conver to numpy

directional_bias_amplification.py

@@ -15,6 +15,8 @@
 
 import evaluate
 
+import numpy as np
+
 _DESCRIPTION = """
 Directional Bias Amplification is a metric that captures the amount of bias (i.e., a conditional probability) that is amplified. 
 This metric was introduced in the ICML 2021 paper "Directional Bias Amplification" (https://arxiv.org/abs/2102.12594).
@@ -60,7 +62,7 @@ class DirectionalBiasAmplification(evaluate.EvaluationModule):
 
     def _compute(self, predictions, references, attributes):
 
-        task_preds, task_labels, attribute_labels = predictions, references, attributes
+        task_preds, task_labels, attribute_labels = np.array(predictions), np.array(references), np.array(attributes)
 
         assert len(task_labels.shape) == 2 and len(attribute_labels.shape) == 2, 'Please read the shape of the expected inputs, which should be "num samples" by "num classification items"'
         assert len(task_labels) == len(attribute_labels) == len(task_preds), 'Please make sure the number of samples in the three input arrays is the same.'