{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boosted decision tree tutorial\n",
    "\n",
    "***\n",
    "## Introduction:\n",
    "#### Boosted decision tree code:\n",
    "We'll use the python API for the [XGBoost (eXtreme Gradient Boosting) library](https://github.com/dmlc/xgboost).\n",
    "\n",
    "#### Data:\n",
    "[Atlas](https://home.cern/about/experiments/atlas) hosted a [Kaggle](https://www.kaggle.com/) competition for identifying Higgs to tau tau events, [the Higgs Boson Machine Learning Challenge](https://www.kaggle.com/c/higgs-boson/data). The training data for this event contains 250,000 labeled, simulated Atlas events in csv format described [here](https://www.kaggle.com/c/higgs-boson/data) and [here](https://higgsml.lal.in2p3.fr/files/2014/04/documentation_v1.8.pdf). You can download it youself, or we have a small subset (10k events) in the \"data\" directory in this repository that we use here.\n",
    "\n",
    "#### Data handling:\n",
    "If you don't have [pandas](http://pandas.pydata.org/), you should get pandas. It's an amazing tool for exploring data in Python.\n",
    "***\n",
    "#### Install XGBoost:\n",
    "Assuming you have python, numpy, matplotlib, and pandas installed, you just need to install XGBoost. Detailed installation instructions are [here](https://xgboost.readthedocs.io/en/latest/build.html). If you're on Ubuntu, just do this (in your terminal):\n",
    "```bash\n",
    "git clone --recursive https://github.com/dmlc/xgboost\n",
    "cd xgboost\n",
    "make\n",
    "```\n",
    "***\n",
    "#### Links:\n",
    "A lot of this was borrowed from other sources. These sources and other good places for information about XGBoost and BDTs in general are here:\n",
    "\n",
    "XGBoost demo: [Example of how to use XGBoost Python Module to run Kaggle Higgs competition](https://github.com/dmlc/xgboost/tree/master/demo/kaggle-higgs)\n",
    "\n",
    "Blog post by phunther: [Winning solution of Kaggle Higgs competition: what a single model can do?](https://no2147483647.wordpress.com/2014/09/17/winning-solution-of-kaggle-higgs-competition-what-a-single-model-can-do/)\n",
    "\n",
    "XGBoost Kaggle Higgs solution: https://github.com/hetong007/higgsml\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
  "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xgboost as xgb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data\n",
    "#### Load data:\n",
    "First, load in the data and look at it. We've taken a 10k event subsample of the Kaggle training data. Then we'll put it in the right format for xgboost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/h90BP1_bb.root')",
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/h90BP2_bb.root')",
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/h95BP1_bb.root')",
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/h95BP3_bb.root')",
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/h96BP1_bb.root')",
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/h96BP2_bb.root')",
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/h96BP3_bb.root')",
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/h96BP6_bb.root')",
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/h_bb.root')",
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/z_bb.root')",
    "data= pd.read_csv('home/ahmed/bb-quark_Signals/bb.root')",
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of data: (1000000, 12)\n",
      "Number of events: 1000000\n",
      "Number of columns: 12\n",
      "\n",
      "List of features in dataset:\n",
      "EventId\n",
      "b1_pt\n",
      "b2_pt\n",
      "b1_eta\n",
      "b2_eta\n",
      "h_mass\n",
      "h_pt\n",
      "h_eta\n",
      "DR_b\n",
      "scalarHt_Ht\n",
      "Weight\n",
      "Label\n"
     ]
    }
   ],
   "source": [
    "print('Size of data: {}'.format(data.shape))\n",
    "print('Number of events: {}'.format(data.shape[0]))\n",
    "print('Number of columns: {}'.format(data.shape[1]))\n",
    "\n",
    "print ('\\nList of features in dataset:')\n",
    "for col in data.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set has 1000,000 events with 12 columns each. It looks like the first column is an identifier, and shouldn't be used as a feature. The last two columns \"Weight\" and \"Label\", are the weights and labels from the simulation, and also shouldn't be used as features (this information is all contained in the documentation).\n",
