Coverage for mlprodict/onnxrt/ops_cpu/op_linear_regressor.py: 100%
Shortcuts on this page
r m x toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
Shortcuts on this page
r m x toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
1# -*- encoding: utf-8 -*-
2# pylint: disable=E0203,E1101,C0111
3"""
4@file
5@brief Runtime operator.
6"""
7import numpy
8from ._op import OpRunUnaryNum
9from ._op_numpy_helper import numpy_dot_inplace
12class LinearRegressor(OpRunUnaryNum):
14 atts = {'coefficients': None, 'intercepts': None,
15 'targets': 1, 'post_transform': b'NONE'}
17 def __init__(self, onnx_node, desc=None, **options):
18 OpRunUnaryNum.__init__(self, onnx_node, desc=desc,
19 expected_attributes=LinearRegressor.atts,
20 **options)
21 if not isinstance(self.coefficients, numpy.ndarray):
22 raise TypeError( # pragma: no cover
23 "coefficient must be an array not {}.".format(
24 type(self.coefficients)))
25 n = self.coefficients.shape[0] // self.targets
26 self.coefficients = self.coefficients.reshape(self.targets, n).T
28 def _run(self, x): # pylint: disable=W0221
29 score = numpy_dot_inplace(self.inplaces, x, self.coefficients)
30 if self.intercepts is not None:
31 score += self.intercepts
32 if self.post_transform == b'NONE':
33 pass
34 else:
35 raise NotImplementedError( # pragma: no cover
36 "Unknown post_transform: '{}'.".format(
37 self.post_transform))
38 return (score, )