348 lines
14 KiB
Python
348 lines
14 KiB
Python
from copy import deepcopy
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import quapy as qp
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import numpy as np
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import itertools
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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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from os.path import exists
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from glob import glob
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class AbstractProtocol(metaclass=ABCMeta):
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@abstractmethod
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def __call__(self):
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"""
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Implements the protocol. Yields one sample at a time
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:return: yields one sample at a time
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"""
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...
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def total(self):
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"""
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Indicates the total number of samples that the protocol generates.
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:return: The number of samples to generate if known, or `None` otherwise.
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"""
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return None
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class AbstractStochasticSeededProtocol(AbstractProtocol):
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"""
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An AbstractStochasticSeededProtocol is a protocol that generates, via any random procedure (e.g.,
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via random sapling), sequences of `LabelledCollection` samples. The protocol abstraction enforces
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the object to be instantiated using a seed, so that the sequence can be completely replicated.
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In order to make this functionality possible, the classes extending this abstraction need to
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implement only two functions, :meth:`samples_parameters` which generates all the parameters
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needed for extracting the samples, and :meth:`sample` that, given some parameters as input,
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deterministically generates a sample.
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:param seed: the seed for allowing to replicate any sequence of samples. Default is None, meaning that
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the sequence will be different every time the protocol is called.
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"""
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_random_seed = -1 # means "not set"
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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 == -1:
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raise ValueError('The random seed has never been initialized. '
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'Set it to None not to impose replicability.')
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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.collator(self.sample(params))
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def collator(self, sample, *args):
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return sample
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class OnLabelledCollectionProtocol:
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RETURN_TYPES = ['sample_prev', 'labelled_collection']
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def get_labelled_collection(self):
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return self.data
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def on_preclassified_instances(self, pre_classifications, in_place=False):
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assert len(pre_classifications) == len(self.data), \
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f'error: the pre-classified data has different shape ' \
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f'(expected {len(self.data)}, found {len(pre_classifications)})'
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if in_place:
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self.data.instances = pre_classifications
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return self
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else:
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new = deepcopy(self)
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return new.on_preclassified_instances(pre_classifications, in_place=True)
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@classmethod
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def get_collator(cls, return_type='sample_prev'):
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assert return_type in cls.RETURN_TYPES, \
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f'unknown return type passed as argument; valid ones are {cls.RETURN_TYPES}'
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if return_type=='sample_prev':
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return lambda lc:lc.Xp
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elif return_type=='labelled_collection':
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return lambda lc:lc
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class APP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
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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 data: a `LabelledCollection` from which the samples will be drawn
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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 10)
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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=10, random_seed=None, return_type='sample_prev'):
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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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self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
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def prevalence_grid(self):
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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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: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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dimensions = self.data.n_classes
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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) <= 1.0)]
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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():
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index = self.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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def total(self):
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return F.num_prevalence_combinations(self.n_prevalences, self.data.n_classes, self.repeats)
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class NPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
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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 data: a `LabelledCollection` from which the samples will be drawn
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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. Default is 100.
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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, repeats=100, random_seed=None, return_type='sample_prev'):
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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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self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
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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 = self.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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def total(self):
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return self.repeats
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class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
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"""
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A variant of :class:`APP` that, instead of using a grid of equidistant prevalence values,
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relies on the Kraemer algorithm for sampling unit (k-1)-simplex uniformly at random, with
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k the number of classes. This protocol covers the entire range of prevalence values in a
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statistical sense, i.e., unlike APP there is no guarantee that it is covered precisely
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equally for all classes, but it is preferred in cases in which the number of possible
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combinations of the grid values of APP makes this endeavour intractable.
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:param data: a `LabelledCollection` from which the samples will be drawn
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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. Default is 100.
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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, repeats=100, random_seed=None, return_type='sample_prev'):
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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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self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
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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=self.data.n_classes, size=self.repeats):
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index = self.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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def total(self):
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return self.repeats
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# class LoadSamplesFromDirectory(AbstractProtocol):
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#
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# def __init__(self, folder_path, loader_fn, classes=None, **loader_kwargs):
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# assert exists(folder_path), f'folder {folder_path} does not exist'
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# assert callable(loader_fn), f'the passed load_fn does not seem to be callable'
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# self.folder_path = folder_path
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# self.loader_fn = loader_fn
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# self.classes = classes
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# self.loader_kwargs = loader_kwargs
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# self._list_files = None
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#
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# def __call__(self):
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# for file in self.list_files:
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# yield LabelledCollection.load(file, loader_func=self.loader_fn, classes=self.classes, **self.loader_kwargs)
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#
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# @property
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# def list_files(self):
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# if self._list_files is None:
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# self._list_files = sorted(glob(self.folder_path, '*'))
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# return self._list_files
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#
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# def total(self):
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# return len(self.list_files)
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class CovariateShiftPP(AbstractStochasticSeededProtocol):
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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(CovariateShiftPP, self).__init__(random_seed)
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self.A = domainA
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self.B = domainB
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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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if isinstance(mixture_points, int):
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self.mixture_points = np.linspace(0, 1, mixture_points)[::-1]
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else:
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self.mixture_points = np.asarray(mixture_points)
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assert all(np.logical_and(self.mixture_points >= 0, self.mixture_points<=1)), \
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'mixture_model datatype not understood (expected int or a sequence of real values in [0,1])'
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self.random_seed = random_seed
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def samples_parameters(self):
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indexesA, indexesB = [], []
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for propA in self.mixture_points:
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for _ in range(self.repeats):
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nA = int(np.round(self.sample_size * propA))
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nB = self.sample_size-nA
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sampleAidx = self.A.sampling_index(nA, *self.prevalence)
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sampleBidx = self.B.sampling_index(nB, *self.prevalence)
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indexesA.append(sampleAidx)
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indexesB.append(sampleBidx)
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return list(zip(indexesA, indexesB))
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def sample(self, indexes):
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indexesA, indexesB = indexes
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sampleA = self.A.sampling_from_index(indexesA)
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sampleB = self.B.sampling_from_index(indexesB)
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return (sampleA+sampleB).Xp
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def total(self):
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return self.repeats * len(self.mixture_points)
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