wehub-resource-sync
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
def calculate_measure(tp, fn, fp):
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# avoid nan
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if tp == 0:
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return 0, 0, 0
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p = tp * 1.0 / (tp + fp)
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r = tp * 1.0 / (tp + fn)
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if (p + r) > 0:
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f1 = 2.0 * (p * r) / (p + r)
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else:
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f1 = 0
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return p, r, f1
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class Measure(object):
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def __init__(self, num_classes, target_class):
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"""
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Args:
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num_classes: number of classes.
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target_class: target class we focus on, used to print info and do early stopping.
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"""
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self.num_classes = num_classes
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self.target_class = target_class
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self.true_positives = {}
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self.false_positives = {}
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self.false_negatives = {}
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self.target_best_f1 = 0.0
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self.target_best_f1_epoch = 0
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self.reset_info()
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def reset_info(self):
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"""
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reset info after each epoch.
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"""
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self.true_positives = {
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cur_class: [] for cur_class in range(self.num_classes)
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}
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self.false_positives = {
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cur_class: [] for cur_class in range(self.num_classes)
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}
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self.false_negatives = {
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cur_class: [] for cur_class in range(self.num_classes)
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}
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def append_measures(self, predictions, labels):
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predicted_classes = predictions.argmax(dim=1)
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for cl in range(self.num_classes):
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cl_indices = labels == cl
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pos = predicted_classes == cl
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hits = predicted_classes[cl_indices] == labels[cl_indices]
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tp = hits.sum()
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fn = hits.size(0) - tp
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fp = pos.sum() - tp
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self.true_positives[cl].append(tp.cpu())
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self.false_negatives[cl].append(fn.cpu())
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self.false_positives[cl].append(fp.cpu())
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def get_each_timestamp_measure(self):
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precisions = []
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recalls = []
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f1s = []
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for i in range(len(self.true_positives[self.target_class])):
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tp = self.true_positives[self.target_class][i]
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fn = self.false_negatives[self.target_class][i]
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fp = self.false_positives[self.target_class][i]
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p, r, f1 = calculate_measure(tp, fn, fp)
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precisions.append(p)
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recalls.append(r)
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f1s.append(f1)
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return precisions, recalls, f1s
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def get_total_measure(self):
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tp = sum(self.true_positives[self.target_class])
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fn = sum(self.false_negatives[self.target_class])
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fp = sum(self.false_positives[self.target_class])
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p, r, f1 = calculate_measure(tp, fn, fp)
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return p, r, f1
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def update_best_f1(self, cur_f1, cur_epoch):
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if cur_f1 > self.target_best_f1:
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self.target_best_f1 = cur_f1
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self.target_best_f1_epoch = cur_epoch
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