forked from moreo/QuaPy
138 lines
4.8 KiB
Python
138 lines
4.8 KiB
Python
import numpy as np
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from scipy.sparse import issparse, dok_matrix
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.model_selection import train_test_split
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from quapy.functional import artificial_prevalence_sampling
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from scipy.sparse import vstack
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class LabelledCollection:
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def __init__(self, instances, labels, n_classes=None):
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self.instances = instances if issparse(instances) else np.asarray(instances)
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self.labels = np.asarray(labels, dtype=int)
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n_docs = len(self)
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if n_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.arange(n_classes)
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self.index = {class_i: np.arange(n_docs)[self.labels == class_i] for class_i in self.classes_}
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@classmethod
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def load(cls, path:str, loader_func:callable):
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return LabelledCollection(*loader_func(path))
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@classmethod
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def load_dataset(cls, train_path, test_path):
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training = cls.load(train_path)
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test = cls.load(test_path)
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return Dataset(training, test)
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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[ci]) for ci 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) == 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}) out of range (sum={sum(prevs)})'
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taken = 0
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indexes_sample = []
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for i, class_i 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_i])
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index_sample = self.index[class_i][
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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 sampling(self, size, *prevs, shuffle=True):
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index = self.sampling_index(size, *prevs, shuffle=shuffle)
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return self.sampling_from_index(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, n_classes=self.n_classes)
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def split_stratified(self, train_prop=0.6):
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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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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 __add__(self, other):
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if issparse(self.instances) and issparse(other.documents):
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docs = vstack([self.instances, other.documents])
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elif isinstance(self.instances, list) and isinstance(other.documents, list):
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docs = self.instances + other.documents
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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(docs, labels)
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class Dataset:
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def __init__(self, training: LabelledCollection, test: LabelledCollection, vocabulary: dict = None):
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assert training.n_classes == test.n_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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@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 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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