Merge pull request #15 from UBC-MDS/mape

added MAPE function and renamed MSE function

src/matrics_calculator/MAPE.py

@@ -0,0 +1,35 @@
+# Mean Absolute Percentage Error (MAPE) calculation
+def mean_absolute_percentage_error(y_true, y_pred):
+    """
+    Calculate the Mean Absolute Percentage Error (MAPE) metric for regression.
+
+    This function computes the average percentage difference between the predicted values (`y_pred`)
+    and the actual values (`y_true`). It measures the relative magnitude of errors in prediction, 
+    expressed as a percentage. MAPE is widely used to evaluate regression models, especially when 
+    relative error matters more than absolute error.
+
+    Parameters:
+    ----------
+    y_true : array-like
+        True values of the target variable.
+    y_pred : array-like
+        Predicted values from the model.
+
+    Returns:
+    -------
+    float
+        The Mean Absolute Percentage Error (as a percentage).
+
+    Notes:
+    ------
+    MAPE is defined as:
+        MAPE = (1 / n) * sum(|(y_true - y_pred) / y_true|) * 100
+    where n is the number of observations.
+
+    Examples:
+    ---------
+    >>> y_true = [100, 200, 300]
+    >>> y_pred = [110, 190, 290]
+    >>> mean_absolute_percentage_error(y_true, y_pred)
+    3.3333
+    """

src/matrics_calculator/matrics_calculator.py → src/matrics_calculator/MSE.py

@@ -1,10 +1,4 @@
 
-
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-
-
-
-
 # Mean Squared Error (MSE) calculation
 def mean_squared_error(y_true, y_pred): 
     """