{ "cells": [ { "cell_type": "markdown", "id": "f2da72a3-830f-4a34-b767-230ed0dbf154", "metadata": {}, "source": [ "# Results of HI estimation using MLP-256" ] }, { "cell_type": "markdown", "id": "e3de787a-3db3-47c0-a1c6-a2b36cc6586f", "metadata": {}, "source": [ "This notebook presents experimental results of anomaly detection on the Tennessee Eastman Process dataset using the model AutoEncoderMLP-256.\n", "\n", "Importing libraries." ] }, { "cell_type": "code", "execution_count": 6, "id": "0d2bf80e-d11f-4c3d-ac75-e4f0cfd418f6", "metadata": { "scrolled": true }, "outputs": [], "source": [ "from ice.health_index_estimation.datasets import Milling\n", "from ice.health_index_estimation.models import MLP\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import torch\n", "from tqdm.auto import trange\n" ] }, { "cell_type": "markdown", "id": "41bd9f60-51bf-4bb8-adc8-33a6abf923a1", "metadata": {}, "source": [ "Initializing model class and train/test data split" ] }, { "cell_type": "code", "execution_count": null, "id": "814ba92e-e394-49a4-ac06-7b68361cf078", "metadata": {}, "outputs": [], "source": [ "dataset_class = Milling()\n", "\n", "data, target = dataset_class.df, dataset_class.target \n", "test_data, test_target = dataset_class.test, dataset_class.test_target " ] }, { "cell_type": "markdown", "id": "767c37e2-1750-46d5-9cd7-f628e7322305", "metadata": {}, "source": [ "Training" ] }, { "cell_type": "code", "execution_count": 19, "id": "f9bd5cb4-3cbd-48f2-bb70-8154f2a8e051", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/5 [00:00