def analyze(problem, X, Y, second_order=False, print_to_console=False):
"""Perform a fractional factorial analysis
Returns a dictionary with keys 'ME' (main effect) and 'IE' (interaction
effect). The techniques bulks out the number of parameters with dummy
parameters to the nearest 2**n. Any results involving dummy parameters
could indicate a problem with the model runs.
Arguments
---------
problem: dict
The problem definition
X: numpy.matrix
The NumPy matrix containing the model inputs
Y: numpy.array
The NumPy array containing the model outputs
second_order: bool, default=False
Include interaction effects
print_to_console: bool, default=False
Print results directly to console
Returns
-------
Si: dict
A dictionary of sensitivity indices, including main effects ``ME``,
and interaction effects ``IE`` (if ``second_order`` is True)
Examples
--------
>>> X = sample(problem)
>>> Y = X[:, 0] + (0.1 * X[:, 1]) + ((1.2 * X[:, 2]) * (0.2 + X[:, 0]))
>>> analyze(problem, X, Y, second_order=True, print_to_console=True)
"""
problem = extend_bounds(problem)
num_vars = problem['num_vars']
X = generate_contrast(problem)
main_effect = (1. / (2 * num_vars)) * np.dot(Y, X)
Si = dict((k, [None] * num_vars)
for k in ['names', 'ME'])
Si['ME'] = main_effect
Si['names'] = problem['names']
if print_to_console:
print("Parameter ME")
for j in range(num_vars):
print("%s %f" % (problem['names'][j], Si['ME'][j]))
if second_order == True:
interaction_names, interaction_effects = interactions(problem,
Y,
print_to_console)
Si['names'].append(interaction_names)
Si['IE'] = interaction_effects
return Si
def analyze(problem, X, Y, second_order=False, print_to_console=False):
problem = extend_bounds(problem)
num_vars = problem['num_vars']
X = generate_contrast(problem)
main_effect = (1. / (2 * num_vars)) * np.dot(Y, X)
Si = dict((k, [None] * num_vars)
for k in ['names', 'ME'])
Si['ME'] = main_effect
Si['names'] = problem['names']
if print_to_console:
print("Parameter ME")
for j in range(num_vars):
print("%s %f" % (problem['names'][j], Si['ME'][j]))
if second_order == True:
interaction_names, interaction_effects = interactions(problem,
Y,
print_to_console)
Si['names'].append(interaction_names)
Si['IE'] = interaction_effects
return Si
def test_extend_bounds():
problem = {
'bounds': np.repeat([-1, 1], 12).reshape(2, 12).T,
'num_vars': 12,
'names': ["x" + str(x + 1) for x in range(12)]
}
actual = extend_bounds(problem)
expected = {
'names': [
'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11',
'x12', 'dummy_0', 'dummy_1', 'dummy_2', 'dummy_3'
],
'bounds': [
np.array([-1, 1]),
np.array([-1, 1]),
np.array([-1, 1]),
np.array([-1, 1]),
np.array([-1, 1]),
np.array([-1, 1]),
np.array([-1, 1]),
np.array([-1, 1]),
np.array([-1, 1]),
np.array([-1, 1]),
np.array([-1, 1]),
np.array([-1, 1]),
np.array([0, 1]),
np.array([0, 1]),
np.array([0, 1]),
np.array([0, 1])
],
'num_vars':
16
}
assert_equal(actual, expected)
def analyze(problem, X, Y, second_order=False, print_to_console=False):
"""Perform a fractional factorial analysis
Returns a dictionary with keys 'ME' (main effect) and 'IE' (interaction
effect). The techniques bulks out the number of parameters with dummy
parameters to the nearest 2**n. Any results involving dummy parameters
could indicate a problem with the model runs.
Arguments
---------
problem: dict
The problem definition
X: numpy.matrix
The NumPy matrix containing the model inputs
Y: numpy.array
The NumPy array containing the model outputs
second_order: bool, default=False
Include interaction effects
print_to_console: bool, default=False
Print results directly to console
Returns
-------
Si: dict
A dictionary of sensitivity indices, including main effects ``ME``,
and interaction effects ``IE`` (if ``second_order`` is True)
Examples
--------
>>> X = sample(problem)
>>> Y = X[:, 0] + (0.1 * X[:, 1]) + ((1.2 * X[:, 2]) * (0.2 + X[:, 0]))
>>> analyze(problem, X, Y, second_order=True, print_to_console=True)
"""
problem = extend_bounds(problem)
num_vars = problem['num_vars']
X = generate_contrast(problem)
main_effect = (1. / (2 * num_vars)) * np.dot(Y, X)
Si = dict((k, [None] * num_vars)
for k in ['names', 'ME'])
Si['ME'] = main_effect
Si['names'] = problem['names']
if print_to_console:
print("Parameter ME")
for j in range(num_vars):
print("%s %f" % (problem['names'][j], Si['ME'][j]))
if second_order == True:
interaction_names, interaction_effects = interactions(problem,
Y,
print_to_console)
Si['names'].append(interaction_names)
Si['IE'] = interaction_effects
return Si
def analyze(problem,
X,
Y,
second_order=False,
print_to_console=False,
seed=None):
"""Perform a fractional factorial analysis
Returns a dictionary with keys 'ME' (main effect) and 'IE' (interaction
effect). The techniques bulks out the number of parameters with dummy
parameters to the nearest 2**n. Any results involving dummy parameters
could indicate a problem with the model runs.
Compatible with
---------------
* `ff`
Parameters
----------
problem: dict
The problem definition
X: numpy.matrix
The NumPy matrix containing the model inputs
Y: numpy.array
The NumPy array containing the model outputs
second_order: bool, default=False
Include interaction effects
print_to_console: bool, default=False
Print results directly to console
Returns
-------
Si: dict
A dictionary of sensitivity indices, including main effects ``ME``,
and interaction effects ``IE`` (if ``second_order`` is True)
References
----------
.. [1] Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D.,
Saisana, M., Tarantola, S., 2008.
Global Sensitivity Analysis: The Primer.
Wiley, West Sussex, U.K.
https://dx.doi.org/10.1002/9780470725184
Examples
--------
>>> X = sample(problem)
>>> Y = X[:, 0] + (0.1 * X[:, 1]) + ((1.2 * X[:, 2]) * (0.2 + X[:, 0]))
>>> analyze(problem, X, Y, second_order=True, print_to_console=True)
"""
if seed:
np.random.seed(seed)
problem = extend_bounds(problem)
num_vars = problem['num_vars']
X = generate_contrast(problem)
main_effect = (1. / (2 * num_vars)) * np.dot(Y, X)
Si = ResultDict((k, [None] * num_vars) for k in ['names', 'ME'])
Si['ME'] = main_effect
Si['names'] = problem['names']
if second_order:
interaction_names, interaction_effects = interactions(problem, Y)
Si['interaction_names'] = interaction_names
Si['IE'] = interaction_effects
Si.to_df = MethodType(to_df, Si)
if print_to_console:
for S in Si.to_df():
print(S)
return Si