keras-multilabel-embedding 0.1.1

Multi-label embeddings with fixed number of labels

import keras_multilabel_embedding as tml
import tensorflow as tf

# a sequence of multi-label data points
x_ids = [[1, 2, 4], [0, 1, 2], [2, 1, 4], [3, 2, 1]]
x_ids = tf.constant(x_ids)

# initialize layer
layer = tml.MultiLabelEmbedding(
    vocab_size=5, embed_size=300, random_state=42)

# predict
y = layer(x_ids)

Multi-label embeddings with variable number of labels

import keras_multilabel_embedding as tml
import tensorflow as tf

# a sequence of multi-label data points
x_ids = [[1, 2, 4], [0, 1, 2], [2, 1], [3]]

# initialize layer
layer = tml.MultiLabelEmbedding(
    vocab_size=5, embed_size=300, random_state=42)

# predict
y = layer(x_ids)

Installation

The keras-multilabel-embedding git repo is available as PyPi package

pip install keras-multilabel-embedding
pip install git+ssh://git@github.com/ulf1/keras-multilabel-embedding.git

Install a virtual environment

python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt --no-cache-dir
pip install -r requirements-dev.txt --no-cache-dir
pip install -r requirements-demo.txt --no-cache-dir

(If your git repo is stored in a folder with whitespaces, then don’t use the subfolder .venv. Use an absolute path without whitespaces.)

Python commands

• Jupyter for the examples: jupyter lab

• Check syntax: flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')

• Run Unit Tests: PYTHONPATH=. pytest

Publish

pandoc README.md --from markdown --to rst -s -o README.rst
python setup.py sdist
twine upload -r pypi dist/*

Clean up

find . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venv

Support

Please open an issue for support.

Contributing

Please contribute using Github Flow. Create a branch, add commits, and open a pull request.