Convolutional_AE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c5ac1d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Choose the ATLAS ML training  kernel from the dropdown menu!\n",
    "import numpy as np\n",
    "import h5py\n",
    "import math\n",
    "import os\n",
    "import pathlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Dense,ZeroPadding2D, BatchNormalization, Activation, Layer, Conv2D,AveragePooling2D,UpSampling2D,Reshape,Flatten\n",
    "from tensorflow.keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50b2e16b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9.1\n"
     ]
    }
   ],
   "source": [
    "# Must be TF version 2\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93887d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from func import load_model, save_model, SetStyle\n",
    "SetStyle()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33a207a",
   "metadata": {},
   "source": [
    "## Load background dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa793615",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_folder = '/global/cfs/cdirs/ntrain5/atlas-ml-training/anomaly_detection/'\n",
    "# You should already have run\n",
    "# python create_datasets.py --bkg_file background_for_training.h5 --output_bkg_name 'BKG' --signals_files 'Ato4l_lepFilter_13TeV.h5' --output_signal_names 'Ato4l_lepFilter_13TeV' --events=1000000\n",
    "filename = 'BKG_dataset.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a7d8f3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(640000, 57)\n",
      "(640000, 19, 3, 1)\n"
     ]
    }
   ],
   "source": [
    "# load background training data\n",
    "with h5py.File(dataset_folder+filename, 'r') as file:\n",
    "    X_train = np.array(file['X_train'])\n",
    "    X_test = np.array(file['X_test'])\n",
    "    X_val = np.array(file['X_val'])\n",
    "\n",
    "print(X_train.shape)\n",
    "X_train = np.reshape(X_train, (-1, 19,3,1))\n",
    "X_test = np.reshape(X_test, (-1, 19,3,1))\n",
    "X_val = np.reshape(X_val, (-1, 19,3,1))\n",
    "\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a110cc7-6ab3-468b-a502-2b69da54628e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot one of these pictures\n",
    "plt.figure(figsize=(8,10))\n",
    "for i in range(3):\n",
    "    plt.subplot(1,3,i+1)\n",
    "    to_plot = X_train[i].copy()\n",
    "    to_plot[to_plot == 0.0] = np.nan\n",
    "    plt.imshow(to_plot, interpolation='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7326ebec",
   "metadata": {},
   "source": [
    "## Define the CNN autoencoder <img src=\"assets/cnn_AE.png\" width=600 height=400 />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a735ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_shape = (19,3,1)\n",
    "latent_dimension = 8\n",
    "num_node=10\n",
    "activation = \"swish\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2b6ce46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 19, 3, 1)]        0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 19, 3, 10)         90        \n",
      "                                                                 \n",
      " average_pooling2d (AverageP  (None, 9, 3, 10)         0         \n",
      " ooling2D)                                                       \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 270)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 8)                 2168      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 270)               2430      \n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 9, 3, 10)          0         \n",
      "                                                                 \n",
      " up_sampling2d (UpSampling2D  (None, 18, 3, 10)        0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " zero_padding2d (ZeroPadding  (None, 19, 3, 10)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 19, 3, 1)          90        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,778\n",
      "Trainable params: 4,778\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-29 10:30:51.854229: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-07-29 10:30:52.461666: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14637 MB memory:  -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:1a:00.0, compute capability: 7.0\n"
     ]
    }
   ],
   "source": [
    "#encoder\n",
    "input_encoder = Input(shape=(image_shape))\n",
    "x = Conv2D(num_node, kernel_size=(3, 3),\n",
    "         use_bias=False, padding='same',activation=activation)(input_encoder)\n",
    "x = AveragePooling2D(pool_size = (2, 1))(x)\n",
    "x = Flatten()(x)\n",
    "enc = Dense(latent_dimension,activation='relu')(x)\n",
    "encoder = Model(inputs=input_encoder, outputs=enc)\n",
    "#decoder\n",
    "x = Dense(num_node*9*3,activation=activation)(enc)\n",
    "x = Reshape((9,3,num_node))(x)\n",
    "x = UpSampling2D((2, 1))(x)\n",
    "x = ZeroPadding2D(((1, 0),(0,0)))(x)\n",
    "dec = Conv2D(1, kernel_size=(3,3), use_bias=False,padding='same',activation='linear')(x)\n",
    "\n",
    "\n",
    "autoencoder = Model(inputs=input_encoder, outputs=dec)\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ac31e11",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer = keras.optimizers.Adam(), loss='mse')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "763cd21d",
   "metadata": {},
   "source": [
    "## Trainning and saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2844c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 50\n",
    "BATCH_SIZE = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "883c0086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-29 10:30:54.313841: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8302\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64/64 [==============================] - 2s 10ms/step - loss: 70.1730 - val_loss: 45.4779\n",
      "Epoch 2/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 23.8417 - val_loss: 10.7717\n",
      "Epoch 3/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 7.6389 - val_loss: 5.8197\n",
      "Epoch 4/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 4.8892 - val_loss: 4.2835\n",
      "Epoch 5/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 3.7144 - val_loss: 3.3602\n",
