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

* chore(Linter): reformatted code with black

* Create black.yaml

training/walk_forward/inference.py

@@ -1,49 +1,79 @@
 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 utils.helpers import get_last_non_na_index
 
+
 def walk_forward_inference(
-                    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)
+    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)
 
-    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_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]
-    model_index_offset = get_last_non_na_index(model_over_time, inference_from) if pd.isna(model_over_time[inference_from]) else 0 
-    first_model = model_over_time[inference_from - model_index_offset] if pd.isna(model_over_time[inference_from]) else model_over_time[inference_from]
+    model_index_offset = (
+        get_last_non_na_index(model_over_time, inference_from)
+        if pd.isna(model_over_time[inference_from])
+        else 0
+    )
+    first_model = (
+        model_over_time[inference_from - model_index_offset]
+        if pd.isna(model_over_time[inference_from])
+        else 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 = []
 
     for index in tqdm(range(inference_from, inference_till)):
-        
-        last_model_index = index - ((index - inference_from) % retrain_every) - model_index_offset
-        train_window_start = X.index[inference_from] if expanding_window else X.index[index - window_size - 1]
+
+        last_model_index = (
+            index - ((index - inference_from) % retrain_every) - model_index_offset
+        )
+        train_window_start = (
+            X.index[inference_from]
+            if expanding_window
+            else X.index[index - window_size - 1]
+        )
 
         current_model = model_over_time[X.index[last_model_index]]
-        current_transformations = [transformation_over_time[X.index[last_model_index]] for transformation_over_time in transformations_over_time]
+        current_transformations = [
+            transformation_over_time[X.index[last_model_index]]
+            for transformation_over_time in transformations_over_time
+        ]
 
-        if current_model.predict_window_size == 'window_size': 
-            next_timestep = X.loc[train_window_start:X.index[index]]
-        else: 
+        if current_model.predict_window_size == "window_size":
+            next_timestep = X.loc[train_window_start : X.index[index]]
+        else:
             # we need to get a Dataframe out of it, since the transformation step always expects a 2D array, but it's equivalent to X.iloc[index]
-            next_timestep = X.loc[X.index[index]:X.index[index]]
-        
+            next_timestep = X.loc[X.index[index] : X.index[index]]
+
         for transformation in current_transformations:
             next_timestep = transformation.transform(next_timestep)
 
@@ -54,7 +84,9 @@ def walk_forward_inference(
 
         predictions[X.index[index]] = prediction
         if inference_from == index and len(probabilities.columns) != len(probs):
-            probabilities = probabilities.reindex(columns = ["prob_" + str(num) for num in range(0, len(probs.T))])
+            probabilities = probabilities.reindex(
+                columns=["prob_" + str(num) for num in range(0, len(probs.T))]
+            )
         probabilities.loc[X.index[index]] = probs
 
     return predictions, probabilities