[feat] Provide explicit col dtypes from user side

autoPyTorch/data/base_feature_validator.py

@@ -1,20 +1,14 @@
 import logging
-from typing import List, Optional, Union
+from typing import Dict, List, Optional, Union
 
 import numpy as np
 
-import pandas as pd
-
-from scipy.sparse import spmatrix
-
 from sklearn.base import BaseEstimator
 
+from autoPyTorch.data.utils import SupportedFeatTypes, list_to_pandas
 from autoPyTorch.utils.logging_ import PicklableClientLogger
 
 
-SupportedFeatTypes = Union[List, pd.DataFrame, np.ndarray, spmatrix]
-
-
 class BaseFeatureValidator(BaseEstimator):
     """
     A class to pre-process features. In this regards, the format of the data is checked,
@@ -27,8 +21,8 @@ class BaseFeatureValidator(BaseEstimator):
         column_transformer (Optional[BaseEstimator])
             Host a encoder object if the data requires transformation (for example,
             if provided a categorical column in a pandas DataFrame)
-        transformed_columns (List[str])
-            List of columns that were encoded.
+        enc_columns (Optional[List[str]]):
+            The list of column names that should be encoded.
     """
     def __init__(
         self,
@@ -37,11 +31,11 @@ def __init__(
         # Register types to detect unsupported data format changes
         self.feat_type: Optional[List[str]] = None
         self.data_type: Optional[type] = None
-        self.dtypes: List[str] = []
+        self.dtypes: Dict[str, str] = {}
         self.column_order: List[str] = []
 
         self.column_transformer: Optional[BaseEstimator] = None
-        self.transformed_columns: List[str] = []
+        self.enc_columns: List[str] = []
 
         self.logger: Union[
             PicklableClientLogger, logging.Logger
@@ -75,7 +69,8 @@ def fit(
 
         # If a list was provided, it will be converted to pandas
         if isinstance(X_train, list):
-            X_train, X_test = self.list_to_dataframe(X_train, X_test)
+            X_train = list_to_pandas(X_train, self.logger)
+            X_test = list_to_pandas(X_test, self.logger) if X_test is not None else None
 
         self._check_data(X_train)
 

autoPyTorch/data/base_target_validator.py

@@ -1,18 +1,14 @@
 import logging
-from typing import List, Optional, Union, cast
+from typing import Optional, Union, cast
 
 import numpy as np
 
 import pandas as pd
 
-from scipy.sparse import spmatrix
-
 from sklearn.base import BaseEstimator
 
 from autoPyTorch.utils.logging_ import PicklableClientLogger
-
-
-SupportedTargetTypes = Union[List, pd.Series, pd.DataFrame, np.ndarray, spmatrix]
+from autoPyTorch.data.utils import SupportedTargetTypes
 
 
 class BaseTargetValidator(BaseEstimator):