fix(WalkForward): major bug where we passed in "window of windows of data" is resolved, refactored walk_forward_train_test() and load_data()

utils/walk_forward.py

@@ -0,0 +1,44 @@
+import pandas as pd
+from sklearn.base import clone
+from utils.typing import SKLearnModel
+import numpy as np
+
+def walk_forward_train_test(
+                            model_name: str,
+                            model: SKLearnModel,
+                            X: pd.DataFrame,
+                            y: pd.Series,
+                            window_size: int,
+                            retrain_every: int
+                        ) -> tuple[list[SKLearnModel], list[float]]:
+                        
+    print("Training: ", model_name)
+    predictions = pd.Series(index=y.index)
+    models = pd.Series(index=y.index)
+
+    train_from = window_size
+    train_till = y.index[-1]
+    
+    iterations_since_retrain = 0
+
+    for i in range(train_from, train_till):
+
+        iterations_since_retrain += 1
+        window_start = i - window_size
+        window_end = i
+        X_slice = X[window_start:window_end]
+        y_slice = y[window_start:window_end]
+
+        if iterations_since_retrain >= retrain_every or pd.isna(models[i-1]):
+            current_model = clone(model)
+            current_model.fit(X_slice.to_numpy(), y_slice)
+            iterations_since_retrain = 0
+        else:
+            current_model = models[i-1]
+
+        models[window_end] = current_model
+
+        next_timestep = X.iloc[window_end+1].to_numpy().reshape(1, -1)
+        predictions[window_end+1] = current_model.predict(next_timestep).item()
+
+    return models, predictions