245 lines
9.2 KiB
Python
245 lines
9.2 KiB
Python
import itertools
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from collections.abc import Generator
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from contextlib import ExitStack
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from abc import ABCMeta, abstractmethod
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from quapy.data import LabelledCollection
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import quapy.functional as F
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# 0.1.7
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# change the LabelledCollection API (removing protocol-related samplings)
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# need to change the two references to the above in the wiki / doc, and code examples...
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# removed artificial_prevalence_sampling from functional
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# class AbstractProtocol(metaclass=ABCMeta):
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# def __call__(self):
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# for g in self.gen():
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# yield g
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#
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# @abstractmethod
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# def gen(self):
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# ...
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class AbstractStochasticProtocol(metaclass=ABCMeta):
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def __init__(self, seed=None):
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self.random_seed = seed
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@property
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def random_seed(self):
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return self._random_seed
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@random_seed.setter
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def random_seed(self, seed):
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self._random_seed = seed
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@abstractmethod
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def samples_parameters(self):
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"""
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This function has to return all the necessary parameters to replicate the samples
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:return: a list of parameters, each of which serves to deterministically generate a sample
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"""
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...
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@abstractmethod
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def sample(self, params):
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"""
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Extract one sample determined by the given parameters
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:param params: all the necessary parameters to generate a sample
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:return: one sample (the same sample has to be generated for the same parameters)
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"""
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...
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def __call__(self):
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with ExitStack() as stack:
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if self.random_seed is not None:
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stack.enter_context(qp.util.temp_seed(self.random_seed))
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for params in self.samples_parameters():
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yield self.sample(params)
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class APP(AbstractStochasticProtocol):
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"""
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Implementation of the artificial prevalence protocol (APP).
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The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g.,
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[0, 0.05, 0.1, 0.15, ..., 1], if `n_prevalences=21`), and generating all valid combinations of
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prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ...,
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[1, 0, 0] prevalence values of size `sample_size` will be yielded). The number of samples for each valid
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combination of prevalence values is indicated by `repeats`.
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:param sample_size: integer, number of instances in each sample
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:param n_prevalences: the number of equidistant prevalence points to extract from the [0,1] interval for the
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grid (default is 21)
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:param repeats: number of copies for each valid prevalence vector (default is 1)
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:param random_seed: allows replicating samples across runs (default None)
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"""
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def __init__(self, data:LabelledCollection, sample_size, n_prevalences=21, repeats=1, random_seed=None):
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super(APP, self).__init__(random_seed)
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self.data = data
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self.sample_size = sample_size
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self.n_prevalences = n_prevalences
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self.repeats = repeats
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def prevalence_grid(self, dimensions):
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"""
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Generates vectors of prevalence values from an exhaustive grid of prevalence values. The
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number of prevalence values explored for each dimension depends on `n_prevalences`, so that, if, for example,
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`n_prevalences=11` then the prevalence values of the grid are taken from [0, 0.1, 0.2, ..., 0.9, 1]. Only
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valid prevalence distributions are returned, i.e., vectors of prevalence values that sum up to 1. For each
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valid vector of prevalence values, `repeat` copies are returned. The vector of prevalence values can be
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implicit (by setting `return_constrained_dim=False`), meaning that the last dimension (which is constrained
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to 1 - sum of the rest) is not returned (note that, quite obviously, in this case the vector does not sum up to
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1). Note that this method is deterministic, i.e., there is no random sampling anywhere.
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:param dimensions: the number of classes
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:return: a `np.ndarray` of shape `(n, dimensions)` if `return_constrained_dim=True` or of shape
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`(n, dimensions-1)` if `return_constrained_dim=False`, where `n` is the number of valid combinations found
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in the grid multiplied by `repeat`
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"""
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s = np.linspace(0., 1., self.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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prevs = np.asarray(prevs).reshape(len(prevs), -1)
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if self.repeats > 1:
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prevs = np.repeat(prevs, self.repeats, axis=0)
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return prevs
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def samples_parameters(self):
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indexes = []
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for prevs in self.prevalence_grid(dimensions=self.data.n_classes):
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index = data.sampling_index(self.sample_size, *prevs)
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indexes.append(index)
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return indexes
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def sample(self, index):
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return self.data.sampling_from_index(index)
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class NPP(AbstractStochasticProtocol):
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"""
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A generator of samples that implements the natural prevalence protocol (NPP). The NPP consists of drawing
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samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.
