{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ScUlcCzL6lso"
   },
   "source": [
    "# Results of anomaly detection using AutoEncoderMLP-256"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0AwvypL47HEj"
   },
   "source": [
    "This notebook presents experimental results of anomaly detection on the Tennessee Eastman Process dataset using the model AutoEncoderMLP-256."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NTwVP4MW6qw3"
   },
   "source": [
    "Importing libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "mEsTIAZQSedM"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tqdm.auto import trange\n",
    "from ice.anomaly_detection.datasets import AnomalyDetectionReinartzTEP, AnomalyDetectionSmallTEP\n",
    "from ice.anomaly_detection.models import AutoEncoderMLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YM554ghK6yHr"
   },
   "source": [
    "Downloading the TEP dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241,
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     ]
    },
    "id": "9ENTzoh4Sp-P",
    "outputId": "9d290f71-25a1-4d30-dc13-008196352394"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e81d3629e4e24f0babe7bfef1c5c66c1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Reading data/reinartz_tep/df.csv:   0%|          | 0/5600000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a066605238741c485983da59da2a416",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Reading data/reinartz_tep/target.csv:   0%|          | 0/5600000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eed8ae7a6e52412b8489b054487f208e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Reading data/reinartz_tep/train_mask.csv:   0%|          | 0/5600000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = AnomalyDetectionReinartzTEP()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kb6wNfsa672V"
   },
   "source": [
    "Training the model and calculation of metrics."
   ]
  },
  {
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    "id": "R0qkb5YtTwHl",
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   "outputs": [
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       "Creating sequence of samples:   0%|          | 0/560 [00:00<?, ?it/s]"
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       "Creating sequence of samples:   0%|          | 0/2240 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
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     },
     "metadata": {},
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   ],
   "source": [
    "metrics = []\n",
    "for i in trange(5):\n",
    "    torch.random.manual_seed(i)\n",
    "    model = AutoEncoderMLP(\n",
    "        window_size=32,\n",
    "        batch_size=512,\n",
    "        lr=0.001,\n",
    "        num_epochs=20,\n",
    "        verbose=False,\n",
    "        device='cuda'\n",
    "    )\n",
    "    model.fit(\n",
    "        dataset.df[dataset.train_mask])\n",
    "    metrics.append(\n",
    "        model.evaluate(\n",
    "            dataset.df[dataset.test_mask], dataset.target[dataset.test_mask]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yycmxbsC6_7x"
   },
   "source": [
    "Printing metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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     "text": [
      "Accuracy: 0.8028 ± 0.0007\n",
      "TPR/FPR: 0.7378 ± 0.0022 / 0.0366 ± 0.0034\n"
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    "acc = []\n",
    "tpr = []\n",
    "fpr = []\n",
    "for metrics_i in metrics:\n",
    "    acc.append(metrics_i[\"accuracy\"])\n",
    "    tpr.append(metrics_i[\"true_positive_rate\"])\n",
    "    fpr.append(metrics_i[\"false_positive_rate\"])\n",
    "tpr_mean, tpr_std = np.mean(tpr, axis=0), np.std(tpr, axis=0)\n",
    "fpr_mean, fpr_std = np.mean(fpr, axis=0), np.std(fpr, axis=0)\n",
    "\n",
    "print(f'Accuracy: {np.mean(acc):.4f} ± {2*np.std(acc):.4f}')\n",
    "for i in range(len(tpr_mean)):\n",
    "    print(f'TPR/FPR: {tpr_mean[i]:.4f} ± {2*tpr_std[i]:.4f} / {fpr_mean[i]:.4f} ± {2*fpr_std[i]:.4f}')"
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