# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
[docs]def calculate_ap(rec, prec):
"""
Computes the AP under the precision recall curve.
"""
rec, prec = rec.reshape(rec.size, 1), prec.reshape(prec.size, 1)
z, o = np.zeros((1, 1)), np.ones((1, 1))
mrec, mpre = np.vstack((z, rec, o)), np.vstack((z, prec, z))
for i in range(len(mpre) - 2, -1, -1):
mpre[i] = max(mpre[i], mpre[i + 1])
indices = np.where(mrec[1:] != mrec[0:-1])[0] + 1
ap = 0
for i in indices:
ap = ap + (mrec[i] - mrec[i - 1]) * mpre[i]
return ap
[docs]def get_precision_recall(targets, scores, weights=None):
"""
[P, R, score, ap] = get_precision_recall(targets, scores, weights)
Args:
targets: number of occurrences of this class in the ith image
scores: score for this image
weights: 0 or 1 whether where 0 means we should ignore the sample
Returns:
P, R: precision and recall
score: score which corresponds to the particular precision and recall
ap: average precision
"""
if weights is not None:
sortweights = weights
else:
sortweights = np.ones((targets.shape[0],), dtype=np.float)
valid_inds = np.where(sortweights == 1)
targets = targets[valid_inds]
scores = scores[valid_inds]
# binarize targets
targets = np.array(targets > 0, dtype=np.float32)
tog = np.hstack(
(
targets[:, np.newaxis].astype(np.float64),
scores[:, np.newaxis].astype(np.float64),
)
)
ind = np.argsort(scores)
ind = ind[::-1]
score = np.array([tog[i, 1] for i in ind])
sortcounts = np.array([tog[i, 0] for i in ind])
tp = sortcounts
fp = sortcounts.copy()
for i in range(sortcounts.shape[0]):
if sortcounts[i] >= 1:
fp[i] = 0.0
elif sortcounts[i] < 1:
fp[i] = 1.0
P = np.cumsum(tp) / (np.cumsum(tp) + np.cumsum(fp))
numinst = np.sum(targets)
R = np.cumsum(tp) / numinst
ap = calculate_ap(R, P)
return P, R, score, ap