Change weighted loss to categorical and fix for test adversarial trai…

…ner (#214)
automl · Feb 28, 2022 · 7f83ce3 · 7f83ce3
1 parent 1b5e1c3
commit 7f83ce3
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Showing 6 changed files with 20 additions and 49 deletions.
diff --git a/autoPyTorch/pipeline/components/training/trainer/AdversarialTrainer.py b/autoPyTorch/pipeline/components/training/trainer/AdversarialTrainer.py
@@ -24,7 +24,7 @@ class AdversarialTrainer(BaseTrainerComponent):
     def __init__(
             self,
             epsilon: float,
-            weighted_loss: int = 0,
+            weighted_loss: bool = False,
             random_state: Optional[np.random.RandomState] = None,
             use_stochastic_weight_averaging: bool = False,
             use_snapshot_ensemble: bool = False,
@@ -159,8 +159,8 @@ def get_hyperparameter_search_space(
         dataset_properties: Optional[Dict] = None,
         weighted_loss: HyperparameterSearchSpace = HyperparameterSearchSpace(
             hyperparameter="weighted_loss",
-            value_range=[1],
-            default_value=1),
+            value_range=[True, False],
+            default_value=True),
         la_steps: HyperparameterSearchSpace = HyperparameterSearchSpace(
             hyperparameter="la_steps",
             value_range=(5, 10),
@@ -226,16 +226,9 @@ def get_hyperparameter_search_space(
                 parent_hyperparameter=parent_hyperparameter
             )
 
-        """
+        # TODO, decouple the weighted loss from the trainer
         if dataset_properties is not None:
             if STRING_TO_TASK_TYPES[dataset_properties['task_type']] in CLASSIFICATION_TASKS:
                 add_hyperparameter(cs, weighted_loss, CategoricalHyperparameter)
-        """
-        # TODO, decouple the weighted loss from the trainer. Uncomment the code above and
-        # remove the code below. Also update the method signature, so the weighted loss
-        # is not a constant.
-        if dataset_properties is not None:
-            if STRING_TO_TASK_TYPES[dataset_properties['task_type']] in CLASSIFICATION_TASKS:
-                add_hyperparameter(cs, weighted_loss, Constant)
 
         return cs
diff --git a/autoPyTorch/pipeline/components/training/trainer/StandardTrainer.py b/autoPyTorch/pipeline/components/training/trainer/StandardTrainer.py
@@ -13,7 +13,8 @@
 
 
 class StandardTrainer(BaseTrainerComponent):
-    def __init__(self, weighted_loss: int = 0,
+    def __init__(self,
+                 weighted_loss: bool = False,
                  use_stochastic_weight_averaging: bool = False,
                  use_snapshot_ensemble: bool = False,
                  se_lastk: int = 3,

diff --git a/autoPyTorch/pipeline/components/training/trainer/base_trainer.py b/autoPyTorch/pipeline/components/training/trainer/base_trainer.py
@@ -206,7 +206,7 @@ class BaseTrainerComponent(autoPyTorchTrainingComponent):
     """
     Base class for training
     Args:
-        weighted_loss (int, default=0): In case for classification, whether to weight
+        weighted_loss (bool, default=False): In case for classification, whether to weight
             the loss function according to the distribution of classes in the target
         use_stochastic_weight_averaging (bool, default=True): whether to use stochastic
             weight averaging. Stochastic weight averaging is a simple average of
@@ -221,7 +221,7 @@ class BaseTrainerComponent(autoPyTorchTrainingComponent):
         random_state:
         **lookahead_config:
     """
-    def __init__(self, weighted_loss: int = 0,
+    def __init__(self, weighted_loss: bool = False,
                  use_stochastic_weight_averaging: bool = True,
                  use_snapshot_ensemble: bool = True,
                  se_lastk: int = 3,
@@ -587,8 +587,8 @@ def get_hyperparameter_search_space(
         dataset_properties: Optional[Dict] = None,
         weighted_loss: HyperparameterSearchSpace = HyperparameterSearchSpace(
             hyperparameter="weighted_loss",
-            value_range=[1],
-            default_value=1),
+            value_range=[True, False],
+            default_value=True),
         la_steps: HyperparameterSearchSpace = HyperparameterSearchSpace(
             hyperparameter="la_steps",
             value_range=(5, 10),
@@ -636,16 +636,9 @@ def get_hyperparameter_search_space(
             parent_hyperparameter=parent_hyperparameter
         )
 
