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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/fitbenchmarking/controllers/horace_controller.py

"""
Implements a controller to the lm implementation in Herbert/Horace
"""

import matlab
import numpy as np

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


class HoraceController(MatlabMixin, Controller):
    """
    Controller for fit in Herbert
    """

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

    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._fit_params = None

    def setup(self):
        """
        Setup for Matlab fitting
        """
        # Initialize Horace in the Matlab engine
        self.eng.evalc('horace_on')

        # Convert initial params into matlab array
        self.eng.workspace['x_mat'] = matlab.double(
            self.data_x.tolist())
        self.eng.workspace['y_mat'] = matlab.double(
            np.zeros(self.data_y.shape).tolist())
        self.eng.workspace['e_mat'] = matlab.double(
            np.ones(self.data_y.shape).tolist())
        self.eng.evalc("W = struct('x', x_mat, 'y', y_mat, 'e', e_mat)")
        self.eng.workspace['initial_params'] = matlab.double(
            [self.initial_params])

        # 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 = @(x, p) double(eval_f(p))')

        # Setup multifit data structures
        self.eng.evalc('kk = multifit(W)')
        self.eng.evalc('kk = kk.set_fun(f_wrapper)')
        self.eng.evalc('kk = kk.set_pin(initial_params)')

    def fit(self):
        """
        Run problem with Horace
        """
        self.eng.evalc('[fitted_data, fit_params] = kk.fit')
        self._fit_params = self.eng.workspace['fit_params']

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

        self.final_params = self._fit_params['p'][0]

1	"""
2	Implements a controller to the lm implementation in Herbert/Horace
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 HoraceController(MatlabMixin, Controller):	!
13	"""
14	Controller for fit in Herbert
15	"""
16
17	algorithm_check = {	!
18	'all': ['lm-lsqr'],
19	'ls': ['lm-lsqr'],
20	'deriv_free': ['lm-lsqr'],
21	'general': [],
22	'simplex': [],
23	'trust_region': [],
24	'levenberg-marquardt': ['lm-lsqr'],
25	'gauss_newton': [],
26	'bfgs': [],
27	'conjugate_gradient': [],
28	'steepest_descent': [],
29	'global_optimization': []}
30
31	incompatible_problems = ['mantid']	!
32
33	def __init__(self, cost_func):	!
34	"""
35	Initialises variables used for temporary storage.
36
37	:param cost_func: Cost function object selected from options.
38	:type cost_func: subclass of
39	:class:`~fitbenchmarking.cost_func.base_cost_func.CostFunc`
40	"""
41	super().__init__(cost_func)	!
42	self._fit_params = None	!
43
44	def setup(self):	!
45	"""
46	Setup for Matlab fitting
47	"""
48	# Initialize Horace in the Matlab engine
49	self.eng.evalc('horace_on')	!
50
51	# Convert initial params into matlab array
52	self.eng.workspace['x_mat'] = matlab.double(	!
53	self.data_x.tolist())
54	self.eng.workspace['y_mat'] = matlab.double(	!
55	np.zeros(self.data_y.shape).tolist())
56	self.eng.workspace['e_mat'] = matlab.double(	!
57	np.ones(self.data_y.shape).tolist())
58	self.eng.evalc("W = struct('x', x_mat, 'y', y_mat, 'e', e_mat)")	!
59	self.eng.workspace['initial_params'] = matlab.double(	!
60	[self.initial_params])
61
62	# serialize cost_func.eval_r and open within matlab engine
63	# so that matlab fitting function can be called
NEW 64	self.eng.workspace['eval_f'] = self.py_to_mat('eval_r')	!
65	self.eng.evalc('f_wrapper = @(x, p) double(eval_f(p))')	!
66
67	# Setup multifit data structures
68	self.eng.evalc('kk = multifit(W)')	!
69	self.eng.evalc('kk = kk.set_fun(f_wrapper)')	!
70	self.eng.evalc('kk = kk.set_pin(initial_params)')	!
71
72	def fit(self):	!
73	"""
74	Run problem with Horace
75	"""
76	self.eng.evalc('[fitted_data, fit_params] = kk.fit')	!
77	self._fit_params = self.eng.workspace['fit_params']	!
78
79	def cleanup(self):	!
80	"""
81	Convert the result to a numpy array and populate the variables results
82	will be read from.
83	"""
84	if int(self._fit_params['converged']) == 0:	!
85	self.flag = 2	!
86	else:
87	self.flag = 0	!
88
89	self.final_params = self._fit_params['p'][0]	!

fitbenchmarking / fitbenchmarking / 2330948078

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