diff options
| author | Daniel Friesel <daniel.friesel@uos.de> | 2022-02-02 13:38:58 +0100 |
|---|---|---|
| committer | Daniel Friesel <daniel.friesel@uos.de> | 2022-02-02 13:38:58 +0100 |
| commit | 5dff95b21fea5198976168b3635d29395a2dfd1d (patch) | |
| tree | 5e4d591b034e657c079d9154ceb3dfc327a60b2e | |
| parent | 8c118d357ad873349a2ae00ccf9bcd093c448df0 (diff) | |
build_dtree: improve debug output
| -rw-r--r-- | lib/parameters.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/lib/parameters.py b/lib/parameters.py index 38b4262..158ba93 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -905,7 +905,7 @@ class ModelAttribute: ) if fit_parameters.shape[1] == 0: logger.warning( - f"Cannot generate CART due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" + f"Cannot generate CART for {self.name} {self.attr} due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" ) self.model_function = df.StaticFunction(np.mean(data)) return @@ -935,7 +935,7 @@ class ModelAttribute: ) if fit_parameters.shape[1] == 0: logger.warning( - f"Cannot run XGBoost due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" + f"Cannot run XGBoost for {self.name} {self.attr} due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" ) self.model_function = df.StaticFunction(np.mean(data)) return @@ -960,14 +960,14 @@ class ModelAttribute: ) if fit_parameters.shape[1] == 0: logger.warning( - f"Cannot generate LMT due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" + f"Cannot generate LMT for {self.name} {self.attr} due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" ) self.model_function = df.StaticFunction(np.mean(data)) return try: lmt.fit(fit_parameters, data) except np.linalg.LinAlgError as e: - logger.error(f"LMT generated failed: {e}") + logger.error(f"LMT generation for {self.name} {self.attr} failed: {e}") self.model_function = df.StaticFunction(np.mean(data)) return self.model_function = df.LMTFunction( @@ -977,7 +977,7 @@ class ModelAttribute: if loss_ignore_scalar and not with_function_leaves: logger.warning( - "build_dtree called with loss_ignore_scalar=True, with_function_leaves=False. This does not make sense." + "build_dtree {self.name} {self.attr} called with loss_ignore_scalar=True, with_function_leaves=False. This does not make sense." ) self.model_function = self._build_dtree( @@ -1103,8 +1103,8 @@ class ModelAttribute: if ffs_feasible: # try generating a function. if it fails, model_function is a StaticFunction. ma = ModelAttribute( - "tmp", - "tmp", + self.name + "_", + self.attr, data, parameters, self.param_names, |