Linear make prediction

libmultilabel/linear/linear.py

@@ -540,3 +540,20 @@ def predict_values(model, x: sparse.csr_matrix) -> np.ndarray:
         ], 'csr')
 
     return (x * model['weights']).A + model['threshold']
+
+def predict_topk(preprocessor, x: np.ndarray, top_k: int = 5) -> 'list[tuple(str)]':
+    """Make top k predictions from decision values.
+
+    Args:
+        preprocessor: The preprocessor object from ``Preprocessor`` API used to load and preprocess the data.
+        x (np.ndarray): A matrix of decision values with dimension number of instances * number of classes.
+        top_k (int): Determine how many classes per instance should be predicted.
+
+    Returns:
+        list of tuples which contain predicted top k labels.
+    """
+    top_k_ind = np.argpartition(x, -top_k)[:, -top_k:]
+    pred_result = np.zeros(x.shape)
+    np.put_along_axis(pred_result, top_k_ind, 1, -1)
+    pred_result[pred_result != 1] = 0
+    return preprocessor.binarizer.inverse_transform(pred_result)