    "\n",
    "Now we can look at how many events are signal and background:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of signal events: 3372\n",
      "Number of background events: 6628\n",
      "Fraction signal: 0.3372\n"
     ]
    }
   ],
   "source": [
    "# look at column labels --- notice last one is \"Label\" and first is \"EventId\" also \"Weight\"\n",
    "print('Number of signal events: {}'.format(len(data[data.Label == 's'])))\n",
    "print('Number of background events: {}'.format(len(data[data.Label == 'b'])))\n",
    "print('Fraction signal: {}'.format(len(data[data.Label == 's'])/(float)(len(data[data.Label == 's']) + len(data[data.Label == 'b']))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Format data:\n",
    "Now we should get the data into an XGBoost-friendly format. We can create DMatrix objects that will be used to train the BDT model. For now, we'll use all 30 of the features for training.\n",
    "\n",
    "First, we'll sliceup the data into training and testing sets. Here, we take 20% for the test set, which is arbitrary.\n",
    "\n",
    "In this file, all samples are independent and ordered randomly, so we can just grab a chunk. Check out [scikit-learn Cross-validation](http://scikit-learn.org/stable/modules/cross_validation.html) for dividing up samples in a responsible way.\n",
    "\n",
    "We can also change the data type of the \"Label\" column to the pandas type \"category\" for easier use later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Label'] = data.Label.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train = data[:8000]\n",
    "data_test = data[8000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check to make sure we did it right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples: 8000\n",
      "Number of testing samples: 2000\n",
      "\n",
      "Number of signal events in training set: 2688\n",
      "Number of background events in training set: 5312\n",
      "Fraction signal: 0.336\n"
     ]
    }
   ],
   "source": [
    "print('Number of training samples: {}'.format(len(data_train)))\n",
    "print('Number of testing samples: {}'.format(len(data_test)))\n",
    "\n",
    "print('\\nNumber of signal events in training set: {}'.format(len(data_train[data_train.Label == 's'])))\n",
    "print('Number of background events in training set: {}'.format(len(data_train[data_train.Label == 'b'])))\n",
    "print('Fraction signal: {}'.format(len(data_train[data_train.Label == 's'])/(float)(len(data_train[data_train.Label == 's']) + len(data_train[data_train.Label == 'b']))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DMatrix object takes as arguments:\n",
    "- data --- the features\n",
    "- label --- 1/0 or true/false for binary data (we have to convert our label to bool from string s/b)\n",
    "- missing --- how missing values are represented (here as -999.0)\n",
    "- feature_names --- the names of all of the features (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = data.columns[1:-2]  # we skip the first and last two columns because they are the ID, weight, and label\n",
    "train = xgb.DMatrix(data=data_train[feature_names],label=data_train.Label.cat.codes,\n",
    "                    missing=-999.0,feature_names=feature_names)\n",
    "test = xgb.DMatrix(data=data_test[feature_names],label=data_test.Label.cat.codes,\n",
    "                   missing=-999.0,feature_names=feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if we did it right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples: 8000\n",
      "Number of testing samples: 2000\n",
      "\n",
      "Number of signal events in training set: 2688\n"
     ]
    }
   ],
   "source": [
    "print('Number of training samples: {}'.format(train.num_row()))\n",
    "print('Number of testing samples: {}'.format(test.num_row()))\n",
    "\n",
    "print('\\nNumber of signal events in training set: {}'.format(len(np.where(train.get_label())[0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make the model\n",
    "#### Set hyperparameters:\n",
    "The XGBoost hyperparameters are defined [here](https://github.com/dmlc/xgboost/blob/master/doc/parameter.md). For a nice description of what they all mean, and tips on tuning them, see [this guide](https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/).\n",
    "\n",
