1-layerNN_testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import six\n",
    "import sys\n",
    "import os\n",
    "from optparse import OptionParser\n",
    "from keras.models import load_model, Model, model_from_json\n",
    "from argparse import ArgumentParser\n",
    "from keras import backend as K\n",
    "import numpy as np\n",
    "# fix random seed for reproducibility\n",
    "seed = 42\n",
    "np.random.seed(seed)\n",
    "import h5py\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "sys.path.append(os.getcwd() + '/..')\n",
    "from models.constraints import ZeroSomeWeights\n",
    "from keras.utils.generic_utils import get_custom_objects\n",
    "get_custom_objects().update({\"ZeroSomeWeights\": ZeroSomeWeights})\n",
    "import yaml\n",
    "from train import parse_config, get_features\n",
    "from layers.quantized_layers import Clip, BinaryDense, TernaryDense, QuantizedDense\n",
    "from models.models import binary_tanh, ternary_tanh, quantized_relu\n",
    "from eval import makeRoc, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('j_ptfrac', 'j_pt', 'j_eta', 'j_mass', 'j_tau1_b1', 'j_tau2_b1', 'j_tau3_b1', 'j_tau1_b2', 'j_tau2_b2', 'j_tau3_b2', 'j_tau32_b1', 'j_tau32_b2', 'j_zlogz', 'j_c1_b0', 'j_c1_b1', 'j_c1_b2', 'j_c2_b1', 'j_c2_b2', 'j_d2_b1', 'j_d2_b2', 'j_d2_a1_b1', 'j_d2_a1_b2', 'j_m2_b1', 'j_m2_b2', 'j_n2_b1', 'j_n2_b2', 'j_tau1_b1_mmdt', 'j_tau2_b1_mmdt', 'j_tau3_b1_mmdt', 'j_tau1_b2_mmdt', 'j_tau2_b2_mmdt', 'j_tau3_b2_mmdt', 'j_tau32_b1_mmdt', 'j_tau32_b2_mmdt', 'j_c1_b0_mmdt', 'j_c1_b1_mmdt', 'j_c1_b2_mmdt', 'j_c2_b1_mmdt', 'j_c2_b2_mmdt', 'j_d2_b1_mmdt', 'j_d2_b2_mmdt', 'j_d2_a1_b1_mmdt', 'j_d2_a1_b2_mmdt', 'j_m2_b1_mmdt', 'j_m2_b2_mmdt', 'j_n2_b1_mmdt', 'j_n2_b2_mmdt', 'j_mass_trim', 'j_mass_mmdt', 'j_mass_prun', 'j_mass_sdb2', 'j_mass_sdm1', 'j_multiplicity', 'j_g', 'j_q', 'j_w', 'j_z', 'j_t', 'j_undef')\n"
     ]
    }
   ],
   "source": [
    "data_path = '../data/processed-pythia82-lhc13-all-pt1-50k-r1_h022_e0175_t220_nonu_truth.z'\n",
    "import h5py\n",
    "f = h5py.File(data_path, 'r')\n",
    "treeArray = f['t_allpar_new'][()]\n",
    "print(treeArray.dtype.names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading configuration from train_config_twolayer.yml\n",
      "{'Inputs': ['j_zlogz', 'j_c1_b0_mmdt', 'j_c1_b1_mmdt', 'j_c2_b1_mmdt', 'j_d2_b1_mmdt', 'j_d2_a1_b1_mmdt', 'j_m2_b1_mmdt', 'j_n2_b1_mmdt', 'j_mass_mmdt', 'j_multiplicity'], 'Labels': ['j_t'], 'KerasModel': 'two_layer_model', 'KerasModelRetrain': 'two_layer_model_constraint', 'KerasLoss': 'binary_crossentropy', 'L1Reg': 0, 'NormalizeInputs': 0, 'ConvInputs': 0, 'InputType': 'Dense'}\n"
