def test_approximate_gaussian_process(self):
from sklearn.gaussian_process.kernels import Matern
num_vars = 1
univariate_variables = [stats.uniform(-1, 2)] * num_vars
variable = pya.IndependentMultivariateRandomVariable(
univariate_variables)
num_samples = 100
train_samples = pya.generate_independent_random_samples(
variable, num_samples)
# Generate random function
nu = np.inf # 2.5
kernel = Matern(0.5, nu=nu)
X = np.linspace(-1, 1, 1000)[np.newaxis, :]
alpha = np.random.normal(0, 1, X.shape[1])
train_vals = kernel(train_samples.T, X.T).dot(alpha)[:, np.newaxis]
gp = approximate(train_samples, train_vals, 'gaussian_process', {
'nu': nu,
'noise_level': 1e-8
}).approx
error = np.linalg.norm(gp(X)[:, 0] -
kernel(X.T, X.T).dot(alpha)) / np.sqrt(
X.shape[1])
assert error < 1e-5
def test_approximate_polynomial_chaos_custom_poly_type(self):
benchmark = setup_benchmark('ishigami', a=7, b=0.1)
nvars = benchmark.variable.num_vars()
# for this test purposefully select wrong variable to make sure
# poly_type overide is activated
univariate_variables = [stats.beta(5, 5, -np.pi, 2 * np.pi)] * nvars
variable = IndependentMultivariateRandomVariable(univariate_variables)
var_trans = AffineRandomVariableTransformation(variable)
# specify correct basis so it is not chosen from variable
poly_opts = {'poly_type': 'legendre', 'var_trans': var_trans}
options = {
'poly_opts': poly_opts,
'variable': variable,
'options': {
'max_num_step_increases': 1
}
}
ntrain_samples = 400
train_samples = np.random.uniform(-np.pi, np.pi,
(nvars, ntrain_samples))
train_vals = benchmark.fun(train_samples)
approx = approximate(train_samples,
train_vals,
method='polynomial_chaos',
options=options).approx
nsamples = 100
error = compute_l2_error(approx,
benchmark.fun,
approx.var_trans.variable,
nsamples,
rel=True)
print(error)
assert error < 1e-4
assert np.allclose(approx.mean(), benchmark.mean, atol=error)
def test_approximate_sparse_grid_user_options(self):
nvars = 3
benchmark = setup_benchmark('ishigami', a=7, b=0.1)
univariate_variables = [stats.uniform(0, 1)] * nvars
errors = []
def callback(approx):
nsamples = 1000
error = compute_l2_error(approx, benchmark.fun,
approx.variable_transformation.variable,
nsamples)
errors.append(error)
univariate_quad_rule_info = [
pya.clenshaw_curtis_in_polynomial_order,
pya.clenshaw_curtis_rule_growth
]
options = {
'univariate_quad_rule_info': univariate_quad_rule_info,
'max_nsamples': 110,
'tol': 0,
'verbose': False
}
approx = approximate(benchmark.fun, univariate_variables,
'sparse-grid', callback, options)
assert np.min(errors) < 1e-12
def help_cross_validate_pce_degree(self, solver_type, solver_options):
print(solver_type, solver_options)
num_vars = 2
univariate_variables = [stats.uniform(-1, 2)] * num_vars
variable = pya.IndependentMultivariateRandomVariable(
univariate_variables)
var_trans = pya.AffineRandomVariableTransformation(variable)
poly = pya.PolynomialChaosExpansion()
poly_opts = pya.define_poly_options_from_variable_transformation(
var_trans)
poly.configure(poly_opts)
degree = 3
poly.set_indices(pya.compute_hyperbolic_indices(num_vars, degree, 1.0))
# factor of 2 does not pass test but 2.2 does
num_samples = int(poly.num_terms() * 2.2)
coef = np.random.normal(0, 1, (poly.indices.shape[1], 2))
coef[pya.nchoosek(num_vars + 2, 2):, 0] = 0
# for first qoi make degree 2 the best degree
poly.set_coefficients(coef)
train_samples = pya.generate_independent_random_samples(
variable, num_samples)
train_vals = poly(train_samples)
true_poly = poly
poly = approximate(
train_samples, train_vals, 'polynomial_chaos', {
'basis_type': 'hyperbolic_cross',
'variable': variable,
'options': {
'verbose': 3,
'solver_type': solver_type,
'min_degree': 1,
'max_degree': degree + 1,
'linear_solver_options': solver_options
}
}).approx
num_validation_samples = 10
validation_samples = pya.generate_independent_random_samples(
variable, num_validation_samples)
