def parse(self):
"""
Parse the NIST problem file into a Fitting Problem.
:return: The fully parsed fitting problem
:rtype: fitbenchmarking.parsing.fitting_problem.FittingProblem
"""
fitting_problem = FittingProblem()
equation, data, starting_values, name = self._parse_line_by_line()
data = self._parse_data(data)
fitting_problem.data_x = data[:, 1]
fitting_problem.data_y = data[:, 0]
if len(data[0, :]) > 2:
fitting_problem.data_e = data[:, 2]
fitting_problem.name = name
# String containing a mathematical expression
fitting_problem.equation = self._parse_equation(equation)
fitting_problem.starting_values = starting_values
fitting_problem.function = \
nist_func_definition(function=fitting_problem.equation,
param_names=starting_values[0].keys())
return fitting_problem
def load_expectation(filename):
"""
Load an expected fitting problem from a json file.
:param filename: The path to the expectation file
:type filename: string
:return: A fitting problem to test against
:rtype: fitbenchmarking.parsing.FittingProblem
"""
with open(filename, 'r') as f:
expectation_dict = load(f)
expectation = FittingProblem(OPTIONS)
expectation.name = expectation_dict['name']
expectation.start_x = expectation_dict['start_x']
expectation.end_x = expectation_dict['end_x']
expectation.data_x = np.array(expectation_dict['data_x'])
expectation.data_y = np.array(expectation_dict['data_y'])
expectation.data_e = expectation_dict['data_e']
if expectation.data_e is not None:
expectation.data_e = np.array(expectation.data_e)
expectation.starting_values = expectation_dict['starting_values']
expectation.value_ranges = expectation_dict['value_ranges']
return expectation
def test_verify_problem(self):
"""
Test that verify only passes if all required values are set.
"""
fitting_problem = FittingProblem(self.options)
with self.assertRaises(exceptions.FittingProblemError):
fitting_problem.verify()
self.fail('verify() passes when no values are set.')
fitting_problem.starting_values = [OrderedDict([('p1', 1), ('p2', 2)])]
with self.assertRaises(exceptions.FittingProblemError):
fitting_problem.verify()
self.fail('verify() passes starting values are set.')
fitting_problem.data_x = np.array([1, 2, 3, 4, 5])
with self.assertRaises(exceptions.FittingProblemError):
fitting_problem.verify()
self.fail('verify() passes when data_x is set.')
fitting_problem.data_y = np.array([1, 2, 3, 4, 5])
with self.assertRaises(exceptions.FittingProblemError):
fitting_problem.verify()
self.fail('verify() passes when data_y is set.')
fitting_problem.function = lambda x, p1, p2: p1 + p2
try:
fitting_problem.verify()
except exceptions.FittingProblemError:
self.fail('verify() fails when all required values set.')
fitting_problem.data_x = [1, 2, 3]
with self.assertRaises(exceptions.FittingProblemError):
fitting_problem.verify()
self.fail('verify() passes for x values not numpy.')
def test_eval_r(self):
"""
Test that eval_r is correct
"""
fitting_problem = FittingProblem()
self.assertRaises(exceptions.FittingProblemError,
fitting_problem.eval_r,
params=[1, 2, 3],
x=2)
fitting_problem.function = lambda x, p1: x + p1
x_val = np.array([1, 8, 11])
y_val = np.array([6, 10, 20])
eval_result = fitting_problem.eval_r(x=x_val, y=y_val, params=[5])
self.assertTrue(all(eval_result == np.array([0, -3, 4])))
e_val = np.array([2, 4, 1])
eval_result = fitting_problem.eval_r(x=x_val,
y=y_val,
e=e_val,
params=[5])
self.assertTrue(all(eval_result == np.array([0, -0.75, 4])))
fitting_problem.data_x = np.array([20, 21, 22])
fitting_problem.data_y = np.array([20, 30, 35])
eval_result = fitting_problem.eval_r(params=[5])
self.assertTrue(all(eval_result == np.array([-5, 4, 8])))
fitting_problem.data_e = np.array([2, 5, 10])
eval_result = fitting_problem.eval_r(params=[5])
self.assertTrue(all(eval_result == np.array([-2.5, 0.8, 0.8])))
def test_verify_problem(self):
"""
Test that verify only passes if all required values are set.