     ]
    }
   ],
   "source": [
    "from collections import namedtuple\n",
    "Option = namedtuple(\"MyStruct\", \"inputModel inputFile tree config jsonModel\")\n",
    "\n",
    "options = Option(\n",
    "    #inputModel = '../weights/KERAS_1layer_weights.h5',\n",
    "    inputModel = '../1layerNN_weights/KERAS_check_best_model_weights.h5',\n",
    "    inputFile = '../data/processed-pythia82-lhc13-all-pt1-50k-r1_h022_e0175_t220_nonu_truth.z',\n",
    "    tree = 't_allpar_new',\n",
    "    config = 'train_config_twolayer.yml',\n",
    "    jsonModel = '../weights/KERAS_1layer.json'\n",
    ")\n",
    "\n",
    "print(\"Loading configuration from\", options.config)\n",
    "config = open(options.config, 'r')\n",
    "yamlConfig =  yaml.load(config, Loader=yaml.FullLoader)\n",
    "print(yamlConfig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys: <KeysViewHDF5 ['t_allpar_new']>\n"
     ]
    }
   ],
   "source": [
    "X_train_val, X_test, y_train_val, y_test, labels  = get_features(options, yamlConfig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:507: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3831: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:167: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:179: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:183: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:192: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:199: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "fc1_relu (Dense)             (None, 32)                352       \n",
      "_________________________________________________________________\n",
      "output_sigmoid (Dense)       (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 385\n",
      "Trainable params: 385\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "json_file = open(options.jsonModel, 'r')\n",
    "model = json_file.read()\n",
    "json_file.close()\n",
    "model = model_from_json(model, custom_objects={'ZeroSomeWeights':ZeroSomeWeights,\n",
    "                                                           'BinaryDense': BinaryDense,\n",
    "                                                           'TernaryDense': TernaryDense,\n",
    "                                                           'QuantizedDense': QuantizedDense,\n",
