diff --git a/src/bayesvalidrox/surrogate_models/engine.py b/src/bayesvalidrox/surrogate_models/engine.py
index 0c4f15263973aa2329e601960740015e1c6f630e..a6d2447c6cae42413261361d2560feb678b928f7 100644
--- a/src/bayesvalidrox/surrogate_models/engine.py
+++ b/src/bayesvalidrox/surrogate_models/engine.py
@@ -439,7 +439,7 @@ class Engine:
if itr_no > 1:
pc_model = prev_meta_model_dict[itr_no - 1]
self.SeqDes._y_hat_prev, _ = pc_model.eval_metamodel(
- samples=x_full[-1].reshape(1, -1))
+ x_full[-1].reshape(1, -1))
del prev_meta_model_dict[itr_no - 1]
if itr_no == 1 and self.ExpDesign.tradeoff_scheme == 'adaptive':
# TODO: this was added just as a fix, needs to be reworked
@@ -448,7 +448,7 @@ class Engine:
# print(x_prev.shape)
pc_model = prev_meta_model_dict[itr_no]
self.SeqDes._y_hat_prev, _ = pc_model.eval_metamodel(
- samples=x_prev)
+ x_prev)
# Optimal Bayesian Design
# self.MetaModel.ExpDesignFlag = 'sequential'
@@ -474,7 +474,7 @@ class Engine:
if self.ExpDesign.adapt_verbose:
from .adaptPlot import adaptPlot
y_hat, std_hat = self.MetaModel.eval_metamodel(
- samples=x_new
+ x_new
)
adaptPlot(
self.MetaModel, y_new, y_hat, std_hat,
@@ -701,7 +701,7 @@ class Engine:
output_names = self.out_names
# TODO: Evaluate MetaModel on the experimental design and ValidSet
- OutputRS, _ = metamod.eval_metamodel(samples=samples)
+ OutputRS, _ = metamod.eval_metamodel(samples)
logLik_data = np.zeros(n_samples)
logLik_model = np.zeros(n_samples)
@@ -861,7 +861,7 @@ class Engine:
)
# Monte Carlo simulation for the candidate design
- Y_MC, std_MC = self.MetaModel.eval_metamodel(samples=X_MC)
+ Y_MC, std_MC = self.MetaModel.eval_metamodel(X_MC)
# Likelihood computation (Comparison of data and
# simulation results via PCE with candidate design)
@@ -1000,7 +1000,7 @@ class Engine:
# Run the PCE model with the generated samples
valid_PCE_runs, _ = self.MetaModel.eval_metamodel(
- samples=self.ExpDesign.valid_samples)
+ self.ExpDesign.valid_samples)
rms_error = {}
valid_error = {}
diff --git a/src/bayesvalidrox/surrogate_models/sequential_design.py b/src/bayesvalidrox/surrogate_models/sequential_design.py
index 2fb70febc0c1fc88bc73f7d12022d7a41f7d5b5f..a8039dd6194ae8e1d0398ec3255feea4cb83b71a 100644
--- a/src/bayesvalidrox/surrogate_models/sequential_design.py
+++ b/src/bayesvalidrox/surrogate_models/sequential_design.py
@@ -548,7 +548,7 @@ class SequentialDesign:
# New adaptive trade-off according to Liu et al. (2017)
# Mean squared error for last design point
last_EDX = old_EDX[-1].reshape(1, -1)
- lastPCEY, _ = self.MetaModel.eval_metamodel(samples=last_EDX)
+ lastPCEY, _ = self.MetaModel.eval_metamodel(last_EDX)
pce_y = np.array(list(lastPCEY.values()))[:, 0]
y = np.array(list(old_EDY.values()))[:, -1, :]
mseError = mean_squared_error(pce_y, y)
@@ -607,7 +607,7 @@ class SequentialDesign:
NCandidate = candidates.shape[0]
U_J_d = np.zeros(NCandidate)
# Evaluate all candidates
- y_can, std_can = self.MetaModel.eval_metamodel(samples=candidates)
+ y_can, std_can = self.MetaModel.eval_metamodel(candidates)
# loop through candidates
for idx, X_can in tqdm(enumerate(candidates), ascii=True,
desc="BAL Design"):
@@ -659,7 +659,7 @@ class SequentialDesign:
# Run the Metamodel for the candidate
X_can = X_can.reshape(1, -1)
- Y_PC_can, std_PC_can = MetaModel.eval_metamodel(samples=X_can)
+ Y_PC_can, std_PC_can = MetaModel.eval_metamodel(X_can)
score = None
if util_func.lower() == 'alm':
@@ -876,14 +876,14 @@ class SequentialDesign:
# Compute the mean and std based on the MetaModel
# pce_means, pce_stds = self._compute_pce_moments(MetaModel)
if var.lower() == 'alc':
- Y_MC, Y_MC_std = MetaModel.eval_metamodel(samples=X_MC)
+ Y_MC, Y_MC_std = MetaModel.eval_metamodel(X_MC)
