chore(Linter): reformatted code with black (#211)

* chore(Linter): reformatted code with black

* Create black.yaml

tests/test_evaluation.py

@@ -1,4 +1,3 @@
-
 import numpy as np
 import pandas as pd
 from training.walk_forward import walk_forward_train, walk_forward_inference
@@ -8,10 +7,10 @@ from sklearn.base import BaseEstimator, ClassifierMixin
 
 no_of_rows = 100
 
+
 def __generate_even_odd_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Series]:
-    ''' Test data, where X[n][any_column] == 1 if n is even, else 0
-    '''
-    
+    """Test data, where X[n][any_column] == 1 if n is even, else 0"""
+
     no_columns = 6
     X = [[-1 if row % 2 == 0 else 1] * no_columns for row in range(no_of_rows)]
     assert X[0][0] == -1
@@ -20,7 +19,7 @@ def __generate_even_odd_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Series]:
     assert X[3][0] == 1
     X = pd.DataFrame(X)
 
-    y = [-1 if (row+1) % 2 == 0 else 1 for row in range(no_of_rows)]
+    y = [-1 if (row + 1) % 2 == 0 else 1 for row in range(no_of_rows)]
     assert y[0] == 1
     assert y[1] == -1
     assert y[2] == 1
@@ -32,14 +31,14 @@ def __generate_even_odd_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Series]:
 
 
 class EvenOddStubModel(BaseEstimator, ClassifierMixin, 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_transformation = "original"
     only_column = None
-    predict_window_size = 'single_timestamp'
+    predict_window_size = "single_timestamp"
 
     def __init__(self, window_length) -> None:
         super().__init__()
@@ -59,12 +58,12 @@ class EvenOddStubModel(BaseEstimator, ClassifierMixin, Model):
 
 def test_evaluation():
     X, y = __generate_even_odd_test_data(no_of_rows)
-    
+
     window_length = 10
     retrain_every = 10
 
-    model = EvenOddStubModel(window_length = window_length)
-    
+    model = EvenOddStubModel(window_length=window_length)
+
     model_over_time = walk_forward_train(
         model=model,
         X=X,
@@ -74,20 +73,21 @@ def test_evaluation():
         window_size=window_length,
         retrain_every=retrain_every,
         from_index=None,
-        transformations_over_time=[])
+        transformations_over_time=[],
+    )
     predictions, _ = walk_forward_inference(
-        model_name='test',
+        model_name="test",
         model_over_time=model_over_time,
         transformations_over_time=[],
         X=X,
         expanding_window=False,
         window_size=window_length,
-        retrain_every = retrain_every,
+        retrain_every=retrain_every,
         from_index=None,
     )
-    
+
     # verify if predictions are the same as y
-    for i in range(window_length+2, no_of_rows):
+    for i in range(window_length + 2, no_of_rows):
         assert predictions[i] == y[i]
 
     fake_forward_returns = y * 0.1
@@ -97,11 +97,8 @@ def test_evaluation():
         forward_returns=fake_forward_returns,
         y_pred=processed_predictions_to_match_returns,
         y_true=y,
-        no_of_classes='two',
-        discretize=True
+        no_of_classes="two",
+        discretize=True,
     )
 
-    assert result['accuracy'] == 100.0
-    
-
-    
+    assert result["accuracy"] == 100.0