ryfan-rs/unsupervisedscalablerepresentationlearningtimeseries

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

Unsupervised Scalable Representation Learning for Multivariate Time Series -- Code

This is the code corresponding to the experiments conducted for the work "Unsupervised Scalable Representation Learning for Multivariate Time Series" (Jean-Yves Franceschi, Aymeric Dieuleveut and Martin Jaggi) [arXiv] [HAL].

Requirements

Experiments were done with the following package versions for Python 3.6:

• Numpy (numpy) v1.15.2;
• Matplotlib (matplotlib) v3.0.0;
• Orange (Orange) v3.18.0;
• Pandas (pandas) v0.23.4;
• python-weka-wrapper3(v0.1.6) (for multivariate time series);
• PyTorch (torch) v0.4.1 with CUDA 9.0;
• Scikit-learn (sklearn) v0.20.0;
• Scipy (scipy) v1.1.0.

This code should execute correctly with updated versions of these packages.

Datasets

The datasets manipulated in this code can be downloaded on the following locations:

• the UCR archive: https://www.cs.ucr.edu/~eamonn/time_series_data_2018/;
• the UEA archive: http://www.timeseriesclassification.com/;
• the Individual Household Electric Power Consumption dataset: https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption.

Files

Core

• losses folder: implements the triplet loss in the cases of a training set with all time series of the same length, and a training set with time series of unequal lengths;
• networks folder: implements encoder and its building blocks (dilated convolutions, causal CNN);
• scikit_wrappers.py file: implements classes inheriting Scikit-learn classifiers that wrap an encoder and a SVM classifier.
• utils.py file: implements custom PyTorch datasets;
• default_hyperparameters.json file: example of a JSON file containing the hyperparameters of a pair (encoder, classifier).

Tests

• ucr.py file: handles learning on the UCR archive (see usage below);
• uea.py file: handles learning on the UEA archive (see usage below);
• transfer_ucr.py file: handles transfer learning on the UCR archive (see usage below);
• combine_ucr.py file: combines learned pairs of (encoder, classifier) for the UCR archive) (see usage below);
• combine_uea.py file: combines learned pairs of (encoder, classifier) for the UEA archive) (see usage below);
• HouseholdPowerConsumption.ipynb file: Jupyter notebook containing experiments on the Individual Household Electric Power Consumption dataset.

Results and Visualization

• results_ucr.csv file: CSV file compiling all results (with those of concurrent methods) on the UCR archive;
• results_uea.csv file: CSV file compiling all results (with those of concurrent methods) on the UEA archive;
• cd.ipynb file: Jupyter notebook containing the code to produce a critical difference diagram;
• stat_plots.ipynb file: Jupyter notebook containing the code to produce boxplots and histograms on the results for the UCR archive.

Usage

Training on the UCR and UEA archives

To train a model on the MALLAT dataset from the UCR archive:

python3 ucr.py --dataset MALLAT --path path/to/MALLAT/folder/ --save_path '/path/to/save/models' --hyper default_hyperparameters.json [--cuda --gpu 0]

Adding the --load option allows to load a model from the specified save path. Training on the UEA archive with uea.py is done in a similar way.

Further Documentation

See the code documentation for more details. ucr.py, uea.py, transfer_ucr.py, combine_ucr.py and combine_uea.py can be called with the -h option for additional help.

Pretrained Models

Pretrained models are available on request.