    "                                                           'binary_tanh': binary_tanh,\n",
    "                                                           'ternary_tanh': ternary_tanh,\n",
    "                                                           'quantized_relu': quantized_relu,\n",
    "                                                           'Clip': Clip})\n",
    "# load weights into new model\n",
    "model.load_weights(options.inputModel)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the weight provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot ROC curve\n"
     ]
    },
    {
     "data": {
      "image/png": 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e798RSdWw0AoJsKAwxphifbIoncc+Wc6VLeoSf2dohgRYUBhjTJE+W5LO3z5exuXN6xB/Z1TIhgRYUBhjzBlmL93OXz9aRnSzOoy/qxvVKoduSIAFhTHGnGbush08MnMp3SJqM+HuqJAPCbCgMMaYU75cvpOHZy4l6uLaTLy7G+dVruTrkvyCBYUxxgBfr9jJn2csoUvTC/hwWDeqV7GQOMmCwhgT8r5ZuYuHEpfQuekFTBre3UKiEAsKY0xIm79qFw9OX0yHJuFMGtaNGhYSZwiqoLBJAY0xZ+O71bt5YPpi2jUOZ/Lw7tSsGubrkvxSUAWFTQpojHHqh7V7uD9hMZc2PJ8pw7tzvoVEsYIqKIwxxomkdXu4Z+oiWl1Yg6nDexBezULCEwsKY0xI+XH9XuKmLqJF/RpMG9GD8PMsJEpSYlCISPvyKMQYY7ztvxv2Ejcljeb1apAwsgcXnFfZ1yUFBCc9ivdFJEVE7heRC7xekTHGeMHPG/cxcnIazepWJ2FkD2pVt5BwqsSgUNUrgVigKZAmItNFpLfXKzPGmDLyy6Z9jJicSkQdV0jUtpA4K47GKFR1A/AU8DhwNfCWiKwVkYHeLM4YY85V8ub9jJiURtNa55Ewqgd1alTxdUkBx8kYRUcReQNYA1wL9FPVS92v3/ByfcYYU2rrDuQx7MNUGteqxvRR0dS1kCgVJ7cgvgOMA/6uqsdOrlTVHSLylNcqM8aYc5C25QCvL8qmSe3qTB/Vg3o1LSRKy0lQ3AgcU9U8ABGpAFRV1aOqOtWr1RljTCks+v0AQyemUKuKkDgqmvo1q/q6pIDmZIziO6BageXz3OuMMcbvLN56kKETU6lXswqPd69K/fMtJM6Vk6CoqqpZJxfcr8/zXknGGFM6S7cdYuiEFOrUqExiXDS1qto9xWXByb/iERHpenJBRCKBYx62N8aYcrc8/RB3TlhIreqVSRwVTcPwaiV/yDjiZIziYWCWiOxwLzcEBnuvJGOMOTsrt2dwx/iFhFcLIzEumkYXWEiUpRKDQlVTRaQN0BoQYK2q5ni9MmOMcWDl9gxixy+kZtUwEkdF09hCosw5fUJHNyDCvX0XEUFVp3itKmOMcWD1jkzumLCQGlUqMSMumqa1bfjUG0oMChGZCjQHlgJ57tUKWFAYY3xmzc5MYscnUy2sIomjLCS8yUmPIgpoq6rq7WKKIiKXAE8C4ap6qy9qMMb4l3W7DhM7fiFVKrlC4qI6FhLe5OSqp5XAhaXZuYhMFJE9IrKy0Pq+IrJORDaKyBhP+1DVzao6ojTHN8YEn/W7D3P7uGTCKgqJcdFE1K3u65KCnpMeRV1gtYikAMdPrlTV/g4+OwnXFCCnTlOJSEXgXaA3kA6kisgcoCIwttDnh6vqHgfHMcaEgI17XCFRsYIwfVQ0zSwkyoWToPhHaXeuqgtEJKLQ6u7ARlXdDCAiM4ABqjoW+GNpj2WMCW4b92QxJH4h4AqJ5vVq+LqkkCFOhh5E5GKgpap+JyLnARVV9bCjA7iC4gtVbe9evhXoq6oj3ct3Aj1U9cFiPl8HeBFXD2S8O1CK2i4OiANo0KBB5IwZM5yUd4asrCxq1AitH0Brc2gI5DbvOpLPSynZ5Ksypns1GtVwdsd1ILe5NM61vddcc80iVY0qvN7JVU+jcP0Cro3r6qfGwPtAr1LWIkWsKzatVHU/cG9JO1XVeCAeICoqSnv27Fmq4pKSkijtZwOVtTk