Comparison of Gradient-based Optimization Methods for the Time Series Sample Mean Problem in DTW Space

The program contained in this repository realizes the experiments described in the respective paper.

Every operation described here is assumed to be executed from the root directory of this repository (the directory of this file).

Requirements

This programm requires Python 3.4, while Python 3.6+ is recommended. To install the package requirements, just run:

pip install -r app/requirements.txt

As alway, it is advisable to create separated virtual environment. If you want to run this program in a Docker container, this step can be skipped (see Deploy on the daigpu3 VM for that).

Run an experiment

To run an experiment, one simply has to execute the main.py in the app folder like this:

app/main.py

The main.py takes several command-line arguments, which can be seen by executing app/main.py --help:

Run optimizing experiment for DTW mean computation.

positional arguments:
  CONFIG                the configuration to use in config folder

optional arguments:
  -h, --help            show this help message and exit
  -r PATH, --results PATH
                        path to store the results
  -d PATH, --datasets PATH
                        path of the datasets folders

If no CONFIG is given the default.json config is executed. If no results and dataset paths are given, the datasets and results folder relative to this directory are assumed.

Configure experiment

To configure an experiment, a respective configuration file has to be placed in the app/config folder. The config file has to comply with the following format:

{
    "DATASETS" : ["Coffee"],
    "OPTIMIZERS" : {
        "sgld-120": {"method": "sgld", "n_coverage": 120, "batch_size": 3},
        "adam-100": {"method": "adam", "n_coverage": 100, "batch_size": 3},
        "ssg-100": {"method": "ssg", "n_coverage": 100, "batch_size": 3}
    },
    "NUM_ITERATIONS" : 3
}

For further explanation of the parameters, please refer to the paper.

To run the configuration in a file named example.json, the command app/main.py example or app/main.py example.json have to be executed.

Deploy on the daigpu3 VM

To run your experiments on the daigpu3 VM (or any other system using Docker), follow these steps.

1. Log into the VM and clone or copy the repository.
2. cd into the repository.
3. If there are not already downloaded, you may use the scripts/download_datasets.sh script to load the UCR Time Series Classification Archive datasets to the correct directory.
4. Run scripts/run_experiment.sh CONFIG while replacing CONFIG with the correct config name. This scripts assumes the datasets and results directory to be subdirectories of this directory.

NOTE: This implementation highly profits from multiprocessing. So it advised to run it on a machine with a great number of processor cores (the daigpu3 machine has 88 (v)cores).

Result interpretation

The results are saved in the following format:

dataset, optimizer, iteration_id, variation, runtime

For an idea how to further analyze these results, you may refer to the respective Jupyter Notebook.

Extendability

This experimental setup can be easily extended to use other optimization methods, by placing the file containing the python code for the new optimization method in the folder: app/optimization/methods

The name of the python module has to match the respective field in the config files used to run experiments with it. For example

...
"OPTIMIZER": {"method": "example_method", ... }
...

would require a file: app/optimization/methods/example_method.py

The module hast to implement a run function with the following signature:

run(X, z, f, batch_size, n_coverage, n_epochs, d_converged, rng)

This function has to return the optimal solution and a vector of Fréchet variations in the following forms:

# z_ is optimal solution
# f is vector of Fréchet variations

return z_, f

Contact details

If you have further inquiries, please contact the authors via:

[email protected] or
[email protected]