Python package for multiple instance learning (MIL) for large n_instance dataset.
- support count-based multiple instance assumptions (see wikipedia)
- support multi-class setting
- support scikit-learn Clustering algorithms (such as
MiniBatchKMeans) - fast even if n_instance is large
pip install clustermil# Prepare follwing dataset
#
# - bags ... list of np.ndarray
# (num_instance_in_the_bag * num_features)
# - lower_threshold ... np.ndarray (num_bags * num_classes)
# - upper_threshold ... np.ndarray (num_bags * num_classes)
#
# bags[i_bag] contains not less than lower_thrshold[i_bag, i_class]
# i_class instances.
# Prepare single-instance clustering algorithms
from sklearn.cluster import MiniBatchKMeans
n_clusters = 100
clustering = MiniBatchKMeans(n_clusters=n_clusters)
clusters = clustering.fit_predict(np.vstack(bags)) # flatten bags into instances
# Prepare one-hot encoder
from sklearn.preprocessing import OneHotEncoder
onehot_encoder = OneHotEncoder()
onehot_encoder.fit(clusters)
# generate ClusterMilClassifier with helper function
from clustermil import generate_mil_classifier
milclassifier = generate_mil_classifier(
clustering,
onehot_encoder,
bags,
lower_threshold,
upper_threshold,
n_clusters)
# after multiple instance learning,
# you can predict instance class
milclassifier.predict([instance_feature])See tests/test_classification.py for an example of a fully working test data generation process.
clustermil is available under the MIT License.