# -*- encoding: utf-8 -*-
# pylint: disable=E0203,E1101,C0111
"""
Runtime operator.
:githublink:`%|py|7`
"""
from collections import OrderedDict
import numpy
from ._op_helper import _get_typed_class_attribute
from ._op import OpRunClassifierProb, RuntimeTypeError
from ._op_classifier_string import _ClassifierCommon
from ._new_ops import OperatorSchema
from .op_svm_classifier_ import ( # pylint: disable=E0611,E0401
RuntimeSVMClassifierFloat,
RuntimeSVMClassifierDouble,
)
[docs]class SVMClassifierCommon(OpRunClassifierProb, _ClassifierCommon):
[docs] def __init__(self, dtype, onnx_node, desc=None,
expected_attributes=None, **options):
OpRunClassifierProb.__init__(self, onnx_node, desc=desc,
expected_attributes=expected_attributes,
**options)
self._init(dtype=dtype)
[docs] def _get_typed_attributes(self, k):
return _get_typed_class_attribute(self, k, self.__class__.atts)
[docs] def _find_custom_operator_schema(self, op_name):
"""
Finds a custom operator defined by this runtime.
:githublink:`%|py|34`
"""
if op_name == "SVMClassifierDouble":
return SVMClassifierDoubleSchema()
raise RuntimeError( # pragma: no cover
"Unable to find a schema for operator '{}'.".format(op_name))
[docs] def _init(self, dtype):
self._post_process_label_attributes()
if dtype == numpy.float32:
self.rt_ = RuntimeSVMClassifierFloat(20)
elif dtype == numpy.float64:
self.rt_ = RuntimeSVMClassifierDouble(20)
else:
raise RuntimeTypeError( # pragma: no cover
"Unsupported dtype={}.".format(dtype))
atts = [self._get_typed_attributes(k)
for k in SVMClassifier.atts]
self.rt_.init(*atts)
[docs] def _run(self, x): # pylint: disable=W0221
"""
This is a C++ implementation coming from
:epkg:`onnxruntime`.
`svm_classifier.cc
<https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/core/providers/cpu/ml/svm_classifier.cc>`_.
See class :class:`RuntimeSVMClassifier
<mlprodict.onnxrt.ops_cpu.op_svm_classifier_.RuntimeSVMClassifier>`.
:githublink:`%|py|61`
"""
label, scores = self.rt_.compute(x)
if scores.shape[0] != label.shape[0]:
scores = scores.reshape(label.shape[0],
scores.shape[0] // label.shape[0])
return self._post_process_predicted_label(label, scores)
[docs]class SVMClassifier(SVMClassifierCommon):
atts = OrderedDict([
('classlabels_ints', numpy.empty(0, dtype=numpy.int64)),
('classlabels_strings', []),
('coefficients', numpy.empty(0, dtype=numpy.float32)),
('kernel_params', numpy.empty(0, dtype=numpy.float32)),
('kernel_type', b'NONE'),
('post_transform', b'NONE'),
('prob_a', numpy.empty(0, dtype=numpy.float32)),
('prob_b', numpy.empty(0, dtype=numpy.float32)),
('rho', numpy.empty(0, dtype=numpy.float32)),
('support_vectors', numpy.empty(0, dtype=numpy.float32)),
('vectors_per_class', numpy.empty(0, dtype=numpy.float32)),
])
[docs] def __init__(self, onnx_node, desc=None, **options):
SVMClassifierCommon.__init__(
self, numpy.float32, onnx_node, desc=desc,
expected_attributes=SVMClassifier.atts,
**options)
[docs]class SVMClassifierDouble(SVMClassifierCommon):
atts = OrderedDict([
('classlabels_ints', numpy.empty(0, dtype=numpy.int64)),
('classlabels_strings', []),
('coefficients', numpy.empty(0, dtype=numpy.float64)),
('kernel_params', numpy.empty(0, dtype=numpy.float64)),
('kernel_type', b'NONE'),
('post_transform', b'NONE'),
('prob_a', numpy.empty(0, dtype=numpy.float64)),
('prob_b', numpy.empty(0, dtype=numpy.float64)),
('rho', numpy.empty(0, dtype=numpy.float64)),
('support_vectors', numpy.empty(0, dtype=numpy.float64)),
('vectors_per_class', numpy.empty(0, dtype=numpy.float64)),
])
[docs] def __init__(self, onnx_node, desc=None, **options):
SVMClassifierCommon.__init__(
self, numpy.float64, onnx_node, desc=desc,
expected_attributes=SVMClassifierDouble.atts,
**options)
[docs]class SVMClassifierDoubleSchema(OperatorSchema):
"""
Defines a schema for operators added in this package
such as :class:`SVMClassifierDouble <mlprodict.onnxrt.ops_cpu.op_svm_classifier.SVMClassifierDouble>`.
:githublink:`%|py|119`
"""
[docs] def __init__(self):
OperatorSchema.__init__(self, 'SVMClassifierDouble')
self.attributes = SVMClassifierDouble.atts
