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 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
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
mimllearning-0.5.5.tar.gz
(1.0 MB
view hashes)
Built Distribution
mimllearning-0.5.5-py3-none-any.whl
(798.7 kB
view hashes)
Close
Hashes for mimllearning-0.5.5-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d7ec3212e36d8edfd573165431cce833b73ce7fe57bcfafffa7186ecf037c18c |
|
MD5 | 06dc089550fb765513f2ca6e948b452c |
|
BLAKE2b-256 | aa8bcce0331c25aaa262cc52c832dda485ed5f220ad9578f5912ccebb48d15a2 |