"""
fitting_problem = FittingProblem()
with self.assertRaises(TypeError):
fitting_problem.verify()
self.fail('verify() passes when no values are set.')
fitting_problem.starting_values = [['p1', [1]], ['p2', [2]]]
with self.assertRaises(TypeError):
fitting_problem.verify()
self.fail('verify() passes starting values are set.')
fitting_problem.data_x = np.array([1, 2, 3, 4, 5])
with self.assertRaises(TypeError):
fitting_problem.verify()
self.fail('verify() passes when data_x is set.')
fitting_problem.data_y = np.array([1, 2, 3, 4, 5])
with self.assertRaises(TypeError):
fitting_problem.verify()
self.fail('verify() passes when data_y is set.')
fitting_problem.functions = [[lambda x, p1, p2: p1 + p2, [1, 2]]]
try:
fitting_problem.verify()
except TypeError:
self.fail('verify() fails when all required values set.')
fitting_problem.data_x = [1, 2, 3]
with self.assertRaises(TypeError):
fitting_problem.verify()
self.fail('verify() passes for x values not numpy.')
def setup_nist_expected_problem(self):
prob = FittingProblem()
prob.name = 'Misra1a'
prob.equation = 'b1*(1-exp(-b2*x))'
prob.starting_values = [['b1', [500.0, 250.0]],
['b2', [0.0001, 0.0005]]]
data_pattern = self.setup_misra1a_expected_data_points()
prob.data_x = data_pattern[:, 1]
prob.data_y = data_pattern[:, 0]
prob.ref_residual_sum_sq = 1.2455138894e-01
return prob
def test_eval_j(self):
"""
Test that eval_j is correct
"""
def f(x, p1, p2):
return p1 * np.exp(p2 * x)
def J(x, p):
return np.column_stack(
(-np.exp(p[1] * x), -x * p[0] * np.exp(p[1] * x)))
fitting_problem = FittingProblem()
fitting_problem.function = f
fitting_problem.data_x = np.array([1, 2, 3, 4, 5])
fitting_problem.data_y = np.array([1, 2, 4, 8, 16])
params = [6, 0.1]
eval_result = fitting_problem.eval_j(params=params)
actual = J(x=fitting_problem.data_x, p=params)
self.assertTrue(np.isclose(actual, eval_result).all())
def test_eval_r_norm(self):
"""
Test that eval_r_norm is correct
"""
fitting_problem = FittingProblem()
fitting_problem.function = lambda x, p1: x + p1
x_val = np.array([1, 8, 11])
y_val = np.array([6, 10, 20])
e_val = np.array([0.5, 10, 0.1])
eval_result = fitting_problem.eval_r_norm(params=[5],
x=x_val,
y=y_val,
e=e_val)
self.assertEqual(eval_result, 1600.09)
fitting_problem.data_x = x_val
fitting_problem.data_y = y_val
eval_result = fitting_problem.eval_r_norm(params=[5])
self.assertEqual(eval_result, 25)
def parse(self):
"""
Parse the Fitbenchmark problem file into a Fitting Problem.
