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) [NeurIPS] [arXiv] [HAL], presented at NeurIPS 2019. A previous version was presented at the 2nd LLD workshop at ICLR 2019.

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 (requires Oracle JDK 8 or OpenJDK 8);
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);
sparse_labeling.ipynb file: file containing code to reproduce the results of training an SVM on our representations for different numbers of available labels;
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;
results_sparse_labeling_TwoPatterns.csv file: CSV file compiling means and standard variations of five runs of learning an SVM on our representations and the ResNet architecture described in the paper for different numbers of available labels;
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;
models folder: contains a pretrained model for the UCR dataset CricketX.

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.

Hyperparameters

Hyperparameters are described in Section S2.2 of the paper.

For the UCR and UEA hyperparameters, two values were switched by mistake. One should read, as reflected in the example configuration file:

number of output channels of the causal network (before max pooling): 160;

dimension of the representations: 320.

instead of

number of output channels of the causal network (before max pooling): 320;

dimension of the representations: 160.

Pretrained Models

Pretrained models are downloadable at https://data.lip6.fr/usrlts/.

White-Link/UnsupervisedScalableRepresentationLearningTimeSeries