Associated with https://github.com/pykale/pykale/discussions/147

Description

Idea is to test the feasibility of adding an interactive notebook e.g. with myBinder or Google Collab (or both).

Status

Work in progress

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• Select a reviewer if ready for review. Use the suggested one if unsure.

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).

Fixes #164.

Description

Adds a notebook tutorial for the bindingdb_deepdta example.

Status

Work in progress

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• Select a reviewer if ready for review. Use the suggested one if unsure.

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).

Fixes NA.

Description

Lays the foundations for adding unit and regression tests to pykale, and reporting the coverage to codecov.

A folder structure is proposed that mimics that of the pykale module. Each .py file in pykale is given a tests_<filename>.py which is to contain unit tests for all functionality in its companion file.

Regression tests are given a separate folder. I propose these are based on existing examples.

A folder has been created (with an empty .csv file) to hold data needed for testing. Depending on amount and format of required data this will need to be revisited. For example a few baseline .csv files would be fine, but loads of images or video may be better held elsewhere e.g. linked via a DOI.

The following must be executed by CI post tests to report data to codecov:

coverage xml
bash <(curl -s https://codecov.io/bash)

Status

Work in progress

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).

Description

Major update to contribution guidelines and pre-commit hooks ready to be enforced.

• Contribution guidelines have been updated and will guide our future developments. Please review it by reading through it completely. Your feedback is very important. Imagine you are new to PyKale and see whether by reading this doc you know what and how to do for various involvements (except release and management). This doc will be very important for us to standardize our practices and for new comers to join this effort.
• ReadMe has been lightly updated on how to contribute.
• Pre-commit hooks have been tested locally and configurations have been updated. The only hook not yet active is mypy, which needs deeper changes to code that the code authors should do (to discuss).
• Auto-fix some style issues using pre-commit hooks. You do not have to review these changes. They are minor (mostly whitespace/ending problems) and I have randomly checked to be safe. See the commit description if you want to learn more.
  • The changes to requirements.txt are not ideal but we will revisit them later when discussing dependancies.
• Further manual fixes for remaining flake8 errors, all in the imports of main.py of Examples. Now all sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) in Examples have been removed. You should install (the latest) PyKale to use kale API in examples.
• Simplified PR template.

Summary: Major changes to CONTRIBUTING.md that need all to review carefully. Minor changes to README.md and others that you may skip if you like.

We will discuss any unresolved issues in our PyKale meeting this Thursday. We should all use pre-commit hooks after resolving all questions. Of course, you are welcome to try out first (see contributing guidelines). I did and it is beautiful.

Status

Ready

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).

Fixes NA.

Description

Moves CI tests to a better range of python versions.

Status

Completed

If you've been given access to pykale with role Triage or above, on the right (delete these after selection):

• When your pull request is ready for review, select a reviewer. Use the suggested one if unsure.

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Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).

… Output is formatted like an image

Fixes #{issue_number}.

Description

Provide an interface to read numeric data from comma separated text. The text file should contain tabular data with one row per example. Features are represented as columns. Additional columns can differentiate subjects, domains, categories, and example indexes. It is currently required for the data to be "balanced": same number of examples for each subject, domain, and category. This is because the function returns a numpy array, similar to an image.

Status

Ready/Work in progress/Hold

If you've been given access to pykale with role Triage or above, on the right (delete these after selection):

• Select one most appropriate label.
• When your pull request is ready for review, select a reviewer. Use the suggested one if unsure.

If you cannot see an option to select a reviewer/label, that means one maintainer will get notified upon your pull request and then select for you.

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).
• [ ] Breaking change (fix or new feature that would cause existing functionality to change).
• [ ] New tests added to cover the changes.
• [ ] In-line docstrings updated.
• [ ] Source for documentation at docs manually updated for new API.

🐛 Bug

When running the test_csv_logger.py file, it is unable to locate the test data folder which contains the relevant test files.

To reproduce

pytest tests/utils/test_csv_logger.py -v

** Stack trace/error message **

E           FileNotFoundError: [Errno 2] No such file or directory: 'tests/test_data/parameters.json'

kale/utils/csv_logger.py:66: FileNotFoundError```

## Expected Behaviour

I expected the csv_logger test to pass and for the script to be able to access the test data.

## Environment

PyTorch version: 1.9.0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 10.15.7 (x86_64)
GCC version: Could not collect
Clang version: 12.0.0 (clang-1200.0.32.2)
CMake version: Could not collect
Libc version: N/A

Python version: 3.8.8 (default, Apr 13 2021, 12:59:45)  [Clang 10.0.0 ] (64-bit runtime)
Python platform: macOS-10.15.7-x86_64-i386-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.20.1
[pip3] numpydoc==1.1.0
[pip3] pytorch-lightning==1.3.8
[pip3] pytorch-memlab==0.2.3
[pip3] torch==1.9.0
[pip3] torchaudio==0.9.0a0+33b2469
[pip3] torchmetrics==0.4.1
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.10.0
[conda] blas                      1.0                         mkl  
[conda] ffmpeg                    4.3                  h0a44026_0    pytorch
[conda] mkl                       2021.2.0           hecd8cb5_269  
[conda] mkl-service               2.3.0            py38h9ed2024_1  
[conda] mkl_fft                   1.3.0            py38h4a7008c_2  
[conda] mkl_random                1.2.1            py38hb2f4e1b_2  
[conda] numpy                     1.20.1           py38hd6e1bb9_0  
[conda] numpy-base                1.20.1           py38h585ceec_0  
[conda] numpydoc                  1.1.0              pyhd3eb1b0_1  
[conda] pytorch                   1.9.0                   py3.8_0    pytorch
[conda] pytorch-lightning         1.3.8                    pypi_0    pypi
[conda] pytorch-memlab            0.2.3                    pypi_0    pypi
[conda] torch                     1.9.0                    pypi_0    pypi
[conda] torchaudio                0.9.0                      py38    pytorch
[conda] torchmetrics              0.4.1                    pypi_0    pypi
[conda] torchsummary              1.5.1                    pypi_0    pypi
[conda] torchvision               0.10.0                   pypi_0    pypi

