forked from moreo/QuaPy
566 lines
26 KiB
Python
566 lines
26 KiB
Python
import itertools
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from functools import cached_property
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from typing import Iterable
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import numpy as np
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from scipy.sparse import issparse
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from scipy.sparse import vstack
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from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold
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from numpy.random import RandomState
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from quapy.functional import strprev
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from quapy.util import temp_seed
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class LabelledCollection:
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"""
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A LabelledCollection is a set of objects each with a label attached to each of them.
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This class implements several sampling routines and other utilities.
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:param instances: array-like (np.ndarray, list, or csr_matrix are supported)
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:param labels: array-like with the same length of instances
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:param classes: optional, list of classes from which labels are taken. If not specified, the classes are inferred
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from the labels. The classes must be indicated in cases in which some of the labels might have no examples
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(i.e., a prevalence of 0)
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"""
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def __init__(self, instances, labels, classes=None):
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if issparse(instances):
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self.instances = instances
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elif isinstance(instances, list) and len(instances) > 0 and isinstance(instances[0], str):
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# lists of strings occupy too much as ndarrays (although python-objects add a heavy overload)
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self.instances = np.asarray(instances, dtype=object)
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else:
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self.instances = np.asarray(instances)
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self.labels = np.asarray(labels)
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n_docs = len(self)
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if classes is None:
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self.classes_ = np.unique(self.labels)
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self.classes_.sort()
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else:
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self.classes_ = np.unique(np.asarray(classes))
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self.classes_.sort()
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if len(set(self.labels).difference(set(classes))) > 0:
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raise ValueError(f'labels ({set(self.labels)}) contain values not included in classes_ ({set(classes)})')
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self.index = {class_: np.arange(n_docs)[self.labels == class_] for class_ in self.classes_}
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@classmethod
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def load(cls, path: str, loader_func: callable, classes=None, **loader_kwargs):
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"""
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Loads a labelled set of data and convert it into a :class:`LabelledCollection` instance. The function in charge
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of reading the instances must be specified. This function can be a custom one, or any of the reading functions
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defined in :mod:`quapy.data.reader` module.
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:param path: string, the path to the file containing the labelled instances
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:param loader_func: a custom function that implements the data loader and returns a tuple with instances and
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labels
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:param classes: array-like, the classes according to which the instances are labelled
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:param loader_kwargs: any argument that the `loader_func` function needs in order to read the instances, i.e.,
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these arguments are used to call `loader_func(path, **loader_kwargs)`
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:return: a :class:`LabelledCollection` object
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"""
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return LabelledCollection(*loader_func(path, **loader_kwargs), classes)
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def __len__(self):
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"""
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Returns the length of this collection (number of labelled instances)
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:return: integer
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"""
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return self.instances.shape[0]
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def prevalence(self):
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"""
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Returns the prevalence, or relative frequency, of the classes in the codeframe.
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:return: a np.ndarray of shape `(n_classes)` with the relative frequencies of each class, in the same order
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as listed by `self.classes_`
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"""
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return self.counts() / len(self)
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def counts(self):
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"""
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Returns the number of instances for each of the classes in the codeframe.
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:return: a np.ndarray of shape `(n_classes)` with the number of instances of each class, in the same order
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as listed by `self.classes_`
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"""
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return np.asarray([len(self.index[class_]) for class_ in self.classes_])
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@property
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def n_classes(self):
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"""
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The number of classes
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:return: integer
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"""
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return len(self.classes_)
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@property
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def binary(self):
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"""
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Returns True if the number of classes is 2
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:return: boolean
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"""
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return self.n_classes == 2
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def sampling_index(self, size, *prevs, shuffle=True, random_state=None):
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"""
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Returns an index to be used to extract a random sample of desired size and desired prevalence values. If the
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prevalence values are not specified, then returns the index of a uniform sampling.
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For each class, the sampling is drawn with replacement if the requested prevalence is larger than
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the actual prevalence of the class, or without replacement otherwise.
