# Copyright (c) 2017, Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can be
# found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
from . import _SVC as _SVC
from ..._deps import _HAS_SKLEARN
if _HAS_SKLEARN:
from ._sklearn_util import check_fitted
from . import _sklearn_util
from sklearn.svm import NuSVC as _NuSVC
sklearn_class = _NuSVC
model_type = "classifier"
def convert(model, feature_names, target):
"""Convert a Nu-Support Vector Classification (NuSVC) model to the protobuf spec.
Parameters
----------
model: NuSVC
A trained NuSVC encoder model.
feature_names: [str], optional (default=None)
Name of the input columns.
target: str, optional (default=None)
Name of the output column.
Returns
-------
model_spec: An object of type Model_pb.
Protobuf representation of the model
"""
if not (_HAS_SKLEARN):
raise RuntimeError(
"scikit-learn not found. scikit-learn conversion API is disabled."
)
_sklearn_util.check_expected_type(model, _NuSVC)
return _SVC.convert(model, feature_names, target)
def supports_output_scores(model):
return _SVC.supports_output_scores(model)
def get_output_classes(model):
if not (_HAS_SKLEARN):
raise RuntimeError(
"scikit-learn not found. scikit-learn conversion API is disabled."
)
check_fitted(model, lambda m: hasattr(m, "support_vectors_"))
return _SVC.get_output_classes(model)
def get_input_dimension(model):
if not (_HAS_SKLEARN):
raise RuntimeError(
"scikit-learn not found. scikit-learn conversion API is disabled."
)
check_fitted(model, lambda m: hasattr(m, "support_vectors_"))
return _SVC.get_input_dimension(model)
