tq_NN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load in packages and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import uproot\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.backend import clear_session\n",
    "\n",
    "from functions import get_eff_faker_err, train_test_split_by_part, get_eff_faker_vs_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take in .root into arrays\n",
    "arrays = (uproot.open(\"TTbar_PU200_D49_prompt.root\")[\"L1TrackNtuple/eventTree\"]\n",
    "                .arrays(\"*\", namedecode=\"utf-8\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['trk_pt', 'trk_eta', 'trk_phi', 'trk_d0', 'trk_z0', 'trk_chi2', 'trk_chi2rphi', 'trk_chi2rz', 'trk_bendchi2', 'trk_nstub', 'trk_lhits', 'trk_dhits', 'trk_seed', 'trk_hitpattern', 'trk_phiSector', 'trk_genuine', 'trk_loose', 'trk_unknown', 'trk_combinatoric', 'trk_fake', 'trk_matchtp_pdgid', 'trk_matchtp_pt', 'trk_matchtp_eta', 'trk_matchtp_phi', 'trk_matchtp_z0', 'trk_matchtp_dxy', 'tp_pt', 'tp_eta', 'tp_phi', 'tp_dxy', 'tp_d0', 'tp_z0', 'tp_d0_prod', 'tp_z0_prod', 'tp_pdgid', 'tp_nmatch', 'tp_nstub', 'tp_eventid', 'tp_charge', 'matchtrk_pt', 'matchtrk_eta', 'matchtrk_phi', 'matchtrk_z0', 'matchtrk_d0', 'matchtrk_chi2', 'matchtrk_chi2rphi', 'matchtrk_chi2rz', 'matchtrk_bendchi2', 'matchtrk_nstub', 'matchtrk_lhits', 'matchtrk_dhits', 'matchtrk_seed', 'matchtrk_hitpattern'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A look at what variables you can use as inputs to your model\n",
    "arrays.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create train and test samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select features from data and put in proper format\n",
    "# 'trk_fake' is the track quality variable, truth data\n",
    "\n",
    "features = ['trk_pt','trk_eta','trk_phi','trk_z0','trk_chi2rphi','trk_chi2rz','trk_bendchi2','trk_bendchi2']\n",
    "X = np.empty((len(arrays[features[0]].flatten()),len(features)))\n",
    "for i in range(len(features)):\n",
    "    X[:,i] = arrays[features[i]].flatten()\n",
    "\n",
    "y = arrays['trk_fake'].flatten()\n",
    "y[y==2] = 1\n",
    "\n",
    "# Grab pdgid for certain studies\n",
    "pdgid = arrays['trk_matchtp_pdgid'].flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get rid of any nan instances for training purposes (doesn't converge)\n",
    "find_nan = np.argwhere(np.isnan(X))\n",
    "X = np.delete(X, find_nan[:,0], 0)\n",
    "y = np.delete(y, find_nan[:,0])\n",
    "pdgid = np.delete(pdgid, find_nan[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create train and test sets with 2500 of each mu, elec, had, fake in train, rest in test\n",
