From d9d117387bb11900e7857c23ef2abec1f040962f Mon Sep 17 00:00:00 2001 From: lhenry15 Date: Thu, 10 Sep 2020 00:53:23 -0500 Subject: [PATCH] add description of package Former-commit-id: 3d87bcb1029be1797b4f1baabe6b3d6801c97f8a [formerly cbcddaf8a282794044d1f3acdd64022b20e5a9ce] [formerly 764508528891119c2cec8c46c6bd6778a6edcaba [formerly e032c4e235b58d434f5a4d9d8cabfed7dc6fafc2]] [formerly dd1416058f2828e94c6113e3623264c26d20875d [formerly 866b60825c57d3b79a5fc4d3e23617c070bd3116] [formerly b23d3e0f29293a6fa8f7015e5e02e18cbf437e8b [formerly 4b592e70b9e9d73acc7ffa13a30ad74693f52925]]] [formerly 32a62d604858a2bccb8af951db28557c825a7533 [formerly eb99525a3278b6c210507a78a487cc2b820211e4] [formerly f722fae61031af7ee43df0db7f16aea34e5d531d [formerly 18f17251afba6fed4baad716985a865abb169a89]] [formerly 937aecd02379a35d18ae4756fded046de68cca47 [formerly 66acef77184bf349b05140746c0e964bdd99d7c4] [formerly e4790d1cffc963eeb7fe32bae12d3d6cb9fcc5a6 [formerly 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ff0a12f..96f61be 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Time-series Outlie Detection System -TODS is an full-stack automated machine learning system for outlier detection on multivariate time-series data. TODS provides exahaustive modules for building machine learning-based outlier detection systems including: data processing, time series processing, feature analysis (extraction), detection algorithms, and reinforcement module. The functionalities provided via these modules including: data preprocessing for general purposes, time series data smoothing/transformation, extracting features from time/frequency domains, various detection algorithms, and involving human expertises to calibrate the system. Specifically, three application scenarios on time-series outlier detection are provided: point-wise detection (time points as outliers), pattern-wise detection (subsequences as outliers), and system-wise detection (sets of time series as outliers), and wide-range of corresponding algorithms are provided in TODS. This package is developed by [DATA Lab @ Texas A&M University](https://people.engr.tamu.edu/xiahu/index.html). +TODS is an full-stack automated machine learning system for outlier detection on multivariate time-series data. TODS provides exahaustive modules for building machine learning-based outlier detection systems including: data processing, time series processing, feature analysis (extraction), detection algorithms, and reinforcement module. The functionalities provided via these modules including: data preprocessing for general purposes, time series data smoothing/transformation, extracting features from time/frequency domains, various detection algorithms, and involving human expertises to calibrate the system. Three common outlier detection scenarios on time-series data can be performed: point-wise detection (time points as outliers), pattern-wise detection (subsequences as outliers), and system-wise detection (sets of time series as outliers), and wide-range of corresponding algorithms are provided in TODS. This package is developed by [DATA Lab @ Texas A&M University](https://people.engr.tamu.edu/xiahu/index.html). TODS is featured for: * **Full Sack Machine Learning System** which supports exhaustive components from preprocessings, feature extraction, detection algorithms and also human-in-the loop interface. @@ -9,13 +9,6 @@ TODS is featured for: * **Automated Machine Learning** aims on providing knowledge-free process that construct optimal pipeline based on the given data by automatically searching the best combination from all of the existing modules. -## Axolotl -Running pre-defined pipeline -``` -python examples/build_AutoEncoder_pipeline.py -python examples/run_predefined_pipeline.py -``` - ## Installation This package works with **Python 3.6** and pip 19+. You need to have the following packages installed on the system (for Debian/Ubuntu): @@ -115,31 +108,6 @@ best_output = best_pipeline_result.output # Evaluate the best pipeline best_scores = search.evaluate(best_pipeline).scores ``` - -# Dataset -Datasets are located in `datasets/anomaly`. `raw_data` is the raw time series data. `transform.py` is script to transform the raw data to D3M format. `template` includes some templates for generating D3M data. If you run `transform.py`, the script will load the raw `kpi` data and create a folder named `kpi` in D3M format. - -The generated csv file will have the following columns: `d3mIndex`, `timestamp`, `value`, `'ground_truth`. In the example kpi dataset, there is only one value. For other datasets there could be multiple values. The goal of the pipline is to predict the `ground_truth` based on `timestamp` and the value(s). - -There is a nice script to check whether the dataset is in the right format. Run -``` -python3 datasets/validate.py datasets/anomaly/kpi/ -``` -The expected output is as follows: -``` -Validating problem '/home/grads/d/daochen/tods/tods/datasets/anomaly/kpi/SCORE/problem_TEST/problemDoc.json'. -Validating dataset '/home/grads/d/daochen/tods/tods/datasets/anomaly/kpi/SCORE/dataset_TEST/datasetDoc.json'. -Validating problem '/home/grads/d/daochen/tods/tods/datasets/anomaly/kpi/kpi_problem/problemDoc.json'. -Validating problem '/home/grads/d/daochen/tods/tods/datasets/anomaly/kpi/TEST/problem_TEST/problemDoc.json'. -Validating dataset '/home/grads/d/daochen/tods/tods/datasets/anomaly/kpi/TEST/dataset_TEST/datasetDoc.json'. -Validating dataset '/home/grads/d/daochen/tods/tods/datasets/anomaly/kpi/kpi_dataset/datasetDoc.json'. -Validating dataset '/home/grads/d/daochen/tods/tods/datasets/anomaly/kpi/TRAIN/dataset_TRAIN/datasetDoc.json'. -Validating problem '/home/grads/d/daochen/tods/tods/datasets/anomaly/kpi/TRAIN/problem_TRAIN/problemDoc.json'. -Validating all datasets and problems. -There are no errors. -``` -Of course, you can also create other datasets with `transform.py`. But for now, we can focus on this example dataset since other datasets are usually in the same format. - # Example In D3M, our goal is to provide a **solution** to a **problem** on a **dataset**. Here, solution is a pipline which consists of data processing, classifiers, etc.