{ "cells": [ { "cell_type": "markdown", "id": "34ffe5a3-f269-4630-af59-05af2f656400", "metadata": {}, "source": [ "[![Static Badge](https://img.shields.io/badge/notebook-open_in_colab-blue?style=flat&logo=googlecolab&color=blue)](https://colab.research.google.com/drive/1QsYjUX3DgS8ccvDtxEgLeHHmbtPViIqV?usp=drive_link)\n", "\n", "### We recommend using the [Google Colab](https://colab.research.google.com/drive/1QsYjUX3DgS8ccvDtxEgLeHHmbtPViIqV?usp=drive_link) verion of the notebook!" ] }, { "cell_type": "markdown", "id": "f48bfa5b-05ca-493e-a892-0eff6dbcb4a6", "metadata": {}, "source": [ "# Convert UCR data to Orion format\n", "\n", "In this notebook we download the data and reformat it\n", "as Orion pipelines expect.\n", "\n", "### Download the data" ] }, { "cell_type": "code", "execution_count": 1, "id": "ac981ead", "metadata": { "id": "ac981ead" }, "outputs": [], "source": [ "# download dataset & unzip\n", "\n", "import io\n", "import os\n", "import urllib\n", "import zipfile\n", "\n", "DATA_URL = 'https://www.cs.ucr.edu/~eamonn/time_series_data_2018/UCR_TimeSeriesAnomalyDatasets2021.zip'\n", "\n", "response = urllib.request.urlopen(DATA_URL)\n", "bytes_io = io.BytesIO(response.read())\n", "\n", "with zipfile.ZipFile(bytes_io) as zf:\n", " zf.extractall()" ] }, { "cell_type": "code", "execution_count": 2, "id": "8c719486", "metadata": { "id": "8c719486" }, "outputs": [], "source": [ "DATA_PATH = os.path.join('AnomalyDatasets_2021',\n", " 'UCR_TimeSeriesAnomalyDatasets2021',\n", " 'FilesAreInHere',\n", " 'UCR_Anomaly_FullData')\n", "\n", "SAVE_TO = 'UCR'\n", "os.makedirs(SAVE_TO, exist_ok=True)" ] }, { "cell_type": "code", "execution_count": 3, "id": "23207f6e", "metadata": { "id": "23207f6e" }, "outputs": [], "source": [ "import csv\n", "import numpy as np\n", "import pandas as pd\n", "from tqdm import tqdm" ] }, { "cell_type": "markdown", "id": "3c5b24d9", "metadata": { "id": "3c5b24d9" }, "source": [ "#### Format\n", "\n", "012_UCR_Anomaly_tiltAPB1_100000_114283_114350.txt\n", "\n", "- `012` Dataset number\n", "- `tiltAPB1` Mnemonic name\n", "- `100000` From 1 to X is training data\n", "- `114283` Begin anomaly\n", "- `114350` End anomaly" ] }, { "cell_type": "code", "execution_count": 4, "id": "bd411a2b", "metadata": { "id": "bd411a2b", "outputId": "c69079c0-b034-4a55-8492-3fb96492db47" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " timestamp value\n", "0 1222819200 1990.0\n", "1 1222819500 1996.0\n", "2 1222819800 1958.0\n", "3 1222820100 1958.0\n", "4 1222820400 1923.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def build_df(data, start=0):\n", " index = np.array(range(start, start + len(data)))\n", " step = 300\n", " initial_time = 1222819200\n", " timestamp = index * step + initial_time\n", "\n", " if len(data.shape) > 1 and data.shape[1] > 1:\n", " print(\"MULTIVARIATE\")\n", " df = pd.DataFrame(data)\n", " df['timestamp'] = timestamp\n", " else:\n", " df = pd.DataFrame({'timestamp': timestamp, 'value': data.reshape(-1, )})\n", "\n", " df['timestamp'] = df['timestamp'].astype('int64')\n", " return df\n", "\n", "df = build_df(np.loadtxt(DATA_PATH + '/204_UCR_Anomaly_CHARISfive_12412_15000_15070.txt'))\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "d17be27d", "metadata": { "id": "d17be27d", "outputId": "1cd182c7-452d-485b-cb96-338452d315f6" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 250/250 [00:19<00:00, 12.84it/s]\n" ] } ], "source": [ "files = os.listdir(DATA_PATH)\n", "file_names, train_sizes, intervals = [], [], []\n", "\n", "for file in tqdm(files):\n", " file_num_str, _, _, file_name, train_size_str, begin_str, end_str = file.split(\"_\")\n", "\n", " train_size, begin_anomaly = int(train_size_str), int(begin_str)\n", " end_anomaly = int(end_str.split('.')