amethyst

A low-code recommendation engine generation tool
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

Amethyst is a low-code, easy to use, GPU-powered recommender engine generator based on PyTorch. It requires only three parameters to rank/predict best items for users and vice-versa

• User ID (unique identifier for each user)
• Item ID (unique identifier for each item)
• User-Item Ratings (user-item rating/interaction scores)

Since all the underlying data operations are being handled by Pandas, amethyst supports a wide variety of database/data storage formats like SQL, NoSQL, CSV, TSV, etc.

The resultant recommendation scores are also obtained as a Pandas Dataframe, which helps in a flexible integration with your application.

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Built With

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Getting Started

This is an example of how you can generate your own collaborative recommendation engine. To get a local copy up and running follow these simple example steps.

Prerequisites

• Python>=3.7

Installation

1. Clone the repo

   git clone https://github.com/radioactive11/amethyst.git

2. Create and activate virtual environment

   python3 -m venv venv
   source venv/bin/activate

3. Install the tool

   python3 setup.py install

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Usage

A recommendation engine can be generated in 4 easy steps:

1. Import the data
2. Select an algorithm
3. Train the model
4. Evaluate the model's performance

Data Split ⚗️

from amethyst.dataloader import split

df = pd.read_csv("./movielens100k.csv")
df_train, df_test = split.stratified_split(
                                    df,
                                    0.8, 
                                    user_col='userID',
                                    item_col='itemID',
                                    filter_col='item'
)

Load Data 📥

from amethyst.dataloader import dataset

df = pd.read_csv("movielens100k.csv")

# from Data Split
df_train, df_test = split.stratified_split(df)

train = dataset.Dataloader.dataloader(df_train.itertuples(index=False))
test = dataset.Dataloader.dataloader(df_test.itertuples(index=False))

Train (BiVAECF) ⚙️

from amethyst.models.bivaecf.bivaecf import BiVAECF
import torch


bivae = BiVAECF(
    k=50,
    encoder_structure=[100],
    act_fn=["tanh"],
    likelihood="pois",
    n_epochs=500,
    batch_size=256,
    learning_rate=0.001,
    seed=42,
    use_gpu=torch.cuda.is_available(),
    verbose=True
)

bivae.fit(train, test)
bivae.save("model.pkl")

Train (IBPR) ⚙️

from amethyst.models.ibpr.ibprcf import IBPR
import torch


ibpr = IBPR(
        k=20,
        max_iter=100,
        alpha_=0.05,
        lambda_=0.001,
        batch_size=100,
        trainable=True,
        verbose=False,
        init_params=None)

ibpr.fit(train, test)
ibpr.save("model.pkl")

Predict/Rank 📈

from amethyst.models.predictions import rank
from amethyst.models.bivaecf.bivaecf import BiVAECF


bivae = BiVAECF(
    k=50,
    encoder_structure=[100],
    act_fn=["tanh"],
    likelihood="pois",
    n_epochs=500,
    batch_size=256,
    learning_rate=0.001,
    seed=42,
    use_gpu=torch.cuda.is_available(),
    verbose=True
)

bivae.load("mode.pkl")

predictions = rank(bivae, test, user_col='userID', item_col='itemID')

# predictions is a Pandas Dataframe
predictions.to_csv("predictions.csv", index=False)

Evaluate 📈

from amethyst.models.predictions import rank
from amethyst.eval.eval_methods import map_at_k, precision_at_k, recall_k



bivae = BiVAECF(
    k=50,
    encoder_structure=[100],
    act_fn=["tanh"],
    likelihood="pois",
    n_epochs=500,
    batch_size=256,
    learning_rate=0.001,
    seed=42,
    use_gpu=torch.cuda.is_available(),
    verbose=True
)

bivae.load("mode.pkl")

predictions = rank(bivae, test, user_col='userID', item_col='itemID')
eval_map = map_at_k(test, predictions, k=10)
pk = precision_at_k(test, predictions, k=10)
rk = recall_k(test, predictions)

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Roadmap

• [] Build API Wrapper
• [] Use Elastic Search to save recommendations
• [] Add more algorithms
• [] Add content-based recommendation generation

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

Arijit Roy - @_radioactive11_ - roy.arijit2001@gmail.com

Project Link: https://github.com/radioactive11/amethyst

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Acknowledgments

• BiVAECF
• IBPR

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