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:param size: integer, the requested size
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:param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since
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it is constrained. E.g., for binary collections, only the prevalence `p` for the first class (as listed in
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`self.classes_` can be specified, while the other class takes prevalence value `1-p`
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:param shuffle: if set to True (default), shuffles the index before returning it
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:param random_state: seed for reproducing sampling
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:return: a np.ndarray of shape `(size)` with the indexes
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"""
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if len(prevs) == 0: # no prevalence was indicated; returns an index for uniform sampling
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return self.uniform_sampling_index(size, random_state=random_state)
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if len(prevs) == self.n_classes - 1:
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prevs = prevs + (1 - sum(prevs),)
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assert len(prevs) == self.n_classes, 'unexpected number of prevalences'
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assert sum(prevs) == 1, f'prevalences ({prevs}) wrong range (sum={sum(prevs)})'
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# Decide how many instances should be taken for each class in order to satisfy the requested prevalence
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# accurately, and the number of instances in the sample (exactly). If int(size * prevs[i]) (which is
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# <= size * prevs[i]) examples are drawn from class i, there could be a remainder number of instances to take
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# to satisfy the size constrain. The remainder is distributed along the classes with probability = prevs.
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# (This aims at avoiding the remainder to be placed in a class for which the prevalence requested is 0.)
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n_requests = {class_: round(size * prevs[i]) for i, class_ in enumerate(self.classes_)}
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remainder = size - sum(n_requests.values())
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with temp_seed(random_state):
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# due to rounding, the remainder can be 0, >0, or <0
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if remainder > 0:
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# when the remainder is >0 we randomly add 1 to the requests for each class;
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# more prevalent classes are more likely to be taken in order to minimize the impact in the final prevalence
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for rand_class in np.random.choice(self.classes_, size=remainder, p=prevs):
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n_requests[rand_class] += 1
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elif remainder < 0:
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# when the remainder is <0 we randomly remove 1 from the requests, unless the request is 0 for a chosen
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# class; we repeat until remainder==0
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while remainder!=0:
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rand_class = np.random.choice(self.classes_, p=prevs)
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if n_requests[rand_class] > 0:
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n_requests[rand_class] -= 1
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remainder += 1
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indexes_sample = []
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for class_, n_requested in n_requests.items():
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n_candidates = len(self.index[class_])
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index_sample = self.index[class_][
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np.random.choice(n_candidates, size=n_requested, replace=(n_requested > n_candidates))
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] if n_requested > 0 else []
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indexes_sample.append(index_sample)
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indexes_sample = np.concatenate(indexes_sample).astype(int)
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if shuffle:
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indexes_sample = np.random.permutation(indexes_sample)
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return indexes_sample
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def uniform_sampling_index(self, size, random_state=None):
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"""
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Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn
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with replacement if the requested size is greater than the number of instances, or without replacement
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otherwise.
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:param size: integer, the size of the uniform sample
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:param random_state: if specified, guarantees reproducibility of the split.
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:return: a np.ndarray of shape `(size)` with the indexes
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"""
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if random_state is not None:
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ng = RandomState(seed=random_state)
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else:
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ng = np.random
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return ng.choice(len(self), size, replace=size > len(self))
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def sampling(self, size, *prevs, shuffle=True, random_state=None):
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"""
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Return a random sample (an instance of :class:`LabelledCollection`) of desired size and desired prevalence
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values. For each class, the sampling is drawn without replacement if the requested prevalence is larger than
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the actual prevalence of the class, or with replacement otherwise.
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:param size: integer, the requested size
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:param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since
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it is constrained. E.g., for binary collections, only the prevalence `p` for the first class (as listed in
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`self.classes_` can be specified, while the other class takes prevalence value `1-p`
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:param shuffle: if set to True (default), shuffles the index before returning it
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:param random_state: seed for reproducing sampling
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:return: an instance of :class:`LabelledCollection` with length == `size` and prevalence close to `prevs` (or
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prevalence == `prevs` if the exact prevalence values can be met as proportions of instances)
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"""
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prev_index = self.sampling_index(size, *prevs, shuffle=shuffle, random_state=random_state)
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return self.sampling_from_index(prev_index)
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def uniform_sampling(self, size, random_state=None):
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"""
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Returns a uniform sample (an instance of :class:`LabelledCollection`) of desired size. The sampling is drawn
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with replacement if the requested size is greater than the number of instances, or without replacement
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otherwise.
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:param size: integer, the requested size
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:param random_state: if specified, guarantees reproducibility of the split.
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:return: an instance of :class:`LabelledCollection` with length == `size`
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"""
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unif_index = self.uniform_sampling_index(size, random_state=random_state)
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return self.sampling_from_index(unif_index)
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def sampling_from_index(self, index):
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"""
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Returns an instance of :class:`LabelledCollection` whose elements are sampled from this collection using the
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index.
