numpy based machine learning package with sklearn-like API
Project description
Tulia: a comprehensive machine learning project entirely from scratch, utilizing the power of Python and numpy.
Features
Simplicity
By encapsulating both the training and predicting logic within just a couple of classes, complexity is greatly reduced compared to popular frameworks that heavily rely on abstraction. Moreover, the library provided here offers a streamlined approach by maintaining only essential parameters in the model class.
Familiar approach
This library uses sklearn API to build the codebase.
Example usage
from src.linear import LinearRegression
X_train, X_test, y_train, y_test = ...
lr = LinearRegression(n_steps=10_000, learning_rate=1e-4)
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
mse = mean_squared_error(y_pred, y_test) # Here mean_squared_error() is a pseudocode.
Installation
To use in code
pip install tulia
Download a whole library
git clone https://github.com/chuvalniy/Tulia.git
pip install -r requirements.txt
Testing
Every machine learning model is provided with unit test that verifies correctness of fit and predict methods.
Execute the following command in your project directory to run the tests.
pytest -v
License
MIT License
Project details
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