assert np.allclose(poly(validation_samples),
true_poly(validation_samples))
poly = copy.deepcopy(true_poly)
approx_res = cross_validate_pce_degree(
poly,
train_samples,
train_vals,
1,
degree + 1,
solver_type=solver_type,
linear_solver_options=solver_options)
assert np.allclose(approx_res.degrees, [2, 3])
def test_approximate_sparse_grid_default_options(self):
nvars = 3
benchmark = setup_benchmark('ishigami', a=7, b=0.1)
univariate_variables = [stats.uniform(0, 1)] * nvars
approx = approximate(benchmark.fun, univariate_variables,
'sparse-grid')
nsamples = 100
error = compute_l2_error(approx, benchmark.fun,
approx.variable_transformation.variable,
nsamples)
assert error < 1e-12
def test_pce_basis_expansion(self):
num_vars = 2
univariate_variables = [stats.uniform(-1, 2)] * num_vars
variable = pya.IndependentMultivariateRandomVariable(
univariate_variables)
var_trans = pya.AffineRandomVariableTransformation(variable)
poly = pya.PolynomialChaosExpansion()
poly_opts = pya.define_poly_options_from_variable_transformation(
var_trans)
poly.configure(poly_opts)
degree, hcross_strength = 7, 0.4
poly.set_indices(
pya.compute_hyperbolic_indices(num_vars, degree, hcross_strength))
num_samples = poly.num_terms() * 2
degrees = poly.indices.sum(axis=0)
coef = np.random.normal(
0, 1, (poly.indices.shape[1], 2)) / (degrees[:, np.newaxis] + 1)**2
# set some coefficients to zero to make sure that different qoi
# are treated correctly.
I = np.random.permutation(coef.shape[0])[:coef.shape[0] // 2]
coef[I, 0] = 0
I = np.random.permutation(coef.shape[0])[:coef.shape[0] // 2]
coef[I, 1] = 0
poly.set_coefficients(coef)
train_samples = pya.generate_independent_random_samples(
variable, num_samples)
train_vals = poly(train_samples)
true_poly = poly
poly = approximate(
train_samples, train_vals, 'polynomial_chaos', {
'basis_type': 'expanding_basis',
'variable': variable,
'options': {
'max_num_expansion_steps_iter': 1,
'verbose': 3,
'max_num_terms': 1000,
'max_num_step_increases': 2,
'max_num_init_terms': 33
}
}).approx
num_validation_samples = 100
validation_samples = pya.generate_independent_random_samples(
variable, num_validation_samples)
validation_samples = train_samples
error = np.linalg.norm(
poly(validation_samples) -
true_poly(validation_samples)) / np.sqrt(num_validation_samples)
assert np.allclose(poly(validation_samples),
true_poly(validation_samples),
atol=1e-8), error
def test_approximate_polynomial_chaos_default_options(self):
nvars = 3
benchmark = setup_benchmark('ishigami', a=7, b=0.1)
univariate_variables = [stats.uniform(0, 1)] * nvars
approx = approximate(benchmark.fun,
univariate_variables,
method='polynomial-chaos')
nsamples = 100
error = compute_l2_error(approx, benchmark.fun,
approx.variable_transformation.variable,
nsamples)
assert error < 1e-12
def test_pce_basis_expansion(self):
num_vars = 2
univariate_variables = [stats.uniform(-1, 2)] * num_vars
variable = pya.IndependentMultivariateRandomVariable(
univariate_variables)
var_trans = pya.AffineRandomVariableTransformation(variable)
poly = pya.PolynomialChaosExpansion()
poly_opts = pya.define_poly_options_from_variable_transformation(
var_trans)
poly.configure(poly_opts)
degree, hcross_strength = 7, 0.4
poly.set_indices(
pya.compute_hyperbolic_indices(num_vars, degree, hcross_strength))
num_samples = poly.num_terms() * 2
degrees = poly.indices.sum(axis=0)
poly.set_coefficients((np.random.normal(0, 1, poly.indices.shape[1]) /
(degrees + 1)**2)[:, np.newaxis])
train_samples = pya.generate_independent_random_samples(
variable, num_samples)
train_vals = poly(train_samples)
true_poly = poly
poly = approximate(train_samples, train_vals, 'polynomial_chaos', {
'basis_type': 'expanding_basis',
'variable': variable
})
num_validation_samples = 100
validation_samples = pya.generate_independent_random_samples(
variable, num_validation_samples)
validation_samples = train_samples
error = np.linalg.norm(
poly(validation_samples) -
