Package Health Score

58 / 100

security
No known security issues
popularity
Limited
maintenance
Inactive
community
Sustainable

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Popularity

Limited

GitHub Stars: 8
Forks: 0
Contributors: 3

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package boost-loss, we found that it has been starred 8 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Jan 26, 2024
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

Yes

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Maintenance

Inactive

Commit Frequency

Open Issues: 1
Open PR: 12
Last Release: 10 months ago
Last Commit: 10 months ago

Further analysis of the maintenance status of boost-loss based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

We found that boost-loss demonstrates a positive version release cadence with at least one new version released in the past 12 months.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 3
Funding: No

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: >=3.8,<4.0

Age: 1 year
Latest Release: 10 months ago
Dependencies: 6 Direct
Versions: 21
Maintainers: 1
Wheels: OS Independent

Readme

Boost Loss

License

Utilities for easy use of custom losses in CatBoost, LightGBM, XGBoost. This sounds very simple, but in reality it took a lot of work.

Installation

Install this via pip (or your favourite package manager):

pip install boost-loss

Usage

Basic Usage

import numpy as np

from boost_loss import LossBase
from numpy.typing import NDArray


class L2Loss(LossBase):
    def loss(self, y_true: NDArray, y_pred: NDArray) -&gt; NDArray:
        return (y_true - y_pred) ** 2 / 2

    def grad(self, y_true: NDArray, y_pred: NDArray) -&gt; NDArray: # dL/dy_pred
        return - (y_true - y_pred)

    def hess(self, y_true: NDArray, y_pred: NDArray) -&gt; NDArray: # d^2L/dy_pred^2
        return np.ones_like(y_true)

import lightgbm as lgb

from boost_loss import apply_custom_loss
from sklearn.datasets import load_boston


X, y = load_boston(return_X_y=True)
apply_custom_loss(lgb.LGBMRegressor(), L2Loss()).fit(X, y)

Built-in losses are available. [^bokbokbok]

from boost_loss.regression import LogCoshLoss

`torch.autograd` Loss [^autograd]

import torch

from boost_loss.torch import TorchLossBase


class L2LossTorch(TorchLossBase):
    def loss_torch(self, y_true: torch.Tensor, y_pred: torch.Tensor) -&gt; torch.Tensor:
        return (y_true - y_pred) ** 2 / 2

Contributors ✨

Thanks goes to these wonderful people (emoji key):

_34j
💻 🤔 📖

This project follows the all-contributors specification. Contributions of any kind welcome!

[^bokbokbok]: Inspired by orchardbirds/bokbokbok [^autograd]: Inspired by TomerRonen34/treeboost_autograd

boost-loss dependencies

attrs catboost lightgbm pyhumps scikit-learn xgboost

FAQs

What is boost-loss?

Utilities for easy use of custom losses in CatBoost, LightGBM, XGBoost. Visit Snyk Advisor to see a full health score report for boost-loss, including popularity, security, maintenance & community analysis.

Is boost-loss popular?

The python package boost-loss receives a total of 344 weekly downloads. As such, boost-loss popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is boost-loss well maintained?

We found indications that boost-loss is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is boost-loss safe to use?

The python package boost-loss was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 19 November-2024, at 00:21 (UTC).

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boost-loss

Utilities for easy use of custom losses in CatBoost, LightGBM, XGBoost For more information about how to use this package see README

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Package Health Score

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Popularity

Direct Usage Popularity

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Community

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Package

Readme

Boost Loss

Installation

Usage

Basic Usage

`torch.autograd` Loss [^autograd]

Contributors ✨

boost-loss dependencies

FAQs

What is boost-loss?

Is boost-loss popular?

Is boost-loss well maintained?

Is boost-loss safe to use?

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Fix identified vulnerabilities

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Package Health Score

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Direct Usage Popularity

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Commit Frequency

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Readme

Boost Loss

Installation

Usage

Basic Usage

torch.autograd Loss [^autograd]

Contributors ✨

boost-loss dependencies

FAQs

What is boost-loss?

Is boost-loss popular?

Is boost-loss well maintained?

Is boost-loss safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

Monitor for new issues

`torch.autograd` Loss [^autograd]