[0])\n", " file_name = file_num_str + \"-\" + file_name\n", "\n", " # get timestamp from data\n", " df = build_df(np.loadtxt(os.path.join(DATA_PATH, file)))\n", " begin_anomaly = int(df.timestamp.iloc[begin_anomaly])\n", " end_anomaly = int(df.timestamp.iloc[end_anomaly])\n", "\n", " # train - test split\n", " train_df = df.iloc[: train_size]\n", " test_df = df.iloc[train_size: ]\n", "\n", " # save file\n", " train_df.to_csv(SAVE_TO + '/{}-train.csv'.format(file_name), index=False)\n", " test_df.to_csv(SAVE_TO + '/{}-test.csv'.format(file_name), index=False)\n", " df.to_csv(SAVE_TO + '/{}.csv'.format(file_name), index=False)\n", "\n", " file_names.append(file_name)\n", " train_sizes.append(train_size)\n", " intervals.append([begin_anomaly, end_anomaly])" ] }, { "cell_type": "code", "execution_count": 6, "id": "3e639635", "metadata": { "id": "3e639635" }, "outputs": [], "source": [ "rows = []\n", "for index, file_name in enumerate(file_names):\n", " row = [file_name, [intervals[index]]]\n", " rows.append(row)" ] }, { "cell_type": "code", "execution_count": 7, "id": "66d7cd5f", "metadata": { "id": "66d7cd5f", "outputId": "9d910330-194a-47df-a59a-24a0ac0c48ec" }, "outputs": [ { "data": { "text/plain": [ "[['183-qtdbSel100MLII', [[1226839200, 1226959200]]],\n", " ['194-sddb49', [[1243204200, 1243279200]]],\n", " 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" ['174-insectEPG2', [[1225219200, 1225226700]]],\n", " ['182-qtdbSel1005V', [[1226539200, 1226659200]]],\n", " ['153-PowerDemand2', [[1229826300, 1229934300]]],\n", " ['014-DISTORTEDECG3', [[1227919200, 1227949200]]],\n", " ['095-NOISECIMIS44AirTemperature4', [[1224483900, 1224498300]]],\n", " ['237-mit14157longtermecg', [[1249687200, 1249930200]]],\n", " ['238-mit14157longtermecg', [[1244599200, 1244653200]]],\n", " ['078-DISTORTEDresperation1', [[1255897200, 1255942800]]],\n", " ['156-TkeepFifthMARS', [[1224615600, 1224644700]]],\n", " ['116-CIMIS44AirTemperature4', [[1224483900, 1224498300]]],\n", " ['011-DISTORTEDECG1', [[1226359200, 1226449200]]],\n", " ['007-DISTORTEDCIMIS44AirTemperature3', [[1224775200, 1224782400]]],\n", " ['169-gait3', [[1240789200, 1240969200]]],\n", " ['218-STAFFIIIDatabase', [[1286035200, 1286230200]]],\n", " ['054-DISTORTEDWalkingAceleration5', [[1224595200, 1224612900]]],\n", " ['209-Fantasia', [[1230910200, 1231000200]]],\n", " ['046-DISTORTEDPowerDemand3', [[1229840700, 1229862300]]],\n", " ['025-DISTORTEDInternalBleeding14', [[1224501300, 1224509400]]],\n", " ['094-NOISEBIDMC1', [[1224439200, 1224499200]]],\n", " ['142-InternalBleeding6', [[1223861400, 1223907900]]],\n", " ['159-TkeepSecondMARS', [[1225618200, 1225621200]]],\n", " ['206-CHARISten', [[1231543200, 1231561200]]],\n", " ['138-InternalBleeding19', [[1224075300, 1224078300]]],\n", " ['123-ECG4', [[1227859200, 1227949200]]],\n", " ['135-InternalBleeding16', [[1224075300, 1224078900]]],\n", " ['192-s20101mML2', [[1233551400, 1233581400]]],\n", " ['185-resperation11', [[1256059200, 1256059500]]],\n", " ['115-CIMIS44AirTemperature3', [[1224775200, 1224782400]]],\n", " ['140-InternalBleeding4', [[1224221700, 1224329100]]],\n", " ['101-NOISELab2Cmac011215EPG4', [[1228036200, 1228075200]]],\n", " ['080-DISTORTEDresperation2', [[1273294200, 1273294500]]],\n", " ['119-ECG1', [[1226359200, 1226449200]]],\n", " ['200-tiltAPB4', [[1243217700, 1243218000]]],\n", " ['090-DISTORTEDtiltAPB2', [[1260066900, 1260314700]]],\n", " ['177-insectEPG5', [[1225369200, 1225369500]]],\n", " ['205-CHARISfive', [[1231517700, 1231544700]]],\n", " ['061-DISTORTEDgait3', [[1240789200, 1240969200]]],\n", " ['047-DISTORTEDPowerDemand4', [[1230020700, 1230042300]]],\n", " ['117-CIMIS44AirTemperature5', [[1224274800, 1224289200]]],\n", " ['145-Lab2Cmac011215EPG1', [[1227982200, 