:return: The fully parsed fitting problem
:rtype: fitbenchmarking.parsing.fitting_problem.FittingProblem
"""
fitting_problem = FittingProblem()
self._entries = self._get_fitbenchmark_data_problem_entries()
self._parsed_func = self._parse_function()
fitting_problem.name = self._entries['name']
data_points = self._get_data_points()
fitting_problem.data_x = data_points[:, 0]
fitting_problem.data_y = data_points[:, 1]
if data_points.shape[1] > 2:
fitting_problem.data_e = data_points[:, 2]
# String containing the function name(s) and the starting parameter
# values for each function
self._mantid_equation = self._entries['function']
fitting_problem.functions = self._fitbenchmark_func_definitions()
# Print number of equations until better way of doing this is looked at
equation_count = len(self._parsed_func)
fitting_problem.equation = '{} Functions'.format(equation_count)
fitting_problem.starting_values = self._get_starting_values()
# start and end values in x range
if 'fit_parameters' in self._entries:
start_x, end_x = self._get_x_range()
fitting_problem.start_x = start_x
fitting_problem.end_x = end_x
return fitting_problem
def test_correct_data_single_fit(self):
"""
Tests that correct data gives the expected result
"""
fitting_problem = FittingProblem(self.options)
x_data = np.array([-0.5, 0.0, 1.0, 0.5, 1.5, 2.0, 2.5, 3.0, 4.0])
y_data = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])
e_data = np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 9.0])
start_x = 0.5
end_x = 2.5
expected_x_data = np.array([0.5, 1.0, 1.5, 2.0, 2.5])
expected_y_data = np.array([3.0, 2.0, 4.0, 5.0, 6.0])
expected_e_data = np.array([40.0, 30.0, 50.0, 60.0, 70.0])
fitting_problem.data_x = x_data
fitting_problem.data_y = y_data
fitting_problem.data_e = e_data
fitting_problem.start_x = start_x
fitting_problem.end_x = end_x
fitting_problem.correct_data()
self.assertTrue(
np.isclose(fitting_problem.data_x[fitting_problem.sorted_index],
expected_x_data).all())
self.assertTrue(
np.isclose(fitting_problem.data_y[fitting_problem.sorted_index],
expected_y_data).all())
self.assertTrue(
np.isclose(fitting_problem.data_e[fitting_problem.sorted_index],
expected_e_data).all())
self.options.cost_func_type = "nlls"
fitting_problem.correct_data()
self.assertTrue(
np.isclose(fitting_problem.data_x[fitting_problem.sorted_index],
expected_x_data).all())
self.assertTrue(
np.isclose(fitting_problem.data_y[fitting_problem.sorted_index],
expected_y_data).all())
self.assertIs(fitting_problem.data_e, None)
def parse(self):
"""
Parse the NIST problem file into a Fitting Problem.
:return: The fully parsed fitting problem
:rtype: fitbenchmarking.parsing.fitting_problem.FittingProblem
"""
fitting_problem = FittingProblem(self.options)
equation, data, starting_values, name = self._parse_line_by_line()
data = self._parse_data(data)
fitting_problem.data_x = data[:, 1]
fitting_problem.data_y = data[:, 0]
if len(data[0, :]) > 2:
fitting_problem.data_e = data[:, 2]
fitting_problem.name = name
# String containing a mathematical expression
fitting_problem.equation = self._parse_equation(equation)
fitting_problem.starting_values = starting_values
fitting_problem.function = \
nist_func_definition(function=fitting_problem.equation,
param_names=starting_values[0].keys())
fitting_problem.format = "nist"
try:
jacobian = self._parse_jacobian(name)
fitting_problem.jacobian = \
nist_jacobian_definition(jacobian=jacobian,
param_names=starting_values[0].keys())
except NoJacobianError:
LOGGER.warn("WARNING: Could not find analytic Jacobian "
"information for {} problem".format(name))
return fitting_problem
def parse(self):
fitting_problem = FittingProblem()
equation, data, starting_values, name = self._parse_line_by_line()
data = self._parse_data(data)
fitting_problem.data_x = data[:, 1]
fitting_problem.data_y = data[:, 0]
if len(data[0, :]) > 2:
fitting_problem.data_e = data[:, 2]
fitting_problem.name = name
# String containing a mathematical expression
fitting_problem.equation = self._parse_equation(equation)
fitting_problem.starting_values = starting_values
fitting_problem.functions = \
nist_func_definitions(function=fitting_problem.equation,
startvals=fitting_problem.starting_values)
return fitting_problem
def test_correct_data_multi_fit(self):
"""
Tests correct data on a multifit problem.
