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