diff --git a/examples/principal_component_analysis/example_principalcomponentanalysis.py b/examples/principal_component_analysis/example_principalcomponentanalysis.py
index 53f9c304ebe2b2e02af0e928c583c3c72a9495e2..ff9cb628f6b5af3f570b4b10b979ceabba4ae4c8 100644
--- a/examples/principal_component_analysis/example_principalcomponentanalysis.py
+++ b/examples/principal_component_analysis/example_principalcomponentanalysis.py
@@ -109,7 +109,7 @@ if __name__ == "__main__":
# Select if you want to preserve the spatial/temporal depencencies
MetaModelOpts.dim_red_method = 'PCA'
MetaModelOpts.var_pca_threshold = 99.999
- MetaModelOpts.n_pca_components = 10#5#10
+ MetaModelOpts.n_pca_components = 9#10#5#10
#MetaModelOpts.n_bootstrap_itrs = 2
# Select your metamodel method
@@ -144,7 +144,7 @@ if __name__ == "__main__":
# Create the engine
engine = Engine(MetaModelOpts, Model, ExpDesign)
- engine.train_normal()
+ engine.train_normal(verbose = True)
print('Surrogate has been trained')
# =====================================================
diff --git a/src/bayesvalidrox/surrogate_models/surrogate_models.py b/src/bayesvalidrox/surrogate_models/surrogate_models.py
index adac29fcdf60c6a23184fbc324da18283ee7133e..629bdd7a5caab1eda13894d900b991d1b2fe0ede 100644
--- a/src/bayesvalidrox/surrogate_models/surrogate_models.py
+++ b/src/bayesvalidrox/surrogate_models/surrogate_models.py
@@ -504,6 +504,10 @@ class MetaModel:
None.
"""
+ # Use settings
+ self.verbose = verbose
+ self.parallel = parallel
+
# Check size of inputs
X = np.array(X)
for key in y.keys():
@@ -581,8 +585,7 @@ class MetaModel:
# Start transformation
pca, target, n_comp = self.pca_transformation(
- Output[b_indices], verbose=False
- )
+ Output[b_indices])
self.pca[f'b_{b_i + 1}'][key] = pca
# Store the number of components for fast bootsrtrapping
if fast_bootstrap and b_i == 0:
@@ -635,13 +638,13 @@ class MetaModel:
if fast_bootstrap and b_i == 0:
first_out[key] = copy.deepcopy(out)
+ # Update the coefficients with OLS during bootstrap-iters
if b_i > 0 and fast_bootstrap:
- # fast bootstrap
out = self.update_pce_coeffs(
X_train_b, target, first_out[key])
+ # Create a dict to pass the variables
for i in range(target.shape[1]):
- # Create a dict to pass the variables
self.deg_dict[f'b_{b_i + 1}'][key][f"y_{i + 1}"] = out[i]['degree']
self.q_norm_dict[f'b_{b_i + 1}'][key][f"y_{i + 1}"] = out[i]['qnorm']
self.coeffs_dict[f'b_{b_i + 1}'][key][f"y_{i + 1}"] = out[i]['coeffs']
@@ -666,7 +669,7 @@ class MetaModel:
Parameters
----------
X : array of shape (n_samples, n_params)
- Training set.
+ Training set. These samples should be already transformed.
y : array of shape (n_samples, n_outs)
The (transformed) model responses.
out_dict : dict
@@ -679,9 +682,15 @@ class MetaModel:
The updated training output dictionary.
"""
- # TODO: why is X not used here?
