Add coalescer

automl · May 25, 2021 · 838e63b · 838e63b
1 parent 294ac60
commit 838e63b
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diff --git a/autoPyTorch/pipeline/tabular_classification.py b/autoPyTorch/pipeline/tabular_classification.py
@@ -18,6 +18,9 @@
 from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.TabularColumnTransformer import (
     TabularColumnTransformer
 )
+from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.coalescer import (
+    CoalescerChoice
+)
 from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.encoding.base_encoder_choice import (
     EncoderChoice
 )
@@ -246,6 +249,7 @@ def _get_pipeline_steps(self, dataset_properties: Optional[Dict[str, Any]],
 
         steps.extend([
             ("imputer", SimpleImputer(random_state=self.random_state)),
+            ("coalescer", CoalescerChoice(default_dataset_properties, random_state=self.random_state)),
             ("encoder", EncoderChoice(default_dataset_properties, random_state=self.random_state)),
             ("scaler", ScalerChoice(default_dataset_properties, random_state=self.random_state)),
             ("feature_preprocessor", FeatureProprocessorChoice(default_dataset_properties,

diff --git a/autoPyTorch/pipeline/tabular_regression.py b/autoPyTorch/pipeline/tabular_regression.py
@@ -16,6 +16,9 @@
 from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.TabularColumnTransformer import (
     TabularColumnTransformer
 )
+from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.coalescer import (
+    CoalescerChoice
+)
 from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.encoding.base_encoder_choice import (
     EncoderChoice
 )
@@ -190,6 +193,7 @@ def _get_pipeline_steps(self, dataset_properties: Optional[Dict[str, Any]]) -> L
 
         steps.extend([
             ("imputer", SimpleImputer(random_state=self.random_state)),
+            ("coalescer", CoalescerChoice(default_dataset_properties, random_state=self.random_state)),
             ("encoder", EncoderChoice(default_dataset_properties, random_state=self.random_state)),
             ("scaler", ScalerChoice(default_dataset_properties, random_state=self.random_state)),
             ("feature_preprocessor", FeatureProprocessorChoice(default_dataset_properties,