beluga-ml 1.1.0

A Python library to help make your Machine Learning easier

Beluga - make predictions, get metrics

Beluga is a Python library that provides easy access to all of the metrics you need in your multiclass classification tasks. We were inspired by this friendly Beluga whale to help others in their Machine Learning projects.

Check out the Issues for future functionality and progress such as support for regression tasks and metric visualisations.

The official PyPi release can be found here.

Overview

• Get various metrics on your Machine Learning predictions
• Print your metrics or incorporate them into downstream analysis
• Visualise your metrics (Coming Soon)

Installation

To install this library you can use Pypi via pip

pip install beluga_ml

Usage

Import beluga into your project

import beluga

Documentation

Methods in metrics have following parameters:

• predictions - (Iterable) predictions output from your model
• ground_truth - (Iterable) ground truth values to compare against
• raw - (bool). Optional. Use to get metrics in a dictionary instead of printing. Default: False.

Methods list:

true_positive: Number of correctly classified labels of the positive class

true_negative: Number of correctly classified labels of the negative class

false_positive: Number of incorrectly classified labels of the postive class

false_negative: Number of incorrectly classified labels of the negative class

precision: Percentage of positive class predictions that are correct

recall: Percentage of correctly classified labels from the positive class

sensitivity: (see recall)

specificity: Percentage of correctly classified labels from the negative class

f1: The harmomic mean of precision and recall

accuracy: Percentage of correctly classified labels

Examples

beluga.metrics.true_positive([1, 1, 1, 0, 0], [1, 0, 1, 0, 0])
>>> True Positive
    ==============
    0       2.0000
    1       2.0000
    ==============

beluga.metrics.recall(['cat', 'dog', 'dog'], ['cat', 'dog', 'dog'])
>>> Recall
    ==============
    cat     1.0000
    dog     1.0000
    ==============

beluga.metrics.f1(['Elon Musk', 'Tim Cook', 'robot'], ['Elon Musk', 'Tim Cook', 'Mark Zuckerberg'])
>>> F1 score
    ========================
    Elon Musk         1.0000
    Mark Zuckerberg   0.0000
    Tim Cook          1.0000
    ========================

Running Code

Run the code from the home direcotry for any development as follows:

python -m beluga.metrics

This should return nothing as all development tests have been removed.

Tests

Run the tests from the library home directory with the following:

python -m setup pytest

Check the coverage of these tests using:

pytest --cov=beluga tests/ --cov-report term-missing

License

GPL-3.0 License

Beluga uses open source packages to work properly:

• numpy - The fundamental package for scientific computing with Python.

And of course beluga itself is open source with a public repository on GitHub.