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funasr_local/utils/compute_eer.py

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+import numpy as np
+from sklearn.metrics import roc_curve
+import argparse
+
+
+def _compute_eer(label, pred, positive_label=1):
+    """
+    Python compute equal error rate (eer)
+    ONLY tested on binary classification
+
+    :param label: ground-truth label, should be a 1-d list or np.array, each element represents the ground-truth label of one sample
+    :param pred: model prediction, should be a 1-d list or np.array, each element represents the model prediction of one sample
+    :param positive_label: the class that is viewed as positive class when computing EER
+    :return: equal error rate (EER)
+    """
+
+    # all fpr, tpr, fnr, fnr, threshold are lists (in the format of np.array)
+    fpr, tpr, threshold = roc_curve(label, pred, pos_label=positive_label)
+    fnr = 1 - tpr
+
+    # the threshold of fnr == fpr
+    eer_threshold = threshold[np.nanargmin(np.absolute((fnr - fpr)))]
+
+    # theoretically eer from fpr and eer from fnr should be identical but they can be slightly differ in reality
+    eer_1 = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
+    eer_2 = fnr[np.nanargmin(np.absolute((fnr - fpr)))]
+
+    # return the mean of eer from fpr and from fnr
+    eer = (eer_1 + eer_2) / 2
+    return eer, eer_threshold
+
+
+def compute_eer(trials_path, scores_path):
+    labels = []
+    for one_line in open(trials_path, "r"):
+        labels.append(one_line.strip().rsplit(" ", 1)[-1] == "target")
+    labels = np.array(labels, dtype=int)
+
+    scores = []
+    for one_line in open(scores_path, "r"):
+        scores.append(float(one_line.strip().rsplit(" ", 1)[-1]))
+    scores = np.array(scores, dtype=float)
+
+    eer, threshold = _compute_eer(labels, scores)
+    return eer, threshold
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("trials", help="trial list")
+    parser.add_argument("scores", help="score file, normalized to [0, 1]")
+    args = parser.parse_args()
+
+    eer, threshold = compute_eer(args.trials, args.scores)
+    print("EER is {:.4f} at threshold {:.4f}".format(eer * 100.0, threshold))
+
+
+if __name__ == '__main__':
+    main()