Merge pull request #172 from kwanit1142/master

Added Mean Absolute Percentage Error

MLlib/loss_func.py

@@ -394,3 +394,34 @@ def loss(X, Y, W):
         sigmoid = Sigmoid()
         H = sigmoid.activations(np.dot(X, W).T)
         return np.sqrt((1 / M) * (np.sum(np.log(Y + 1) - np.log(H + 1))))
+
+
+class MeanAbsolutePrecentageError():
+    """""
+    Calcute Mean Absolute Percentage Loss
+    """
+
+    @staticmethod
+    def loss(X, Y, W):
+        """
+            Calculate  Mean Squared Log Loss
+
+            PARAMETERS
+            ==========
+
+            X:ndarray(dtype=float,ndim=1)
+              input vector
+            Y:ndarray(dtype=float)
+              output vector
+            W:ndarray(dtype=float)
+              Weights
+
+            RETURNS
+            =======
+
+            array of Mean Absolute Percentage Loss
+        """
+
+        y_pred = np.dot(X, W).T
+        L = np.sum(np.true_divide((np.abs(Y - y_pred) * 100), Y)) / X.shape[0]
+        return L

README.md

@@ -94,4 +94,4 @@ Follow the following steps to get started with contributing to the repository.
 | Log_cosh | [loss_func.py](https://github.com/RoboticsClubIITJ/ML-DL-implementation/blob/master/MLlib/loss_func.py#L248) | | | F1 Score | [metrics.py](https://github.com/RoboticsClubIITJ/ML-DL-implementation/blob/master/MLlib/metrics.py#L85) 
 | Huber | [loss_func.py](https://github.com/RoboticsClubIITJ/ML-DL-implementation/blob/master/MLlib/loss_func.py#L300) | | | F-B Theta | [metrics.py](https://github.com/RoboticsClubIITJ/ML-DL-implementation/blob/master/MLlib/metrics.py#L88) 
 | Mean Squared Log Error | [loss_func.py](https://github.com/RoboticsClubIITJ/ML-DL-implementation/blob/master/MLlib/loss_func.py#L367) | | | Specificity | [metrics.py](https://github.com/RoboticsClubIITJ/ML-DL-implementation/blob/master/MLlib/metrics.py#L86) 
-
+| Mean Absolute Percentage Error | [loss_func.py](https://github.com/RoboticsClubIITJ/ML-DL-implementation/blob/master/MLlib/loss_func.py#L399)

setup.py

@@ -27,7 +27,7 @@
     install_requires=[
         'numpy>=1.18.0',
         'matplotlib>=3.0.0',
-        'scipy'
+        'scipy',
         'pandas'
     ],
 )