# Old Experimental design
oldExpDesignX = self.ExpDesign.X
oldExpDesignY = self.ExpDesign.Y
# Evaluate the PCE metamodels at that location ???
- Y_PC_can, Y_std_can = MetaModel.eval_metamodel(samples=X_can)
+ Y_PC_can, Y_std_can = MetaModel.eval_metamodel(X_can)
PCE_Model_can = deepcopy(MetaModel)
# TODO: this is really not clean, create a workaround for this issue!
engine_can = deepcopy(self.engine)
@@ -916,7 +916,7 @@ class SequentialDesign:
if var.lower() == 'mi':
# Mutual information based on Krause et al.
# Adapted from Beck & Guillas (MICE) paper
- _, std_PC_can = engine_can.MetaModel.eval_metamodel(samples=X_can)
+ _, std_PC_can = engine_can.MetaModel.eval_metamodel(X_can)
std_can = {key: std_PC_can[key] for key in out_names}
std_old = {key: Y_std_can[key] for key in out_names}
@@ -934,8 +934,7 @@ class SequentialDesign:
# metrics, 51 (2009), pp. 130–145.
# Evaluate the MetaModel at the given samples
- _, Y_MC_std_can = engine_can.MetaModel.eval_metamodel(
- samples=X_MC)
+ _, Y_MC_std_can = engine_can.MetaModel.eval_metamodel(X_MC)
# Compute the score
score = []
@@ -962,7 +961,7 @@ class SequentialDesign:
)
# Evaluate the MetaModel at the given samples
- Y_MC, std_MC = PCE_Model_can.eval_metamodel(samples=X_MC)
+ Y_MC, std_MC = PCE_Model_can.eval_metamodel(X_MC)
# Likelihood computation (Comparison of data and simulation
# results via PCE with candidate design)
@@ -1299,7 +1298,7 @@ class SequentialDesign:
output_names = self.out_names
# TODO: Evaluate MetaModel on the experimental design and ValidSet
- OutputRS, _ = MetaModel.eval_metamodel(samples=samples)
+ OutputRS, _ = MetaModel.eval_metamodel(samples)
logLik_data = np.zeros(n_samples)
logLik_model = np.zeros(n_samples)
@@ -1396,7 +1395,7 @@ class SequentialDesign:
)
# Monte Carlo simulation for the candidate design
- Y_MC, std_MC = self.MetaModel.eval_metamodel(samples=X_MC)
+ Y_MC, std_MC = self.MetaModel.eval_metamodel(X_MC)
# Likelihood computation (Comparison of data and
# simulation results via PCE with candidate design)
@@ -1534,8 +1533,7 @@ class SequentialDesign:
valid_model_runs = self.ExpDesign.valid_model_runs
# Run the PCE model with the generated samples
- valid_PCE_runs, _ = self.MetaModel.eval_metamodel(
- samples=self.ExpDesign.valid_samples)
+ valid_PCE_runs, _ = self.MetaModel.eval_metamodel(self.ExpDesign.valid_samples)
rms_error = {}
valid_error = {}