from sklearn.utils.estimator_checks import check_estimator

import xam


# Preprocessing

def test_cycle_transformer():
    assert check_estimator(xam.preprocessing.CycleTransformer) is None

def test_supervised_imputer():

    # This is needed for going through the same testing hoops as scikit-learn's Imputer
    xam.preprocessing.SupervisedImputer.__name__ = 'Imputer'

    assert check_estimator(xam.preprocessing.SupervisedImputer) is None

## Binning

def test_bayesian_blocks_binner():
    assert check_estimator(xam.preprocessing.BayesianBlocksBinner) is None

def test_equal_frequency_binner():
    assert check_estimator(xam.preprocessing.EqualFrequencyBinner) is None

def test_equal_width_binner():
    assert check_estimator(xam.preprocessing.EqualWidthBinner) is None

def test_mdlp_binner():
    assert check_estimator(xam.preprocessing.MDLPBinner) is None


# NLP

# def test_top_terms_classifier():
#     assert check_estimator(xam.nlp.TopTermsClassifier) is None

@@ -198,18 +198,19 @@
 est_
-supervised
+conditional
 _imp
@@ -324,34 +324,35 @@
 eprocessing.
-Supervised
+Conditional
 Imputer.__na
@@ -418,18 +418,19 @@
 ing.
-Supervised
+Conditional
 Impu

Update tests