[JTH] first implementation for kfold in RBF model

Author: tausiaj

bluemath_tk/core/metrics.py

@@ -0,0 +1,143 @@
+import numpy as np
+
+
+def bias(X_true: np.ndarray, X_pred: np.ndarray) -> float:
+    """
+    Calculate the BIAS.
+
+    Parameters
+    ----------
+    X_true : np.ndarray
+        True values.
+    X_pred : np.ndarray
+        Predicted values.
+
+    Returns
+    -------
+    float
+        The BIAS value.
+    """
+
+    if len(X_true) != len(X_pred):
+        raise ValueError("X_true and X_pred must have the same length")
+
+    return float(sum(X_true - X_pred) / len(X_true))
+
+
+def si(X_true: np.ndarray, X_pred: np.ndarray) -> float:
+    """
+    Calculate the Scatter Index (SI).
+
+    Parameters
+    ----------
+    X_true : np.ndarray
+        True values.
+    X_pred : np.ndarray
+        Predicted values.
+
+    Returns
+    -------
+    float
+        The Scatter Index value.
+    """
+
+    if len(X_true) != len(X_pred):
+        raise ValueError("X_true and X_pred must have the same length")
+
+    return float(
+        np.sqrt(
+            sum(((X_true - X_true.mean()) - (X_pred - X_pred.mean())) ** 2)
+            / sum(X_true**2)
+        )
+    )
+
+
+def mse(X_true: np.ndarray, X_pred: np.ndarray) -> float:
+    """
+    Calculate Mean Squared Error (MSE).
+
+    Parameters
+    ----------
+    X_true : np.ndarray
+        True values.
+    X_pred : np.ndarray
+        Predicted values.
+
+    Returns
+    -------
+    float
+        Mean squared error.
+    """
+
+    if len(X_true) != len(X_pred):
+        raise ValueError("X_true and X_pred must have the same length")
+
+    return float(np.mean((X_true - X_pred) ** 2))
+
+
+def mae(X_true: np.ndarray, X_pred: np.ndarray) -> float:
+    """
+    Calculate Mean Absolute Error (MAE).
+
+    Parameters
+    ----------
+    X_true : np.ndarray
+        True values.
+    X_pred : np.ndarray
+        Predicted values.
+
+    Returns
+    -------
+    float
+        Mean absolute error.
+    """
+
+    if len(X_true) != len(X_pred):
+        raise ValueError("X_true and X_pred must have the same length")
+
+    return float(np.mean(np.abs(X_true - X_pred)))
+
+
+def rmse(X_true: np.ndarray, X_pred: np.ndarray) -> float:
+    """
+    Calculate Root Mean Squared Error (RMSE).
+
+    Parameters
+    ----------
+    X_true : np.ndarray
+        True values.
+    X_pred : np.ndarray
+        Predicted values.
+
+    Returns
+    -------
+    float
+        Root mean squared error.
+    """
+
+    return float(np.sqrt(mse(X_true, X_pred)))
+
+
+def r2(X_true: np.ndarray, X_pred: np.ndarray) -> float:
+    """
+    Calculate the R² score.
+
+    Parameters
+    ----------
+    X_true : np.ndarray
+        True values.
+    X_pred : np.ndarray
+        Predicted values.
+
+    Returns
+    -------
+    float
+        The R² score.
+    """
+
+    if len(X_true) != len(X_pred):
+        raise ValueError("X_true and X_pred must have the same length")
+
+    return float(
+        1.0 - np.sum((X_true - X_pred) ** 2) / np.sum((X_true - X_true.mean()) ** 2)
+    )