MIML Learning Library
Project description
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, scikit-learn, scipy, mil, tensorflow and keras. 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
$ pip install scipy
$ pip install mil
$ pip install tensorflow
$ pip install keras==2.12.0
Usage
Datasets
import pkg_resources
from miml.data.load_datasets import load_dataset
dataset_train = load_dataset(pkg_resources.resource_filename('miml', 'datasets/miml_birds_random_80train.arff'),
delimiter="'")
dataset_test = load_dataset(pkg_resources.resource_filename('miml', 'datasets/miml_birds_random_20test.arff'),
delimiter="'")
Classifier
from miml.classifier import MIMLtoMIBRClassifier, AllPositiveAPRClassifier
classifier_mi = MIMLtoMIBRClassifier(AllPositiveAPRClassifier())
classifier_mi.fit(dataset_train)
results_mi=classifier_mi.evaluate(dataset_test)
Report
from miml.report import Report
report = Report()
report.to_string(dataset_test.get_labels_by_bag(), results_ml)
print("")
report.to_csv(dataset_test.get_labels_by_bag(), results_ml)
License
MIML library is released under the GNU General Public License GPLv3.
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