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

* chore(Linter): reformatted code with black

* Create black.yaml

training/walk_forward/inference_batched.py

@@ -1,36 +1,64 @@
 import pandas as pd
-from training.types import ModelOverTime, TransformationsOverTime, PredictionsSeries, ProbabilitiesDataFrame
+from training.types import (
+    ModelOverTime,
+    TransformationsOverTime,
+    PredictionsSeries,
+    ProbabilitiesDataFrame,
+)
 from utils.helpers import get_first_valid_return_index
 from tqdm import tqdm
 from typing import Optional
 from data_loader.types import XDataFrame
 from tqdm import tqdm
 
-def walk_forward_inference_batched(
-                    model_name: str,
-                    model_over_time: ModelOverTime,
-                    transformations_over_time: TransformationsOverTime,
-                    X: XDataFrame,
-                    expanding_window: bool,
-                    window_size: int,
-                    retrain_every: int,
-                    from_index: Optional[pd.Timestamp],
-                ) -> tuple[PredictionsSeries, ProbabilitiesDataFrame]:
-    predictions = pd.Series(index=X.index, dtype='object').rename(model_name)
-    probabilities = pd.DataFrame(index=X.index, columns=['0', '1'])
 
-    inference_from = max(get_first_valid_return_index(model_over_time), get_first_valid_return_index(X.iloc[:,0])) if from_index is None else X.index.to_list().index(from_index)
+def walk_forward_inference_batched(
+    model_name: str,
+    model_over_time: ModelOverTime,
+    transformations_over_time: TransformationsOverTime,
+    X: XDataFrame,
+    expanding_window: bool,
+    window_size: int,
+    retrain_every: int,
+    from_index: Optional[pd.Timestamp],
+) -> tuple[PredictionsSeries, ProbabilitiesDataFrame]:
+    predictions = pd.Series(index=X.index, dtype="object").rename(model_name)
+    probabilities = pd.DataFrame(index=X.index, columns=["0", "1"])
+
+    inference_from = (
+        max(
+            get_first_valid_return_index(model_over_time),
+            get_first_valid_return_index(X.iloc[:, 0]),
+        )
+        if from_index is None
+        else X.index.to_list().index(from_index)
+    )
     inference_till = X.shape[0]
     first_model = model_over_time[inference_from]
 
     if first_model.only_column is not None:
         X = X[[column for column in X.columns if first_model.only_column in column]]
-    
-    if first_model.data_transformation == 'original':
+
+    if first_model.data_transformation == "original":
         transformations_over_time = []
 
-    batch_indices = range(inference_from, inference_till, retrain_every) if inference_till - inference_from > retrain_every else [inference_from]
-    batched_results = [__inference_from_window(index, index + retrain_every, X, model_over_time, transformations_over_time, expanding_window, window_size) for index in tqdm(batch_indices)]
+    batch_indices = (
+        range(inference_from, inference_till, retrain_every)
+        if inference_till - inference_from > retrain_every
+        else [inference_from]
+    )
+    batched_results = [
+        __inference_from_window(
+            index,
+            index + retrain_every,
+            X,
+            model_over_time,
+            transformations_over_time,
+            expanding_window,
+            window_size,
+        )
+        for index in tqdm(batch_indices)
+    ]
     for batch in batched_results:
         for index, prediction, probs in batch:
             predictions[X.index[index]] = prediction
@@ -38,12 +66,24 @@ def walk_forward_inference_batched(
 
     return predictions, probabilities
 
-def __inference_from_window(index_start: int, index_end: int, X: XDataFrame, model_over_time: ModelOverTime, transformations_over_time: TransformationsOverTime, expanding_window: bool, window_size: int) -> list[tuple[int, float, pd.Series]]:
+
+def __inference_from_window(
+    index_start: int,
+    index_end: int,
+    X: XDataFrame,
+    model_over_time: ModelOverTime,
+    transformations_over_time: TransformationsOverTime,
+    expanding_window: bool,
+    window_size: int,
+) -> list[tuple[int, float, pd.Series]]:
     current_model = model_over_time[X.index[index_start]]
-    current_transformations = [transformation_over_time[X.index[index_start]] for transformation_over_time in transformations_over_time]
+    current_transformations = [
+        transformation_over_time[X.index[index_start]]
+        for transformation_over_time in transformations_over_time
+    ]
 
     input_data = X.iloc[index_start:index_end]
-        
+
     for transformation in current_transformations:
         input_data = transformation.transform(input_data)
 
@@ -51,6 +91,9 @@ def __inference_from_window(index_start: int, index_end: int, X: XDataFrame, mod
 
     predictions = current_model.predict(input_data)
     probs = current_model.predict_proba(input_data)
-    results = [(index_start + index, predictions[index], probs[index]) for index in range(len(predictions))]
-    
-    return results
+    results = [
+        (index_start + index, predictions[index], probs[index])
+        for index in range(len(predictions))
+    ]
+
+    return results