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

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Author: pre-commit-ci[bot]

machine_learning/loss_functions.py

@@ -667,26 +667,26 @@ def mean_absolute_percentage_error(
     y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
 ) -> float:
     """
-    Calculate the Mean Absolute Percentage Error (MAPE) between y_true and y_pred.
-　  MAPE is calculated by dividing the absolute error by the absolute value of
-    the actual measurement and then averaging.
-    SMAPE = (1/n) * Σ( |y_true - y_pred| / |y_true|)
-    Reference: 
-    https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
-    Parameters:
-    - y_true: The true values (ground truth)
-    - y_pred: The predicted values
-    - epsilon: Small constant to avoid division by zero
-    >>> true_values = np.array([100, 200, 300, 400])
-    >>> predicted_values = np.array([110, 190, 310, 420])
-    >>> float(symmetric_mean_absolute_percentage_error(true_values, predicted_values))
-    0.058333333333333334
-    >>> true_labels = np.array([100, 200, 300])
-    >>> predicted_probs = np.array([110, 190, 310, 420])
-    >>> symmetric_mean_absolute_percentage_error(true_labels, predicted_probs)
-    Traceback (most recent call last):
-        ...
-    ValueError: Input arrays must have the same length.
+        Calculate the Mean Absolute Percentage Error (MAPE) between y_true and y_pred.
+    MAPE is calculated by dividing the absolute error by the absolute value of
+        the actual measurement and then averaging.
+        SMAPE = (1/n) * Σ( |y_true - y_pred| / |y_true|)
+        Reference:
+        https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
+        Parameters:
+        - y_true: The true values (ground truth)
+        - y_pred: The predicted values
+        - epsilon: Small constant to avoid division by zero
+        >>> true_values = np.array([100, 200, 300, 400])
+        >>> predicted_values = np.array([110, 190, 310, 420])
+        >>> float(symmetric_mean_absolute_percentage_error(true_values, predicted_values))
+        0.058333333333333334
+        >>> true_labels = np.array([100, 200, 300])
+        >>> predicted_probs = np.array([110, 190, 310, 420])
+        >>> symmetric_mean_absolute_percentage_error(true_labels, predicted_probs)
+        Traceback (most recent call last):
+            ...
+        ValueError: Input arrays must have the same length.
     """
     if len(y_true) != len(y_pred):
         raise ValueError("Input arrays must have the same length.")