def test_akmcs_expected_feasibility():
from UQpy.surrogates.kriging.regression_models.LinearRegression import LinearRegression
from UQpy.surrogates.kriging.correlation_models.ExponentialCorrelation import ExponentialCorrelation
marginals = [Normal(loc=0., scale=4.), Normal(loc=0., scale=4.)]
x = MonteCarloSampling(distributions=marginals,
nsamples=20,
random_state=1)
model = PythonModel(model_script='series.py', model_object_name="series")
rmodel = RunModel(model=model)
regression_model = LinearRegression()
correlation_model = ExponentialCorrelation()
K = Kriging(
regression_model=regression_model,
correlation_model=correlation_model,
optimizations_number=10,
correlation_model_parameters=[1, 1],
optimizer=MinimizeOptimizer('l-bfgs-b'),
)
# OPTIONS: 'U', 'EFF', 'Weighted-U'
learning_function = ExpectedFeasibility(eff_a=0,
eff_epsilon=2,
eff_stop=0.001)
a = AdaptiveKriging(distributions=marginals,
runmodel_object=rmodel,
surrogate=K,
learning_nsamples=10**3,
n_add=1,
learning_function=learning_function,
random_state=2)
a.run(nsamples=25, samples=x.samples)
assert a.samples[23, 0] == 1.366058523912817
assert a.samples[20, 1] == -12.914668932772358
def test_akmcs_expected_improvement_global_fit():
from UQpy.surrogates.kriging.regression_models.LinearRegression import LinearRegression
from UQpy.surrogates.kriging.correlation_models.ExponentialCorrelation import ExponentialCorrelation
marginals = [Normal(loc=0., scale=4.), Normal(loc=0., scale=4.)]
x = MonteCarloSampling(distributions=marginals,
nsamples=20,
random_state=1)
model = PythonModel(model_script='series.py', model_object_name="series")
rmodel = RunModel(model=model)
regression_model = LinearRegression()
correlation_model = ExponentialCorrelation()
K = Kriging(
regression_model=regression_model,
correlation_model=correlation_model,
optimizations_number=10,
correlation_model_parameters=[1, 1],
optimizer=MinimizeOptimizer('l-bfgs-b'),
)
# OPTIONS: 'U', 'EFF', 'Weighted-U'
learning_function = ExpectedImprovementGlobalFit()
a = AdaptiveKriging(distributions=marginals,
runmodel_object=rmodel,
surrogate=K,
learning_nsamples=10**3,
n_add=1,
learning_function=learning_function,
random_state=2)
a.run(nsamples=25, samples=x.samples)
assert a.samples[23, 0] == 11.939859785098493
assert a.samples[20, 1] == -8.429899469300118
def test_akmcs_u():
from UQpy.surrogates.kriging.regression_models.LinearRegression import LinearRegression
from UQpy.surrogates.kriging.correlation_models.ExponentialCorrelation import ExponentialCorrelation
marginals = [Normal(loc=0., scale=4.), Normal(loc=0., scale=4.)]
x = MonteCarloSampling(distributions=marginals,
nsamples=20,
random_state=1)
model = PythonModel(model_script='series.py', model_object_name="series")
rmodel = RunModel(model=model)
regression_model = LinearRegression()
correlation_model = ExponentialCorrelation()
K = Kriging(regression_model=regression_model,
correlation_model=correlation_model,
optimizer=MinimizeOptimizer('l-bfgs-b'),
optimizations_number=10,
correlation_model_parameters=[1, 1],
random_state=0)
# OPTIONS: 'U', 'EFF', 'Weighted-U'
learning_function = UFunction(u_stop=2)
a = AdaptiveKriging(distributions=marginals,
runmodel_object=rmodel,
surrogate=K,
learning_nsamples=10**3,
n_add=1,
learning_function=learning_function,
random_state=2)
a.run(nsamples=25, samples=x.samples)
assert a.samples[23, 0] == -4.141979058326188
assert a.samples[20, 1] == -1.6476534435429009
def test_akmcs_samples_error():
from UQpy.surrogates.kriging.regression_models.LinearRegression import LinearRegression
from UQpy.surrogates.kriging.correlation_models.ExponentialCorrelation import ExponentialCorrelation
marginals = [Normal(loc=0., scale=4.), Normal(loc=0., scale=4.)]
