2330948078

Committed 16 May 2022 - 9:26 coverage decreased (-0.2%) to 86.542%

Build Type

github

Committed by GitHub

Commit Message

Merge f94846412 into ed08b4d37

Pull Request Pull Request #1036: Enable max runtime in curve fitting

Run Details

0 of 46 new or added lines in 6 files covered. (0.0%)

5 existing lines in 4 files now uncovered.

3421 of 3953 relevant lines covered (86.54%)

0.87 hits per line

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0.0

/fitbenchmarking/controllers/matlab_stats_controller.py

"""
Implements a controller for MATLAB Statistics Toolbox
"""

import matlab
import numpy as np

from fitbenchmarking.controllers.base_controller import Controller
from fitbenchmarking.controllers.matlab_mixin import MatlabMixin


class MatlabStatsController(MatlabMixin, Controller):
    """
    Controller for MATLAB Statistics Toolbox fitting (nlinfit)
    """

    algorithm_check = {
        'all': ['Levenberg-Marquardt'],
        'ls': ['Levenberg-Marquardt'],
        'deriv_free': [],
        'general': [],
        'simplex': [],
        'trust_region': ['Levenberg-Marquardt'],
        'levenberg-marquardt': ['Levenberg-Marquardt'],
        'gauss_newton': [],
        'bfgs': [],
        'conjugate_gradient': [],
        'steepest_descent': [],
        'global_optimization': []}

    controller_name = 'matlab_stats'

    incompatible_problems = ['mantid']

    def __init__(self, cost_func):
        """
        Initialises variables used for temporary storage.

        :param cost_func: Cost function object selected from options.
        :type cost_func: subclass of
                :class:`~fitbenchmarking.cost_func.base_cost_func.CostFunc`
        """
        super().__init__(cost_func)
        self.x_data_mat = None
        self.y_data_mat = None
        self._status = None
        self.result = None

    def setup(self):
        """
        Setup for Matlab fitting
        """
        # Convert initial params into matlab array
        self.y_data_mat = matlab.double(np.zeros(self.data_y.shape).tolist())
        self.initial_params_mat = matlab.double([self.initial_params])
        self.x_data_mat = matlab.double(self.data_x.tolist())

        # serialize cost_func.eval_r and open within matlab engine
        # so that matlab fitting function can be called
        self.eng.workspace['eval_f'] = self.py_to_mat('eval_r')
        self.eng.evalc('f_wrapper = @(p, x)double(eval_f(p))')

    def fit(self):
        """
        Run problem with Matlab Statistics Toolbox
        """

        self.result = self.eng.nlinfit(self.x_data_mat, self.y_data_mat,
                                       self.eng.workspace['f_wrapper'],
                                       self.initial_params_mat, nargout=1)
        self._status = 0 if self.result is not None else 1

    def cleanup(self):
        """
        Convert the result to a numpy array and populate the variables results
        will be read from.
        """
        if self._status == 0:
            self.flag = 0
        else:
            self.flag = 2

        self.final_params = self.result[0]

1	"""
2	Implements a controller for MATLAB Statistics Toolbox
3	"""
4
5	import matlab	!
6	import numpy as np	!
7
8	from fitbenchmarking.controllers.base_controller import Controller	!
9	from fitbenchmarking.controllers.matlab_mixin import MatlabMixin	!
10
11
12	class MatlabStatsController(MatlabMixin, Controller):	!
13	"""
14	Controller for MATLAB Statistics Toolbox fitting (nlinfit)
15	"""
16
17	algorithm_check = {	!
18	'all': ['Levenberg-Marquardt'],
19	'ls': ['Levenberg-Marquardt'],
20	'deriv_free': [],
21	'general': [],
22	'simplex': [],
23	'trust_region': ['Levenberg-Marquardt'],
24	'levenberg-marquardt': ['Levenberg-Marquardt'],
25	'gauss_newton': [],
26	'bfgs': [],
27	'conjugate_gradient': [],
28	'steepest_descent': [],
29	'global_optimization': []}
30
31	controller_name = 'matlab_stats'	!
32
33	incompatible_problems = ['mantid']	!
34
35	def __init__(self, cost_func):	!
36	"""
37	Initialises variables used for temporary storage.
38
39	:param cost_func: Cost function object selected from options.
40	:type cost_func: subclass of
41	:class:`~fitbenchmarking.cost_func.base_cost_func.CostFunc`
42	"""
43	super().__init__(cost_func)	!
44	self.x_data_mat = None	!
45	self.y_data_mat = None	!
46	self._status = None	!
47	self.result = None	!
48
49	def setup(self):	!
50	"""
51	Setup for Matlab fitting
52	"""
53	# Convert initial params into matlab array
54	self.y_data_mat = matlab.double(np.zeros(self.data_y.shape).tolist())	!
55	self.initial_params_mat = matlab.double([self.initial_params])	!
56	self.x_data_mat = matlab.double(self.data_x.tolist())	!
57
58	# serialize cost_func.eval_r and open within matlab engine
59	# so that matlab fitting function can be called
NEW 60	self.eng.workspace['eval_f'] = self.py_to_mat('eval_r')	!
61	self.eng.evalc('f_wrapper = @(p, x)double(eval_f(p))')	!
62
UNCOV 63	def fit(self):	!
64	"""
65	Run problem with Matlab Statistics Toolbox
66	"""
67
68	self.result = self.eng.nlinfit(self.x_data_mat, self.y_data_mat,	!
69	self.eng.workspace['f_wrapper'],
70	self.initial_params_mat, nargout=1)
71	self._status = 0 if self.result is not None else 1	!
72
73	def cleanup(self):	!
74	"""
75	Convert the result to a numpy array and populate the variables results
76	will be read from.
77	"""
78	if self._status == 0:	!
79	self.flag = 0	!
80	else:
81	self.flag = 2	!
82
83	self.final_params = self.result[0]	!

fitbenchmarking / fitbenchmarking / 2330948078

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