diff options
| -rw-r--r-- | lib/parameters.py | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/lib/parameters.py b/lib/parameters.py index ac69075..e4fe03f 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -892,7 +892,7 @@ class ModelAttribute: self.model_function = mf return True else: - logger.warning(f"CART generation for {self.name} {self.attr} faled") + logger.warning(f"{self.name}:{self.attr}:CART failed") self.model_function = df.StaticFunction( np.mean(self.data), n_samples=len(self.data) ) @@ -915,7 +915,7 @@ class ModelAttribute: self.model_function = mf return True else: - logger.warning(f"DECART generation for {self.name} {self.attr} faled") + logger.warning(f"{self.name}:{self.attr}:DECART failed") self.model_function = df.StaticFunction( np.mean(self.data), n_samples=len(self.data) ) @@ -936,17 +936,17 @@ class ModelAttribute: if len(self.stats.distinct_values_by_param_index[param_index]) < 2: ignore_param_indexes.append(param_index) x = df.FOLFunction( - self.median, - self.param_names, + np.mean(self.data), n_samples=self.data.shape[0], - num_args=self.arg_count, + param_names=self.param_names, + arg_count=self.arg_count, ) x.fit(self.param_values, self.data, ignore_param_indexes=ignore_param_indexes) if x.fit_success: self.model_function = x return True else: - logger.warning(f"Fit of first-order linear model function failed.") + logger.warning(f"{self.name}:{self.attr}:FOL failed") self.model_function = df.StaticFunction( np.mean(self.data), n_samples=len(self.data) ) @@ -964,7 +964,7 @@ class ModelAttribute: self.model_function = mf return True else: - logger.warning(f"LMT generation for {self.name} {self.attr} faled") + logger.warning(f"{self.name}:{self.attr}:LMT failed") self.model_function = df.StaticFunction( np.mean(self.data), n_samples=len(self.data) ) @@ -1006,7 +1006,7 @@ class ModelAttribute: self.model_function = mf return True else: - logger.warning(f"LightGBM generation for {self.name} {self.attr} faled") + logger.warning(f"{self.name}:{self.attr}:LightGBM failed") self.model_function = df.StaticFunction( np.mean(self.data), n_samples=len(self.data) ) @@ -1024,7 +1024,7 @@ class ModelAttribute: self.model_function = mf return True else: - logger.warning(f"XGB generation for {self.name} {self.attr} faled") + logger.warning(f"{self.name}:{self.attr}:XGBoost failed") self.model_function = df.StaticFunction( np.mean(self.data), n_samples=len(self.data) ) |