Registry / ai-ml / scikit-multilearn

scikit-multilearn

library0.2.0pypypiunverified

A BSD-licensed library for multi-label classification built on top of scikit-learn. Current version is 0.2.0. The project appears to be in maintenance mode with no recent releases; last PyPI release was in 2018.

ai-ml

pip install scikit-multilearn

package ↗homepage ↗

INSTALL

IMPORT

SIG · SCIKIT-MULTILEARN

scikit-multilearn

ai-mlpythonv0.2.0

harness data pending

Install & Compatibility

Where this runs

No compatibility data collected yet for this library.

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

BinaryRelevance

✓ from skmultilearn.problem_transform import BinaryRelevance

✗ from skmultilearn.problem_transform import BinaryRelevance

Quick example using BinaryRelevance with a RandomForest base classifier.

import numpy as np from skmultilearn.problem_transform import BinaryRelevance from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_multilabel_classification X, Y = make_multilabel_classification(n_samples=100, n_features=20, n_classes=5, random_state=42) classifier = BinaryRelevance(classifier=RandomForestClassifier(), require_dense=[True, True]) classifier.fit(X, Y) predictions = classifier.predict(X) print(predictions.shape)

Debug

Known issues

deprecatedThe library has not been updated since 2018; consider using scikit-multilearn v2 (if available) or alternatives like 'skmultilearn' fork.

fix

Check for newer forks or use 'pip install scikit-multilearn==0.2.0' (still old).

affects: >=0.2.0

gotchaMany methods require `require_dense` parameter to be set to [True, True] for classifiers that expect dense arrays, otherwise confusing errors occur.

fix

Always pass `require_dense=[True, True]` to adapters like BinaryRelevance when using sklearn classifiers.

affects: all

gotchaThe module name in imports is 'skmultilearn', not 'scikit_multilearn' or 'sklearn_multilearn'.

fix

Use 'import skmultilearn' or 'from skmultilearn import ...'.

affects: all

breakingVersion 0.2.0 dropped Python 2 support; Python 3.5+ required.

fix

Upgrade to Python 3.5 or later.

affects: >=0.2.0

Upgrade

Version history

0.2.0latest on PyPI · released Dec 10, 2018

Audit

Dependencies

scikit-learnrequiredCore dependency for base classifiers and utilities.

numpyrequiredRequired for array operations.

scipyrequiredRequired for sparse matrix support.

Agent activity

15 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/scikit-multilearn/ ↗

homepagehttp://scikit.ml/ ↗