fixed_fp_rate_analysis.py

from sklearn.metrics import roc_curve, det_curve

def get_threshold_from_rate(thresholds, rate_array, rate):
    index = np.where(rate_array >= rate)[0][0]
    return thresholds[index]

def get_value_from_threshold(values, thresholds, threshold):
    try:
        thr_index = np.where(thresholds <= threshold)[0][0]
    except IndexError:
        thr_index = 0
    return values[thr_index]

metrics = {}
metrics["fpr"], metrics["tpr"], metrics["threshold_roc"] = roc_curve(y_true, preds)
_, metrics["fnr"], metrics["threshold_det"] = det_curve(y_true, preds)

print("---" * 35)
for fpr_rate in [0.00005, 0.0001, 0.0005, 0.001]:
    threshold = get_threshold_from_rate(metrics["threshold_roc"], metrics["fpr"], fpr_rate)
    tpr_rate = get_value_from_threshold(metrics["tpr"], metrics["threshold_roc"], threshold)
    print(f"{encoding:>20} : False Positive rate: {fpr_rate*100:>5.3f}% | Detection rate: {tpr_rate*100:>5.2f}% | Threshold: {threshold:>5.4f} ")
    print("---" * 35)