+ # TODO: why is X not used here? -> Uses self.univ_p_val instead
+ # This should be changed to either check if the univ_p_val are accurate
+ # or to always construct them from the X-values
# Make a copy
final_out_dict = copy.deepcopy(out_dict)
+
+ # Create the univ_p_val
+ univ_p_val = self.univ_p_val
+# univ_p_val = self.univ_basis_vals(X, n_max=np.max(self.pce_deg))
# Loop over the points
for i in range(y.shape[1]):
@@ -692,7 +701,7 @@ class MetaModel:
basis_indices = out_dict[i]['multi_indices']
# Evaluate the multivariate polynomials on CollocationPoints
- psi = create_psi(basis_indices, self.univ_p_val)
+ psi = create_psi(basis_indices, univ_p_val)#self.univ_p_val)
# Calulate the cofficients of surrogate model
updated_out = self.regression(
@@ -752,8 +761,6 @@ class MetaModel:
return univ_basis
- # -------------------------------------------------------------------------
-
# -------------------------------------------------------------------------
def regression(self, X, y, basis_indices, reg_method=None, sparsity=True):
"""
@@ -1091,7 +1098,7 @@ class MetaModel:
return returnVars
# -------------------------------------------------------------------------
- def pca_transformation(self, target, verbose=False):
+ def pca_transformation(self, target):
"""
Transforms the targets (outputs) via Principal Component Analysis.
The number of features is set by `self.n_pca_components`.
@@ -1101,9 +1108,6 @@ class MetaModel:
----------
target : array of shape (n_samples,)
Target values.
- verbose : bool
- Set to True to get more information during functtion call.
- The default is False.
Returns
-------
@@ -1124,8 +1128,9 @@ class MetaModel:
# Switch to var_pca if n_pca_components is too large
if n_pca_components >= n_features:
n_pca_components = None
- print('')
- print('WARNING: Too many components are set for PCA. The transformation will proceed based on explainable variance.')
+ if self.verbose:
+ print('')
+ print('WARNING: Too many components are set for PCA. The transformation will proceed based on explainable variance.')
# Calculate n_pca_components based on decomposition of the variance
if n_pca_components is None:
@@ -1148,13 +1153,13 @@ class MetaModel:
n_pca_components = min(n_samples, n_features, n_components)
# Print out a report
- if verbose:
- print()
- print('-' * 50)
- print(f"PCA transformation is performed with {n_pca_components}"
- " components.")
- print('-' * 50)
- print()
+ #if self.verbose:
+ # print()
+ # print('-' * 50)
+ # print(f"PCA transformation is performed with {n_pca_components}"
+ # " components.")
+ # print('-' * 50)
+ # print()
# Fit and transform with the selected number of components
pca = sklearnPCA(n_components=n_pca_components, svd_solver='arpack')
@@ -1189,7 +1194,6 @@ class MetaModel:
method='user'
)
# Compute univariate bases for the given samples
- #print('Creating the univariate basis.')
univ_p_val = None
if self.meta_model_type.lower() != 'gpe':
univ_p_val = self.univ_basis_vals(
@@ -1203,7 +1207,6 @@ class MetaModel:
std_pred_b = {}
b_i = 0
# Loop over bootstrap iterations
- #print('Looping over bootstrap iterations.')
for b_i in range(self.n_bootstrap_itrs):
# Extract model dictionary
@@ -1215,8 +1218,6 @@ class MetaModel:
# Loop over outputs
mean_pred = {}
std_pred = {}
- #print(model_dict.items())
- #print('Looping over the output timesteps')
for output, values in model_dict.items():
mean = np.empty((len(samples), len(values)))
@@ -1285,14 +1286,13 @@ class MetaModel:
finite_rows = np.isfinite(
mean_pred_all[output]).all(axis=2).all(axis=1)
outs = np.asarray(mean_pred_all[output])[finite_rows]
- # Compute mean
+
+ # Compute mean and stdev
mean_pred[output] = np.mean(outs, axis=0)
- # Compute standard deviation
if self.n_bootstrap_itrs > 1:
std_pred[output] = np.std(outs, axis=0)
else:
- # TODO: this b_i seems off here
- std_pred[output] = std_pred_b[b_i][output]
+ std_pred[output] = std_pred_b[self.n_bootstrap_itrs-1][output]
return mean_pred, std_pred