recohut 0.0.11

A python library for building recommender systems.

recohut

a python library for building recommender systems.
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About The Project

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Built With

• Python
• PyTorch
• Lightning
• nbdev

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Getting Started

To get a local copy up and running follow these simple example steps.

Prerequisites

• pytorch

  pip install torch
  

• lightning

  pip install pytorch-lightning
  

Installation

pip install recohut

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Usage

# import the required modules
from recohut.datasets.movielens import ML1mDataModule
from recohut.models.nmf import NMF
from recohut.trainers.pl_trainer import pl_trainer

# build the dataset
class Args:
    def __init__(self):
        self.data_dir = '/content/data'
        self.min_rating = 4
        self.num_negative_samples = 99
        self.min_uc = 5
        self.min_sc = 5
        self.val_p = 0.2
        self.test_p = 0.2
        self.num_workers = 2
        self.normalize = False
        self.batch_size = 32
        self.seed = 42
        self.shuffle = True
        self.pin_memory = True
        self.drop_last = False
        self.split_type = 'stratified'

args = Args()

ds = ML1mDataModule(**args.__dict__)
ds.prepare_data()

# build the model
model = NMF(n_items=ds.data.num_items, n_users=ds.data.num_users, embedding_dim=20)

# train and evaluate the matrix factorization model
pl_trainer(model, ds, max_epochs=5)

Check this quick tutorial.

For more examples, please refer to the Documentation and Tutorials.

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Roadmap

• [] RecSys Model Deployment and MLOps features
• [] RL agents and environment specific to recommender systems
• [] Visualization utilities and EDA

See the open issues for a full list of proposed features (and known issues).

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Sparsh A.

@sparsh-ai

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Acknowledgments

• nbdev team for providing supporting tools to build this library.
• colab team for providing running VMs instances for development and testing.

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