1542

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Build # 1542

Build Type

Pull #514

travis-ci

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web-flow

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Pull Request Pull Request #514: Adding option toggle for 3rd party output

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16.28

/fitbenchmarking/core/fitting_benchmarking.py

"""
Main module of the tool, this holds the master function that calls
lower level functions to fit and benchmark a set of problems
for a certain fitting software.
"""

from __future__ import absolute_import, division, print_function

from fitbenchmarking.core.fitbenchmark_one_problem import fitbm_one_prob
from fitbenchmarking.parsing.parser_factory import parse_problem_file
from fitbenchmarking.utils import misc, output_grabber
from fitbenchmarking.utils.log import get_logger

LOGGER = get_logger()


def fitbenchmark_group(group_name, options, data_dir):
    """
    Gather the user input and list of paths. Call benchmarking on these.

    :param group_name: is the name (label) for a group. E.g. the name for
                       the group of problems in "NIST/low_difficulty" may be
                       picked to be NIST_low_difficulty
    :type group_name: str
    :param options: dictionary containing software used in fitting
                    the problem, list of minimizers and location of
                    json file contain minimizers
    :type options: fitbenchmarking.utils.options.Options
    :param data_dir: full path of a directory that holds a group of problem
                     definition files
    :type date_dir: str

    :return: prob_results array of fitting results for
             the problem group and the location of the results
    :rtype: tuple(list, str)
    """
    grabbed_output = output_grabber.OutputGrabber(options)

    # Extract problem definitions
    problem_group = misc.get_problem_files(data_dir)

    results = []

    name_count = {}
    template_prob_name = " Running data from: {}"
    for i, p in enumerate(problem_group):
        with grabbed_output:
            parsed_problem = parse_problem_file(p, options)
        parsed_problem.correct_data()
        name = parsed_problem.name
        name_count[name] = 1 + name_count.get(name, 0)
        count = name_count[name]

        # Put in placeholder for the count.
        # This will be fixed in the results after all problems have ran
        parsed_problem.name = name + '<count> {}</count>'.format(count)

        tmp_prob_name = template_prob_name.format(name + ' ' + str(count))
        decorator = '#' * (3 + len(
            tmp_prob_name + str(i + 1) + str(len(problem_group))))
        print("\n{0}\n{1} {2}/{3}\n{0}\n".format(decorator, tmp_prob_name,
                                                 i + 1, len(problem_group)))
        info_str = " Running data from: {} {}/{}".format(
            parsed_problem.name, i + 1, len(problem_group))
        LOGGER.info('#' * len(info_str))
        LOGGER.info(info_str)
        LOGGER.info('#' * len(info_str))

        problem_results = fitbm_one_prob(problem=parsed_problem,
                                         options=options)
        results.extend(problem_results)

    # Used to group elements in list by name
    results_dict = {}
    # Get list of names which need an index so they are unique
    names_to_update = [name for name in name_count if name_count[name] > 1]

    for problem_result in results:
        # First fix name
        name_segs = problem_result.name.split('<count>')
        if name_segs[0] in names_to_update:
            name = name_segs[0] + name_segs[1].replace('</count>', '')
        else:
            name = name_segs[0] + name_segs[1].split('</count>')[1]
        problem_result.name = name

        # Now group by name
        try:
            results_dict[name].append(problem_result)
        except KeyError:
            results_dict[name] = [problem_result]

    results = [results_dict[r] for r in
               sorted(results_dict.keys(), key=str.lower)]

    return results

1	"""
2	Main module of the tool, this holds the master function that calls
3	lower level functions to fit and benchmark a set of problems
4	for a certain fitting software.
5	"""
6
7	from __future__ import absolute_import, division, print_function	1✔
8
9	from fitbenchmarking.core.fitbenchmark_one_problem import fitbm_one_prob	1✔
10	from fitbenchmarking.parsing.parser_factory import parse_problem_file	1✔
11	from fitbenchmarking.utils import misc, output_grabber	1✔
12	from fitbenchmarking.utils.log import get_logger	1✔
13
14	LOGGER = get_logger()	1✔
15
16
17	def fitbenchmark_group(group_name, options, data_dir):	1✔
18	"""
19	Gather the user input and list of paths. Call benchmarking on these.
20
21	:param group_name: is the name (label) for a group. E.g. the name for
22	the group of problems in "NIST/low_difficulty" may be
23	picked to be NIST_low_difficulty
24	:type group_name: str
25	:param options: dictionary containing software used in fitting
26	the problem, list of minimizers and location of
27	json file contain minimizers
28	:type options: fitbenchmarking.utils.options.Options
29	:param data_dir: full path of a directory that holds a group of problem
30	definition files
31	:type date_dir: str
32
33	:return: prob_results array of fitting results for
34	the problem group and the location of the results
35	:rtype: tuple(list, str)
36	"""
37	grabbed_output = output_grabber.OutputGrabber(options)	×
38
39	# Extract problem definitions
40	problem_group = misc.get_problem_files(data_dir)	×
41
42	results = []	×
43
44	name_count = {}	×
45	template_prob_name = " Running data from: {}"	×
46	for i, p in enumerate(problem_group):	×
47	with grabbed_output:	×
48	parsed_problem = parse_problem_file(p, options)	×
49	parsed_problem.correct_data()	×
50	name = parsed_problem.name	×
51	name_count[name] = 1 + name_count.get(name, 0)	×
52	count = name_count[name]	×
53
54	# Put in placeholder for the count.
55	# This will be fixed in the results after all problems have ran
56	parsed_problem.name = name + '<count> {}</count>'.format(count)	×
57
58	tmp_prob_name = template_prob_name.format(name + ' ' + str(count))	×
59	decorator = '#' * (3 + len(	×
60	tmp_prob_name + str(i + 1) + str(len(problem_group))))
61	print("\n{0}\n{1} {2}/{3}\n{0}\n".format(decorator, tmp_prob_name,	×
62	i + 1, len(problem_group)))
63	info_str = " Running data from: {} {}/{}".format(	×
64	parsed_problem.name, i + 1, len(problem_group))
65	LOGGER.info('#' * len(info_str))	×
66	LOGGER.info(info_str)	×
67	LOGGER.info('#' * len(info_str))	×
68
69	problem_results = fitbm_one_prob(problem=parsed_problem,	×
70	options=options)
71	results.extend(problem_results)	×
72
73	# Used to group elements in list by name
74	results_dict = {}	×
75	# Get list of names which need an index so they are unique
76	names_to_update = [name for name in name_count if name_count[name] > 1]	×
77
78	for problem_result in results:	×
79	# First fix name
80	name_segs = problem_result.name.split('<count>')	×
81	if name_segs[0] in names_to_update:	×
82	name = name_segs[0] + name_segs[1].replace('</count>', '')	×
83	else:
84	name = name_segs[0] + name_segs[1].split('</count>')[1]	×
85	problem_result.name = name	×
86
87	# Now group by name
88	try:	×
89	results_dict[name].append(problem_result)	×
90	except KeyError:	×
91	results_dict[name] = [problem_result]	×
92
93	results = [results_dict[r] for r in	×
94	sorted(results_dict.keys(), key=str.lower)]
95
96	return results	×

fitbenchmarking / fitbenchmarking / 1542

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