forked from moreo/QuaPy
127 lines
5.1 KiB
Python
127 lines
5.1 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_linspace(n_prevalences=21, repeat=1, smooth_limits_epsilon=0.01):
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"""
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Produces a uniformly separated values of prevalence. By default, produces an array 21 prevalences, with step 0.05
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and with the limits smoothed, i.e.:
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[0.01, 0.05, 0.10, 0.15, ..., 0.90, 0.95, 0.99]
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:param n_prevalences: the number of prevalence values to sample from the [0,1] interval (default 21)
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:param repeat: number of times each prevalence is to be repeated (defaults to 1)
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:param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1
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:return: an array of uniformly separated prevalence values
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"""
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p = np.linspace(0., 1., num=n_prevalences, endpoint=True)
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p[0] += smooth_limits_epsilon
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p[-1] -= smooth_limits_epsilon
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if p[0] > p[1]:
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raise ValueError(f'the smoothing in the limits is greater than the prevalence step')
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if repeat > 1:
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p = np.repeat(p, repeat)
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return p
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def prevalence_from_labels(labels, n_classes):
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if labels.ndim != 1:
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raise ValueError(f'param labels does not seem to be a ndarray of label predictions')
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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 posteriors.ndim != 2:
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raise ValueError(f'param posteriors does not seem to be a ndarray of posteior probabilities')
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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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def normalize_prevalence(prevalences):
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assert prevalences.ndim==1, 'unexpected shape'
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accum = prevalences.sum()
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if accum > 0:
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return prevalences / accum
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else:
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# if all classifiers are trivial rejectors
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return np.ones_like(prevalences) / prevalences.size
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def num_prevalence_combinations(n_prevpoints:int, n_classes:int, n_repeats:int=1):
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"""
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Computes the number of prevalence combinations in the n_classes-dimensional simplex if nprevpoints equally distant
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prevalences are generated and n_repeats repetitions are requested
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:param n_classes: number of classes
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:param n_prevpoints: number of prevalence points.
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:param n_repeats: number of repetitions for each prevalence combination
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:return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the
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number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]
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"""
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__cache={}
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def __f(nc,np):
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if (nc,np) in __cache: # cached result
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return __cache[(nc,np)]
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if nc==1: # stop condition
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return 1
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else: # recursive call
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x = sum([__f(nc-1, np-i) for i in range(np)])
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__cache[(nc,np)] = x
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return x
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return __f(n_classes, n_prevpoints) * n_repeats
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def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repeats:int=1):
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"""
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Searches for the largest number of (equidistant) prevalence points to define for each of the n_classes classes so that
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the number of valid prevalences generated as combinations of prevalence points (points in a n_classes-dimensional
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simplex) do not exceed combinations_budget.
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:param n_classes: number of classes
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:param n_repeats: number of repetitions for each prevalence combination
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:param combinations_budget: maximum number of combinatios allowed
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:return: the largest number of prevalence points that generate less than combinations_budget valid prevalences
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"""
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assert n_classes > 0 and n_repeats > 0 and combinations_budget > 0, 'parameters must be positive integers'
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n_prevpoints = 1
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while True:
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combinations = num_prevalence_combinations(n_prevpoints, n_classes, n_repeats)
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if combinations > combinations_budget:
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return n_prevpoints-1
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else:
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n_prevpoints += 1
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