107 lines
3.3 KiB
Markdown
107 lines
3.3 KiB
Markdown
# ProtoTorch Models
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[](https://travis-ci.org/si-cim/prototorch_models)
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[](https://pypi.org/project/prototorch_models/)
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Pre-packaged prototype-based machine learning models using ProtoTorch and
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PyTorch-Lightning.
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## Installation
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To install this plugin, simply run the following command:
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```sh
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pip install prototorch_models
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```
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**Installing the models plugin should automatically install a suitable version
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of** [ProtoTorch](https://github.com/si-cim/prototorch). The plugin should then
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be available for use in your Python environment as `prototorch.models`.
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## Contribution
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This repository contains definition for [git hooks](https://githooks.com).
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[Pre-commit](https://pre-commit.com) gets installed as development dependency with prototorch.
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Please install the hooks by running
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```bash
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pre-commit install
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pre-commit install --hook-type commit-msg
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```
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before creating the first commit.
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## Available models
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### LVQ Family
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- Learning Vector Quantization 1 (LVQ1)
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- Generalized Learning Vector Quantization (GLVQ)
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- Generalized Relevance Learning Vector Quantization (GRLVQ)
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- Generalized Matrix Learning Vector Quantization (GMLVQ)
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- Limited-Rank Matrix Learning Vector Quantization (LiRaMLVQ)
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- Localized and Generalized Matrix Learning Vector Quantization (LGMLVQ)
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- Learning Vector Quantization Multi-Layer Network (LVQMLN)
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- Siamese GLVQ
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- Cross-Entropy Learning Vector Quantization (CELVQ)
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- Soft Learning Vector Quantization (SLVQ)
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- Robust Soft Learning Vector Quantization (RSLVQ)
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- Probabilistic Learning Vector Quantization (PLVQ)
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### Other
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- k-Nearest Neighbors (KNN)
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- Neural Gas (NG)
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- Growing Neural Gas (GNG)
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## Work in Progress
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- Classification-By-Components Network (CBC)
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- Learning Vector Quantization 2.1 (LVQ2.1)
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- Self-Organizing-Map (SOM)
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## Planned models
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- Median-LVQ
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- Generalized Tangent Learning Vector Quantization (GTLVQ)
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- Self-Incremental Learning Vector Quantization (SILVQ)
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## Development setup
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It is recommended that you use a virtual environment for development. If you do
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not use `conda`, the easiest way to work with virtual environments is by using
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[virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/). Once
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you've installed it with `pip install virtualenvwrapper`, you can do the
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following:
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```sh
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export WORKON_HOME=~/pyenvs
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mkdir -p $WORKON_HOME
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source /usr/local/bin/virtualenvwrapper.sh # location may vary
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mkvirtualenv pt
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```
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Once you have a virtual environment setup, you can start install the `models`
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plugin with:
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```sh
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workon pt
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git clone git@github.com:si-cim/prototorch_models.git
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cd prototorch_models
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git checkout dev
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pip install -e .[all] # \[all\] if you are using zsh or MacOS
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```
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To assist in the development process, you may also find it useful to install
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`yapf`, `isort` and `autoflake`. You can install them easily with `pip`. **Also,
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please avoid installing Tensorflow in this environment. It is known to cause
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problems with PyTorch-Lightning.**
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## FAQ
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### How do I update the plugin?
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If you have already cloned and installed `prototorch` and the
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`prototorch_models` plugin with the `-e` flag via `pip`, all you have to do is
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navigate to those folders from your terminal and do `git pull` to update.
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