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:param index: np.ndarray
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:return: an instance of :class:`LabelledCollection`
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"""
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documents = self.instances[index]
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labels = self.labels[index]
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return LabelledCollection(documents, labels, classes=self.classes_)
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def split_stratified(self, train_prop=0.6, random_state=None):
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"""
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Returns two instances of :class:`LabelledCollection` split with stratification from this collection, at desired
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proportion.
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:param train_prop: the proportion of elements to include in the left-most returned collection (typically used
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as the training collection). The rest of elements are included in the right-most returned collection
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(typically used as a test collection).
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:param random_state: if specified, guarantees reproducibility of the split.
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:return: two instances of :class:`LabelledCollection`, the first one with `train_prop` elements, and the
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second one with `1-train_prop` elements
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"""
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tr_docs, te_docs, tr_labels, te_labels = train_test_split(
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self.instances, self.labels, train_size=train_prop, stratify=self.labels, random_state=random_state
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)
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training = LabelledCollection(tr_docs, tr_labels, classes=self.classes_)
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test = LabelledCollection(te_docs, te_labels, classes=self.classes_)
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return training, test
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def split_random(self, train_prop=0.6, random_state=None):
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"""
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Returns two instances of :class:`LabelledCollection` split randomly from this collection, at desired
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proportion.
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:param train_prop: the proportion of elements to include in the left-most returned collection (typically used
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as the training collection). The rest of elements are included in the right-most returned collection
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(typically used as a test collection).
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:param random_state: if specified, guarantees reproducibility of the split.
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:return: two instances of :class:`LabelledCollection`, the first one with `train_prop` elements, and the
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second one with `1-train_prop` elements
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"""
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indexes = np.random.RandomState(seed=random_state).permutation(len(self))
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if isinstance(train_prop, int):
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assert train_prop < len(self), \
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'argument train_prop cannot be greater than the number of elements in the collection'
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splitpoint = train_prop
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elif isinstance(train_prop, float):
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assert 0 < train_prop < 1, \
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'argument train_prop out of range (0,1)'
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splitpoint = int(np.round(len(self)*train_prop))
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left, right = indexes[:splitpoint], indexes[splitpoint:]
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training = self.sampling_from_index(left)
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test = self.sampling_from_index(right)
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return training, test
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def __add__(self, other):
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"""
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Returns a new :class:`LabelledCollection` as the union of this collection with another collection.
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Both labelled collections must have the same classes.
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:param other: another :class:`LabelledCollection`
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:return: a :class:`LabelledCollection` representing the union of both collections
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"""
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if not all(np.sort(self.classes_)==np.sort(other.classes_)):
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raise NotImplementedError(f'unsupported operation for collections on different classes; '
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f'expected {self.classes_}, found {other.classes_}')
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return LabelledCollection.join(self, other)
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@classmethod
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def join(cls, *args: Iterable['LabelledCollection']):
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"""
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Returns a new :class:`LabelledCollection` as the union of the collections given in input.
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:param args: instances of :class:`LabelledCollection`
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:return: a :class:`LabelledCollection` representing the union of both collections
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"""
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args = [lc for lc in args if lc is not None]
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assert len(args) > 0, 'empty list is not allowed for mix'
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assert all([isinstance(lc, LabelledCollection) for lc in args]), \
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'only instances of LabelledCollection allowed'
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first_instances = args[0].instances
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first_type = type(first_instances)
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assert all([type(lc.instances)==first_type for lc in args[1:]]), \
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'not all the collections are of instances of the same type'
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if issparse(first_instances) or isinstance(first_instances, np.ndarray):
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first_ndim = first_instances.ndim
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assert all([lc.instances.ndim == first_ndim for lc in args[1:]]), \
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'not all the ndarrays are of the same dimension'
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if first_ndim > 1:
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first_shape = first_instances.shape[1:]
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assert all([lc.instances.shape[1:] == first_shape for lc in args[1:]]), \
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'not all the ndarrays are of the same shape'
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if issparse(first_instances):
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instances = vstack([lc.instances for lc in args])
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else:
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instances = np.concatenate([lc.instances for lc in args])
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elif isinstance(first_instances, list):
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instances = list(itertools.chain(lc.instances for lc in args))
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else:
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raise NotImplementedError('unsupported operation for collection types')
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labels = np.concatenate([lc.labels for lc in args])
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classes = np.unique(labels).sort()
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return LabelledCollection(instances, labels, classes=classes)
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@property
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def Xy(self):
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"""
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Gets the instances and labels. This is useful when working with `sklearn` estimators, e.g.:
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>>> svm = LinearSVC().fit(*my_collection.Xy)
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:return: a tuple `(instances, labels)` from this collection
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"""
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return self.instances, self.labels
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@property
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def Xp(self):
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"""
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Gets the instances and the true prevalence. This is useful when implementing evaluation protocols from
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a :class:`LabelledCollection` object.
