semvec/pypi-packages huggingface dataset:
ranking-table-tennis 2021.3.30
A ranking table tennis system
Requires Python: >=3.6

# ranking-table-tennis

Ranking system for table tennis players.

The system keeps a record of championship points and rating points.

Championship points aim at tracking the tournament achievements of players.
Their computation is based on the best rounds reached by players in each tournament.

Rating points aim at tracking the relative skill level of players. 
Their computation is based on the outcomes of one-versus-one matches.

## Installation

This system has been developed for a Linux environment.

System-wide installation

    sudo pip3 install ranking-table-tennis

Single-user installation (recommended)

    pip3 install --user ranking-table-tennis

Uninstallation

    [sudo] pip3 uninstall ranking-table-tennis
    
## Update

System-wide update

    sudo pip3 install -U ranking-table-tennis
    
Single-user update (recommended)

    pip3 install --user -U ranking-table-tennis

## Usage

The commands must be run in a bash terminal.

1. Fill a sheet with the tournament matches. It must be saved in the Tournaments spreadsheet (xlsx).

   Players and Initial Ranking sheets must be in the same spreadsheet (it is used as a database).

2. Run `rtt preprocess`. 

   The scripts will read the Tournament spreadsheet and will ask for missing information of new players (city, affiliation, initial rating points, and category).
   This information will be saved in the Players and Initial Ranking sheets.

3. Run `rtt compute`.

   It will ask for the tournament that you want to process. 0 will compute all from the beggining.
   The outcome will be saved in the Ranking spreadsheet.

4. Run `rtt publish`.

   It will ask for the index of the tournament that you want to publish. 
The outcome will be saved in a new spreadsheet.

## Development

Install locally from source (source directory will immediately affect the installed package
without needing to re-install): 
    
    pip3 install --user --editable .
    
Update version at `setup.py` and then create a source distribution

    python3 setup.py sdist bdist_wheel
    
Upload to PyPI
    
    twine upload dist/* 
    
Upload to TestPyPI

    twine upload --repository-url https://test.pypi.org/legacy/ dist/*
office31 0.1.3
Revised splits for Office31 domain adaptation experiments
Requires Python: >=3.6

# Splits for Office31 domain adaptation tasks

This repository contains the revised protocol for creating Office31 splits for few shot domain adaptation.

Contrary to the usual splits, we define an independent test split here (split using a hardcoded seed), and let the train-val split vary according to a user-defined random seed.

## Installation
```bash
pip install office31
```

## Usage
Getting the splits is a simple as:

```python
from office31 import office31

train, val, test = office31(
    source_name = "webcam",
    target_name = "amazon",
    seed=1,
    same_to_diff_class_ratio=3,
    image_resize=(240, 240),
    group_in_out=True, # groups data: ((img_s, img_t), (lbl_s, _lbl_t))
    framework_conversion="tensorflow",
    office_path = None, #automatically downloads to "~/data"
)
```

The function automatically downloads and unpacks the data if necessary. It then creates the splits using the [Dataset Ops library](https://github.com/LukasHedegaard/datasetops). 
Depending on your choice of machine learning library, the dataset can be converted to Tensorflow or PyTorch (assuming either is pre-installed) using Dataset Ops.