From 2d9aa47712698e380cb2bbb7abe0092c61871c15 Mon Sep 17 00:00:00 2001
From: faridm69 <faridmohammadi69@gmail.com>
Date: Wed, 23 Sep 2020 18:26:22 +0200
Subject: [PATCH] [surrogate][GPE] modified the kernels.
---
.../surrogate_models/surrogate_models.py | 16 ++++++++--------
1 file changed, 8 insertions(+), 8 deletions(-)
diff --git a/BayesValidRox/surrogate_models/surrogate_models.py b/BayesValidRox/surrogate_models/surrogate_models.py
index 1748f3090..636688190 100644
--- a/BayesValidRox/surrogate_models/surrogate_models.py
+++ b/BayesValidRox/surrogate_models/surrogate_models.py
@@ -866,10 +866,10 @@ class Metamodel:
def PCATransformation(self, Output):
# Transform via Principal Component Analysis
- from sklearn.decomposition import PCA as sklearnPCA
+ from sklearn.decomposition import IncrementalPCA as sklearnPCA
# from sklearn.decomposition import TruncatedSVD as TruncatedSVD
n_samples, n_features = Output.shape
- covar_matrix = sklearnPCA(n_components=None, svd_solver='full')
+ covar_matrix = sklearnPCA(n_components=None)#, svd_solver='full')
covar_matrix.fit(Output)
var = np.cumsum(np.round(covar_matrix.explained_variance_ratio_, decimals=5)*100)
try:
@@ -879,7 +879,7 @@ class Metamodel:
nComponents = min(n_samples, n_features, selected_n_components)
- pca = sklearnPCA(n_components=nComponents, svd_solver='full')
+ pca = sklearnPCA(n_components=nComponents)#, svd_solver='full')
OutputMatrix = pca.fit_transform(Output)
return pca, OutputMatrix
@@ -893,10 +893,10 @@ class Metamodel:
ConstantKernel)
- kernels = [1.0 * RBF(length_scale=1.0, length_scale_bounds=(1e-5, 1e5)),
- 1.0 * RationalQuadratic(length_scale=0.2, alpha=1.0),
+ kernels = [np.var(y) * RBF(length_scale=1.0, length_scale_bounds=(1e-5, 1e5)),
+ np.var(y) * RationalQuadratic(length_scale=0.2, alpha=1.0),
# 1.0 * ExpSineSquared(length_scale=1.0, length_scale_bounds=(1e-05, 1e05)),
- 1.0 * Matern(length_scale=1.0, length_scale_bounds=(1e-5, 1e5),
+ np.var(y) * Matern(length_scale=1.0, length_scale_bounds=(1e-5, 1e5),
nu=1.5)]
if autoSelect:# Automatic selection of the kernel
@@ -914,7 +914,7 @@ class Metamodel:
gp = gp[np.argmax(BME)]
else:
- gp = GaussianProcessRegressor(kernel=kernels[0], n_restarts_optimizer=3,
+ gp = GaussianProcessRegressor(kernel=kernels[-1], n_restarts_optimizer=3,
normalize_y=True)
gp.fit(X, y)
@@ -2186,7 +2186,7 @@ class Metamodel:
# TODO: Plot PCE vs Components
# PCA = self.pca[Outkey]
- # self.plot_components(PCA, Y, PCEOutputs_mean, PCEOutputs_std, plotname='PCA_PCE_'+Outkey)
+ # self.plot_components(PCA, Y[Outkey], PCEOutputs_mean, PCEOutputs_std, plotname='PCA_PCE_'+Outkey)
if self.index is None:
if self.DimRedMethod.lower() == 'pca':
--
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