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‎.gitignore

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.vscode/
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data/
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wandb/
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artifacts/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/

‎.pre-commit-config.yaml

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# See https://pre-commit.com for more information
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# See https://pre-commit.com/hooks.html for more hooks
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repos:
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- repo: https://github.com/psf/black
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rev: 19.10b0
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hooks:
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- id: black
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language_version: python3
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- repo: https://github.com/kynan/nbstripout
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rev: master
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hooks:
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- id: nbstripout
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files: ".ipynb"
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v2.4.0
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hooks:
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- id: trailing-whitespace
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- id: end-of-file-fixer
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- id: check-yaml
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- id: check-added-large-files
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- id: check-merge-conflict
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- id: trailing-whitespace
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- repo: https://github.com/pre-commit/mirrors-isort
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rev: v5.6.0
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hooks:
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- id: isort

‎LICENSE

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MIT License
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Copyright (c) 2021 Zachary Barry
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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# convit-hcs-moa-prediction
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Prediction of drug mechanism-of-action (MoA) from high content screening images.
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## Background
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![](https://data.broadinstitute.org/bbbc/BBBC021/aurora-kinase-inhibitor.png)
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Example image from [BBBC021](https://bbbc.broadinstitute.org/BBBC021) dataset
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used in this work.
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## Getting started
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1. Clone the repository:
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```
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git clone https://github.com/zbarry/pytorch-hcs.git
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```
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2. Install the environment and the `pytorch_hcs` package:
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```
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cd pytorch-hcs
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conda install -c conda-forge mamba -y
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mamba env update
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```
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(the `mamba` install is optional but recommended as a conda replacement)
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This will create a `pytorch-hcs` environment and `pip install` the Python package in one go.
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3. Acquire the BBBC021 dataset
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In a notebook or Python terminal, run the following:
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```python
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```

‎environment.yml

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name: pytorch-hcs
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channels:
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- pytorch
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- nvidia
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- conda-forge
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- defaults
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dependencies:
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- python
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- numpy
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- scipy
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- scikit-image
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- pandas
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- pyjanitor
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# - opencv
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- holoviews
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- hvplot
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- datashader
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- panel
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- jupyterlab
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- pyviz_comms
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- ipywidgets
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# - umap-learn # until supervised densmap officially released
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- hdbscan
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- wandb
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- pytorch
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- torchvision
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- cudatoolkit
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- pytorch-lightning
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- h5py
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- pyarrow
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- pylint
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- mypy
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- flake8
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- pre-commit
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- black
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- pip
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- pip:
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- opencv-python
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- git+https://github.com/lmcinnes/umap.git
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- git+https://github.com/zbarry/pybbbc.git
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- '-e .'

‎mypy.ini

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[mypy]
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ignore_missing_imports = True

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