x = MonteCarloSampling(distributions=marginals,
nsamples=20,
random_state=0)
model = PythonModel(model_script='series.py', model_object_name="series")
rmodel = RunModel(model=model)
regression_model = LinearRegression()
correlation_model = ExponentialCorrelation()
K = Kriging(regression_model=regression_model,
correlation_model=correlation_model,
optimizer=MinimizeOptimizer('l-bfgs-b'),
optimizations_number=10,
correlation_model_parameters=[1, 1],
random_state=1)
# OPTIONS: 'U', 'EFF', 'Weighted-U'
learning_function = WeightedUFunction(weighted_u_stop=2)
with pytest.raises(NotImplementedError):
a = AdaptiveKriging(distributions=[Normal(loc=0., scale=4.)] * 3,
runmodel_object=rmodel,
surrogate=K,
learning_nsamples=10**3,
n_add=1,
learning_function=learning_function,
random_state=2,
samples=x.samples)
def test_dist_object():
"""Validate dist_object, when dist_object is a distribution object."""
with pytest.raises(BeartypeCallHintPepParamException):
MonteCarloSampling(distributions='abc', random_state=np.random.RandomState(123))
def test_run_random_state():
"""Check if random_state attribute is not an integer, np.random.RandomState object or None, when when 'run' method
is called."""
with pytest.raises(BeartypeCallHintPepParamException):
MonteCarloSampling(distributions=dist1).run(nsamples=5, random_state='abc')
def test_random_state2():
"""Check if random_state attribute is not an integer, np.random.RandomState object or None, when dist_object is a
list of multiple distribution class object."""
with pytest.raises(BeartypeCallHintPepParamException):
MonteCarloSampling(distributions=[dist1, dist2], random_state='abc')
def test_nsamples_not_integer():
"""Validate error check, when nsamples is not an integer, while calling 'run' method."""
with pytest.raises(BeartypeCallHintPepParamException):
MonteCarloSampling(distributions=dist1, random_state=np.random.RandomState(123)).run(nsamples='abc')
import numpy as np
import pytest
from beartype.roar import BeartypeCallHintPepParamException
from UQpy.distributions import Normal, MultivariateNormal
from UQpy.sampling import MonteCarloSampling
dist1 = Normal(loc=0., scale=1.)
dist2 = Normal(loc=0., scale=1.)
x = MonteCarloSampling(distributions=dist1, nsamples=5, random_state=np.random.RandomState(123))
x.transform_u01()
y = MonteCarloSampling(distributions=[dist1, dist2])
y.run(nsamples=5, random_state=123)
y.transform_u01()
# Call run method multiple time, to cover lines where samples are append to existing ones
z1 = MonteCarloSampling(distributions=dist1, nsamples=2, random_state=123)
z1.run(nsamples=2)
z2 = MonteCarloSampling(distributions=[dist1, dist2], nsamples=2, random_state=np.random.RandomState(123))
z2.run(nsamples=2)
# Same object as z2, just to cover lines where, random_state is an integer
z3 = MonteCarloSampling(distributions=[dist1, dist2], nsamples=2, random_state=123)
z4 = MonteCarloSampling(distributions=[MultivariateNormal([0, 0])], nsamples=2,
random_state=np.random.RandomState(123))
z4.run(nsamples=2)
z4.transform_u01()
dist3 = Normal(loc=0., scale=1.)
del dist3.rvs
z5 = MonteCarloSampling(distributions=[dist3], random_state=np.random.RandomState(123))
from UQpy.sampling import MonteCarloSampling, AdaptiveKriging
from UQpy.run_model.RunModel import RunModel
from UQpy.distributions import Uniform
from local_BraninHoo import function
import time
from UQpy.utilities.MinimizeOptimizer import MinimizeOptimizer
# %% md
#
# Using UQpy :class:`MonteCarloSampling` class to generate samples for two random variables, which are uniformly
# distributed
# %%
marginals = [Uniform(loc=-5, scale=15), Uniform(loc=0, scale=15)]
x = MonteCarloSampling(distributions=marginals, nsamples=20)