"""
fitting_problem = FittingProblem(self.options)
fitting_problem.multifit = True
x_data = [
np.array([-0.5, 0.0, 1.0, 0.5, 1.5, 2.0, 2.5, 3.0, 4.0]),
np.array([-0.5, 0.0, 1.0, 0.5, 1.4, 2.0, 2.5, 3.0, 4.0]),
np.array([-0.5, 0.0, 1.0, 0.5, 1.7, 2.0, 2.5, 3.0, 4.0])
]
y_data = [
np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]),
np.array([0.0, 1.0, 2.0, 3.0, 24.0, 5.0, 6.0, 7.0, 8.0]),
np.array([0.0, 1.0, 2.8, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])
]
e_data = [
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0]),
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0]),
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0])
]
start_x = [0.5, 1.1, 0.0]
end_x = [2.5, 2.6, 1.0]
expected_x_data = [
np.array([0.5, 1.0, 1.5, 2.0, 2.5]),
np.array([1.4, 2.0, 2.5]),
np.array([0.0, 0.5, 1.0])
]
expected_y_data = [
np.array([3.0, 2.0, 4.0, 5.0, 6.0]),
np.array([24.0, 5.0, 6.0]),
np.array([1.0, 3.0, 2.8])
]
expected_e_data = [
np.array([40.0, 30.0, 50.0, 60.0, 1.0]),
np.array([50.0, 60.0, 1.0]),
np.array([20.0, 40.0, 30.0])
]
fitting_problem.data_x = x_data
fitting_problem.data_y = y_data
fitting_problem.data_e = e_data
fitting_problem.start_x = start_x
fitting_problem.end_x = end_x
fitting_problem.correct_data()
for i in range(3):
self.assertTrue(
np.isclose(
fitting_problem.data_x[i][fitting_problem.sorted_index[i]],
expected_x_data[i]).all())
self.assertTrue(
np.isclose(
fitting_problem.data_y[i][fitting_problem.sorted_index[i]],
expected_y_data[i]).all())
self.assertTrue(
np.isclose(
fitting_problem.data_e[i][fitting_problem.sorted_index[i]],
expected_e_data[i]).all())
self.options.cost_func_type = "nlls"
fitting_problem.correct_data()
for i in range(3):
self.assertTrue(
np.isclose(
fitting_problem.data_x[i][fitting_problem.sorted_index[i]],
expected_x_data[i]).all())
self.assertTrue(
np.isclose(
fitting_problem.data_y[i][fitting_problem.sorted_index[i]],
expected_y_data[i]).all())
self.assertIs(fitting_problem.data_e[i], None)
def parse(self):
"""
Parse the Fitbenchmark problem file into a Fitting Problem.