      "Epoch 6/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 2.9965 - val_loss: 2.7902\n",
      "Epoch 7/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 2.5499 - val_loss: 2.4227\n",
      "Epoch 8/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 2.2521 - val_loss: 2.1647\n",
      "Epoch 9/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 2.0346 - val_loss: 1.9596\n",
      "Epoch 10/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 1.8420 - val_loss: 1.7542\n",
      "Epoch 11/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 1.6304 - val_loss: 1.5160\n",
      "Epoch 12/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 1.3816 - val_loss: 1.2391\n",
      "Epoch 13/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 1.1076 - val_loss: 0.9669\n",
      "Epoch 14/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.8511 - val_loss: 0.7367\n",
      "Epoch 15/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.6517 - val_loss: 0.5632\n",
      "Epoch 16/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.5259 - val_loss: 0.4719\n",
      "Epoch 17/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.4460 - val_loss: 0.4225\n",
      "Epoch 18/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.4035 - val_loss: 0.3756\n",
      "Epoch 19/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3787 - val_loss: 0.3579\n",
      "Epoch 20/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3596 - val_loss: 0.3503\n",
      "Epoch 21/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3489 - val_loss: 0.3364\n",
      "Epoch 22/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3391 - val_loss: 0.3270\n",
      "Epoch 23/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3366 - val_loss: 0.3207\n",
      "Epoch 24/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3230 - val_loss: 0.3127\n",
      "Epoch 25/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3169 - val_loss: 0.3126\n",
      "Epoch 26/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3158 - val_loss: 0.3046\n",
      "Epoch 27/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3092 - val_loss: 0.2970\n",
      "Epoch 28/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3043 - val_loss: 0.2946\n",
      "Epoch 29/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.3050 - val_loss: 0.2910\n",
      "Epoch 30/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2953 - val_loss: 0.2869\n",
      "Epoch 31/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2930 - val_loss: 0.2833\n",
      "Epoch 32/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2915 - val_loss: 0.2861\n",
      "Epoch 33/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2885 - val_loss: 0.2805\n",
      "Epoch 34/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2874 - val_loss: 0.2740\n",
      "Epoch 35/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2820 - val_loss: 0.2734\n",
      "Epoch 36/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2779 - val_loss: 0.3075\n",
      "Epoch 37/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2858 - val_loss: 0.2704\n",
      "Epoch 38/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2734 - val_loss: 0.2649\n",
      "Epoch 39/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2726 - val_loss: 0.2635\n",
      "Epoch 40/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2705 - val_loss: 0.2644\n",
      "Epoch 41/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2702 - val_loss: 0.2598\n",
      "Epoch 42/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2688 - val_loss: 0.2603\n",
      "Epoch 43/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2667 - val_loss: 0.2657\n",
      "Epoch 44/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2676 - val_loss: 0.2555\n",
      "Epoch 45/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2621 - val_loss: 0.2665\n",
      "Epoch 46/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2643 - val_loss: 0.2537\n",
      "Epoch 47/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2618 - val_loss: 0.2531\n",
      "Epoch 48/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2672 - val_loss: 0.2517\n",
      "Epoch 49/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2577 - val_loss: 0.2512\n",
      "Epoch 50/50\n",
      "64/64 [==============================] - 0s 6ms/step - loss: 0.2586 - val_loss: 0.2535\n"
     ]
    }
   ],
   "source": [
    "history = autoencoder.fit(X_train, X_train, epochs = EPOCHS, batch_size = BATCH_SIZE,\n",
    "                  validation_data=(X_val, X_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "efb8efe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'CNN_AE'\n",
    "model_directory = 'CNNS/'\n",
    "if not os.path.exists(model_directory+model_name):\n",
    "    os.makedirs(model_directory+model_name)\n",
    "save_model(model_directory+model_name, autoencoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7989f156",
   "metadata": {},
   "source": [
    "## Let's take a look at the background reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bfe5ce9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6250/6250 [==============================] - 6s 896us/step\n"
     ]
    }
   ],
   "source": [
    "bkg_prediction = autoencoder.predict(X_test,batch_size=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46bdc3d3",
   "metadata": {},
   "source": [
    "## Now let's take a look at the signals! <img src=\"assets/NP.png\" width=800 height=200 />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2987e8c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add correct signal labels\n",
    "signal_labels = ['A to 4 leptons',\n",
    "                 'h to tau tau',\n",
    "                 'leptoquark',\n",
    "                 'charged h to tau nu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "93f5107a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add correct path to signal files\n",
    "signals_file = ['Ato4l_lepFilter_13TeV_dataset.h5',\n",
    "                'hToTauTau_13TeV_PU20_dataset.h5',\n",
    "                'leptoquark_LOWMASS_lepFilter_13TeV_dataset.h5',\n",
