def test_return_result_options(self, classifier):
cl = classifier()
if isinstance(cl, tuple(UNSUPERVISED_CLASSIFIERS)):
cl.fit(self.X_train)
else:
cl.fit(self.X_train, self.y_train)
prediction_default = cl.predict(self.X_test)
assert isinstance(prediction_default, pd.MultiIndex)
with rl.option_context('classification.return_type', 'index'):
prediction_multiindex = cl.predict(comparison_vectors=self.X_train)
assert isinstance(prediction_multiindex, pd.MultiIndex)
with rl.option_context('classification.return_type', 'array'):
prediction_ndarray = cl.predict(comparison_vectors=self.X_train)
assert isinstance(prediction_ndarray, np.ndarray)
with rl.option_context('classification.return_type', 'series'):
prediction_series = cl.predict(comparison_vectors=self.X_train)
assert isinstance(prediction_series, pd.Series)
with pytest.raises(ValueError):
with rl.option_context('classification.return_type',
'unknown_return_type'):
cl.predict(comparison_vectors=self.X_train)
def test_options_context():
with rl.option_context("indexing.pairs", "multiindex"):
rl.options.indexing.pairs = "multiindex"
assert rl.get_option("indexing.pairs") == "multiindex"