From ddcb03266bc640cba041ebb09af80ecce6cb218d Mon Sep 17 00:00:00 2001 From: Md Mijanur Rahman Date: Sat, 16 Mar 2024 22:49:51 +0100 Subject: [PATCH] Update Authors section in README.md --- README.md | 65 +------------------------------------------------------ 1 file changed, 1 insertion(+), 64 deletions(-) diff --git a/README.md b/README.md index 352984b5..bb30d70e 100644 --- a/README.md +++ b/README.md @@ -59,67 +59,4 @@ You can find the results in the following table: ## Authors -* [**Md Mijanur Rahman**](https://github.com/mijanr) -# 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: -```bash -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: -```bash -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: -```bash -python main.py "dataset_name=[Handwriting]" -``` -For a multirun case: -```bash -python main.py --multirun "dataset_name=[Handwriting]" -``` -To run for a specific model: -```bash -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. -## Mlruns -All the runs are stored in the `mlruns` directory. To visualize the runs, run the following command in the terminal, in the root directory of the repository: -```bash -mlflow ui -``` -This will start a server, and the runs can be visualized in the browser at `localhost:5000`. - -## Requirements -requirements.yml file contains all the dependencies required to run the code. To install all the dependencies, run the following command in the terminal, given that anaconda is installed: -```bash -conda env create -f requirements.yaml -``` -This will create a conda environment named `ts_cl` with all the dependencies installed. -It insall Pytorch with CPU support. To install Pytorch with GPU support, follow the instructions given [here](https://pytorch.org/get-started/locally/). - -## Datasets -This repository uses the datasets from the [UEA & UCR Time Series Classification Repository](https://www.timeseriesclassification.com/). The datasets are automatically downloaded and stored in the `data` directory. - -## Models -We use the classification models available in [tsai library](https://timeseriesai.github.io/tsai/). Models can be added to this repository by adding the corresponding config file in the `config` directory, and the corresponding model file in the `codes/models` directory. - -## Results -You can find the results in the following table: - - - - - - -## Authors -* [**Md Mijanur Rahman**](https://github.com/mijanr) - +* [**Md Mijanur Rahman**](https://github.com/mijanr) \ No newline at end of file