Metrics for SED evaluation

metrics.py

import numpy as np
import sklearn.metrics as skmetrics
from scipy import stats

def roc(y_true, y_pred, average=None):
    return skmetrics.roc_auc_score(y_true, y_pred, average=average)


def mAP(y_true, y_pred, average=None):
    return skmetrics.average_precision_score(y_true, y_pred, average=average)


def d_prime(auc):
    return stats.norm().ppf(auc) * np.sqrt(2.0)

y_true = np.array([0,1,1,1])
y_pred = np.array([0.5,0.2,0.1,0.8])
mAP = np.nan_to_num(mAP(y_true, y_pred))
auc = np.nan_to_num(roc(y_true, y_pred))
dprime = np.nan_to_num(d_prime(np.mean(auc)))