feat(Transformations): added Transformations abstraction & handling in walk_forward_train() & inference() (#161)

* feat(Transformations): added Transformations abstraction & handling in walk_forward_train() & inference()

* fix(WalkForward): use Dataframes to call Transformation.fit_transform()

* feat(WalkForward): restored option for models to recieve unscaled data

* fix(Transformations): output DataFrame as expected

* fix(Tests): missing new property

transformations/base.py

@@ -0,0 +1,31 @@
+from __future__ import annotations
+from typing import Literal, Optional, Union
+from abc import ABC, abstractmethod
+import pandas as pd
+
+class Transformation(ABC):
+
+    @abstractmethod
+    def fit(self, X: pd.DataFrame, y: Optional[pd.Series]) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def fit_transform(self, X: pd.DataFrame, y: Optional[pd.Series] = None) -> pd.DataFrame:
+        raise NotImplementedError
+
+    @abstractmethod
+    def transform(self, X: pd.DataFrame) -> pd.DataFrame:
+        raise NotImplementedError
+
+    @abstractmethod
+    def clone(self) -> Transformation:
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_name(self) -> str:
+        raise NotImplementedError
+    
+
+
+    
+    

transformations/sklearn.py

@@ -0,0 +1,34 @@
+from __future__ import annotations
+from transformations.base import Transformation
+from typing import Literal, Optional, Union
+from sklearn.base import clone, BaseEstimator
+import pandas as pd
+
+class SKLearnTransformation(Transformation):
+
+    transformer: BaseEstimator
+
+    def __init__(self, transformer: BaseEstimator):
+        self.transformer = transformer
+
+    def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None) -> None:
+        self.transformer.fit(X, y)
+    
+    def fit_transform(self, X: pd.DataFrame, y: Optional[pd.Series]) -> pd.DataFrame:
+        self.fit(X, y)
+        return self.transform(X)
+
+    def transform(self, X: pd.DataFrame) -> pd.DataFrame:
+        return pd.DataFrame(self.transformer.transform(X), index = X.index, columns = X.columns)
+
+    def clone(self) -> SKLearnTransformation:
+        return SKLearnTransformation(clone(self.transformer))
+
+    def get_name(self) -> str:
+        return self.transformer.__class__.__name__
+
+    
+
+
+    
+