[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

machine_learning/adaboost.py

@@ -111,7 +111,9 @@ def _build_stump(
             thresholds = np.unique(feature_matrix[:, feature])
             for threshold in thresholds:
                 for polarity in [1, -1]:
-                    pred = self._stump_predict(feature_matrix, feature, threshold, polarity)
+                    pred = self._stump_predict(
+                        feature_matrix, feature, threshold, polarity
+                    )
                     error = np.sum(sample_weights * (pred != target_signed))
 
                     # Keep stump with lowest weighted error
@@ -148,4 +150,4 @@ def _stump_predict(
         else:
             pred[feature_matrix[:, feature] > threshold] = -1
 
-        return pred
+        return pred

machine_learning/arima.py

@@ -12,12 +12,13 @@
 array([10.99999999, 12.00000001])
 """
 
-
 import numpy as np
 
 
 class ARIMAModel:
-    def __init__(self, ar_order: int = 1, diff_order: int = 0, ma_order: int = 0) -> None:
+    def __init__(
+        self, ar_order: int = 1, diff_order: int = 0, ma_order: int = 0
+    ) -> None:
         """Initialize ARIMA model.
         Args:
             ar_order: Autoregressive order (p)
@@ -51,9 +52,11 @@ def fit(self, time_series: np.ndarray) -> "ARIMAModel":
         y = np.asarray(time_series)
         y_diff = self.difference(y, self.diff_order)
         # Build lagged feature matrix
-        feature_matrix = np.column_stack([np.roll(y_diff, i) for i in range(1, self.ar_order + 1)])
-        feature_matrix = feature_matrix[self.ar_order:]
-        target = y_diff[self.ar_order:]
+        feature_matrix = np.column_stack(
+            [np.roll(y_diff, i) for i in range(1, self.ar_order + 1)]
+        )
+        feature_matrix = feature_matrix[self.ar_order :]
+        target = y_diff[self.ar_order :]
         # Add intercept
         feature_matrix = np.hstack(
             [np.ones((feature_matrix.shape[0], 1)), feature_matrix]
@@ -79,10 +82,10 @@ def predict(self, time_series: np.ndarray, n_periods: int = 1) -> np.ndarray:
         array([10.99999999, 12.00000001])
         """
         y = np.asarray(time_series)
-        y_pred = list(y[-self.ar_order:])
+        y_pred = list(y[-self.ar_order :])
         for _ in range(n_periods):
             # Build feature vector for prediction
-            features = [1] + y_pred[-self.ar_order:][::-1]
+            features = [1] + y_pred[-self.ar_order :][::-1]
             next_val = np.dot(features, self.coef_)
             y_pred.append(next_val)
-        return np.array(y_pred[self.ar_order:])
+        return np.array(y_pred[self.ar_order :])