-        """
+        # TODO, decouple the weighted loss from the trainer
         if dataset_properties is not None:
             if STRING_TO_TASK_TYPES[dataset_properties['task_type']] in CLASSIFICATION_TASKS:
                 add_hyperparameter(cs, weighted_loss, CategoricalHyperparameter)
-        """
-        # TODO, decouple the weighted loss from the trainer. Uncomment the code above and
-        # remove the code below. Also update the method signature, so the weighted loss
-        # is not a constant.
-        if dataset_properties is not None:
-            if STRING_TO_TASK_TYPES[dataset_properties['task_type']] in CLASSIFICATION_TASKS:
-                add_hyperparameter(cs, weighted_loss, Constant)
 
         return cs
diff --git a/autoPyTorch/pipeline/components/training/trainer/cutout_utils.py b/autoPyTorch/pipeline/components/training/trainer/cutout_utils.py
@@ -20,7 +20,7 @@
 class CutOut:
     def __init__(self, patch_ratio: float,
                  cutout_prob: float,
-                 weighted_loss: int = 0,
+                 weighted_loss: bool = False,
                  random_state: Optional[np.random.RandomState] = None,
                  use_stochastic_weight_averaging: bool = False,
                  use_snapshot_ensemble: bool = False,
@@ -63,9 +63,8 @@ def get_hyperparameter_search_space(
             dataset_properties: Optional[Dict] = None,
             weighted_loss: HyperparameterSearchSpace = HyperparameterSearchSpace(
                 hyperparameter="weighted_loss",
-                value_range=[1],
-                default_value=1
-            ),
+                value_range=[True, False],
+                default_value=True),
             la_steps: HyperparameterSearchSpace = HyperparameterSearchSpace(
                 hyperparameter="la_steps",
                 value_range=(5, 10),
@@ -137,16 +136,9 @@ def get_hyperparameter_search_space(
                 parent_hyperparameter=parent_hyperparameter
             )
 
-        """
+        # TODO, decouple the weighted loss from the trainer
         if dataset_properties is not None:
             if STRING_TO_TASK_TYPES[dataset_properties['task_type']] in CLASSIFICATION_TASKS:
                 add_hyperparameter(cs, weighted_loss, CategoricalHyperparameter)
-        """
-        # TODO, decouple the weighted loss from the trainer. Uncomment the code above and
-        # remove the code below. Also update the method signature, so the weighted loss
-        # is not a constant.
-        if dataset_properties is not None:
-            if STRING_TO_TASK_TYPES[dataset_properties['task_type']] in CLASSIFICATION_TASKS:
-                add_hyperparameter(cs, weighted_loss, Constant)
 
         return cs
diff --git a/autoPyTorch/pipeline/components/training/trainer/mixup_utils.py b/autoPyTorch/pipeline/components/training/trainer/mixup_utils.py
@@ -19,7 +19,7 @@
 
 class MixUp:
     def __init__(self, alpha: float,
-                 weighted_loss: int = 0,
+                 weighted_loss: bool = False,
                  random_state: Optional[np.random.RandomState] = None,
                  use_stochastic_weight_averaging: bool = False,
                  use_snapshot_ensemble: bool = False,
@@ -61,9 +61,8 @@ def get_hyperparameter_search_space(
         dataset_properties: Optional[Dict] = None,
         weighted_loss: HyperparameterSearchSpace = HyperparameterSearchSpace(
             hyperparameter="weighted_loss",
-            value_range=[1],
-            default_value=1
-        ),
+            value_range=[True, False],
+            default_value=True),
         la_steps: HyperparameterSearchSpace = HyperparameterSearchSpace(
             hyperparameter="la_steps",
             value_range=(5, 10),
@@ -128,16 +127,9 @@ def get_hyperparameter_search_space(
                 la_config_space,
                 parent_hyperparameter=parent_hyperparameter
             )
-        """
+        # TODO, decouple the weighted loss from the trainer
         if dataset_properties is not None:
             if STRING_TO_TASK_TYPES[dataset_properties['task_type']] in CLASSIFICATION_TASKS:
                 add_hyperparameter(cs, weighted_loss, CategoricalHyperparameter)
-        """
-        # TODO, decouple the weighted loss from the trainer. Uncomment the code above and
-        # remove the code below. Also update the method signature, so the weighted loss
-        # is not a constant.
-        if dataset_properties is not None:
-            if STRING_TO_TASK_TYPES[dataset_properties['task_type']] in CLASSIFICATION_TASKS:
-                add_hyperparameter(cs, weighted_loss, Constant)
 
         return cs
diff --git a/test/test_pipeline/test_tabular_classification.py b/test/test_pipeline/test_tabular_classification.py
@@ -380,7 +380,7 @@ def test_set_choices_updates(self, fit_dictionary_tabular):
     @pytest.mark.parametrize('lr_scheduler', ['CosineAnnealingWarmRestarts',
                                               'ReduceLROnPlateau'])
     def test_trainer_cocktails(self, fit_dictionary_tabular, mocker, lr_scheduler, trainer):  # noqa F811
-        fit_dictionary_tabular['epochs'] = 20
+        fit_dictionary_tabular['epochs'] = 45
         fit_dictionary_tabular['early_stopping'] = 20
         pipeline = TabularClassificationPipeline(
             dataset_properties=fit_dictionary_tabular['dataset_properties'],