Coverage for mlprodict/onnxrt/ops_cpu/op_linear_regressor.py: 100%

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 

10 

11 

12class LinearRegressor(OpRunUnaryNum): 

13 

14 atts = {'coefficients': None, 'intercepts': None, 

15 'targets': 1, 'post_transform': b'NONE'} 

16 

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 f"coefficient must be an array not {type(self.coefficients)}.") 

24 n = self.coefficients.shape[0] // self.targets 

25 self.coefficients = self.coefficients.reshape(self.targets, n).T 

26 

27 def _run(self, x, attributes=None, verbose=0, fLOG=None): # pylint: disable=W0221 

28 score = numpy_dot_inplace(self.inplaces, x, self.coefficients) 

29 if self.intercepts is not None: 

30 score += self.intercepts 

31 if self.post_transform == b'NONE': 

32 pass 

33 else: 

34 raise NotImplementedError( # pragma: no cover 

35 f"Unknown post_transform: '{self.post_transform}'.") 

36 return (score, )