feat(Model): added own Model class, SkLearnModel wrapper and StaticMomentumModel (#61)

* feat(Model): added own `Model` class, SkLearnModel wrapper and StaticMomentumModel

* fix(Tests): added missing Model variable

* fix(Tests): added missing clone method()

tests/test_evaluation.py

@@ -2,7 +2,7 @@
 import numpy as np
 import pandas as pd
 from training.walk_forward import walk_forward_train_test
-from sklearn.base import BaseEstimator
+from models.base import Model
 from utils.evaluate import evaluate_predictions
 
 no_of_rows = 100
@@ -29,12 +29,15 @@ def __generate_even_odd_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Series]:
 
     return X, y
 
-class EvenOddStubModel(BaseEstimator):
+class EvenOddStubModel(Model):
     '''
     A deteministic model that can predict the future with 100% accuracy
     It verifies that the X[n][any_column] == 1 if n is even,
     '''
 
+    data_scaling = "unscaled"
+    only_column = None
+
     def __init__(self, window_length) -> None:
         super().__init__()
         self.window_length = window_length
@@ -47,6 +50,9 @@ class EvenOddStubModel(BaseEstimator):
     def predict(self, X):
         return np.array([-1 if X[0][0] == 1 else 1])
 
+    def clone(self):
+        return self
+
 
 def test_evaluation():
     X, y = __generate_even_odd_test_data(no_of_rows)

tests/test_walk_forward.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 from training.walk_forward import walk_forward_train_test
-from sklearn.base import BaseEstimator
+from models.base import Model
 
 no_of_rows = 100
 
@@ -27,12 +27,15 @@ def __generate_incremental_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Serie
 
 
 
-class IncrementingStubModel(BaseEstimator):
+class IncrementingStubModel(Model):
     '''
     A deteministic model that can predict the future with 100% accuracy
     It verifies that the X[n][any_column]+1 == y[n]
     '''
 
+    data_scaling = "unscaled"
+    only_column = None
+
     def __init__(self, window_length) -> None:
         super().__init__()
         self.window_length = window_length
@@ -45,6 +48,8 @@ class IncrementingStubModel(BaseEstimator):
     def predict(self, X):
         return np.array([X[0][0] + 1])
 
+    def clone(self):
+        return self
 
 def test_walk_forward_train_test():
     X, y = __generate_incremental_test_data(no_of_rows)