340 lines
14 KiB
Python
340 lines
14 KiB
Python
from abc import abstractmethod
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from typing import List, Union
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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 quapy.functional import artificial_prevalence_sampling, strprev
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# class Sampling:
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#
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# @abstractmethod
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# def load(cls, path: str, loader_func: callable, classes=None): ...
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#
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# @abstractmethod
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# @property
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# def __len__(self): ...
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#
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# @abstractmethod
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# @property
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# def prevalence(self): ...
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#
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# @abstractmethod
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# @property
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# def n_classes(self):
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#
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# @property
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# def binary(self):
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# return self.n_classes == 2
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#
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# def uniform_sampling_index(self, size):
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# return np.random.choice(len(self), size, replace=False)
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#
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# def uniform_sampling(self, size):
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# unif_index = self.uniform_sampling_index(size)
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# return self.sampling_from_index(unif_index)
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#
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# def sampling(self, size, *prevs, shuffle=True):
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# prev_index = self.sampling_index(size, *prevs, shuffle=shuffle)
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# return self.sampling_from_index(prev_index)
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#
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# def sampling_from_index(self, index):
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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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#
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# def split_stratified(self, train_prop=0.6, random_state=None):
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# # with temp_seed(42):
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# tr_docs, te_docs, tr_labels, te_labels = \
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# train_test_split(self.instances, self.labels, train_size=train_prop, stratify=self.labels,
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# random_state=random_state)
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# return LabelledCollection(tr_docs, tr_labels), LabelledCollection(te_docs, te_labels)
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#
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# def artificial_sampling_generator(self, sample_size, n_prevalences=101, repeats=1):
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# dimensions = self.n_classes
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# for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
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# yield self.sampling(sample_size, *prevs)
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#
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# def artificial_sampling_index_generator(self, sample_size, n_prevalences=101, repeats=1):
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# dimensions = self.n_classes
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# for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
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# yield self.sampling_index(sample_size, *prevs)
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#
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# def natural_sampling_generator(self, sample_size, repeats=100):
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# for _ in range(repeats):
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# yield self.uniform_sampling(sample_size)
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#
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# def natural_sampling_index_generator(self, sample_size, repeats=100):
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# for _ in range(repeats):
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# yield self.uniform_sampling_index(sample_size)
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#
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# def __add__(self, other):
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# if other is None:
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# return self
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# elif issparse(self.instances) and issparse(other.instances):
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# join_instances = vstack([self.instances, other.instances])
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# elif isinstance(self.instances, list) and isinstance(other.instances, list):
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# join_instances = self.instances + other.instances
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# elif isinstance(self.instances, np.ndarray) and isinstance(other.instances, np.ndarray):
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# join_instances = np.concatenate([self.instances, other.instances])
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# else:
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# raise NotImplementedError('unsupported operation for collection types')
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# labels = np.concatenate([self.labels, other.labels])
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# return LabelledCollection(join_instances, labels)
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#
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# @property
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# def Xy(self):
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# return self.instances, self.labels
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#
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# def stats(self, show=True):
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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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#
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# def kFCV(self, nfolds=5, nrepeats=1, random_state=0):
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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 LabelledCollection:
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'''
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A LabelledCollection is a set of objects each with a label associated to it.
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'''
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def __init__(self, instances, labels, classes_=None):
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"""
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:param instances: list of objects
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:param labels: list of labels, same length of instances
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:param classes_: optional, list of classes from which labels are taken. When used, must contain the set of values used in labels.
