[ADD] Missing Batchnorm (#317)

* Add batch norm to mlp

* Increase max runtime in test

* Try drop last for running batch norm

* Change order to BN-ReLU

Author: ravinkohli

autoPyTorch/pipeline/components/setup/network_backbone/MLPBackbone.py

@@ -55,6 +55,7 @@ def _add_layer(self, layers: List[nn.Module], in_features: int, out_features: in
 
         """
         layers.append(nn.Linear(in_features, out_features))
+        layers.append(nn.BatchNorm1d(out_features))
         layers.append(_activations[self.config["activation"]]())
         if self.config['use_dropout']:
             layers.append(nn.Dropout(self.config["dropout_%d" % layer_id]))

autoPyTorch/pipeline/components/setup/network_backbone/ShapedMLPBackbone.py

@@ -62,6 +62,7 @@ def _add_layer(self, layers: List[nn.Module],
                    in_features: int, out_features: int, dropout: float
                    ) -> None:
         layers.append(nn.Linear(in_features, out_features))
+        layers.append(nn.BatchNorm1d(out_features))
         layers.append(_activations[self.config["activation"]]())
         if self.config["use_dropout"] and self.config["max_dropout"] > 0.05:
             layers.append(nn.Dropout(dropout))

autoPyTorch/pipeline/components/training/data_loader/base_data_loader.py

@@ -114,7 +114,7 @@ def fit(self, X: Dict[str, Any], y: Any = None) -> torch.utils.data.DataLoader:
             shuffle=True,
             num_workers=X.get('num_workers', 0),
             pin_memory=X.get('pin_memory', True),
-            drop_last=X.get('drop_last', False),
+            drop_last=X.get('drop_last', True),
             collate_fn=custom_collate_fn,
         )
 

test/test_pipeline/test_tabular_classification.py

@@ -460,7 +460,7 @@ def test_train_pipeline_with_runtime(fit_dictionary_tabular_dummy):
     # Convert the training to runtime
     fit_dictionary_tabular_dummy.pop('epochs', None)
     fit_dictionary_tabular_dummy['budget_type'] = 'runtime'
-    fit_dictionary_tabular_dummy['runtime'] = 3
+    fit_dictionary_tabular_dummy['runtime'] = 5
     fit_dictionary_tabular_dummy['early_stopping'] = -1
 
     pipeline = TabularClassificationPipeline(
@@ -474,11 +474,11 @@ def test_train_pipeline_with_runtime(fit_dictionary_tabular_dummy):
     run_summary = pipeline.named_steps['trainer'].run_summary
     budget_tracker = pipeline.named_steps['trainer'].budget_tracker
     assert budget_tracker.budget_type == 'runtime'
-    assert budget_tracker.max_runtime == 3
+    assert budget_tracker.max_runtime == 5
     assert budget_tracker.is_max_time_reached()
 
     # There is no epoch limitation
     assert not budget_tracker.is_max_epoch_reached(epoch=np.inf)
 
-    # More than 200 epochs would have pass in 3 seconds for this dataset
+    # More than 200 epochs would have pass in 5 seconds for this dataset
     assert len(run_summary.performance_tracker['start_time']) > 100