fixes after rebase

Author: ravinkohli

autoPyTorch/api/tabular_classification.py

@@ -432,13 +432,8 @@ def search(
             y_test=y_test,
             resampling_strategy=self.resampling_strategy,
             resampling_strategy_args=self.resampling_strategy_args,
-<<<<<<< HEAD
             dataset_name=dataset_name,
             dataset_compression=self._dataset_compression)
-=======
-            dataset_name=dataset_name
-        )
->>>>>>> [FIX] Enable preprocessing in reg_cocktails (#369)
 
         return self._search(
             dataset=self.dataset,
@@ -479,23 +474,23 @@ def predict(
             raise ValueError("predict() is only supported after calling search. Kindly call first "
                              "the estimator search() method.")
 
-        X_test = self.InputValidator.feature_validator.transform(X_test)
+        X_test = self.input_validator.feature_validator.transform(X_test)
         predicted_probabilities = super().predict(X_test, batch_size=batch_size,
                                                   n_jobs=n_jobs)
 
-        if self.InputValidator.target_validator.is_single_column_target():
+        if self.input_validator.target_validator.is_single_column_target():
             predicted_indexes = np.argmax(predicted_probabilities, axis=1)
         else:
             predicted_indexes = (predicted_probabilities > 0.5).astype(int)
 
         # Allow to predict in the original domain -- that is, the user is not interested
         # in our encoded values
-        return self.InputValidator.target_validator.inverse_transform(predicted_indexes)
+        return self.input_validator.target_validator.inverse_transform(predicted_indexes)
 
     def predict_proba(self,
                       X_test: Union[np.ndarray, pd.DataFrame, List],
                       batch_size: Optional[int] = None, n_jobs: int = 1) -> np.ndarray:
-        if self.InputValidator is None or not self.InputValidator._is_fitted:
+        if self.input_validator is None or not self.input_validator._is_fitted:
             raise ValueError("predict() is only supported after calling search. Kindly call first "
                              "the estimator search() method.")
         X_test = self.input_validator.feature_validator.transform(X_test)

autoPyTorch/api/tabular_regression.py

@@ -432,13 +432,8 @@ def search(
             y_test=y_test,
             resampling_strategy=self.resampling_strategy,
             resampling_strategy_args=self.resampling_strategy_args,
-<<<<<<< HEAD
             dataset_name=dataset_name,
             dataset_compression=self._dataset_compression)
-=======
-            dataset_name=dataset_name
-        )
->>>>>>> [FIX] Enable preprocessing in reg_cocktails (#369)
 
         return self._search(
             dataset=self.dataset,
@@ -465,14 +460,14 @@ def predict(
             batch_size: Optional[int] = None,
             n_jobs: int = 1
     ) -> np.ndarray:
-        if self.InputValidator is None or not self.InputValidator._is_fitted:
+        if self.input_validator is None or not self.input_validator._is_fitted:
             raise ValueError("predict() is only supported after calling search. Kindly call first "
                              "the estimator search() method.")
 
-        X_test = self.InputValidator.feature_validator.transform(X_test)
+        X_test = self.input_validator.feature_validator.transform(X_test)
         predicted_values = super().predict(X_test, batch_size=batch_size,
                                            n_jobs=n_jobs)
 
         # Allow to predict in the original domain -- that is, the user is not interested
         # in our encoded values
-        return self.InputValidator.target_validator.inverse_transform(predicted_values)
+        return self.input_validator.target_validator.inverse_transform(predicted_values)

autoPyTorch/data/base_feature_validator.py

@@ -113,13 +113,13 @@ def _fit(
 
     def _check_data(
         self,
-        X: SUPPORTED_FEAT_TYPES,
+        X: SupportedFeatTypes,
     ) -> None:
         """
         Feature dimensionality and data type checks
 
         Args:
-            X (SUPPORTED_FEAT_TYPES):
+            X (SupportedFeatTypes):
                 A set of features that are going to be validated (type and dimensionality
                 checks) and a encoder fitted in the case the data needs encoding
         """
@@ -145,19 +145,19 @@ def transform(
 
     def list_to_pandas(
         self,
-        X_train: SUPPORTED_FEAT_TYPES,
-        X_test: Optional[SUPPORTED_FEAT_TYPES] = None,
+        X_train: SupportedFeatTypes,
+        X_test: Optional[SupportedFeatTypes] = None,
     ) -> Tuple[pd.DataFrame, Optional[pd.DataFrame]]:
         """
         Converts a list to a pandas DataFrame. In this process, column types are inferred.
 
         If test data is provided, we proactively match it to train data
 
         Args:
-            X_train (SUPPORTED_FEAT_TYPES):
+            X_train (SupportedFeatTypes):
                 A set of features that are going to be validated (type and dimensionality
                 checks) and a encoder fitted in the case the data needs encoding
-            X_test (Optional[SUPPORTED_FEAT_TYPES]):
+            X_test (Optional[SupportedFeatTypes]):
                 A hold out set of data used for checking
         Returns:
             pd.DataFrame:

autoPyTorch/data/tabular_target_validator.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, cast
+from typing import List, Optional, Union, cast
 
 import numpy as np