diff options
| author | Birte Kristina Friesel <birte.friesel@uos.de> | 2023-12-22 06:35:31 +0100 |
|---|---|---|
| committer | Birte Kristina Friesel <birte.friesel@uos.de> | 2023-12-22 06:35:31 +0100 |
| commit | dac3650b6d0f474aaff502d66c04936a945f28cc (patch) | |
| tree | 9ea504d2c1c12d76a826f02e34a245ef0bbaab48 /lib | |
| parent | 4d774509ee12549e40c4c69f6f6e1e5cb01b65e7 (diff) | |
parameters: more debug output
Diffstat (limited to 'lib')
| -rw-r--r-- | lib/parameters.py | 7 |
1 files changed, 6 insertions, 1 deletions
diff --git a/lib/parameters.py b/lib/parameters.py index 4c7cf8b..74f1007 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -383,6 +383,7 @@ class ParallelParamStats: Statistics are computed in parallel with one process per core. Results are written to each ModelAttribute wich was passed via enqueue(). """ + logger.debug("Computing param stats in parallel") with Pool() as pool: results = pool.map(_compute_param_statistics_parallel, self.queue) @@ -774,7 +775,7 @@ class ModelAttribute: param2_values = map(lambda pv: pv[param2_index], self.param_values) param2_numeric_count = sum(map(is_numeric, param2_values)) # If all occurences of (param1, param2) are either (None, None) or (not None, not None), removing one of them is sensible. - # Otherwise, one parameter may decide whether the other one has an effect or not (or what kind of effect it has). This is importent for + # Otherwise, one parameter may decide whether the other one has an effect or not (or what kind of effect it has). This is important for # decision-tree models, so do not remove parameters in that case. params_are_pairwise_none = all( map( @@ -1067,10 +1068,12 @@ class ModelAttribute: ) self.model_function = df.StaticFunction(np.mean(data)) return + logger.debug("Fitting sklearn CART ...") cart.fit(fit_parameters, data) self.model_function = df.CARTFunction( np.mean(data), cart, category_to_index, ignore_index ) + logger.debug("Fitted sklearn CART") return if with_xgboost: @@ -1122,12 +1125,14 @@ class ModelAttribute: ) self.model_function = df.StaticFunction(np.mean(data)) return + logger.debug("Fitting LMT ...") try: lmt.fit(fit_parameters, data) except np.linalg.LinAlgError as e: logger.error(f"LMT generation for {self.name} {self.attr} failed: {e}") self.model_function = df.StaticFunction(np.mean(data)) return + logger.debug("Fitted LMT") self.model_function = df.LMTFunction( np.mean(data), lmt, category_to_index, ignore_index ) |