scikit-activeml 0.0.0

The package scikit-activeml is a library of that covers the most relevant query strategies in active learning and implements tools to work with partially labeled data.

scikit-activeml

scikit-activeml is a Python module for active learning on top of SciPy and scikit-learn. It is distributed under the 3-Clause BSD licence.

The project was initiated in 2020 by the Intelligent Embedded Systems Group at University Kassel.

Installation

The easiest way of installing scikit-activeml is using pip

pip install -U scikit-activeml

Example

The following code implements an Active Learning Cycle with 20 iterations using a Logistic Regression Classifier and Uncertainty Sampling. To use other classifiers, you can simply wrap classifiers from scikit-learn or use classifiers provided by scikit-activeml. Note that the main difficulty using active learning with scikit-learn is the ability to handle unlabeled data which we denote as a specific value (MISSING_LABEL) in the label vector y_true. More query strategies can be found in the documentation.

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from skactiveml.pool import UncertaintySampling
from skactiveml.utils import is_unlabeled, MISSING_LABEL
from skactiveml.classifier import SklearnClassifier

X, y_true = make_classification(random_state=0)
y = np.full(shape=y_true.shape, fill_value=MISSING_LABEL)

clf = SklearnClassifier(LogisticRegression(),
                        classes=np.unique(y_true))
qs = UncertaintySampling(clf, method='entropy')

n_cycles = 20
for c in range(n_cycles):
     unlbld_idx = np.where(is_unlabeled(y))[0]
     X_cand = X[unlbld_idx]
     query_idx = unlbld_idx[qs.query(X_cand=X_cand, X=X, y=y)]
     y[query_idx] = y_true[query_idx]
     clf.fit(X, y)

Development

More information are available in the Developer’s Guide.

Documentation

The doumentation is available here: https://scikit-activeml.readthedocs.io