def test_pprint():
learner = HoeffdingTreeClassifier()
expected_string = "binary_split=False, grace_period=200, leaf_prediction='nba',\n" \
" max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0,\n" \
" no_preprune=False, nominal_attributes=None, remove_poor_atts=False,\n" \
" split_confidence=1e-07, split_criterion='info_gain',\n" \
" stop_mem_management=False, tie_threshold=0.05"
assert _pprint(learner.get_params()) == expected_string
def test_set_params():
learner = HoeffdingTreeClassifier()
original_info = learner.get_info()
params = learner.get_params()
params.update(leaf_prediction='nb',
split_criterion='gini',
remove_poor_atts=True)
learner.set_params(**params)
updated_info = learner.get_info()
assert original_info != updated_info
expected_info = "HoeffdingTreeClassifier(binary_split=False, grace_period=200, leaf_prediction='nb', " \
"max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \
"nominal_attributes=None, remove_poor_atts=True, split_confidence=1e-07, split_criterion='gini', " \
"stop_mem_management=False, tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
