50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
from collections import defaultdict
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import numpy as np
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import itertools
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def artificial_prevalence_sampling(dimensions, n_prevalences=21, repeat=1, return_constrained_dim=False):
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s = np.linspace(0., 1., n_prevalences, endpoint=True)
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s = [s] * (dimensions - 1)
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prevs = [p for p in itertools.product(*s, repeat=1) if sum(p)<=1]
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if return_constrained_dim:
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prevs = [p+(1-sum(p),) for p in prevs]
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prevs = np.asarray(prevs).reshape(len(prevs), -1)
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if repeat>1:
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prevs = np.repeat(prevs, repeat, axis=0)
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return prevs
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def prevalence_from_labels(labels, n_classes):
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unique, counts = np.unique(labels, return_counts=True)
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by_class = defaultdict(lambda:0, dict(zip(unique, counts)))
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prevalences = np.asarray([by_class[ci] for ci in range(n_classes)], dtype=np.float)
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prevalences /= prevalences.sum()
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return prevalences
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def prevalence_from_probabilities(posteriors, binarize: bool = False):
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if binarize:
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predictions = np.argmax(posteriors, axis=-1)
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return prevalence_from_labels(predictions, n_classes=posteriors.shape[1])
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else:
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prevalences = posteriors.mean(axis=0)
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prevalences /= prevalences.sum()
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return prevalences
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def strprev(prevalences, prec=3):
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return '['+ ', '.join([f'{p:.{prec}f}' for p in prevalences]) + ']'
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def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True):
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den = tpr - fpr
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if den == 0:
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den += 1e-8
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adjusted = (prevalence_estim - fpr) / den
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if clip:
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adjusted = np.clip(adjusted, 0., 1.)
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return adjusted
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