    "In general, the tunable parameters in XGBoost are the ones you would see in other gradient boosting libraries. Here, they fall into three categories:\n",
    "1. General parameters - Ex. :which booster to use, number of threads. I won't mess with any of these here.\n",
    "2. Booster parameters - Tune the actual boosting. Ex.: learning rate. These are the ones to optimize.\n",
    "3. Learning task parameters - Define the objective function and the evaluation metrics.\n",
    "\n",
    "Here, we will use the defaults for most parameters and just set a few to see how it's done. The parameters are passed in as a dictionary or list of pairs.\n",
    "\n",
    "Make the parameter dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "param = {}\n",
    "\n",
    "# Booster parameters\n",
    "param['eta']              = 0.1 # learning rate\n",
    "param['max_depth']        = 10  # maximum depth of a tree\n",
    "param['subsample']        = 0.8 # fraction of events to train tree on\n",
    "param['colsample_bytree'] = 0.8 # fraction of features to train tree on\n",
    "\n",
    "# Learning task parameters\n",
    "param['objective']   = 'binary:logistic' # objective function\n",
    "param['eval_metric'] = 'error'           # evaluation metric for cross validation\n",
    "param = list(param.items()) + [('eval_metric', 'logloss')] + [('eval_metric', 'rmse')]\n",
    "\n",
    "num_trees = 100  # number of trees to make"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we set the booster parameters. Again, we just chose a few here to experiment with. These are the paraters to tune to optimize your model. Generally, there is a trade off between speed and accuracy.\n",
    "1. ```eta``` is the learning rate. It determines how much to change the data weights after each boosting iteration. The default is 0.3.\n",
    "2. ```max_depth``` is the maximum depth of any tree. The default is 6.\n",
    "3. ```subsample``` is the fraction of events used to train each new tree. These events are randomly sampled each iteration from the whole sample set. The default is 1 (use every event for each tree).\n",
    "4. ```colsample_bytree``` is the fraction of features available to train each new tree. These features are randomly sampled each iteration from the whole feature set. The default is 1.\n",
    "\n",
    "Next, we set the learning objective to ```binary:logistic```. So, we have two classes that we want to score from 0 to 1. The ```eval_metric``` parameters set what we want to monitor when doing cross validation. (We aren't doing cross validation in this example, but we really should be!) If you want to watch more than one metric, ```param``` must be a list of pairs, instead of a dict. Otherwise, we would just keep resetting the same parameter.\n",
    "\n",
    "Last, we set thenumber of trees to 100. Usually, you would set this number high, and choose a cut off point based on the cross validation. The number of trees is the same as the number of iterations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now train!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "booster = xgb.train(param,train,num_boost_round=num_trees)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a trained model. The next step is to look at it's performance and try to improve the model if we need to. We can try to improve it by improving/adding features, adding more training data, using more boosting iterations, or tuning the hyperparameters (ideally in that order).\n",
    "\n",
    "#### Evaluate:\n",
    "First, let's look at how it does on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\teval-error:0.170500\teval-logloss:0.378685\teval-rmse:0.345281\n"
     ]
    }
   ],
   "source": [
    "print(booster.eval(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the evaluation metrics that we stored in the parameter set.\n",
    "\n",
    "It's pretty hard to interpret the performance of a classifier from a few number. So, let's look at the predictions for the entire test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = booster.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O8LrYPu7+YWrCEhGRbBUlcWwFCoAJ7l6conhERCTL7fBSlZlNNLO3zewIYIC7jytNGmbW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\nLr1RpoeZtQZuBw5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc631dd7710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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49a97YFb8HPBTE0e5uFTl7iuAc5KUbwCuTH1EIiKp9fjj73P//W9SqVLisbxq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\n/nzg8eB9BvAdQTNGUa+Kcqm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc63140ea50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all predictions (both signal and background)\n",
