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

tests/test_evaluation.py

@@ -4,7 +4,6 @@ import pandas as pd
 from training.walk_forward import walk_forward_train, walk_forward_inference
 from models.base import Model
 from utils.evaluate import evaluate_predictions
-from sklearn.preprocessing import MinMaxScaler
 
 no_of_rows = 100
 
@@ -36,7 +35,7 @@ class EvenOddStubModel(Model):
     It verifies that the X[n][any_column] == 1 if n is even,
     '''
 
-    data_scaling = "unscaled"
+    data_transformation = "original"
     only_column = None
     predict_window_size = 'single_timestamp'
 
@@ -68,9 +67,8 @@ def test_evaluation():
     window_length = 10
 
     model = EvenOddStubModel(window_length = window_length)
-    scaler = MinMaxScaler()
     
-    models, scalers = walk_forward_train(
+    model_over_time, transformations_over_time = walk_forward_train(
         model_name='test',
         model=model,
         X=X,
@@ -79,11 +77,11 @@ def test_evaluation():
         expanding_window=False,
         window_size=window_length,
         retrain_every=10,
-        scaler=scaler)
-    predictions, probs = walk_forward_inference(
+        transformations=[])
+    predictions, _ = walk_forward_inference(
         model_name='test',
-        models=models,
-        scalers=scalers,
+        model_over_time=model_over_time,
+        transformations_over_time=transformations_over_time,
         X=X,
         expanding_window=False,
         window_size=window_length