    "X_train, y_train, pdgid_train, X_test, y_test, pdgid_test = train_test_split_by_part(X,y,pdgid,2500,2500,2500,2500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train neural network (NN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/csavard/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /Users/csavard/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7febcd5107f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This creates NN with 2 hidden layers and one output layer\n",
    "# The first hidden layer has 128 nodes, and the second has 64 nodes\n",
    "# The output is a single node with a value between 0 and 1\n",
    "# Visit https://keras.io/ for more information on how to configure NN\n",
    "clf_NN = Sequential()\n",
    "clf_NN.add(Dense(128, input_dim=len(features), activation='relu')) # each Dense layer is a fully-connected layer in the NN being added\n",
    "clf_NN.add(Dense(64, activation='relu'))\n",
    "clf_NN.add(Dense(1, activation='sigmoid'))\n",
    "clf_NN.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "clf_NN.fit(X_train, y_train, epochs=100, batch_size=64, verbose=0) #verbose is supressing output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check NN performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create roc curve with AUC (area under curve) value\n",
    "y_pred = clf_NN.predict(X_test)[:,0]\n",
    "fpr, tpr, dt = roc_curve(y_test,y_pred)\n",
    "auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "plt.plot(fpr,tpr)\n",
    "plt.xlabel('FPR',fontsize=14)\n",
    "plt.ylabel('TPR',fontsize=14)\n",
    "plt.title('AUC = '+str(auc),fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot TPR and FPR vs decision threshold (dt)\n",
    "plt.plot(dt[1:],fpr[1:],label='FPR')\n",
    "plt.plot(dt[1:],tpr[1:],label='TPR')\n",
    "plt.xlabel('decision thresh.',fontsize=14)\n",
    "plt.ylabel('metric',fontsize=14)\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create efficiency/fake rate vs. pt\n",
    "pt, eff, faker, err_eff, err_faker = get_eff_faker_vs_feat('pt',features,X_test,y_test,clf_NN)\n",
    "\n",
    "plt.errorbar(pt,eff,yerr=err_eff,linestyle='None',fmt='.')\n",
    "plt.xlabel('p$_{T}$ (GeV/c)',fontsize=14)\n",
    "plt.ylabel('Efficiency',fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "plt.errorbar(pt,faker,yerr=err_faker,linestyle='None',fmt='.')\n",
    "plt.xlabel('p$_{T}$ (GeV/c)',fontsize=14)\n",
    "plt.ylabel('Fake rate',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ds2+15qYvA89Z2kjyR0meO96SpP6t5eZxnwvazOK6G5osqzU3LZ9v6aXASWOqRZoYfX9DX4u2TVN9T/XhVcd0aj0thzRP1tIbp++Bb374apxWa24qjp6p1VlXNXHG0eTTpjfONPUucloMPRVtmps+nGRpNtYTgd9J8p3hg6rq4qP+TWnKtfmGPi29i9Z6I77vpilNjtWuJD7I4nTeDw5+PsziKOsHl/1Ic2laRhJPyyhutdfVleExrySq6p+N9dWlEenrvsC0jCSeplHcWl2XXbTbjriWJlbf9wWmYSTxrI7inlddXhnau0lTr+/7AtPSbu8o7tnRZRdtQ0JTb5rGNPRpWkZxa3VdNnMaEpp603JfYFr4+5wOXc1obEhoJkzDFODTxN+nlnjjWpLUyCsJzYRpuXk8Lfx9aolXEpKkRoaEJKmRISFJamRISJIaGRKSpEb2bpKkKdRVDzSvJCRJjQwJSVIjQ0KS1MiQkCQ1MiQkSY0MCUlSo05DIsmFSe5Jsi/JNSs8/9IkX0pyOMkru6xNknS0zkIiyTpgO3ARsAW4PMmWZYf9BfA64CNd1SVJatblYLrzgX1VtR8gyc3AJcBdSwdU1TcHzx3psC5JUoMum5tOAw4MbS8M9q1ZkquS7E6y++DBgyMpTpJ0tC5DIivsq6dyoqq6oaq2VtXWjRs3Ps2yJElNugyJBWDz0PYm4P4OX1+StEZdhsQdwNlJzkxyAnAZsKPD15ckrVFnIVFVh4GrgduAu4Fbq2pvkuuSXAyQ5MeSLACvAq5Psrer+iRJR+t0qvCq2gnsXLbv2qHHd7DYDCVJmgCOuJYkNTIkJEmNDAlJUiNDQpLUyJCQJDUyJCRJjQwJSVIjQ0KS1MiQkCQ1MiQkSY0MCUlSI0NCktTIkJAkNTIkJEmNDAlJUiNDQpLUyJCQJDUyJCRJjQwJSVIjQ0KS1MiQkCQ1MiQkSY0MCUlSI0NCktTIkJAkNTIkJEmNDAlJUiNDQpLUyJCQJDUyJCRJjQwJSVIjQ0KS1MiQkCQ16jQkklyY5J4k+5Jcs8LzfyfJLYPnv5jkeV3WJ0l6ss5CIsk6YDtwEbAFuDzJlmWHXQE8VFU/BLwb+PfjqmfPvQ+x/fP72HPvQ+N6CUmaesd3+FrnA/uqaj9AkpuBS4C7ho65BPj1weOPAe9NkqqqURay596HeM2Nu3j88BFOOP44brpyG+edcfIoX0KSZkKXzU2nAQeGthcG+1Y8pqoOAw8Dpyw/UZKrkuxOsvvgwYNrLmTX/gd5/PARjhQ8cfgIu/Y/uOZzSNI86DIkssK+5VcIbY6hqm6oqq1VtXXjxo1rLmTbWadwwvHHsS6w/vjj2HbWUTkkSaLb5qYFYPPQ9ibg/oZjFpIcDzwL+KtRF3LeGSdz05Xb2LX/QbaddYpNTZLUoMuQuAM4O8mZwH3AZcCrlx2zA3gtcDvwSuC/j/p+xJLzzjjZcJCkVXQWElV1OMnVwG3AOuADVbU3yXXA7qraAbwf+FCSfSxeQVzWVX2SpKN1eSVBVe0Edi7bd+3Q40PAq7qsSZLUzBHXkqRGhoQkqZEhIUlqZEhIkhplTD1MO5PkIHBv33U8Bc8BHui7iI7N23uet/cLvudpckZVrToaeepDYlol2V1VW/uuo0vz9p7n7f2C73kW2dwkSWpkSEiSGhkS/bmh7wJ6MG/ved7eL/ieZ473JCRJjbySkCQ1MiQkSY0MiZ4k+c0kX0vylSS/n+TZfdc0bklelWRvkiNJZrbLIECSC5Pck2Rfkmv6rmfcknwgybeSfLXvWrqSZHOSzye5e/D/9Rv7rmkcDIn+fAb40ap6EfB14N/0XE8Xvgr8Y+DP+i5knJKsA7YDFwFbgMuTbOm3qrH7XeDCvovo2GHgzVX1I8A24Jdm8b+zIdGTqvr0YB1vgF0srtQ306rq7qq6p+86OnA+sK+q9lfV48DNwCU91zRWVfVnjGEVyUlWVf+3qr40ePwocDdwWr9VjZ4hMRn+OfCpvovQyJwGHBjaXmAGPzz0PUmeB5wLfLHfSkav00WH5k2SzwKnrvDUW6vqDwfHvJXFy9abuqxtXNq85zmQFfbZ13xGJXkm8N+AN1XVI33XM2qGxBhV1cuP9XyS1wL/EPj741rLu2urvec5sQBsHtreBNzfUy0aoyTrWQyIm6rq433XMw42N/UkyYXAW4CLq+o7fdejkboDODvJmUlOYHGt9h0916QRSxLg/cDdVfWuvusZF0OiP+8FNgCfSXJnkv/cd0HjluTnkywAPw78UZLb+q5pHAYdEq4GbmPxZuatVbW336rGK8lHgduB5ydZSHJF3zV14O8Bvwi8bPA3fGeSn+27qFFzWg5JUiOvJCRJjQwJSVIjQ0KS1MiQkCQ1MiQkSY0MCUlSI0NCktTIkJBGbDCo6u1J3pPkgcE6C2/ruy7pqXAwnTRCg7l8vs3i/E2/zuIo5F8E3gb8QFX9ZX/VSWtnSEgjlOTFwJ3Az1fVHwz2bQb+AviRqvpan/VJa2VzkzRa5wAPAZ8Y2rdx8E+vIjR1DAlptF4M7K6q/ze07xzg3qr6655qkp4yQ0IarXOALy/bdy6LTVDS1DEkpNF6ESuHxPJ90lQwJKQRSbIJOIWhq4bBwjQvxpDQlHL5UmlEqmqBo9e3Pht4JoaEppRXEtJ4nQs8WFUH+i5EeioMCWm8vGmtqeZgOklSI68kJEmNDAlJUiNDQpLUyJCQJDUyJCRJjQwJSVIjQ0KS1Oj/A3x2FK+fpaV6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create efficiency/fake rate vs. eta\n",
    "pt, eff, faker, err_eff, err_faker = get_eff_faker_vs_feat('eta',features,X_test,y_test,clf_NN)\n",
    "\n",
    "plt.errorbar(pt,eff,yerr=err_eff,linestyle='None',fmt='.')\n",
    "plt.xlabel('$\\eta$',fontsize=14)\n",
    "plt.ylabel('Efficiency',fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "plt.errorbar(pt,faker,yerr=err_faker,linestyle='None',fmt='.')\n",
    "plt.xlabel('$\\eta$',fontsize=14)\n",
    "plt.ylabel('Fake rate',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}