miml: Multi-Instance Multi-Label Learning Library for Python

The aim of the library is to ease the development, testing, and comparison of classification algorithms for multi-instance multi-label learning (MIML).

Table of Contents

Installation

Use the package manager pip to install miml.

$ pip install mimllearning

Requirements

The requirement packages for miml library are: numpy and scikit-learn. Installing miml with the package manager does not install the package dependencies. So install them with the package manager manually if not already downloaded.

$ pip install numpy
$ pip install scikit-learn

Documentation

We can find the documentation of the project in this link: Documentation

Usage

Datasets

from miml.data.load_datasets import load_dataset

dataset_train = load_dataset("miml_birds_random_80train.arff", from_library=True)
dataset_test = load_dataset("C:/Users/Damián/Desktop/miml_birds_random_20test.arff")

Classifier

from miml.classifier import MIMLtoMIBRClassifier, AllPositiveAPRClassifier

classifier_mi = MIMLtoMIBRClassifier(AllPositiveAPRClassifier())
classifier_mi.fit(dataset_train)
results_mi=classifier_mi.evaluate(dataset_test)
probs_mi = classifier_mi.predict_proba(dataset_test)

Report

from miml.report import Report

report = Report(results_mi, probs_mi, dataset_test)
report.to_string()
print("")
report.to_csv()

License

MIML library is released under the GNU General Public License GPLv3.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for p82maavd from gravatar.com p82maavd

Credits

Author: Damian Martinez

License

GNU General Public License v3 or later (GPLv3+)

Requires

Python >=3.8

Tags

data-science machine-learning miml multi-instance multilabel python

Classifiers

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