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:param sample_size: integer, the number of instances in each sample
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:param repeats: the number of samples to generate
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"""
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def __init__(self, data:LabelledCollection, sample_size, repeats=1, random_seed=None):
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super(NPP, self).__init__(random_seed)
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self.data = data
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self.sample_size = sample_size
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self.repeats = repeats
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self.random_seed = random_seed
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def samples_parameters(self):
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indexes = []
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for _ in range(self.repeats):
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index = data.uniform_sampling_index(self.sample_size)
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indexes.append(index)
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return indexes
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def sample(self, index):
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return self.data.sampling_from_index(index)
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class USimplexPP(AbstractStochasticProtocol):
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def __init__(self, data: LabelledCollection, sample_size, repeats=1, random_seed=None):
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super(USimplexPP, self).__init__(random_seed)
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self.data = data
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self.sample_size = sample_size
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self.repeats = repeats
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self.random_seed = random_seed
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def samples_parameters(self):
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indexes = []
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for prevs in F.uniform_simplex_sampling(n_classes=data.n_classes, size=self.repeats):
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index = data.sampling_index(self.sample_size, *prevs)
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indexes.append(index)
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return indexes
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def sample(self, index):
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return self.data.sampling_from_index(index)
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class CovariateShift(AbstractStochasticProtocol):
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"""
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Generates mixtures of two domains (A and B) at controlled rates, but preserving the original class prevalence.
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:param domainA:
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:param domainB:
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:param sample_size:
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:param repeats:
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:param prevalence: the prevalence to preserv along the mixtures. If specified, should be an array containing
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one prevalence value (positive float) for each class and summing up to one. If not specified, the prevalence
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will be taken from the domain A (default).
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:param mixture_points: an integer indicating the number of points to take from a linear scale (e.g., 21 will
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generate the mixture points [1, 0.95, 0.9, ..., 0]), or the array of mixture values itself.
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the specific points
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:param random_seed:
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"""
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def __init__(
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self,
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domainA: LabelledCollection,
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domainB: LabelledCollection,
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sample_size,
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repeats=1,
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prevalence=None,
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mixture_points=11,
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random_seed=None):
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super(CovariateShift, self).__init__(random_seed)
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self.data = data
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self.sample_size = sample_size
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self.repeats = repeats
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if prevalence is None:
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self.prevalence = domainA.prevalence()
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else:
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self.prevalence = np.asarray(prevalence)
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assert len(self.prevalence) == domainA.n_classes, \
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f'wrong shape for the vector prevalence (expected {domainA.n_classes})'
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assert F.check_prevalence_vector(self.prevalence), \
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f'the prevalence vector is not valid (either it contains values outside [0,1] or does not sum up to 1)'
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assert isinstance(mixture_points, int) or
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self.random_seed = random_seed
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def samples_parameters(self):
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indexes = []
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for _ in range(self.repeats):
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index = data.uniform_sampling_index(self.sample_size)
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indexes.append(index)
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return indexes
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def sample(self, index):
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return self.data.sampling_from_index(index)
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if __name__=='__main__':
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import numpy as np
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import quapy as qp
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y = [0]*25 + [1]*25 + [2]*25 + [3]*25
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X = [str(i)+'-'+str(yi) for i, yi in enumerate(y)]
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data = LabelledCollection(X, y, classes_=sorted(np.unique(y)))
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# p=CounterExample(1, 8, 10, 5)
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# p = APP(data, sample_size=10, n_prevalences=11, random_seed=42)
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# p = NPP(data, sample_size=10, repeats=10, random_seed=42)
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# p = NPP(data, sample_size=10, repeats=10)
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p = USimplexPP(data, sample_size=10, repeats=10)
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for _ in range(2):
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print('init generator', p.__class__.__name__)
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for i in p():
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# print(i)
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print(i.instances, i.labels, i.prevalence())
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print('done')
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