    "                'hChToTauNu_13TeV_PU20_dataset.h5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7401da92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read signal data\n",
    "signal_data = []\n",
    "for i, label in enumerate(signal_labels):\n",
    "    with h5py.File(dataset_folder+signals_file[i], 'r') as file:\n",
    "        test_data = np.array(file['Data'])\n",
    "    signal_data.append(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3e69187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56/56 [==============================] - 0s 1ms/step\n",
      "692/692 [==============================] - 1s 1ms/step\n",
      "341/341 [==============================] - 0s 1ms/step\n",
      "761/761 [==============================] - 1s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "signal_results = []\n",
    "\n",
    "for i, label in enumerate(signal_labels):\n",
    "    signal_prediction = autoencoder.predict(signal_data[i].reshape(-1,19,3,1),batch_size=1000)\n",
    "    signal_results.append([label, signal_data[i], signal_prediction]) # save [label, true, prediction] for signal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cc9b504",
   "metadata": {},
   "source": [
    "## Before looking into the results, let's save the trained model in case we want to use it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3689de80",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_file = 'cnnae_results'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9a94e1b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(save_file, 'w') as file:\n",
    "    file.create_dataset('BKG_input', data=X_test)\n",
    "    file.create_dataset('BKG_predicted', data = bkg_prediction)\n",
    "    for i, sig in enumerate(signal_results):\n",
    "        file.create_dataset('%s_input' %sig[0], data=sig[1])\n",
    "        file.create_dataset('%s_predicted' %sig[0], data=sig[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b176d70e",
   "metadata": {},
   "source": [
    "# Evaluating the autoencoder\n",
    "## Let's take a look at the reconstruction error for the different signals and background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "68f4636a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from func import mse_loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b552bb7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute loss value (true, predicted)\n",
    "total_loss = []\n",
    "shape=(-1,X_test.shape[1]*X_test.shape[2])\n",
    "total_loss.append(mse_loss(X_test.reshape(shape),bkg_prediction.reshape(shape)))\n",
    "for i, signal_X in enumerate(signal_data):\n",
    "    total_loss.append(mse_loss(signal_X,(signal_results[i][2].reshape(shape))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b2961a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "binning=np.linspace(0,1000,50)\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.hist(total_loss[0], bins=binning, label=\"Background\", density = True, histtype='step', fill=False, linewidth=1.5)\n",
    "for i, label in enumerate(signal_labels):\n",
    "    plt.hist(total_loss[i+1], bins=binning, label=label, density = True, histtype='step', fill=False, linewidth=1.5)\n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"Autoencoder Loss\")\n",
    "plt.ylabel(\"Normalized entries\")\n",
    "plt.title('MSE loss')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ef5b9ca-17a4-4a7a-837e-40081d39a4ef",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Let's now make a Receiver Operating Characteristic (ROC) curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "44785225",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c365dff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = np.concatenate([['Background'], np.array(signal_labels)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e084b093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_background = np.zeros(total_loss[0].shape[0])\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "for i, label in enumerate(labels):\n",
    "    if i == 0: continue # background events\n",
    "    \n",
    "    trueVal = np.concatenate((np.ones(total_loss[i].shape[0]), target_background)) # anomaly=1, bkg=0\n",
    "    predVal_loss = np.concatenate((total_loss[i], total_loss[0]))\n",
    "\n",
    "    fpr_loss, tpr_loss, threshold_loss = roc_curve(trueVal, predVal_loss)\n",
    "\n",
    "    auc_loss = auc(fpr_loss, tpr_loss)\n",
    "    \n",
    "    plt.plot(fpr_loss, tpr_loss, \"-\", label='%s (auc = %.1f%%)'%(label,auc_loss*100.), linewidth=1.5)\n",
    "    \n",
    "    plt.semilogx()\n",
    "    plt.semilogy()\n",
    "    plt.ylabel(\"Signal efficiency\")\n",
    "    plt.xlabel(\"Background efficiency\")\n",
    "    plt.legend(loc='best')\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "plt.title(\"ROC AE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7387c8d-b454-41f8-bf21-eac7e561f428",
   "metadata": {},
   "source": [
    "## Let's now make a Significance Improvement (SIC) curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c9a50200-c659-4f8f-a102-3cae9438b124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_background = np.zeros(total_loss[0].shape[0])\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "for i, label in enumerate(labels):\n",
    "    if i == 0: continue # background events\n",
    "    \n",
    "    trueVal = np.concatenate((np.ones(total_loss[i].shape[0]), target_background)) # anomaly=1, bkg=0\n",
    "    predVal_loss = np.concatenate((total_loss[i], total_loss[0]))\n",
    "\n",
    "    fpr_loss, tpr_loss, threshold_loss = roc_curve(trueVal, predVal_loss)    \n",
    "    plt.plot(tpr_loss,np.ma.divide(tpr_loss,np.sqrt(fpr_loss)), \"-\", label='%s (max = %.1f)'%(label,np.max(np.ma.divide(tpr_loss,np.sqrt(fpr_loss)))), linewidth=1.5)\n",
    "    \n",
    "\n",
    "    plt.ylabel(\"Significance improvement\")\n",
    "    plt.xlabel(\"Background efficiency\")\n",
    "    plt.legend(loc='best')\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "plt.title(\"SIC AE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bc49aa5-e1f3-4f02-8892-6be49332da12",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ATLAS ML training - BDT+DNN",
   "language": "python",
   "name": "atlas-ml-dnn-bdt-tutorial"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}