    "plt.figure();\n",
    "plt.hist(predictions,bins=np.linspace(0,1,50),histtype='step',color='darkgreen',label='All events');\n",
    "# make the plot readable\n",
    "plt.xlabel('Prediction from BDT',fontsize=12);\n",
    "plt.ylabel('Events',fontsize=12);\n",
    "plt.legend(frameon=False);\n",
    "\n",
    "# plot signal and background separately\n",
    "plt.figure();\n",
    "plt.hist(predictions[test.get_label().astype(bool)],bins=np.linspace(0,1,50),\n",
    "         histtype='step',color='midnightblue',label='signal');\n",
    "plt.hist(predictions[~(test.get_label().astype(bool))],bins=np.linspace(0,1,50),\n",
    "         histtype='step',color='firebrick',label='background');\n",
    "# make the plot readable\n",
    "plt.xlabel('Prediction from BDT',fontsize=12);\n",
    "plt.ylabel('Events',fontsize=12);\n",
    "plt.legend(frameon=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:11: RuntimeWarning: invalid value encountered in divide\n",
      "/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZXDT4IR5bfzFz/zhD55zaA1qzUEr1aBMuOI9lq57loONySXGm4zCGquB6LJyc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6314d9250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# choose score cuts:\n",
    "cuts = np.linspace(0,1,500);\n",
    "nsignal = np.zeros(len(8));\n",
    "nbackground = np.zeros(len(3));\n",
    "for i,cut in enumerate(cuts):\n",
    "    nsignal[i] = len(np.where(predictions[test.get_label().astype(bool)] > cut)[0]);\n",
    "    nbackground[i] = len(np.where(predictions[~(test.get_label().astype(bool))] > cut)[0]);\n",
    "    \n",
    "# plot efficiency vs. purity (ROC curve)\n",
    "plt.figure();\n",
    "plt.plot(nsignal/len(data_test[data_test.Label == 's']),nsignal/(nsignal + nbackground),'o-',color='blueviolet');\n",
    "# make the plot readable\n",
    "plt.xlabel('Efficiency',fontsize=12);\n",
    "plt.ylabel('Purity',fontsize=12);\n",
    "plt.legend(frameon=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also very informative to look at the importance of each feature. The \"F score\" is the number of times each feature is used to split the data over all of the trees (times the weight of that tree).\n",
    "\n",
    "There is a built-in function in the XGBoost python API to easily plot this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OUsurIk4HVgH9ow2oqoP8S707TimxGWipqqtE5HsRqaKq24gg/QQa4NJm98c5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\nJBzHcRzHKTb/H1IYUcYM3fVZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc631dd7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(booster,grid=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature that was used the most was \"```DER_mass_MMC```. (For this data the \"DER\" prefix is for derived variables, and \"PRI\" is for raw variables.)\n",
    "\n",
    "We can plot how this feature is ditributed for the signal and background:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EIiIikVJiERGRSCmxiIhIpJRYREQkUkosIiISKSUWERGJlBKLiIhESolFREQipcQiIiKR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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc627d4bc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure();\n",
    "plt.hist(data_train.b1_pt[data_train.Label == 's'],bins=np.linspace(0,800,50),\n",
     "plt.hist(data_train.b2_pt[data_train.Label == 's'],bins=np.linspace(0,800,50),\n",
     "plt.hist(data_train.b1_eta[data_train.Label == 's'],bins=np.linspace(0,800,50),\n",
     "plt.hist(data_train.b2_eta[data_train.Label == 's'],bins=np.linspace(0,800,50),\n",
     "plt.hist(data_train.h_pt[data_train.Label == 's'],bins=np.linspace(0,800,50),\n",
     "plt.hist(data_train.h_mass[data_train.Label == 's'],bins=np.linspace(0,800,50),\n",
     "plt.hist(data_train.h_eta[data_train.Label == 's'],bins=np.linspace(0,800,50),\n",
     "plt.hist(data_train.DR_b[data_train.Label == 's'],bins=np.linspace(0,800,50),\n",
     "plt.hist(data_train.scalarHt_Ht[data_train.Label == 's'],bins=np.linspace(0,800,50),\n",
     
     
     
    "histtype='step',color='midnightblue',label='signal');\n",
    "plt.hist(data_train.b1_pt[data_train.Label == 'b'],bins=np.linspace(0,800,50),\n",