true_poly(validation_samples)) / np.sqrt(num_validation_samples)
assert np.allclose(
poly(validation_samples),true_poly(validation_samples),atol=1e-8),\
error
def test_cross_validate_pce_degree(self):
num_vars = 2
univariate_variables = [stats.uniform(-1, 2)] * num_vars
variable = pya.IndependentMultivariateRandomVariable(
univariate_variables)
var_trans = pya.AffineRandomVariableTransformation(variable)
poly = pya.PolynomialChaosExpansion()
poly_opts = pya.define_poly_options_from_variable_transformation(
var_trans)
poly.configure(poly_opts)
degree = 3
poly.set_indices(pya.compute_hyperbolic_indices(num_vars, degree, 1.0))
num_samples = poly.num_terms() * 2
poly.set_coefficients(
np.random.normal(0, 1, (poly.indices.shape[1], 1)))
train_samples = pya.generate_independent_random_samples(
variable, num_samples)
train_vals = poly(train_samples)
true_poly = poly
poly = approximate(train_samples, train_vals, 'polynomial_chaos', {
'basis_type': 'hyperbolic_cross',
'variable': variable
})
num_validation_samples = 10
validation_samples = pya.generate_independent_random_samples(
variable, num_validation_samples)
assert np.allclose(poly(validation_samples),
true_poly(validation_samples))
poly = copy.deepcopy(true_poly)
poly, best_degree = cross_validate_pce_degree(poly, train_samples,
train_vals, 1,
degree + 2)
assert best_degree == degree
def test_cross_validate_approximation_after_regularization_selection(self):
"""
This test is useful as it shows how to use cross_validate_approximation
to produce a list of approximations on each cross validation fold
once regularization parameters have been chosen.
These can be used to show variance in predictions of values, sensitivity
indices, etc.
Ideally this could be avoided if sklearn stored the coefficients
and alphas for each fold and then we can just find the coefficients
that correspond to the first time the path drops below the best_alpha
"""
num_vars = 2
univariate_variables = [stats.uniform(-1, 2)] * num_vars
variable = pya.IndependentMultivariateRandomVariable(
univariate_variables)
var_trans = pya.AffineRandomVariableTransformation(variable)
poly = pya.PolynomialChaosExpansion()
poly_opts = pya.define_poly_options_from_variable_transformation(
var_trans)
poly.configure(poly_opts)
degree, hcross_strength = 7, 0.4
poly.set_indices(
pya.compute_hyperbolic_indices(num_vars, degree, hcross_strength))
num_samples = poly.num_terms() * 2
degrees = poly.indices.sum(axis=0)
coef = np.random.normal(
0, 1, (poly.indices.shape[1], 2)) / (degrees[:, np.newaxis] + 1)**2
# set some coefficients to zero to make sure that different qoi
# are treated correctly.
I = np.random.permutation(coef.shape[0])[:coef.shape[0] // 2]
coef[I, 0] = 0
I = np.random.permutation(coef.shape[0])[:coef.shape[0] // 2]
coef[I, 1] = 0
poly.set_coefficients(coef)
train_samples = pya.generate_independent_random_samples(
variable, num_samples)
train_vals = poly(train_samples)
true_poly = poly
result = approximate(train_samples, train_vals, 'polynomial_chaos', {
'basis_type': 'expanding_basis',
'variable': variable
})
# Even with the same folds, iterative methods such as Lars, LarsLasso
# and OMP will not have cv_score from approximate and cross validate
# approximation exactly the same because iterative methods interpolate
# residuals to compute cross validation scores
nfolds = 10
linear_solver_options = [{
'alpha': result.reg_params[0]
}, {
'alpha': result.reg_params[1]
}]
indices = [
result.approx.indices[:, np.where(np.absolute(c) > 0)[0]]
for c in result.approx.coefficients.T
]
options = {
'basis_type': 'fixed',
'variable': variable,
'options': {
'linear_solver_options': linear_solver_options,
'indices': indices
}
}
approx_list, residues_list, cv_score = cross_validate_approximation(
train_samples,
train_vals,
options,
nfolds,
'polynomial_chaos',
random_folds='sklearn')
assert (np.all(cv_score < 1e-14) and np.all(result.scores < 1e-14))