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:return: a tuple `(instances, prevalence)` from this collection
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"""
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return self.instances, self.prevalence()
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@property
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def X(self):
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"""
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An alias to self.instances
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:return: self.instances
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"""
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return self.instances
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@property
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def y(self):
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"""
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An alias to self.labels
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:return: self.labels
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"""
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return self.labels
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@property
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def p(self):
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"""
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An alias to self.prevalence()
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:return: self.prevalence()
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"""
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return self.prevalence()
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def stats(self, show=True):
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"""
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Returns (and eventually prints) a dictionary with some stats of this collection. E.g.,:
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>>> data = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=5)
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>>> data.training.stats()
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>>> #instances=3821, type=<class 'scipy.sparse.csr.csr_matrix'>, #features=4403, #classes=[0 1], prevs=[0.081, 0.919]
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:param show: if set to True (default), prints the stats in standard output
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:return: a dictionary containing some stats of this collection. Keys include `#instances` (the number of
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instances), `type` (the type representing the instances), `#features` (the number of features, if the
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instances are in array-like format), `#classes` (the classes of the collection), `prevs` (the prevalence
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values for each class)
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"""
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ninstances = len(self)
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instance_type = type(self.instances[0])
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if instance_type == list:
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nfeats = len(self.instances[0])
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elif instance_type == np.ndarray or issparse(self.instances):
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nfeats = self.instances.shape[1]
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else:
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nfeats = '?'
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stats_ = {'instances': ninstances,
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'type': instance_type,
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'features': nfeats,
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'classes': self.classes_,
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'prevs': strprev(self.prevalence())}
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if show:
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print(f'#instances={stats_["instances"]}, type={stats_["type"]}, #features={stats_["features"]}, '
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f'#classes={stats_["classes"]}, prevs={stats_["prevs"]}')
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return stats_
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def kFCV(self, nfolds=5, nrepeats=1, random_state=None):
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"""
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Generator of stratified folds to be used in k-fold cross validation.
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:param nfolds: integer (default 5), the number of folds to generate
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:param nrepeats: integer (default 1), the number of rounds of k-fold cross validation to run
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:param random_state: integer (default 0), guarantees that the folds generated are reproducible
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:return: yields `nfolds * nrepeats` folds for k-fold cross validation
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"""
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kf = RepeatedStratifiedKFold(n_splits=nfolds, n_repeats=nrepeats, random_state=random_state)
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for train_index, test_index in kf.split(*self.Xy):
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train = self.sampling_from_index(train_index)
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test = self.sampling_from_index(test_index)
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yield train, test
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class Dataset:
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"""
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Abstraction of training and test :class:`LabelledCollection` objects.
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:param training: a :class:`LabelledCollection` instance
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:param test: a :class:`LabelledCollection` instance
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:param vocabulary: if indicated, is a dictionary of the terms used in this textual dataset
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:param name: a string representing the name of the dataset
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"""
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def __init__(self, training: LabelledCollection, test: LabelledCollection, vocabulary: dict = None, name=''):
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assert set(training.classes_) == set(test.classes_), 'incompatible labels in training and test collections'
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self.training = training
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self.test = test
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self.vocabulary = vocabulary
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self.name = name
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@classmethod
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def SplitStratified(cls, collection: LabelledCollection, train_size=0.6):
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"""
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Generates a :class:`Dataset` from a stratified split of a :class:`LabelledCollection` instance.
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See :meth:`LabelledCollection.split_stratified`
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:param collection: :class:`LabelledCollection`
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:param train_size: the proportion of training documents (the rest conforms the test split)
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:return: an instance of :class:`Dataset`
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"""
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return Dataset(*collection.split_stratified(train_prop=train_size))
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@property
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def classes_(self):
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"""
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The classes according to which the training collection is labelled
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:return: The classes according to which the training collection is labelled
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"""
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return self.training.classes_
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@property
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def n_classes(self):
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"""
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The number of classes according to which the training collection is labelled
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|
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:return: integer
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"""
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return self.training.n_classes
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@property
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def binary(self):
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"""
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Returns True if the training collection is labelled according to two classes
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|
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:return: boolean
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"""
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return self.training.binary
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|
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@classmethod
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def load(cls, train_path, test_path, loader_func: callable, classes=None, **loader_kwargs):
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"""
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|
Loads a training and a test labelled set of data and convert it into a :class:`Dataset` instance.
