From 0f6b574fc77c5a45800e8d3afa9d281cda3c6093 Mon Sep 17 00:00:00 2001
From: Daniel Friesel <daniel.friesel@uos.de>
Date: Mon, 10 Jan 2022 10:20:30 +0100
Subject: SKLearnRegressionFunction: handle unseen params in
categorial_to_index mode
---
lib/functions.py | 15 +++++++++++++--
1 file changed, 13 insertions(+), 2 deletions(-)
diff --git a/lib/functions.py b/lib/functions.py
index 4a6dac2..af83146 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -444,10 +444,21 @@ class SKLearnRegressionFunction(ModelFunction):
for i, param in enumerate(param_list):
if not self.ignore_index[i]:
if i in self.categorial_to_index:
- actual_param_list.append(self.categorial_to_index[i][param])
+ try:
+ actual_param_list.append(self.categorial_to_index[i][param])
+ except KeyError:
+ # param was not part of training data. substitute an unused scalar.
+ # Note that all param values which were not part of training data map to the same scalar this way.
+ # This should be harmless.
+ actual_param_list.append(
+ max(self.categorial_to_index[i][param]) + 1
+ )
else:
actual_param_list.append(param)
- return self.regressor.predict(np.array([actual_param_list]))
+ predictions = self.regressor.predict(np.array([actual_param_list]))
+ if predictions.shape == (1,):
+ return predictions[0]
+ return predictions
class AnalyticFunction(ModelFunction):
--
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