0BGqbf9t3hMfjf6VipTA+ioumVYOajj8bqG0uLW+110ksPwBcAWTCqYcY1T+HY6bjelreSU2AHcVsa4wJYVv2HSEmPpmcPGX6qLMLCVN2nATFcVU9cXJBRCrhoQfgQCrQUkSaiUhlYAgw5xz2d4qI9BOR+IyMjLLYnTHGh7buP0rMuGSO5+aRMLIHrS+0kPAVJ0Hxo4j8Hajmflb2LGCuk52LSCLwK9BaRNJFZISq5gIPAvNwPTXvI1VdVbryT6eqc1U1Ljw8vCx2Z4zxkW0HXCFxLCePaSN7cGnD831dUkhzctXTGGAEsAK4B/gKGO9k56oaU8z6r9z7McaY06QfPMqQ+GSyjueSMLIH7RrZH36+5mRSwHxcj0Id5/1yjDGhbPuhYwyJT+Zwdg4JI6Np39hCwh8UGxQi8pGqDhKRFRQxJqGqHb1aWSmISD+gX4sWLXxdijHmLO04dIyY+GQyjuUwbUQPOjSxkPAXnnoUf3H/N2BuglPVucDcqKioUb6uxRjj3K6MbGLGJXPwyAmmjuxBp6YX+LokU0CxQaGqO90vKwA7VTUbQESqAQ3KoTZjTAjYnekKif1ZJ5gyojudLST8jpOrnmYB+QWW89zrjDHmnOzJzCYmPpk9mdlMHt6NrhfV8nVJpghOgqJSwfso3K8re6+k0rP7KIwJHHsOZzNkXDK7MrOZNLw7kRfX9nVJphhOgmKviJyaKVZEBgD7vFdS6dl9FMYEhr2Hj3P7uIXsyshm0rDudIuwkPBnTu6juBdIEJF3cM3TtA24y6tVGWOC1r6s48SOT2b7wWN8OKwb3ZtZSPg7J/dRbAKiRaQGrtlmHc0aa4wxhe3POk7suIVsPXCUiXd3I/qSOr4uyTjg6T6KO1R1mog8Wmg9AKr6updrM8YEkQNHThA7fiFb9h9h4t3duLx5XV+XZBzy1KM4+RDagJmJy264M8Y/HTxygjvGL2TzviNMGBrFFS0sJAKJp6Bo7v7valUNiMth7YY7Y/zPoaMnuGPCQjbuzWLcXVH8oWU9X5dkzpKnq55uFJEw4InyKsYYE1wyjuZw54QUNuzO4oM7I7m6lYVEIPLUo/gG12Ww1UUks8B6AVRVbd5fY0yxMo7lcNfEhazdlckHd0ZyTetzed6Z8SVPPYqnVDUc+FJVzy/wVdNCwhjjSWZ2DndNTGH1zkz+HRvJtW1s1p9A5ikofnX/N9PDNsYYc5rD2TkMnZjCqu0ZvHt7V65rayER6DydeqosIkOBy0VkYOE3VfVT75VVOnbVkzG+lXU8l7s/TGVFegbv3N6V69td6OuSTBnwFBT3ArHABUC/Qu8p4HdBYVc9GeM7R47nMuzDFJZuO8Q7MV3o295CIlh4mmb8J+AnEUlT1QnlWJMxJsC4QiKVxVsP8daQLtzQoaGvSzJlqNgxChF5DEBVJ4jIbYXe+z9vF2aMCQxHT+QyfFIqab8f4M3Bnbmpo4VEsPE0mD2kwOvC91L09UItxpgAc+xEHiMmpZG65QBvDO5Mv06NfF2S8QJPYxRSzOuilo0xISY7J4+RU1JJ/m0/rw/qxIDOjX1dkvESTz0KLeZ1UcvGmBCSnZPHqClp/LJpP6/e2ombuzTxdUnGizz1KDq578gWoFqBu7MFqOr1ykrBLo81xvuyc/KIm7qInzbu4+VbOnJLpIVEsCu2R6GqFQvciV2p0J3ZYeVZpFP2hDtjvOt4bh73TlvEgvV7+efAjtwW1dTXJZly4ORRqMYYw/HcPO6btpikdXsZO7ADg7pZSIQKCwpjTIlO5ObzQMJivl+7hxdvbk9M94t8XZIpRxYUxhiPTuTm88D0xXy3Zg8vDGhHbI+LfV2SKWcWFMaYYuXk5fNQ4mK+Xb2b5/q3487LInxdkvEBT8/MPoyHy2BtqnFjgltOXj5/mbGEeat282y/tgy9PMLXJRkf8TTXU00AEXke2AVMxXVpbCwB9BxtY8zZy83L5+GZS/lqxS6euulShl3RzNclGR9ycuqpj6q+p6qHVTVTVf8N3OLtwowxvpGbl88jHy3jy+U7efLGSxn5h0t8XZLxMSdBkScisSJSUUQqiEgskOftwkpDRPqJSHxGRoavSzEmIOXlK3+dtYy5y3Yw5oY2jLrKQsI4C4rbgUHAbvfXbe51fsduuDOm9PLylb/NWsbspTsY3ac1917d3NclGT/haQoPAFR1CzDA+6UYY3wlL1957OPlfLZkO3+7vhUPXGPT4Jj/KTEoRKQeMAqIKLi9qg73XlnGmPKSn6+M+WQ5nyxO55HrWvHgtS19XZLxMyUGBTAb+C/wHX46NmGMKZ38fOWJT1cwa1E6f+nVkr9cZyFhzuQkKM5T1ce9Xokxplzl5ytPfr6SmWnbeOjaFjxsIWGK4WQw+wsRudHrlRhjyo2q8vTslSSmbOX+ns15tHcrROx5ZKZoToLiL7jC4piIZIrI4QLPpjDGBBhV5ZnZq0hYuJV