This repository contains the code for time-series (TS) classification with various state-of-the-art TS classification models.

Entire pipeline is developed in a way such that an easy integration of mlflow, hydra and optuna sweeper is possible.

1. The simplest way to run a model on a specific dataset is to run the following command in the terminal, in the root directory of the repository:

python main.py

This will run a model on a dataset specified in the config file main_config.yaml, located in the config directory.

2. To optimize hyperparameters of a model, run the following command in the terminal, in the root directory of the repository:

python main.py --multirun

This will run a model on a dataset specified in the config file main_config.yaml, located in the config directory. However, this time, a search space, specified in config/search_space/model_name will be used by optuna to find the optimal hyperparameters. A total number of trial is specified in the main_config.yaml file.

3. To run a model on a specific dataset, run the following command in the terminal, in the root directory of the repository:

python main.py "dataset_name=[Handwriting]" 

For a multirun case:

python main.py --multirun "dataset_name=[Handwriting]"  

To run for a specific model:

python main.py --multirun "dataset_name=[Handwriting]" models=LSTM_FCN"

Model name can be anything that is available in the codes/models directory, given corresponding configs are also available.

Similarly, other parameters can also be specified in the terminal, and passed as arguments.

Requirements

Authors