## Additional context
Add any other context about the problem here.

Description

1. Add tests covering deepdta related modules including:

• kale/embed/seq_nn.py
• kale/loaddata/tdc_datasets.py
• kale/pipeline/deep_dti.py
• kale/predict/decode.py
• kale/prepdata/chem_transform.py

2. Introduce conda virtual environment in test action.

3. Remove MNIST/MNISTM test as they are blocked due to HTTPServer error.

Status

Ready

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).
• [x] New tests added to cover the changes.

Fixes #316.

Description

• Update to support 3.7 and above.
• Update PyTorch >= 1.11.0 to fix this bug.
• Update PyG installation on colab in deepdta example to fix this bug.
• Increase notebook cell timeout limit from 300 to 3000 seconds to reduce timeout errors.

Status

Ready

Types of changes

• [ ] Non-breaking change (fix or new feature that would not break existing functionality).
• [x] Breaking change (fix or new feature that would cause existing functionality to change).
• [ ] New tests added to cover the changes.
• [ ] In-line docstrings updated.
• [ ] Source for documentation at docs manually updated for new API.

Fixes #214

Description

Adds action to run PyKale tutorial notebooks as part of the existing CI testing workflow. Running as a separate action would lead to some duplication of the setup yaml, but faster execution.

Status

Work in progress

On the right (delete these after selection):

• Select a reviewer if ready for review. Use the suggested one if unsure.

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).

Fixes #175.

Description

• Reduce tests for video
• Fix video load
• Remove binder icon

Status

Ready

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).
• [ ] Breaking change (fix or new feature that would cause existing functionality to change).
• [ ] New tests added to cover the changes.
• [ ] In-line docstrings updated.
• [ ] Source for documentation at docs manually updated for new API.

Description

This PR follows PR #291 and #292, upgrading DA Trainers for feature vector input. Trainers on video image input and video feature vector input have two main differences below:

1. Three modality choices in image input: RGB, Flow and joint (RGB+Flow), due to no Audio available now; Five in feature vector: RGB, Flow, Audio, joint and all (RGB+Flow+Audio).
2. One class type in image input: verb; Two in feature: verb, verb+noun.

The main changes are below:

1. Add Audio branch into the pipeline.
2. Update optimizer work with SGD and ADAM.
3. Add adaptive choice into source/target dataset sampling.
4. Add JSON logger for EPIC UDA challenge.
5. Reduce dummy network dimension in test_video_domain_adapter.

Status

Ready

Types of changes

• [ ] Non-breaking change (fix or new feature that would not break existing functionality).
• [x] Breaking change (fix or new feature that would cause existing functionality to change).
• [x] New tests added to cover the changes.
• [x] In-line docstrings updated.
• [ ] Source for documentation at docs manually updated for new API.

Description

Improve video_domain_adapter.py via organizing repeated and redundant codes into BaseAdaptTrainerVideo. This PR follows PR #291 and is followed by a large PR that will merge feature vector into action_dann_lightn example.

Status

Ready

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).
• [ ] Breaking change (fix or new feature that would cause existing functionality to change).
• [x] New tests added to cover the changes.
• [ ] In-line docstrings updated.
• [ ] Source for documentation at docs manually updated for new API.

Description

Update action_dann_lightn example with a dataloader of feature vector input. This dataloader is used in the EPIC challenge. The trainer and models for feature vectors will be updated in the next PR after this one is merged. Coverage in video.py will be improved in the next PR because of most parts are used in training. Changes are summarized below.

1. Add EPIC100DatasetAccess
2. Add new hyperparameters: CLASS_TYPE, INPUT_TYPE and NUM_SEGMENTS
3. Improve video-related trainers and ClassNetVideo for multi-class output.

Status

Ready

Types of changes

• [ ] Non-breaking change (fix or new feature that would not break existing functionality).
• [x] Breaking change (fix or new feature that would cause existing functionality to change).
• [x] New tests added to cover the changes.
• [x] In-line docstrings updated.
• [ ] Source for documentation at docs manually updated for new API.

Implementation of uncertainty estimation for landmark localisation.

Description

Integrates Uncertainty estimation methods including: quantile binning, error bound estimation and evaluation metrics. Provides an example file.

Status

Work in progress

Types of changes

• [x] Non-breaking change (fix or new feature that would not break existing functionality).
• [ ] Breaking change (fix or new feature that would cause existing functionality to change).
• [ ] New tests added to cover the changes.
• [ ] In-line docstrings updated.
• [ ] Source for documentation at docs manually updated for new API.