# %% md
#
# :class:`.RunModel` class is used to define an object to evaluate the model at sample points.
# %%
model = PythonModel(model_script='local_BraninHoo.py',
model_object_name='function')
rmodel = RunModel(model=model)
# %% md
#
# :class:`.Kriging` class defines an object to generate a surrogate model for a given set of data.
# %%
normal1 = normal2 = Normal()
# %% md
#
# Next, we'll construct a :code:`MonteCarloSampling` object :code:`mc` to generate random samples following those
# distributions. Here, we specify an optional initial number of samples, :code:`nsamples` to be generated at the
# object's construction. For teh purposes of this demonstration, we also supply a random seed :code:`random_state`.
#
# We access the generated samples via the :code:`samples` attribute.
# %%
mc = MonteCarloSampling(distributions=[normal1, normal2],
nsamples=5,
random_state=RandomState(123))
mc.samples
# %% md
#
# To generate more samples on :code:`mc` after construction, we call :code:`mc.run` and once again specify
# :code:`nsamples`.
# %%
mc.run(nsamples=2, random_state=RandomState(23))
mc.samples
import shutil
from beartype.roar import BeartypeCallHintPepParamException
from UQpy.run_model.model_execution.PythonModel import PythonModel
from UQpy.run_model import ThirdPartyModel, RunModel
from UQpy.sampling import MonteCarloSampling
from UQpy.run_model.RunModel import RunModel
from UQpy.distributions import Normal
import pytest
import os
import numpy as np
d = Normal(loc=0, scale=1)
x_mcs = MonteCarloSampling(distributions=[d, d, d],
nsamples=5,
random_state=1234)
x_mcs_new = MonteCarloSampling(distributions=[d, d, d],
nsamples=5,
random_state=2345)
verbose_parameter = True
# def test_div_zero():
# with pytest.raises(TypeError):
# model = PythonModel(model_script='python_model.py', model_object_name='SumRVs', fmt=20,
# delete_files=True)
# runmodel_object = RunModel_New(model=model)
#
#
# def test_fmt_1():
# with pytest.raises(TypeError):
# Define the distribution objects.
# %%
dist1 = Uniform(location=15000, scale=10000)
dist2 = Uniform(location=450000, scale=80000)
dist3 = Uniform(location=2.0e8, scale=0.5e8)
# %% md
#
# Draw the samples using MCS.
# %%
x = MonteCarloSampling(distributions=[dist1, dist2, dist3] * 6,
samples_number=5,
random_state=938475)
samples = np.array(x.samples).round(2)
# %% md
#
# Run the model.
# %%
names_ = [
'fc1', 'fy1', 'Es1', 'fc2', 'fy2', 'Es2', 'fc3', 'fy3', 'Es3', 'fc4',
'fy4', 'Es4', 'fc5', 'fy5', 'Es5', 'fc6', 'fy6', 'Es6'
]
opensees_rc6_model = RunModel(samples=samples,
# %%
m = PythonModel(model_script='local_eigenvalue_model.py',
model_object_name="RunPythonModel")
model = RunModel(model=m)
# model = RunModel(model_script='local_eigenvalue_model.py')
model.run(samples=y.samples)
r_srom = model.qoi_list
# %% md
#
# :class:`MonteCarloSampling` class is used to generate 1000 samples.
# %%
x_mcs = MonteCarloSampling(distributions=marginals, nsamples=1000)
# %% md
#
# Run the model 'eigenvalue_model.py' for each sample generated through :class:`.MonteCarloSampling` class.
# %%
model.run(samples=x_mcs.samples, append_samples=False)
r_mcs = model.qoi_list
# %% md
#
# Plot the distribution of each eigenvalue, estimated using :class:`.SROM` and :class:`.MonteCarloSampling` weights.
# %%
def test_dist_object3():
"""Create a MCS object using DistributionND class object. Validate dist_object, when dist_object is not a
list of distribution object."""