    "histtype='step',color='firebrick',label='background');\n",
    "\n",
    "plt.xlabel('b1_pt',fontsize=12);\n",
    "plt.xlabel('b2_pt',fontsize=12);\n",
    "plt.xlabel('b1_eta',fontsize=12);\n",
    "plt.xlabel('b2_eta',fontsize=12);\n",
    "plt.xlabel('h_pt',fontsize=12);\n",
    "plt.xlabel('h_eta',fontsize=12);\n",
    "plt.xlabel('h_mass',fontsize=12);\n",
    "plt.xlabel('DR_b',fontsize=12);\n",
    "plt.xlabel('scalarHt_Ht',fontsize=12);\n",
    "plt.ylabel('Events',fontsize=12);\n",
    "plt.legend(frameon=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is not a lot of discriminating power in that variable. For fun, we can plot it with the next most important feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cm/cncTO/9PBDSUz85QG2tYMDLbKJO5s0Z+tDSU83kJ5uBLDOU+7JWkcdVai5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\n1dVmUlL0pKToSU83EB2t4bn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//7tOOdgoCFshN/x3v0Pl7W0tD2OLbRJnU8UrmxNEjQWc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc627db7710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure();\n",
      "plt.plot(data_train.b1_pt[data_train.Label == 'b'],data_train.PRI_Bquark_pt[data_train.Label == 'b'],\n",
      "plt.plot(data_train.b2_pt[data_train.Label == 'b'],data_train.PRI_Bquark_pt[data_train.Label == 'b'],\n",
      "plt.plot(data_train.b1_eta[data_train.Label == 'b'],data_train.PRI_Bquark_eta[data_train.Label == 'b'],\n",
      "plt.plot(data_train.b2_eta[data_train.Label == 'b'],data_train.PRI_Bquark_eta[data_train.Label == 'b'],\n",
      "plt.plot(data_train.h_mass[data_train.Label == 'b'],data_train.PRI_Bquark_h_mass[data_train.Label == 'b'],\n",
      "plt.plot(data_train.h_pt[data_train.Label == 'b'],data_train.PRI_Bquark_pt_h_pt[data_train.Label == 'b'],\n",
      "plt.plot(data_train.h_eta[data_train.Label == 'b'],data_train.PRI_Bquark_h_eta[data_train.Label == 'b'],\n",
      "plt.plot(data_train.DR_b[data_train.Label == 'b'],data_train.PRI_Bquark_DR_b[data_train.Label == 'b'],\n",
      "plt.plot(data_train.scalarHt_Ht[data_train.Label == 'b'],data_train.PRI_Bquark_scalarHt_Ht[data_train.Label == 'b'],\n",
     
    " 'o',markersize=2,color='firebrick',markeredgewidth=0,alpha=0.8,label='background');\n",
    "plt.plot(data_train.b1_pt[data_train.Label == 's'],data_train.PRI_Bquark_pt[data_train.Label == 's'],\n",
    "plt.plot(data_train.b2_pt[data_train.Label == 's'],data_train.PRI_Bquark_pt[data_train.Label == 's'],\n",
    "plt.plot(data_train.b1_eta[data_train.Label == 's'],data_train.PRI_Bquark_eta[data_train.Label == 's'],\n",
    "plt.plot(data_train.b2_eta[data_train.Label == 's'],data_train.PRI_Bquark_eta[data_train.Label == 's'],\n",
    "plt.plot(data_train.h_mass[data_train.Label == 's'],data_train.PRI_Bquark_h_mass[data_train.Label == 's'],\n",
    "plt.plot(data_train.h_pt[data_train.Label == 's'],data_train.PRI_Bquark_h_pt[data_train.Label == 's'],\n",
    "plt.plot(data_train.h_eta[data_train.Label == 's'],data_train.PRI_Bquark_h_eta[data_train.Label == 's'],\n",
    "plt.plot(data_train.DR_b[data_train.Label == 's'],data_train.PRI_Bquark_DR_b[data_train.Label == 's'],\n",
    "plt.plot(data_train.scalarHt_Ht[data_train.Label == 's'],data_train.PRI_Bquark_scalarHt_Ht[data_train.Label == 's'],\n",
    
    
    
    
    " 'o',markersize=2,color='mediumblue',markeredgewidth=0,alpha=0.8,label='signal');\n",
    "\n",
    "plt.xlim(0,400);\n",
    "plt.ylim(0,200);\n",
    "plt.xlabel('b1_pt',fontsize=12);\n",
    "plt.xlabel('b2_pt',fontsize=12);\n",
    "plt.xlabel('b1_eta',fontsize=12);\n",
    "plt.xlabel('b2_eta',fontsize=12);\n",
    "plt.xlabel('h_pt',fontsize=12);\n",
    "plt.xlabel('h_mass',fontsize=12);\n",
    "plt.xlabel('h_eta',fontsize=12);\n",
    "plt.xlabel('DR_b',fontsize=12);\n",
    "plt.xlabel('scalarHt_Ht',fontsize=12);\n",
    
    
    "plt.ylabel('PRI_b1_pt',fontsize=12);\n",
    "plt.ylabel('PRI_b2_pt',fontsize=12);\n",
    "plt.ylabel('PRI_b1_eta',fontsize=12);\n",
    "plt.ylabel('PRI_b2_eta',fontsize=12);\n",
    "plt.ylabel('PRI_h_pt',fontsize=12);\n",
    "plt.ylabel('PRI_h_mass',fontsize=12);\n",
    "plt.ylabel('PRI_h_eta',fontsize=12);\n",
    "plt.ylabel('PRI_b1_pt',fontsize=12);\n",
    "plt.ylabel('PRI_b1_pt',fontsize=12);\n",
    
    "plt.legend(frameon=False,numpoints=1,markerscale=2);"
   ]
  }
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