Merge pull request #75 from ayrna/to_array_conversion_in_metrics

[ENH] Added to array and probas to preds castings in metrics

dlordinal/metrics/metrics.py

@@ -35,6 +35,9 @@ def ranked_probability_score(y_true, y_proba):
     >>> ranked_probability_score(y_true, y_pred)
     0.506875
     """
+    y_true = np.array(y_true)
+    y_proba = np.array(y_proba)
+
     y_oh = np.zeros(y_proba.shape)
     y_oh[np.arange(len(y_true)), y_true] = 1
 
@@ -72,6 +75,13 @@ def minimum_sensitivity(y_true: np.ndarray, y_pred: np.ndarray) -> float:
     >>> minimum_sensitivity(y_true, y_pred)
     0.5
     """
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
+
+    if len(y_true.shape) > 1:
+        y_true = np.argmax(y_true, axis=1)
+    if len(y_pred.shape) > 1:
+        y_pred = np.argmax(y_pred, axis=1)
 
     sensitivities = recall_score(y_true, y_pred, average=None)
     return np.min(sensitivities)
@@ -101,6 +111,8 @@ def accuracy_off1(y_true: np.ndarray, y_pred: np.ndarray, labels=None) -> float:
     >>> accuracy_off1(y_true, y_pred)
     1.0
     """
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
 
     if len(y_true.shape) > 1:
         y_true = np.argmax(y_true, axis=1)
@@ -141,6 +153,13 @@ def gmsec(y_true: np.ndarray, y_pred: np.ndarray) -> float:
     >>> gmec(y_true, y_pred)
     0.5
     """
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
+
+    if len(y_true.shape) > 1:
+        y_true = np.argmax(y_true, axis=1)
+    if len(y_pred.shape) > 1:
+        y_pred = np.argmax(y_pred, axis=1)
 
     sensitivities = recall_score(y_true, y_pred, average=None)
     return np.sqrt(sensitivities[0] * sensitivities[-1])
@@ -161,6 +180,8 @@ def amae(y_true: np.ndarray, y_pred: np.ndarray):
     amae : float
             Average mean absolute error.
     """
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
 
     if len(y_true.shape) > 1:
         y_true = np.argmax(y_true, axis=1)
@@ -193,6 +214,8 @@ def mmae(y_true: np.ndarray, y_pred: np.ndarray):
     mmae : float
             Maximum mean absolute error.
     """
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
 
     if len(y_true.shape) > 1:
         y_true = np.argmax(y_true, axis=1)