From 75f1a6b10e4d98ab6b3ed661fd01e9d63bc2a3fe Mon Sep 17 00:00:00 2001
From: Farid Mohammadi <farid.mohammadi@iws.uni-stuttgart.de>
Date: Wed, 3 Aug 2022 10:53:43 +0200
Subject: [PATCH] [surrogate] handle the case with all zero outputs.
---
.../surrogate_models/surrogate_models.py | 17 +++--------------
1 file changed, 3 insertions(+), 14 deletions(-)
diff --git a/src/bayesvalidrox/surrogate_models/surrogate_models.py b/src/bayesvalidrox/surrogate_models/surrogate_models.py
index 722221d74..5194a95b4 100644
--- a/src/bayesvalidrox/surrogate_models/surrogate_models.py
+++ b/src/bayesvalidrox/surrogate_models/surrogate_models.py
@@ -701,22 +701,11 @@ class MetaModel():
# The first column must be kept (For mean calculations)
nnz_idx = np.nonzero(clf_poly.coef_)[0]
- # if len(nnz_idx) == 0 or nnz_idx[0] != 0:
- # nnz_idx = np.insert(np.nonzero(clf_poly.coef_)[0], 0, 0)
- # # Remove the zero entries for Bases and PSI if need be
- # if sparsity:
- # sparse_basis_indices = basis_indices.toarray()[nnz_idx]
- # else:
- # sparse_basis_indices = basis_indices[nnz_idx]
- # sparse_X = X[:, nnz_idx]
-
- # # Store the coefficients of the regression model
- # clf_poly.fit(sparse_X, y)
- # coeffs = clf_poly.coef_
-
# This is for the case where all outputs are zero, thereby
# all coefficients are zero
- nnz_idx = np.nonzero(clf_poly.coef_)[0]
+ if len(nnz_idx) == 0 or nnz_idx[0] != 0:
+ nnz_idx = np.insert(np.nonzero(clf_poly.coef_)[0], 0, 0)
+
if sparsity:
sparse_basis_indices = basis_indices.toarray()[nnz_idx]
else:
--
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