{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "ScUlcCzL6lso"
      },
      "source": [
        "# Results of fault diagnosis using MLP-256"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0AwvypL47HEj"
      },
      "source": [
        "This notebook presents experimental results of fault diagnosis on the Tennessee Eastman Process dataset using the model MLP-256."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTwVP4MW6qw3"
      },
      "source": [
        "Importing libraries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "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.fault_diagnosis.datasets import FaultDiagnosisReinartzTEP\n",
        "from ice.fault_diagnosis.models import MLP"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YM554ghK6yHr"
      },
      "source": [
        "Downloading the TEP dataset."
      ]
    },
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        },
        "id": "9ENTzoh4Sp-P",
        "outputId": "9d290f71-25a1-4d30-dc13-008196352394"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0307c5091b4440f874d9b9bdf1790b9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading reinartz_tep:   0%|          | 0.00/1.85G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "00717a9df6994d7b8702d2b190cb15eb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting train_mask.csv:   0%|          | 0.00/91.8M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "38851b785a784ff586de4753a4259e96",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting df.csv:   0%|          | 0.00/4.89G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "462c112bebd94852b0f081c9081aa1b6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting target.csv:   0%|          | 0.00/94.3M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "432609aa7ce54534ac8825986671fe95",
              "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": "e58014e4f6e74eb38ca5efa8ecb1029c",
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            },
            "text/plain": [
              "Reading data/reinartz_tep/target.csv:   0%|          | 0/5600000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "b124305e8b5648129865d94f1cc43dc2",
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            "text/plain": [
              "Reading data/reinartz_tep/train_mask.csv:   0%|          | 0/5600000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
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      ],
      "source": [
        "dataset = FaultDiagnosisReinartzTEP()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSVInzgi61LS"
      },
      "source": [
        "Standardization of the input data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "TyFAC5pWSu-S"
      },
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      "source": [
        "scaler = StandardScaler()\n",
        "dataset.df[dataset.train_mask] = scaler.fit_transform(dataset.df[dataset.train_mask])\n",
        "dataset.df[dataset.test_mask] = scaler.transform(dataset.df[dataset.test_mask])"
      ]
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    {
      "cell_type": "markdown",
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      ]
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      "source": [
        "metrics = []\n",
        "for i in trange(5):\n",
        "    torch.random.manual_seed(i)\n",
        "    model = MLP(\n",
        "        window_size=10,\n",
        "        hidden_dim=256,\n",
        "        batch_size=128,\n",
        "        lr=0.001,\n",
        "        num_epochs=10,\n",
        "        verbose=False,\n",
        "        device='cuda',\n",
        "    )\n",
        "    model.fit(\n",
        "        dataset.df[dataset.train_mask], dataset.target[dataset.train_mask])\n",
        "    metrics.append(\n",
        "        model.evaluate(\n",
        "            dataset.df[dataset.test_mask], dataset.target[dataset.test_mask]))"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "yycmxbsC6_7x"
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      "source": [
        "Printing metrics."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_fG2NRY3-w2",
        "outputId": "dae142c0-e6da-48c3-f82d-4b5146c49d0c"
      },
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8500 ± 0.0029\n",
            "CDR: 0.9582 ± 0.0013\n",
            "TPR/FPR 1: 0.9948 ± 0.0039 / 0.0000 ± 0.0000\n",
            "TPR/FPR 2: 0.9951 ± 0.0008 / 0.0000 ± 0.0000\n",
            "TPR/FPR 3: 0.9457 ± 0.0487 / 0.0000 ± 0.0000\n",
            "TPR/FPR 4: 0.9963 ± 0.0020 / 0.0000 ± 0.0000\n",
            "TPR/FPR 5: 0.2423 ± 0.0490 / 0.0009 ± 0.0004\n",
            "TPR/FPR 6: 0.9984 ± 0.0002 / 0.0000 ± 0.0000\n",
            "TPR/FPR 7: 0.9978 ± 0.0001 / 0.0000 ± 0.0000\n",
            "TPR/FPR 8: 0.9782 ± 0.0136 / 0.0000 ± 0.0000\n",
            "TPR/FPR 9: 0.3068 ± 0.1355 / 0.0001 ± 0.0001\n",
            "TPR/FPR 10: 0.9754 ± 0.0018 / 0.0000 ± 0.0000\n",
            "TPR/FPR 11: 0.9539 ± 0.0130 / 0.0000 ± 0.0000\n",
            "TPR/FPR 12: 0.9098 ± 0.0086 / 0.0000 ± 0.0000\n",
            "TPR/FPR 13: 0.9702 ± 0.0074 / 0.0000 ± 0.0000\n",
            "TPR/FPR 14: 0.9949 ± 0.0006 / 0.0000 ± 0.0000\n",
            "TPR/FPR 15: 0.0000 ± 0.0000 / 0.0000 ± 0.0000\n",
            "TPR/FPR 16: 0.2772 ± 0.0309 / 0.0002 ± 0.0001\n",
            "TPR/FPR 17: 0.9786 ± 0.0006 / 0.0000 ± 0.0000\n",
            "TPR/FPR 18: 0.9635 ± 0.0019 / 0.0000 ± 0.0000\n",
            "TPR/FPR 19: 0.9918 ± 0.0004 / 0.0000 ± 0.0000\n",
            "TPR/FPR 20: 0.9739 ± 0.0006 / 0.0000 ± 0.0000\n",
            "TPR/FPR 21: 0.0000 ± 0.0000 / 0.0000 ± 0.0000\n",
            "TPR/FPR 22: 0.5031 ± 0.1355 / 0.0000 ± 0.0000\n",
            "TPR/FPR 23: 0.8086 ± 0.0242 / 0.0000 ± 0.0000\n",
            "TPR/FPR 24: 0.9890 ± 0.0011 / 0.0000 ± 0.0000\n",
            "TPR/FPR 25: 0.9872 ± 0.0007 / 0.0000 ± 0.0000\n",
            "TPR/FPR 26: 0.9696 ± 0.0064 / 0.0000 ± 0.0000\n",
            "TPR/FPR 27: 0.9742 ± 0.0111 / 0.0000 ± 0.0000\n",
            "TPR/FPR 28: 0.3768 ± 0.0459 / 0.0002 ± 0.0001\n"
          ]
        }
      ],
      "source": [
        "acc = []\n",
        "cdr = []\n",
        "tpr = []\n",
        "fpr = []\n",
        "for metrics_i in metrics:\n",
        "    acc.append(metrics_i[\"accuracy\"])\n",
        "    cdr.append(metrics_i[\"correct_daignosis_rate\"])\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",
        "print(f'CDR: {np.mean(cdr):.4f} ± {2*np.std(cdr):.4f}')\n",
        "for i in range(len(tpr_mean)):\n",
        "    print(f'TPR/FPR {i+1}: {tpr_mean[i]:.4f} ± {2*tpr_std[i]:.4f} / {fpr_mean[i]:.4f} ± {2*fpr_std[i]:.4f}')"
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