1227997200]]],\n", " ['019-DISTORTEDGP711MarkerLFM5z1', [[1224669600, 1224682800]]],\n", " ['131-GP711MarkerLFM5z5', [[1225402800, 1225434000]]],\n", " ['127-GP711MarkerLFM5z1', [[1224669600, 1224682800]]],\n", " ['021-DISTORTEDGP711MarkerLFM5z3', [[1224603600, 1224617100]]],\n", " ['168-gait2', [[1236769200, 1236859200]]],\n", " ['065-DISTORTEDinsectEPG1', [[1224919200, 1224928200]]],\n", " ['154-PowerDemand3', [[1229840700, 1229862300]]],\n", " ['072-DISTORTEDltstdbs30791ES', [[1238599200, 1238659200]]],\n", " ['121-ECG3', [[1227619200, 1227649200]]],\n", " ['076-DISTORTEDresperation10', [[1262029200, 1262383200]]],\n", " ['029-DISTORTEDInternalBleeding18', [[1224164700, 1224195300]]],\n", " ['248-weallwalk', [[1224229800, 1224231300]]],\n", " ['239-taichidbS0715Master', [[1400854200, 1400873400]]],\n", " ['024-DISTORTEDInternalBleeding10', [[1224177000, 1224186000]]],\n", " ['195-sel840mECG1', [[1238230200, 1238341200]]],\n", " ['188-resperation2', [[1273294200, 1273294500]]],\n", " ['112-BIDMC1', [[1224439200, 1224499200]]],\n", " ['122-ECG3', [[1227919200, 1227949200]]],\n", " ['100-NOISELab2Cmac011215EPG1', [[1227982200, 1227997200]]],\n", " ['215-STAFFIIIDatabase', [[1254835200, 1255030200]]],\n", " ['240-taichidbS0715Master', [[1488049200, 1488079200]]],\n", " ['018-DISTORTEDECG4', [[1227919200, 1227949200]]],\n", " ['197-tiltAPB1', [[1257104100, 1257124200]]],\n", " ['050-DISTORTEDTkeepForthMARS', [[1224615600, 1224644700]]],\n", " ['063-DISTORTEDgaitHunt2', [[1232179200, 1232374200]]],\n", " ['075-DISTORTEDqtdbSel100MLII', [[1226839200, 1226959200]]],\n", " ['132-InternalBleeding10', [[1224177000, 1224186000]]],\n", " ['109-1sddb40', [[1238419200, 1238605200]]],\n", " ['176-insectEPG4', [[1224771600, 1224786600]]],\n", " ['098-NOISEInternalBleeding16', [[1224075300, 1224078900]]]]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rows" ] }, { "cell_type": "code", "execution_count": 8, "id": "65e79159", "metadata": { "id": "65e79159" }, "outputs": [], "source": [ "# save anomalies\n", "\n", "with open(SAVE_TO + '/anomalies.csv', 'w', newline='') as file:\n", " writer = csv.writer(file)\n", " writer.writerows(rows)" ] }, { "cell_type": "code", "execution_count": 9, "id": "eaa00b2f", "metadata": { "id": "eaa00b2f" }, "outputs": [ { "data": { "text/html": [ "
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signalevents
0183-qtdbSel100MLII[[1226839200, 1226959200]]
1194-sddb49[[1243204200, 1243279200]]
2069-DISTORTEDinsectEPG5[[1225369200, 1225369500]]
3023-DISTORTEDGP711MarkerLFM5z5[[1225402800, 1225434000]]
4212-Italianpowerdemand[[1231663200, 1231670400]]
.........
245075-DISTORTEDqtdbSel100MLII[[1226839200, 1226959200]]
246132-InternalBleeding10[[1224177000, 1224186000]]
247109-1sddb40[[1238419200, 1238605200]]
248176-insectEPG4[[1224771600, 1224786600]]
249098-NOISEInternalBleeding16[[1224075300, 1224078900]]
\n", "

250 rows × 2 columns

\n", "
" ], "text/plain": [ " signal events\n", "0 183-qtdbSel100MLII [[1226839200, 1226959200]]\n", "1 194-sddb49 [[1243204200, 1243279200]]\n", "2 069-DISTORTEDinsectEPG5 [[1225369200, 1225369500]]\n", "3 023-DISTORTEDGP711MarkerLFM5z5 [[1225402800, 1225434000]]\n", "4 212-Italianpowerdemand [[1231663200, 1231670400]]\n", ".. ... ...\n", "245 075-DISTORTEDqtdbSel100MLII [[1226839200, 1226959200]]\n", "246 132-InternalBleeding10 [[1224177000, 1224186000]]\n", "247 109-1sddb40 [[1238419200, 1238605200]]\n", "248 176-insectEPG4 [[1224771600, 1224786600]]\n", "249 098-NOISEInternalBleeding16 [[1224075300, 1224078900]]\n", "\n", "[250 rows x 2 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_labels = pd.read_csv(SAVE_TO + '/anomalies.csv', header=None, names=['signal', 'events'])\n", "new_labels" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.15" } }, "nbformat": 4, "nbformat_minor": 5 }