<div align="center">
Tulia: a comprehensive machine learning project entirely from scratch, utilizing the power of Python and numpy.
</div>
## 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
```python
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
```sh
pip install tulia
```
### Download a whole library
```sh
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.
```python
pytest -v
```
## License
MIT License
Raw data
{
"_id": null,
"home_page": "https://github.com/chuvalniy/tulia",
"name": "tulia",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "python,machine learning,from scratch,numpy,sklearn-like,random forest,tree,knn",
"author": "Valentin Belyaev",
"author_email": "chuvalik.work@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/05/32/d1392edf53ee0afd66113b15cbc55e9e25e84a21bf10891402e2bd4b6060/tulia-0.2.1.tar.gz",
"platform": null,
"description": "<div align=\"center\">\r\n\r\n\r\n\r\nTulia: a comprehensive machine learning project entirely from scratch, utilizing the power of Python and numpy.\r\n\r\n</div>\r\n\r\n## Features\r\n\r\n### Simplicity\r\n\r\nBy 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.\r\nMoreover, the library provided here offers a streamlined approach by maintaining only essential parameters in the model class. \r\n\r\n### Familiar approach\r\n\r\nThis library uses sklearn API to build the codebase. \r\n\r\n## Example usage\r\n\r\n```python\r\nfrom src.linear import LinearRegression\r\n\r\nX_train, X_test, y_train, y_test = ...\r\n\r\nlr = LinearRegression(n_steps=10_000, learning_rate=1e-4)\r\nlr.fit(X_train, y_train)\r\n\r\ny_pred = lr.predict(X_test)\r\n\r\nmse = mean_squared_error(y_pred, y_test) # Here mean_squared_error() is a pseudocode.\r\n```\r\n\r\n## Installation\r\n\r\n\r\n### To use in code\r\n\r\n```sh\r\npip install tulia\r\n```\r\n\r\n### Download a whole library\r\n\r\n```sh\r\ngit clone https://github.com/chuvalniy/Tulia.git\r\npip install -r requirements.txt\r\n```\r\n\r\n## Testing\r\n\r\nEvery machine learning model is provided with unit test that verifies correctness of fit and predict methods.\r\n\r\nExecute the following command in your project directory to run the tests.\r\n\r\n```python\r\npytest -v\r\n```\r\n\r\n## License\r\nMIT License\r\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "numpy based machine learning package with sklearn-like API",
"version": "0.2.1",
"project_urls": {
"Homepage": "https://github.com/chuvalniy/tulia"
},
"split_keywords": [
"python",
"machine learning",
"from scratch",
"numpy",
"sklearn-like",
"random forest",
"tree",
"knn"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "687ba38decddad2464f89f7a3fb5e9fcf3a3fc3a77907dc4605e3416bc728acc",
"md5": "91af8f6ae3feecd611494175d24a0b59",
"sha256": "66e1564de6bea956a2d08b6b46bc88ae07cd28d59c0cc193877857a5f72810c3"
},
"downloads": -1,
"filename": "tulia-0.2.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "91af8f6ae3feecd611494175d24a0b59",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 15504,
"upload_time": "2024-01-12T03:38:15",
"upload_time_iso_8601": "2024-01-12T03:38:15.456883Z",
"url": "https://files.pythonhosted.org/packages/68/7b/a38decddad2464f89f7a3fb5e9fcf3a3fc3a77907dc4605e3416bc728acc/tulia-0.2.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0532d1392edf53ee0afd66113b15cbc55e9e25e84a21bf10891402e2bd4b6060",
"md5": "14a05ed50d20b65ae626b3a474847840",
"sha256": "9441baadb620f7bf88d91b28dab1afdd01fb5f912eca0799a92e5ce87a82ba2b"
},
"downloads": -1,
"filename": "tulia-0.2.1.tar.gz",
"has_sig": false,
"md5_digest": "14a05ed50d20b65ae626b3a474847840",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 11275,
"upload_time": "2024-01-12T03:38:17",
"upload_time_iso_8601": "2024-01-12T03:38:17.337918Z",
"url": "https://files.pythonhosted.org/packages/05/32/d1392edf53ee0afd66113b15cbc55e9e25e84a21bf10891402e2bd4b6060/tulia-0.2.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-01-12 03:38:17",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "chuvalniy",
"github_project": "tulia",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "tulia"
}
JSON
