZMTMzMxK5WTEzMzMSuVkxMzMzErlZMTMzMxK5WTEzMzMSuVkxMzMzErlZMTMzMxK5WTEzEol\n6ThJ75ZdDzMrj5MRMzMzK5WTETOrC0mjJT0q6V1JSyU9JukU0rtC1pe0PFvGZeWPkTRT0juSXpN0\nu6QvVxzzIElzJX0g6SFJR2bH2CpXZg9J/ytpmaSFkiZK+sv6Xr2ZdcXJiJnVnKS1SS/VehgYBowA\nrgL+DzgdeJ+Vb1z9j+xj65De6jsM+DqwCelljR3H3Aq4C/hNVuYnwI+ByJUZCvwO+FVW5jDSW4Jv\nrMmFmlmP+EV5ZlZzkr5IetPnXhHxcMW+44BrIqLLuxWShgBPA1tExCuSLgW+ERE75sqcA/wIaIuI\nFyXdBHwcEf+cKzOc9NrzzSLijepcoZn1hu+MmFnNRcSbwCTgd5LulXSGpC27+oykXSXdI2m+pHeA\nmdmuji6YIbltHX5fsb4bcEzWNfRuNlD2EdLdk+16cUlmVkVORsysLiLiW8BXSF013wDmStq/s7KS\n1id1r7wHHAP8LXBgtnvdjkMC6ua0Av4L2Dm3DAP+Cniip9diZtW1dtkVMLPWERFPAk8CP5b0W2As\ncC/Qr6LoEGBj4AcRsQBA0k4VZZ4FDqnYNqJifRawU0TMq0L1zaxGfGfEzGpOUpukyyS1S9pa0t6k\nOxRzgPlAf0n7StpE0l8ALwIfAadK2lbSQcBFFYe9DthO0hWStpd0GPAd0h2TjsFwlwMjJP1U0i6S\nBkv6uqTran/VZlaUkxEzq4f3SV0jdwBzSeNHfgFcHhEzSInFZGAx8P2IeJ101+RQUsJyPnAGuSdl\nIuJF4HBSl89s4DTgh6SumQ+zMk8Bo4E2YGpW7hJgUe0u1czWlJ+mMbOmIek04IKI2KjsuphZcR4z\nYmZ9VjZp2kzgdWAkcB7prouZ9SFORsysL9sOOIc02HUh8FNSV42Z9SHupjEzM7NSeQCrmZmZlcrJ\niJmZmZXKyYiZmZmVysmImZmZlcrJiJmZmZXKyYiZmZmV6v8BtahwxFH0lkcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb308e610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for est in tt_folding.estimators:\n",
    "    est.estimators = est.estimators[:2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<rep.plotting.GridPlot at 0xb42178d0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VjDZqGPReorYyFGpoI0OhBpUMhRpUMhRqUMlQqKHpDPmJrpmZmZkNJ/mJrkJv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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3dc4c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparison_report.feature_importance()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils import get_result_with_bootstrap_for_given_part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold prediction using folds column\n",
      "KFold prediction using random classifier (length of data passed not equal to length of train)\n"
     ]
    },
    {
     "data": {
      "image/png": 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SS5rrxIqfA/6mfP/DwDnAmbU79fX1Td7v7e2l1wmaJBVgcHCQwcHBosOQJEmS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\nVBeLCJIkSZIkqS7/PwepJUp8my7oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb420a890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for tagged: 0.59478907895 AUC with untag: 0.575246318076\n",
      "mean AUC after calibration: 0.594936385565 5.97503228835e-07\n"
     ]
    }
   ],
   "source": [
    "models.append(get_result_with_bootstrap_for_given_part(tagging_efficiency, tagging_efficiency_delta, tt_folding, \n",
    "                                                      [data_sw_passed, data_sw_not_passed], 'tt-iso', logistic=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold prediction using folds column\n",
      "KFold prediction using random classifier (length of data passed not equal to length of train)\n"
     ]
    },
    {
     "data": {
      "image/png": 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REQC2bNzO3gdOYvXZZ+cclZSNRQRJkiRpYZ6fUro3Ip4EfC8i7kgpXZ93UGpg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\nK56aU0SS1Fr2jR5GoXD+xPL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WzJSbmr4nQkppf0T8JfDvlN7QV6aUbo+Ivyhv/0fgPcAT\ngE+Vq4RjKaXVecVcCxmPw8XAH0fEGPAI8Ae5BVwDGY9By8t4HH4f+B8RsR94jDZ8L6SU7oiIa4Et\nwK+Bz6SUfp5f1POX5fVGxJOBn1AaHOjXEfEWSl/8j+QW+Dy0U85vp7zeTvm7nXJ0O+XijK/1moh4\nWURsBx4F/izHkOfNPNy+uamdPq/A+4GrIuJWSuMI/k1qvt5gAETEl4AXAcdGxN3AeyldmjLv3DRe\n5ZYkSZIkSZpRK8zOIEmSJEmS6sAigiRJkiRJysQigiRJkiRJysQigiRJkiRJysQigiRJkiRJysQi\ngiRJkiRJysQigiRJkiRJysQigiRJkiRJyuT/B0SARevAZpBqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8d8ced10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for tagged: 0.594269361397 AUC with untag: 0.574481390102\n",
      "mean AUC after calibration: 0.594298376427 5.62882151508e-07\n"
     ]
    }
   ],
   "source": [
