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varunagrawal/average_precision.py

Created December 10, 2018 23:24
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average_precision.py
import numpy as np
def average_precision(recall, precision):
mrec = np.hstack((0, recall, 1))
mpre = np.hstack((0, precision, 0))
for i in range(mpre.size-2, -1, -1):
mpre[i] = max(mpre[i], mpre[i+1])
i = np.ravel(np.where(mrec[1:] != mrec[0:-1])) + 1
ap = np.sum((mrec[i]-mrec[i-1]) * mpre[i])
return ap
def precision_recall(truth, scores, pos_label=1, neg_label=0):
desc_score_idx = np.argsort(-scores, kind='stable')
scores = scores[desc_score_idx]
truth = truth[desc_score_idx]
distinct_value_indices = np.where(np.diff(scores))[0]
threshold_idxs = np.r_[distinct_value_indices, truth.size - 1]
tp = (truth == pos_label).astype(np.float)
fp = (truth == neg_label).astype(np.float)
tps = np.cumsum(tp)[threshold_idxs]
fps = np.cumsum(fp)[threshold_idxs]
precision = tps / (tps + fps)
if tps[-1] == 0:
recall = np.ones(tps.size)
else:
recall = tps / tps[-1]
return precision, recall
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