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"""
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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):
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return LabelledCollection(*loader_func(path), classes)
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def __len__(self):
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return self.instances.shape[0]
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def prevalence(self):
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return self.counts() / len(self)
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def counts(self):
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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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return len(self.classes_)
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@property
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def binary(self):
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return self.n_classes == 2
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def sampling_index(self, size, *prevs, shuffle=True):
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if len(prevs) == 0: # no prevalence was indicated; returns an index for uniform sampling
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return np.random.choice(len(self), size, replace=False)
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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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taken = 0
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indexes_sample = []
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for i, class_ in enumerate(self.classes_):
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if i == self.n_classes - 1:
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n_requested = size - taken
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else:
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n_requested = int(size * prevs[i])
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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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taken += n_requested
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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):
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return np.random.choice(len(self), size, replace=False)
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def uniform_sampling(self, size):
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unif_index = self.uniform_sampling_index(size)
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return self.sampling_from_index(unif_index)
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def sampling(self, size, *prevs, shuffle=True):
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prev_index = self.sampling_index(size, *prevs, shuffle=shuffle)
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return self.sampling_from_index(prev_index)
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def sampling_from_index(self, index):
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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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# with temp_seed(42):
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tr_docs, te_docs, tr_labels, te_labels = \
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train_test_split(self.instances, self.labels, train_size=train_prop, stratify=self.labels,
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random_state=random_state)
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return LabelledCollection(tr_docs, tr_labels), LabelledCollection(te_docs, te_labels)
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def artificial_sampling_generator(self, sample_size, n_prevalences=101, repeats=1):
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dimensions = self.n_classes
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for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
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yield self.sampling(sample_size, *prevs)
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def artificial_sampling_index_generator(self, sample_size, n_prevalences=101, repeats=1):
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dimensions = self.n_classes
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for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
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yield self.sampling_index(sample_size, *prevs)
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def natural_sampling_generator(self, sample_size, repeats=100):
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for _ in range(repeats):
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yield self.uniform_sampling(sample_size)
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def natural_sampling_index_generator(self, sample_size, repeats=100):
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for _ in range(repeats):
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yield self.uniform_sampling_index(sample_size)
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def __add__(self, other):
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if other is None:
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return self
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elif issparse(self.instances) and issparse(other.instances):
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join_instances = vstack([self.instances, other.instances])
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elif isinstance(self.instances, list) and isinstance(other.instances, list):
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join_instances = self.instances + other.instances
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elif isinstance(self.instances, np.ndarray) and isinstance(other.instances, np.ndarray):
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join_instances = np.concatenate([self.instances, other.instances])
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else:
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raise NotImplementedError('unsupported operation for collection types')
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labels = np.concatenate([self.labels, other.labels])
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return LabelledCollection(join_instances, labels)
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@property
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def Xy(self):
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return self.instances, self.labels
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def stats(self, show=True):
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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=0):
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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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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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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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return self.training.classes_
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@property
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def n_classes(self):
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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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return self.training.binary
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@classmethod
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def load(cls, train_path, test_path, loader_func: callable):
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training = LabelledCollection.load(train_path, loader_func)
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test = LabelledCollection.load(test_path, loader_func)
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return Dataset(training, test)
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@property
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def vocabulary_size(self):
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return len(self.vocabulary)
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def stats(self):
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tr_stats = self.training.stats(show=False)
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te_stats = self.test.stats(show=False)
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print(f'Dataset={self.name} #tr-instances={tr_stats["instances"]}, #te-instances={te_stats["instances"]}, '
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f'type={tr_stats["type"]}, #features={tr_stats["features"]}, #classes={tr_stats["classes"]}, '
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f'tr-prevs={tr_stats["prevs"]}, te-prevs={te_stats["prevs"]}')
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return {'train': tr_stats, 'test': te_stats}
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@classmethod
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def kFCV(cls, data: LabelledCollection, nfolds=5, nrepeats=1, random_state=0):
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for i, (train, test) in enumerate(data.kFCV(nfolds=nfolds, nrepeats=nrepeats, random_state=random_state)):
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yield Dataset(train, test, name=f'fold {(i % nfolds) + 1}/{nfolds} (round={(i // nfolds) + 1})')
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def isbinary(data):
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if isinstance(data, Dataset) or isinstance(data, LabelledCollection):
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return data.binary
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return False
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