Adjust base train() return values

howso/client/base.py

@@ -553,12 +553,13 @@ def train(  # noqa: C901
             in cases is applied in order to each of the cases in the series.
         skip_auto_analyze : bool, default False
             When true, the Trainee will not auto-analyze when appropriate.
-            Instead, the return dict will have a `status` key set to
-            "analyze" if an analyze is needed.
+            Instead, the return dict will have a "needs_analyze" flag if an
+            `analyze` is needed.
         skip_reduce_data : bool, default False
             When true, the Trainee will not call `reduce_data` when
-            appropriate. Instead, the return dict will have a `status` key set
-            to "reduce_data" if a call to `reduce_data` is recommended.
+            appropriate. Instead, the return dict will have a
+            "needs_data_reduction" flag if a call to `reduce_data` is
+            recommended.
         train_weights_only : bool, default False
             When true, and accumulate_weight_feature is provided,
             will accumulate all of the cases' neighbor weights instead of
@@ -571,8 +572,9 @@ def train(  # noqa: C901
         Returns
         -------
         dict
-            A dict containing `status` and `details` if there are important
-            messages to share from the Engine. Otherwise, an empty dict.
+            A dict containing variable keys if there are important messages to
+            share from the Engine, such as 'needs_analyze` and
+            'needs_data_reduction'. Otherwise, an empty dict.
         """
         trainee_id = self._resolve_trainee(trainee_id).id
         feature_attributes = self.resolve_feature_attributes(trainee_id)
@@ -656,11 +658,9 @@ def train(  # noqa: C901
                 })
 
                 if response and response.get('status') == 'analyze':
-                    status['status'] = 'analyze'
-                    status['details'] = 'An analyze is strongly recommended for this trainee.'
-                elif response and response.get('status') == 'reduce_data':
-                    status['status'] = 'reduce_data'
-                    status['details'] = 'Data reduction via `reduce_data` is recommended for this trainee.'
+                    status['needs_analyze'] = True
+                if response and response.get('status') == 'reduce_data':
+                    status['needs_data_reduction'] = True
 
                 if batch_scaler is None or gen_batch_size is None:
                     progress.update(batch_size)

howso/engine/trainee.py

@@ -150,6 +150,7 @@ def __init__(
         self._custom_save_path = None
         self._calculated_matrices = {}
         self._needs_analyze: bool = False
+        self._needs_data_reduction: bool = False
 
         # Allow passing project id or the project instance
         if isinstance(project, BaseProject):
@@ -311,6 +312,18 @@ def needs_analyze(self) -> bool:
         """
         return self._needs_analyze
 
+    @property
+    def needs_data_reduction(self) -> bool:
+        """
+        The flag indicating if the Trainee needs its data reduced.
+
+        Returns
+        -------
+        bool
+            A flag indicating if a call to `reduce_data` is recommended.
+        """
+        return self._needs_data_reduction
+
     @property
     def calculated_matrices(self) -> dict[str, DataFrame] | None:
         """
@@ -683,12 +696,9 @@ def train(
             applied in order to each of the cases in the series.
         skip_auto_analyze : bool, default False
             When true, the Trainee will not auto-analyze when appropriate.
-            Instead, the return dict will have a `status` key set to
-            "analyze" if an analyze is needed.
         skip_reduce_data : bool, default False
             When true, the Trainee will not call `reduce_data` when
-            appropriate. Instead, the return dict will have a `status` key set
-            to "reduce_data" if a call to `reduce_data` is recommended.
+            appropriate.
         train_weights_only:  bool, default False
             When true, and accumulate_weight_feature is provided,
             will accumulate all of the cases' neighbor weights instead of
@@ -715,7 +725,8 @@ def train(
                 train_weights_only=train_weights_only,
                 validate=validate,
             )
-            self._needs_analyze = True if status.get('status') == 'analyze' else False
+            self._needs_analyze = status.get('needs_analyze', False)
+            self._needs_data_reduction = status.get('needs_data_reduction', False)
         else:
             raise AssertionError("Client must have the 'train' method.")