:return: The fully parsed fitting problem
:rtype: fitbenchmarking.parsing.fitting_problem.FittingProblem
"""
# pylint: disable=attribute-defined-outside-init
fitting_problem = FittingProblem(self.options)
self._entries = self._get_data_problem_entries()
software = self._entries['software'].lower()
if not (software in import_success and import_success[software][0]):
error = import_success[software][1]
raise MissingSoftwareError('Requirements are missing for {} parser'
': {}'.format(software, error))
self._parsed_func = self._parse_function()
if software == 'mantid' and self._entries['input_file'][0] == '[':
fitting_problem.multifit = True
# NAME
fitting_problem.name = self._entries['name']
# DATA
data_points = [_get_data_points(p) for p in self._get_data_file()]
# FUNCTION
if software == 'mantid':
fitting_problem.function = self._create_mantid_function()
fitting_problem.format = 'mantid'
elif software == 'sasview':
fitting_problem.function = self._create_sasview_function()
fitting_problem.format = 'sasview'
# EQUATION
equation_count = len(self._parsed_func)
if equation_count == 1:
fitting_problem.equation = self._parsed_func[0]['name']
else:
fitting_problem.equation = '{} Functions'.format(equation_count)
# STARTING VALUES
fitting_problem.starting_values = self._get_starting_values()
# PARAMETER RANGES
vr = _parse_range(self._entries.get('parameter_ranges', ''))
fitting_problem.value_ranges = vr if vr != {} else None
# FIT RANGES
fit_ranges_str = self._entries.get('fit_ranges', '')
# this creates a list of strs like '{key: val, ...}' and parses each
fit_ranges = [
_parse_range('{' + r.split('}')[0] + '}')
for r in fit_ranges_str.split('{')[1:]
]
if fitting_problem.multifit:
num_files = len(data_points)
fitting_problem.data_x = [d[:, 0] for d in data_points]
fitting_problem.data_y = [d[:, 1] for d in data_points]
fitting_problem.data_e = [
d[:, 2] if d.shape[1] > 2 else None for d in data_points
]
if not fit_ranges:
fit_ranges = [{} for _ in range(num_files)]
fitting_problem.start_x = [
f['x'][0] if 'x' in f else None for f in fit_ranges
]
fitting_problem.end_x = [
f['x'][1] if 'x' in f else None for f in fit_ranges
]
else:
fitting_problem.data_x = data_points[0][:, 0]
fitting_problem.data_y = data_points[0][:, 1]
if data_points[0].shape[1] > 2:
fitting_problem.data_e = data_points[0][:, 2]
if fit_ranges and 'x' in fit_ranges[0]:
fitting_problem.start_x = fit_ranges[0]['x'][0]
fitting_problem.end_x = fit_ranges[0]['x'][1]
if software == 'mantid':
# String containing the function name(s) and the starting parameter
# values for each function.
fitting_problem.additional_info['mantid_equation'] \
= self._entries['function']
if fitting_problem.multifit:
fitting_problem.additional_info['mantid_ties'] \
= self._parse_ties()
return fitting_problem
def parse(self):
"""
Parse the Fitbenchmark problem file into a Fitting Problem.
:return: The fully parsed fitting problem
:rtype: fitbenchmarking.parsing.fitting_problem.FittingProblem
"""
fitting_problem = FittingProblem()
self._entries = self._get_data_problem_entries()
software = self._entries['software'].lower()
if not (software in import_success and import_success[software][0]):
e = import_success[software][1]
raise MissingSoftwareError('Requirements are missing for {} parser'
': {}'.format(software, e))
self._parsed_func = self._parse_function()
# NAME
fitting_problem.name = self._entries['name']
# DATA
data_points = self._get_data_points()
fitting_problem.data_x = data_points[:, 0]
fitting_problem.data_y = data_points[:, 1]
if data_points.shape[1] > 2:
fitting_problem.data_e = data_points[:, 2]
# FUNCTION
if software == 'mantid':
fitting_problem.function = self._create_mantid_function()
elif software == 'sasview':
fitting_problem.function = self._create_sasview_function()
# EQUATION
equation_count = len(self._parsed_func)
if equation_count == 1:
fitting_problem.equation = self._parsed_func[0]['name']
else:
fitting_problem.equation = '{} Functions'.format(equation_count)
if software == 'mantid':
# String containing the function name(s) and the starting parameter
# values for each function
fitting_problem._mantid_equation = self._entries['function']
# STARTING VALUES
fitting_problem.starting_values = self._get_starting_values()
# PARAMETER RANGES
vr = self._parse_range('parameter_ranges')
fitting_problem.value_ranges = vr if vr != {} else None
# FIT RANGES
fit_ranges = self._parse_range('fit_ranges')
try:
fitting_problem.start_x = fit_ranges['x'][0]
fitting_problem.end_x = fit_ranges['x'][1]
except KeyError:
pass
return fitting_problem
