Python package for multiple instance learning (MIL). This wraps single instance learning algorithms so that they can be fitted to data for MIL.
RandomForestClassifier
, SVC
, LogisticRegression
)pip install milwrap
For more information, see Use scikit-learn models in multiple instance learning based on the count-based assumption.
# Prepare single-instance supervised-learning algorithm
# Note: only supports models with predict_proba() method.
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
# Wrap it with MilCountBasedMultiClassLearner
from milwrap import MilCountBasedMultiClassLearner
mil_learner = MilCountBasedMultiClassLearner(clf)
# 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.
# run multiple instance learning
clf_mil, y_mil = learner.fit(
bags,
lower_threshold,
upper_threshold,
n_classes,
max_iter=10)
# after multiple instance learning,
# you can predict instance class
clf_mil.predict([instance_feature])
See tests/test_countbased.py
for an example of a fully working test data generation process.
milwrap is available under the MIT License.