with pytest.raises(BeartypeCallHintPepParamException):
MonteCarloSampling(distributions=['abc'], random_state=np.random.RandomState(123))
d2 = Uniform(location=0.02, scale=0.01)
d3 = Uniform(location=0.02, scale=0.01)
d4 = Uniform(location=0.0025, scale=0.0075)
d5 = Uniform(location=0.02, scale=0.06)
d6 = Uniform(location=0.02, scale=0.01)
d7 = Uniform(location=0.02, scale=0.01)
d8 = Uniform(location=0.0025, scale=0.0075)
# %% md
#
# Draw the samples using MCS.
# %%
x = MonteCarloSampling(distributions=[d1, d2, d3, d4, d5, d6, d7, d8],
samples_number=12,
random_state=349875)
# %% md
#
# Run the model.
# %%
run_ = RunModel(samples=x.samples,
ntasks=6,
model_script='dyna_script.py',
input_template='dyna_input.k',
var_names=['x0', 'y0', 'z0', 'R0', 'x1', 'y1', 'z1', 'R1'],
model_dir='dyna_test',
cluster=True,
from UQpy.run_model.RunModel import RunModel from UQpy.distributions import Normal from local_series import series import matplotlib.pyplot as plt import time from UQpy.utilities.MinimizeOptimizer import MinimizeOptimizer # %% md # # Using UQpy :class:`.MonteCarloSampling` class to generate samples for two random variables, which are normally # distributed with mean :math:`0` and variance :math:`1`. # %% marginals = [Normal(loc=0., scale=4.), Normal(loc=0., scale=4.)] x = MonteCarloSampling(distributions=marginals, nsamples=20, random_state=1) # %% md # # RunModel class is used to define an object to evaluate the model at sample points. # %% model = PythonModel(model_script='local_series.py', model_object_name='series') rmodel = RunModel(model=model) # %% md # # :class:`.Kriging` class defines an object to generate a surrogate model for a given set of data. # %%
# %% md
#
# Example 1: Three scalar random variables
# ----------------------------------------------------
# In this example, we pass three scalar random variables. Note that this is different from assigning a single variable
# with three components, which will be handled in the following example.
#
# Here we will pass the samples both as an ndarray and as a list. Recall that UQpy converts all samples into an ndarray
# of at least two dimensions internally.
# %%
if pick_model in {'scalar', 'vector', 'all'}:
d = Normal(loc=0, scale=1)
x_mcs = MonteCarloSampling(distributions=[d, d, d],
nsamples=5,
random_state=987979)
names = ['var1', 'var11', 'var111']
# UQpy returns samples as an ndarray. Convert them to a list for part 1.2
x_mcs_list = list(x_mcs.samples)
print(
"Monte Carlo samples of three random variables from a standard normal distribution."
)
print('Samples stored as an array:')
print('Data type:', type(x_mcs.samples))
print('Number of samples:', len(x_mcs.samples))
print('Dimensions of samples:', np.shape(x_mcs.samples))
print('Samples')
print(x_mcs.samples)
print()
# %% md
#
# Towards defining the sampling scheme
# The fire load density is assumed to be uniformly distributed between 50 :math:`MJ/m^2` and 450 :math:`MJ/m^2`.
# The yield strength is assumed to be normally distributed, with the parameters
# being: mean = 250 :math:`MPa` and coefficient of variation of :math:`7%`.
#
# Creating samples using MCS.
# %%
d_n = Normal(loc=50, scale=400)
d_u = Uniform(location=2.50e8, scale=1.75e7)
x_mcs = MonteCarloSampling(distributions=[d_n, d_u],
samples_number=100,
random_state=987979)
# %% md
#
# Running simulations using the previously defined model object and samples
# %%
sample_points = x_mcs.samples
abaqus_sfe_model.run(samples=sample_points)
# %% md
#
# The outputs from the analysis are the values of the performance function.
plt.ylabel(r'Stiffness ($k$)')
plt.grid(True)
plt.tight_layout()
plt.show()
m = PythonModel(model_script='local_Resonance_pfn.py',
model_object_name="RunPythonModel")
model = RunModel(model=m)
# %% md
#
# Monte Carlo Simulation
# %%
x_mcs = MonteCarloSampling(distributions=[d1, d2])
x_mcs.run(nsamples=1000000)
model.run(samples=x_mcs.samples)
A = np.asarray(model.qoi_list) < 0
pf = np.shape(np.asarray(
model.qoi_list)[np.asarray(model.qoi_list) < 0])[0] / 1000000
print(pf)
ntrials = 1
pf_stretch = np.zeros((ntrials, 1))
cov1_stretch = np.zeros((ntrials, 1))
cov2_stretch = np.zeros((ntrials, 1))
m = np.ones(2)
m[0] = 5
