def decision_function(self, X) -> CumlArray:
"""
Calculate the decision function.
"""
X, _, _, _, _ = input_to_host_array(X)
with cuml.internals.exit_internal_api():
return self.multiclass_estimator.decision_function(X)
def predict(self, X) -> CumlArray:
"""
Predict using multi class classifier.
"""
X, _, _, _, _ = input_to_host_array(X)
with cuml.internals.exit_internal_api():
return self.multiclass_estimator.predict(X)
def fit(self, X, y) -> 'MulticlassClassifier':
"""
Fit a multiclass classifier.
"""
if self.strategy == 'ovr':
self.multiclass_estimator = sklearn.multiclass.\
OneVsRestClassifier(self.estimator, n_jobs=None)
elif self.strategy == 'ovo':
self.multiclass_estimator = \
sklearn.multiclass.OneVsOneClassifier(
self.estimator, n_jobs=None)
else:
raise ValueError('Invalid multiclass strategy ' +
str(self.strategy) + ', must be one of '
'{"ovr", "ovo"}')
X, _, _, _, _ = input_to_host_array(X)
y, _, _, _, _ = input_to_host_array(y)
with cuml.internals.exit_internal_api():
self.multiclass_estimator.fit(X, y)
return self
def test_input_to_host_array(dtype, input_type, num_rows, num_cols, order):
input_data, real_data = get_input(input_type, num_rows, num_cols, dtype,
order=order)
if input_type == 'cupy' and input_data is None:
pytest.skip('cupy not installed')
X, X_ptr, n_rows, n_cols, dtype = input_to_host_array(input_data,
order=order)
np.testing.assert_equal(X, real_data)
assert n_rows == num_rows
assert n_cols == num_cols
assert dtype == dtype
del input_data
del real_data