    "models.append(get_result_with_bootstrap_for_given_part(tagging_efficiency, tagging_efficiency_delta, tt_folding, \n",
    "                                                      [data_sw_passed, data_sw_not_passed], 'tt-log', logistic=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison table of different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\epsilon_{tag}, \\%$</th>\n",
       "      <th>$\\Delta \\epsilon_{tag}, \\%$</th>\n",
       "      <th>$D^2$</th>\n",
       "      <th>$\\Delta D^2$</th>\n",
       "      <th>$\\epsilon, \\%$</th>\n",
       "      <th>$\\Delta \\epsilon, \\%$</th>\n",
       "      <th>AUC, with untag</th>\n",
       "      <th>$\\Delta$ AUC, with untag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tt-iso</th>\n",
       "      <td>72.39764091</td>\n",
       "      <td>0.0987203</td>\n",
       "      <td>0.02487980</td>\n",
       "      <td>0.00055256</td>\n",
       "      <td>1.80123890</td>\n",
       "      <td>0.04007942</td>\n",
       "      <td>57.52463181</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tt-log</th>\n",
       "      <td>72.39764091</td>\n",
       "      <td>0.0987203</td>\n",
       "      <td>0.03117677</td>\n",
       "      <td>0.00044998</td>\n",
       "      <td>2.25712467</td>\n",
       "      <td>0.03272223</td>\n",
       "      <td>57.44813901</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        $\\epsilon_{tag}, \\%$  $\\Delta \\epsilon_{tag}, \\%$       $D^2$  \\\n",
       "name                                                                    \n",
       "tt-iso           72.39764091                    0.0987203  0.02487980   \n",
       "tt-log           72.39764091                    0.0987203  0.03117677   \n",
       "\n",
       "        $\\Delta D^2$  $\\epsilon, \\%$  $\\Delta \\epsilon, \\%$  AUC, with untag  \\\n",
       "name                                                                           \n",
       "tt-iso    0.00055256      1.80123890             0.04007942      57.52463181   \n",
       "tt-log    0.00044998      2.25712467             0.03272223      57.44813901   \n",
       "\n",
       "        $\\Delta$ AUC, with untag  \n",
       "name                              \n",
       "tt-iso                         0  \n",
       "tt-log                         0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas.set_option('display.precision', 8)\n",
    "result = pandas.concat(models)\n",
    "result.index = result.name\n",
    "result.drop('name', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing best tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils import prepare_B_data_for_given_part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold prediction using folds column\n",
      "KFold prediction using random classifier (length of data passed not equal to length of train)\n"
     ]
    },
    {
     "data": {
      "image/png": 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lS/MOV5JajnlYkuorpcSPfvQjVq9ePbHuO9/5Dg8//DDnnXdejpFN5pgIkhrSYYcdRl9f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb401dc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for tagged: 0.594269361397 AUC with untag: 0.574481390102\n"
     ]
    },
    {
     "data": {
      "image/png": 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z+OeVV2DkSLjiCnjqKZ1PFhFBiVn8UlEBY8ZAUREUF0MIKpSj0Sj5+flEIhG/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\niVlERCSF/H/S/6XfvNU9LAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3cdd6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Bdata_prepared = prepare_B_data_for_given_part(tt_folding, [data_sw_passed, data_sw_not_passed], logistic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Bdata_prepared.to_csv('models/Bdata_tracks_SS.csv', header=True, index=False)"
   ]
  }
 ],
 "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": "2.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}