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author | Birte Kristina Friesel <birte.friesel@uos.de> | 2024-01-19 08:30:35 +0100 |
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committer | Birte Kristina Friesel <birte.friesel@uos.de> | 2024-01-19 08:30:35 +0100 |
commit | d475b21ac86d0684e059a36641ecaa1f0621e680 (patch) | |
tree | 13c04143523ed3a5451143b264c401f544f86b2c | |
parent | e844c4e325e497ceea5e7ac64d12d77ac5026b95 (diff) |
parameters: nits
-rw-r--r-- | lib/parameters.py | 10 |
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, |