more debug output

2 files changed, 13 insertions, 1 deletions

diff --git a/lib/utils.py b/lib/utils.py
index ed5aa14..69e807c 100644
--- a/lib/utils.py
+++ b/lib/utils.py
@@ -209,6 +209,10 @@ def param_to_ndarray(
     distinct_values = dict()
     category_to_scalar = dict()
 
+    logger.debug(
+        f"converting param_to_ndarray(with_nan={with_nan}, categorial_to_scalar={categorial_to_scalar}, ignore_indexes={ignore_indexes})"
+    )
+
     for param_tuple in param_tuples:
         for i, param in enumerate(param_tuple):
             if not is_numeric(param):
@@ -336,7 +340,11 @@ def observation_dict_to_by_name(observation):
     assert parameter_names == sorted(parameter_names)
     for name in by_name:
         for entry in by_name[name]["param"]:
-            assert len(entry) == len(parameter_names)
+            if len(entry) != len(parameter_names):
+                logger.error(
+                    f"by_name[{name}] has an entry with {len(entry)} parameters. I expect {len(parameter_names)} parameters."
+                )
+                assert len(entry) == len(parameter_names)
         for attribute in by_name[name]["attributes"]:
             by_name[name][attribute] = np.array(by_name[name][attribute])
     return by_name, parameter_names
diff --git a/lib/validation.py b/lib/validation.py
index 8552ca5..3fc5c1a 100644
--- a/lib/validation.py
+++ b/lib/validation.py
@@ -288,18 +288,22 @@ class CrossValidator:
             for idx in validation_subset:
                 validation[name]["param"].append(self.by_name[name]["param"][idx])
 
+        logger.debug("Creating training model instance")
         kwargs = self.kwargs.copy()
         if static:
             kwargs["force_tree"] = False
         training_data = self.model_class(
             training, self.parameters, *self.args, **kwargs
         )
+        logger.debug("Building trainig model")
         training_model = model_getter(training_data)
         kwargs = self.kwargs.copy()
         kwargs["compute_stats"] = False
         kwargs["force_tree"] = False
+        logger.debug("Creating validation model instance")
         validation_data = self.model_class(
             validation, self.parameters, *self.args, **kwargs
         )
+        logger.debug("Done")
 
         return training_data, validation_data.assess(training_model, return_raw=True)