import numpy as np

from modAL.batch import uncertainty_batch_sampling

from modAL.expected_error import expected_error_reduction

from modAL.models import ActiveLearner

from modAL.uncertainty import entropy_sampling, margin_sampling

from sklearn.base import BaseEstimator

class MockClassifier(BaseEstimator):

def __init__(self, n_classes=2):

self.n_classes = n_classes

def fit(self, X, y):

return self

def predict(self, X):

return np.random.randint(0, self.n_classes, shape=(len(X), 1))

def predict_proba(self, X):

return np.ones(shape=(len(X), self.n_classes))/self.n_classes

if __name__ == '__main__':

X_train = np.random.rand(10, 5, 5)

y_train = np.random.randint(0, 2, size=10)

X_pool = np.random.rand(10, 5, 5)

y_pool = np.random.randint(0, 2, size=10)

strategies = [margin_sampling, entropy_sampling, uncertainty_batch_sampling, expected_error_reduction]

for query_strategy in strategies:

print("testing %s..." % query_strategy.__name__)

# max margin sampling

learner = ActiveLearner(

estimator=MockClassifier(), query_strategy=query_strategy,

X_training=X_train, y_training=y_train

)

learner.query(X_pool)

learner.teach(X_pool, y_pool)