7r27O6D6tLSSMR06uerK7sI0JEqrKc3NXMzX5d+KuuoTH+1pImJI5uerpqqLWq+qCsi/HGOMtqsrzX6xm0i9bGHFlM564oY2FhHHEyWD26AKvqwLdgUXAtV6pyBhT5lSVGetOMG/LFoZdEcFTN11qIWEcc3LqqV/BZRFpCrzstYqMMWVKVRn79Vrmbcnl7ssjeOaPbS0kzFkpzfMo0oH2ZV2IMabsqSr//GYd8Qs2c+1FlXi2n4WEOXtOxije5n/PpagAdAaWebOo0hKRfkC/Fi1s+gFjVJVX56/j/R83EdvjInpdsM9CwpSKkx5FGq4xiUXAr8DjqnqHV6sqJZsU0BgXVeX1b9fz7g+biOnelBcGtKeChYQpJSdjFJNFpDLQyr1qnXdLMsacqze/28Db329kcFRTXvxTBypUsJAwpefk1FNPYDKwBdcT7pqKyFC7PNYY//TWfzbwr/9s4NbIJowdaCFhzp2Ty2NfA65X1XUAItIKSAQivVmYMebsvfP9Bl7/dj0Duzbmn7d0tJAwZcLJGEXYyZAAUNX1QJj3SjLGlMZ7SRt5df56bu7SmFdu7URFCwlTRpz0KNJEZAIw1b0ci2tg2xjjJz74cRMvf7OOAZ0b8eptFhKmbDkJivuAB4A/4xqjWAC8582ijDHOjVuwmbFfr6Vfp0a8ZiFhvMBjUIhIRWCC+3LY18unJGOMU+P/u5kXv1rDTR0a8sagTlSqWJp7aI3xzONPlarmAfXcl8caY/zIhz//xv/7cg03tL+QN4d0tpAwXuPk1NMW4GcRmQMcOblSVa2HYYyPTPl1C8/NXU2fdg14K6YLYRYSxoucBMUO91cFwJ5NYYyPTU3+nWdmr6J32wa8HdPVQsJ4nZM7s58rj0KMMSVLWPg7T3++kusurc+7t3elciULCeN9Tu7Mnsv/JgU8KQPXHFAfqGq2NwozxpwuMWUrT362kmvb1OfdWAsJU36c/KRtBrKAce6vTGA3rrmfxnmvNGPMSR+lbuOJT1fQs3U93ovtSpVKFX1dkgkhTsYouqhqwcehzhWRBap6lYis8lZhxhiXWWnbePzT5VzVqh7v3xFJ1TALCVO+nPQo6onIRScX3K/ruRdPeKUqYwwAnyxK57FPlnNli7rE32khYXzDSY/ir8BPIrIJ153ZzYD7RaQ6rllljTFe8PmS7fzt42Vc3rwO4+6KspAwPuPkqqevRKQl0AZXUKx1rdbjwJterg8R+RNwE1AfeFdV53v7mMb42uyl23n0o6VEN6vD+Lu6WUgYnyrx1JOITFTV46q6TFWXAhWBr5zsXEQmisgeEVlZaH1fEVknIhtFZIynfajq56o6CrgbGOzkuMYEsrnLdvDIzKV0i6jNhLujqFbZQsL4lpMxiu0i8m8AEakFfAtMc7j/SUDfgivc80e9C9wAtAViRKStiHQQkS8KfdUv8NGn3J8zJmh9uXwnD89cStTFtflwWDfOq+zk7LAx3iWqhW+RKGIjkX8C4bgeVvSSqn7i+AAiEcAXqtrevXwZ8A9V7eNefgJAVccW83kBXgK+VdXvPBwnDogDaNCgQeSMGTOclniarKwsatSoUarPBiprs39I3ZXLv5cdp3l4Bf4aVZWqlcp2Flh/bLO3hVqbz7W911xzzSJVjSq8vtg/V0RkYIHFFOBp939VRAaq6qelrKUxsK3AcjrQw8P2DwHXAeEi0kJV3y9qI1WNB+IBoqKitGfPnqUqLikpidJ+NlBZm33vm5W7+GD+YrpcVIvJw7tTo0rZ9yT8rc3lIdTa7K32evpp7FdoeQmuJ9v1w3WndmmDoqg/k4rt1qjqW8BbpTyWMX7v29W7eXD6Yjo0CWfSsG5eCQljzkWxP5GqOsxLx0wHmhZYboJr0kFjQs5/1uzm/oRFtGsczjRDj4sAABEJSURBVOTh3alZ1Z4ybPyPk6ueJovIBQWWa4nIxHM4ZirQUkSauZ9zMQSYcw77O0VE+olIfEZGRlnszhiv+mHtHu6btphLG57PlOHdOd9CwvgpJ1c9dVTVQycXVPUg0MXJzkUkEfgVaC0i6SIyQlVzgQeBecAa4CNVLZOpQFR1rqrGhYeHl8XujPGapHV7uGfqIlpdWIOpw3sQXs1CwvgvJydDK4hILXdAICK1HX4OVY0pZv1XOLwXw5hg8+P6vcRNXUTLBjWYNqIH4edZSBj/5uQX/mvALyLysXv5NuBF75VUeiLSD+jXokULX5diTJF+2rCPuClpNK/nCokLzrOnDBv/V+KpJ1WdAtyKa2rxPcBAVZ3q7cJKw049GX/288Z9jJicSrO61UkY2YNa1S0kTGBwegpplYjsBaqCawZZVd3q1cqMCSK/bHKFREQdV0jUtpAwAcTJVU/9RWQD8BvwI7AF+NrLdRkTNJI372fEpDSa1jqPhFE9qFOjiq9LMuasOLnq6QUgGlivqs2AXsDPXq3KmCCR8tsBhk9KpXGtakwfFU1dCwkTgJwERY6q7sd19VMFVf0B6OzlukrF7qMw/iRtywHu/jCFhuFVmT6qB/VqWkiYwOQkKA6JSA1gAZAgIv8Ccr1bVunYYLbxF4t+P8jQiSlceH5VEkdFU79mVV+XZEypOQmKAcBR4BHgG2ATZ84DZYxxW7zVFRL1alZh+qho6p9vIWECm5Mn3B1xv8wXkS+B/epkbnJjQtDSbYcYOiGFOjUqkxgXzYXhFhIm8BXboxCRaBFJEpFPRaSL+yl1K4HdItK3uM/5ko1RGF9ann6IOycspFb1yiSOiqZheDVfl2RMmfB06ukd4P+AROB7YKSqXghcBRT5kCFfszEK4ysrt2dwx/iFXHBeGIlx0TS6wELCBA9PQVFJVeer6ixgl6omA6jq2vIpzZjAsHJ7BrHjF1KzahiJo6JpbCFhgoynoMgv8PpYofdsjMIYYPWOTO6YsJAaVSoxIy6aJrXO83VJxpQ5T4PZnUQkE9cT6aq5X+NethE6E/LW7Mwkdnwy1cIqkjgqmqa1LSRMcPL0hLuK5VmIMYFk3a7DxI5fSJVKFZkRF81FdSwkTPBych9FwLCrnkx5WL/7MLePSyasopAYF83Fdar7uiRjvCqogsKuejLetnGPKyQqVhCmj4qmWV0LCRP8gioojPGmTXuziBm3EHCFRPN6NXxdkjHlwoLCGAc2780iJj4ZVWVGXA9a1LeQMKHDgsKYEvy27wgx45LJy1emj4qmRf2avi7JmHLl6Al3xoSq3/cfISY+mZw8JXFUNK0aWEiY0GM9CmOKsXX/UWLikzmem0fCyB60vtBCwoSmoAoKuzzWlJVtB44SMy6Zozl5JIyM5tKG5/u6JGN8JqiCwi6PNWUh/eBRhsQnk3U8l2kjetC2kYWECW1BFRTGnKvth44RMy6Zw9k5TBvRg/aN7Y8OY2ww2xi3HYeOEROfzKGjOSSM7EGHJhYSxoD1KIwBYFdGNjHjkjl45ARTR/SgY5MLfF2SMX7DehQm5O3OdIXE/qwTTBnRnc5NLSSMKch6FCak7cnMJiY+mT2Z2Uwe3o2uF9XydUnG+B3rUZiQteewqyexKzObycO7E3lxbV+XZIxfsh6FCUkZx5Xbxy1kZ0Y2k4Z1p1uEhYQxxQmqoLAb7owT+7KO83LqMbYfPMbEu7vRvZmFhDGeBFVQ2A13piT7s45zx/iF7D2qTLg7iuhL6vi6JGP8XlAFhTGeHDhygtjxC/lt3xEejqzK5c3r+rokYwKCBYUJCYeOnuCO8QvZvO8I44dG0baOPRLeGKcsKEzQO3TU1ZPYuDeLcXdF8YeW9XxdkjEBxYLCBLWMozncOSGFDbuziL8zkqtbWUgYc7YsKEzQyjiWw10TF7J2Vybv39mVnq3r+7okYwKSBYUJSpnZOQydmMLqnZn8OzaSa9s08HVJxgQsCwoTdA67Q2Ll9gzevb0r17W1kDDmXFhQmKCSdTyXuz9MZUV6Bu/c3pXr213o65KMCXg215MJGkeO5zLswxSWbjvEOzFd6NveQsKYsmA9ChMUjp7IZdikVBZvPcRbQ7pwQ4eGvi7JmKBhQWEC3tETuQyflEralgO8ObgzN3W0kDCmLAVVUNikgKHn2Ik8RkxKI+W3A7wxuDP9OjXydUnGBJ2gCgqbFDC0ZOfkMWpKGsm/7ee1QZ0Y0Lmxr0syJigFVVCY0HEyJH7etI9Xb+3EzV2a+LokY4KWBYUJONk5ecRNXcRPG/fx8i0duSXSQsIYb7KgMAHleG4e901bxIL1e/nnwI7cFtXU1yUZE/QsKEzAcIXEYn5Yt5exAzswqJuFhDHlwYLCBIQTufk8kLCY79fu4cWb2xPT/SJfl2RMyLCgMH4vJy+fB6cv5rs1e3hhQDtie1zs65KMCSkWFMav5eTl89D0JcxfvZvn+rfjzssifF2SMSHHgsL4rZy8fP4yYwnfrNrFs/3aMvTyCF+XZExIsqAwfik3L5+HZy7lqxW7eOqmSxl2RTNfl2RMyLKgMH4nNy+fRz5axpfLd/LkjZcy8g+X+LokY0KaBYXxK3n5yl9nLWPush2MuaENo66ykDDG1ywojN/Iy1dGz1rG7KU7eKxva+69urmvSzLGYEFh/ERevvLYx8v5dMl2/nZ9K+7v2cLXJRlj3CwojM/l5ytjPlnOJ4vTeeS6Vjx4bUtfl2SMKcCCwvhUfr7y989WMGtROn/p1ZK/XGchYYy/saAwPpOfrzz5+UpmpG7joWtb8LCFhDF+ye+DQkQuFZH3ReRjEbnP1/WYsqGqPDNnJYkpW7m/Z3Me7d0KEfF1WcaYIng1KERkoojsEZGVhdb3FZF1IrJRRMZ42oeqrlHVe4FBQJQ36zXlQ1V5ds4qpiVv5d6rmzO6T2sLCWP8mLd7FJOAvgVXiEhF4F3gBqAtECMibUWkg4h8Ueirvvsz/YGfgP94uV7jZarKc3NXM+XX34m76hIe72shYYy/q+TNnavqAhGJKLS6O7BRVTcDiMgMYICqjgX+WMx+5gBzRORLYLr3KjbepKq88MUaJv2yhRFXNuOJG9pYSBgTALwaFMVoDGwrsJwO9ChuYxHpCQwEqgBfedguDohzL2aJyLpS1lcX2FfKzwaqcm/zM/+EZ8rzgGey73NoCLU2n2t7i5zD3xdBUdSfkFrcxqqaBCSVtFNVjQfiS12Vm4ikqWpIjYVYm0ODtTn4eau9vrjqKR0o+AzLJsAOH9RhjDHGAV8ERSrQUkSaiUhlYAgwxwd1GGOMccDbl8cmAr8CrUUkXURGqGou8CAwD1gDfKSqq7xZx1k659NXAcjaHBqszcHPK+0V1WKHB4wxxhj/vzPbGGOMb1lQGGOM8Shkg6KkaUREpIqIzHS/v7CIGwcDjoM2Pyoiq0VkuYj8R0SKvKY6kDidLkZEbhURFZGAvpTSSXtFZJD7+7xKRAL+BlYHP9cXicgPIrLE/bN9oy/qLEvFTY9U4H0Rkbfc/ybLRaTrOR1QVUPuC6gIbAIuASoDy4C2hba5H3jf/XoIMNPXdZdDm68BznO/vi8U2uzeriawAEgGonxdt5e/xy2BJUAt93J9X9ddDm2OB+5zv24LbPF13WXQ7quArsDKYt6/Efga131r0cDCczleqPYoTk0joqongBnAgELbDAAmu19/DPSSwJ5vosQ2q+oPqnrUvZiM6x6XQObk+wzwAvAykF2exXmBk/aOAt5V1YMAqrqnnGssa07arMD57tfhBMF9W6q6ADjgYZMBwBR1SQYuEJGGpT1eqAZFUdOINC5uG3Vd0psB1CmX6rzDSZsLGoHrL5JAVmKbRaQL0FRVvyjPwrzEyfe4FdBKRH4WkWQR6Utgc9LmfwB3iEg6rmmAHiqf0nzqbP9/98gXU3j4AyfTiJzVVCMBwHF7ROQOXFO6X+3VirzPY5tFpALwBnB3eRXkZU6+x5VwnX7qiavH+F8Raa+qh7xcm7c4aXMMMElVXxORy4Cp7jbne788nynT31+h2qNwMo3IqW1EpBKuLqunrp6/czR1iohcBzwJ9FfV4+VUm7eU1OaaQHsgSUS24DqXOyeAB7Sd/lzPVtUcVf0NWIcrOAKVkzaPAD4CUNVfgaq4Js8LZmU6VVKoBoWTaUTmAEPdr28Fvlf3KFGAKrHN7tMwH+AKiUA/dw0ltFlVM1S1rqpGqGoErnGZ/qqa5ptyz5mTn+vPcV20gIjUxXUqanO5Vlm2nLR5K9ALXE/MxBUUe8u1yvI3B7jLffVTNJChqjtLu7OQPPWkqrkicnIakYrARFVdJSLPA2nqev7FBFxd1I24ehJDfFfxuXPY5leAGsAs97j9VlXt77Oiz5HDNgcNh+2dB1wvIquBPGC0qu73XdXnxmGb/wqME5FHcJ1+uTvA/+g7OT1ST6Cue+zlWSAMQFXfxzUWcyOwETgKDDun4wX4v5cxxhgvC9VTT8YYYxyyoDDGGOORBYUxxhiPLCiMMcZ4ZEFhjDHGIwsKE1RE5En3rKjLRWSpiPRwrx8vIm29cLysYtbnuY9/8muMe/0f3PUtFZFqIvKKe/kVEblXRO7ycKxGIvJxWbfBmJLY5bEmaLinZ3gd6Kmqx903lFVWVa9NAiciWapa4yzWv49rJs8P3cuZQL0guAveBDHrUZhg0hDYd/KXrqruOxkSIpJ0cmoOERkhIuvd68aJyDvu9ZPcc/j/IiKbReRW9/oa4no+x2IRWSEiRc1AWyIRGQkMAp4RkQQRmQNUBxaKyGAR+YeI/M29bQsR+U5ElrmP21xEIk4+f0BEKrp7Ianu3tM97vU93e36WETWuo8j7ve6udu2TERSRKSmiPxXRDoXqPFnEelYmvaZ4BWSd2aboDUf1y/h9cB3uJ6n8WPBDUSkEfA0rrn8DwPf43qGwUkNgSuBNrimQfgY1/TjN6tqpruXkiwic0q4u7eaiCwtsDxWVceLyJXAF6r6sbueLFXt7H79jwLbJwAvqepnIlIV1x919Qu8PwLXtAzdRKQK8LOIzHe/1wVoh2tun5+BK0QkBZgJDFbVVBE5HzgGjMc1KeLDItIKqKKqyz20y4Qg61GYoKGqWUAkEIdrLp+ZInJ3oc26Az+q6gFVzQFmFXr/c1XNV9XVQAP3OgH+T0SW4wqgxgXeK84xVe1c4Gum03aISE2gsap+5m5XdoHnhJx0Pa65fJYCC3FNgX9ycr8UVU13z466FIgAWgM7VTXVvc9M9/T5s4A/ikgYMByY5LROEzqsR2GCiqrmAUm4ZoRdgWtix0kFNinp4VMFxwpObhsL1AMiVTVHXDPNVi2Leovh5AFZAjykqvNOWynSk9PbkIfr/3OhiGmmVfWoiHyL60E3g3BNL2/MaaxHYYKGiLQWkYJTZncGfi+0WQpwtYjUEtf08bc42HU4sMcdEtcAXn2WuKpmAuki8ic49fz28wptNg+4z90TQERaiUh1D7tdCzQSkW7u7