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authorBirte Kristina Friesel <birte.friesel@uos.de>2024-01-19 08:30:35 +0100
committerBirte Kristina Friesel <birte.friesel@uos.de>2024-01-19 08:30:35 +0100
commitd475b21ac86d0684e059a36641ecaa1f0621e680 (patch)
tree13c04143523ed3a5451143b264c401f544f86b2c
parente844c4e325e497ceea5e7ac64d12d77ac5026b95 (diff)
parameters: nits
-rw-r--r--lib/parameters.py10
1 files changed, 6 insertions, 4 deletions
diff --git a/lib/parameters.py b/lib/parameters.py
index 45a577b..d0894e4 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -43,12 +43,10 @@ def distinct_param_values(param_tuples):
def _depends_on_param(corr_param, std_param, std_lut, threshold=0.5):
- # if self.use_corrcoef:
- if False:
- return corr_param > 0.1
- elif std_param == 0:
+ if std_param == 0:
# In general, std_param_lut < std_by_param. So, if std_by_param == 0, std_param_lut == 0 follows.
# This means that the variation of param does not affect the model quality -> no influence
+ # assert std_lut == 0
return False
return std_lut / std_param < threshold
@@ -1160,6 +1158,10 @@ class ModelAttribute:
relevance_threshold = float(os.getenv("DFATOOL_PARAM_RELEVANCE_THRESHOLD", 0.5))
+ logger.debug(
+ f"build_dtree(threshold={threshold}, relevance_threshold={relevance_threshold})"
+ )
+
self.model_function = self._build_dtree(
parameters,
data,