Source code for mlprodict.onnxrt.ops_cpu.op_linear_classifier
# -*- encoding: utf-8 -*-
# pylint: disable=E0203,E1101,C0111
"""
Runtime operator.
:githublink:`%|py|7`
"""
import numpy
from scipy.special import expit # pylint: disable=E0611
from ._op import OpRunClassifierProb
from ._op_classifier_string import _ClassifierCommon
from ._op_numpy_helper import numpy_dot_inplace
[docs]class LinearClassifier(OpRunClassifierProb, _ClassifierCommon):
atts = {'classlabels_ints': [], 'classlabels_strings': [],
'coefficients': None, 'intercepts': None,
'multi_class': 0, 'post_transform': b'NONE'}
[docs] def __init__(self, onnx_node, desc=None, **options):
OpRunClassifierProb.__init__(self, onnx_node, desc=desc,
expected_attributes=LinearClassifier.atts,
**options)
self._post_process_label_attributes()
if not isinstance(self.coefficients, numpy.ndarray):
raise TypeError("coefficient must be an array not {}.".format(
type(self.coefficients)))
if len(getattr(self, "classlabels_ints", [])) == 0 and \
len(getattr(self, 'classlabels_strings', [])) == 0:
raise ValueError(
"Fields classlabels_ints or classlabels_strings must be specified.")
self.nb_class = max(len(getattr(self, 'classlabels_ints', [])),
len(getattr(self, 'classlabels_strings', [])))
if len(self.coefficients.shape) != 1:
raise ValueError("coefficient must be an array but has shape {}\n{}.".format(
self.coefficients.shape, desc))
n = self.coefficients.shape[0] // self.nb_class
self.coefficients = self.coefficients.reshape(self.nb_class, n).T
[docs] def _run(self, x): # pylint: disable=W0221
scores = numpy_dot_inplace(self.inplaces, x, self.coefficients)
if self.intercepts is not None:
scores += self.intercepts
if self.post_transform == b'NONE':
pass
elif self.post_transform == b'LOGISTIC':
expit(scores, out=scores)
elif self.post_transform == b'SOFTMAX':
numpy.subtract(scores, scores.max(axis=1)[
:, numpy.newaxis], out=scores)
numpy.exp(scores, out=scores)
numpy.divide(scores, scores.sum(axis=1)[
:, numpy.newaxis], out=scores)
else:
raise NotImplementedError("Unknown post_transform: '{}'.".format(
self.post_transform))
if self.nb_class == 1:
label = numpy.zeros((scores.shape[0],), dtype=x.dtype)
label[scores > 0] = 1
else:
label = numpy.argmax(scores, axis=1)
return self._post_process_predicted_label(label, scores)