Wu62w+u009vAamqGshT6RsvsR6FCSY1gLdF5AIgF9fMmXEFN1DV7SLyf7hO1+wAVuN6eqEnCcBcEUnDdSpnrYNaCo9RfKOqY5w1A4A7gQ/cs6DmALcBBR+0Mx7XKaXF7sHqvcCfituZqp4QkcG4/n2q4RqfuA7IUtVF7quvPjyL+kwIsctjTcgRkRqqmuX+i/ozXFNTf+brunzFPcCfBLQJ8qe+mVKyU08mFP3D/df+SuA3XA/zCUnuG/wWAk9aSJjiWI/CGGOMR9ajMMYY45EFhTHGGI8sKIwxxnhkQWGMMcYjCwpjjDEe/X82U1aZCxIjvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot normalized confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Plot ROC curve\")\n",
    "y_predict = makeRoc(X_test, labels, y_test, model)\n",
    "plt.show()\n",
    "\n",
    "y_test_proba = y_test.argmax(axis=1)\n",
    "y_predict_proba = y_predict.argmax(axis=1)\n",
    "\n",
    "print(\"Plot confusion matrix\")\n",
    "# Compute non-normalized confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test_proba, y_predict_proba)\n",
    "np.set_printoptions(precision=2)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=[l.replace('j_','') for l in labels],\n",
    "                          title='Confusion matrix')\n",
    "plt.show()\n",
    "\n",
    "print(\"Plot normalized confusion matrix\")\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=[l.replace('j_','') for l in labels], normalize=True,\n",
    "                          title='Normalized confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the weight I trained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot ROC curve\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot normalized confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Plot ROC curve\")\n",
    "y_predict = makeRoc(X_test, labels, y_test, model)\n",
    "plt.show()\n",
    "\n",
    "y_test_proba = y_test.argmax(axis=1)\n",
    "y_predict_proba = y_predict.argmax(axis=1)\n",
    "\n",
    "print(\"Plot confusion matrix\")\n",
    "# Compute non-normalized confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test_proba, y_predict_proba)\n",
    "np.set_printoptions(precision=2)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=[l.replace('j_','') for l in labels],\n",
    "                          title='Confusion matrix')\n",
    "plt.show()\n",
    "\n",
    "print(\"Plot normalized confusion matrix\")\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=[l.replace('j_','') for l in labels], normalize=True,\n",
    "                          title='Normalized confusion matrix')\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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}