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|
The function in charge of reading the instances must be specified. This function can be a custom one, or any of
|
|
the reading functions defined in :mod:`quapy.data.reader` module.
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|
|
|
:param train_path: string, the path to the file containing the training instances
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|
:param test_path: string, the path to the file containing the test instances
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|
:param loader_func: a custom function that implements the data loader and returns a tuple with instances and
|
|
labels
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|
:param classes: array-like, the classes according to which the instances are labelled
|
|
:param loader_kwargs: any argument that the `loader_func` function needs in order to read the instances.
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|
See :meth:`LabelledCollection.load` for further details.
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|
:return: a :class:`Dataset` object
|
|
"""
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|
|
|
training = LabelledCollection.load(train_path, loader_func, classes, **loader_kwargs)
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test = LabelledCollection.load(test_path, loader_func, classes, **loader_kwargs)
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|
return Dataset(training, test)
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|
|
|
@property
|
|
def vocabulary_size(self):
|
|
"""
|
|
If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary
|
|
|
|
:return: integer
|
|
"""
|
|
return len(self.vocabulary)
|
|
|
|
@property
|
|
def train_test(self):
|
|
"""
|
|
Alias to `self.training` and `self.test`
|
|
|
|
:return: the training and test collections
|
|
:return: the training and test collections
|
|
"""
|
|
return self.training, self.test
|
|
|
|
def stats(self, show=True):
|
|
"""
|
|
Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:
|
|
|
|
>>> data = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=5)
|
|
>>> data.stats()
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|
>>> Dataset=kindle #tr-instances=3821, #te-instances=21591, type=<class 'scipy.sparse.csr.csr_matrix'>, #features=4403, #classes=[0 1], tr-prevs=[0.081, 0.919], te-prevs=[0.063, 0.937]
|
|
|
|
:param show: if set to True (default), prints the stats in standard output
|
|
:return: a dictionary containing some stats of this collection for the training and test collections. The keys
|
|
are `train` and `test`, and point to dedicated dictionaries of stats, for each collection, with keys
|
|
`#instances` (the number of instances), `type` (the type representing the instances),
|
|
`#features` (the number of features, if the instances are in array-like format), `#classes` (the classes of
|
|
the collection), `prevs` (the prevalence values for each class)
|
|
"""
|
|
tr_stats = self.training.stats(show=False)
|
|
te_stats = self.test.stats(show=False)
|
|
if show:
|
|
print(f'Dataset={self.name} #tr-instances={tr_stats["instances"]}, #te-instances={te_stats["instances"]}, '
|
|
f'type={tr_stats["type"]}, #features={tr_stats["features"]}, #classes={tr_stats["classes"]}, '
|
|
f'tr-prevs={tr_stats["prevs"]}, te-prevs={te_stats["prevs"]}')
|
|
return {'train': tr_stats, 'test': te_stats}
|
|
|
|
@classmethod
|
|
def kFCV(cls, data: LabelledCollection, nfolds=5, nrepeats=1, random_state=0):
|
|
"""
|
|
Generator of stratified folds to be used in k-fold cross validation. This function is only a wrapper around
|
|
:meth:`LabelledCollection.kFCV` that returns :class:`Dataset` instances made of training and test folds.
|
|
|
|
:param nfolds: integer (default 5), the number of folds to generate
|
|
:param nrepeats: integer (default 1), the number of rounds of k-fold cross validation to run
|
|
:param random_state: integer (default 0), guarantees that the folds generated are reproducible
|
|
:return: yields `nfolds * nrepeats` folds for k-fold cross validation as instances of :class:`Dataset`
|
|
"""
|
|
for i, (train, test) in enumerate(data.kFCV(nfolds=nfolds, nrepeats=nrepeats, random_state=random_state)):
|
|
yield Dataset(train, test, name=f'fold {(i % nfolds) + 1}/{nfolds} (round={(i // nfolds) + 1})')
|
|
|
|
|
|
def reduce(self, n_train=100, n_test=100):
|
|
"""
|
|
Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.
|
|
|
|
:param n_train: number of training documents to keep (default 100)
|
|
:param n_test: number of test documents to keep (default 100)
|
|
:return: self
|
|
"""
|
|
self.training = self.training.sampling(n_train, *self.training.prevalence())
|
|
self.test = self.test.sampling(n_test, *self.test.prevalence())
|
|
return self |