import numpy as np from scipy.sparse import issparse from sklearn.model_selection import train_test_split from quapy.functional import artificial_prevalence_sampling from scipy.sparse import vstack class LabelledCollection: def __init__(self, instances, labels, n_classes=None): self.instances = instances if issparse(instances) else np.asarray(instances) self.labels = np.asarray(labels, dtype=int) n_docs = len(self) if n_classes is None: self.classes_ = np.unique(self.labels) self.classes_.sort() else: self.classes_ = np.arange(n_classes) self.index = {class_i: np.arange(n_docs)[self.labels == class_i] for class_i in self.classes_} @classmethod def load(cls, path:str, loader_func:callable): return LabelledCollection(*loader_func(path)) def __len__(self): return self.instances.shape[0] def prevalence(self): return self.counts()/len(self) def counts(self): return np.asarray([len(self.index[ci]) for ci in self.classes_]) @property def n_classes(self): return len(self.classes_) @property def binary(self): return self.n_classes == 2 def sampling_index(self, size, *prevs, shuffle=True): if len(prevs) == 0: # no prevalence was indicated; returns an index for uniform sampling return np.random.choice(len(self), size, replace=False) if len(prevs) == self.n_classes-1: prevs = prevs + (1-sum(prevs),) assert len(prevs) == self.n_classes, 'unexpected number of prevalences' assert sum(prevs) == 1, f'prevalences ({prevs}) wrong range (sum={sum(prevs)})' taken = 0 indexes_sample = [] for i, class_i in enumerate(self.classes_): if i == self.n_classes-1: n_requested = size - taken else: n_requested = int(size * prevs[i]) n_candidates = len(self.index[class_i]) index_sample = self.index[class_i][ np.random.choice(n_candidates, size=n_requested, replace=(n_requested > n_candidates)) ] if n_requested > 0 else [] indexes_sample.append(index_sample) taken += n_requested indexes_sample = np.concatenate(indexes_sample).astype(int) if shuffle: indexes_sample = np.random.permutation(indexes_sample) return indexes_sample # def uniform_sampling_index(self, size): # return np.random.choice(len(self), size, replace=False) # def uniform_sampling(self, size): # unif_index = self.uniform_sampling_index(size) # return self.sampling_from_index(unif_index) def sampling(self, size, *prevs, shuffle=True): prev_index = self.sampling_index(size, *prevs, shuffle=shuffle) return self.sampling_from_index(prev_index) def sampling_from_index(self, index): documents = self.instances[index] labels = self.labels[index] return LabelledCollection(documents, labels, n_classes=self.n_classes) def split_stratified(self, train_prop=0.6): tr_docs, te_docs, tr_labels, te_labels = \ train_test_split(self.instances, self.labels, train_size=train_prop, stratify=self.labels) return LabelledCollection(tr_docs, tr_labels), LabelledCollection(te_docs, te_labels) def artificial_sampling_generator(self, sample_size, n_prevalences=101, repeats=1): dimensions=self.n_classes for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats): yield self.sampling(sample_size, *prevs) def artificial_sampling_index_generator(self, sample_size, n_prevalences=101, repeats=1): dimensions=self.n_classes for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats): yield self.sampling_index(sample_size, *prevs) def natural_sampling_generator(self, sample_size, repeats=100): for _ in range(repeats): yield self.uniform_sampling(sample_size) def natural_sampling_index_generator(self, sample_size, repeats=100): for _ in range(repeats): yield self.uniform_sampling_index(sample_size) def __add__(self, other): if issparse(self.instances) and issparse(other.documents): docs = vstack([self.instances, other.documents]) elif isinstance(self.instances, list) and isinstance(other.documents, list): docs = self.instances + other.documents else: raise NotImplementedError('unsupported operation for collection types') labels = np.concatenate([self.labels, other.labels]) return LabelledCollection(docs, labels) class Dataset: def __init__(self, training: LabelledCollection, test: LabelledCollection, vocabulary: dict = None): assert training.n_classes == test.n_classes, 'incompatible labels in training and test collections' self.training = training self.test = test self.vocabulary = vocabulary @classmethod def SplitStratified(cls, collection: LabelledCollection, train_size=0.6): return Dataset(*collection.split_stratified(train_prop=train_size)) @property def n_classes(self): return self.training.n_classes @property def binary(self): return self.training.binary @classmethod def load(cls, train_path, test_path, loader_func:callable): training = LabelledCollection.load(train_path, loader_func) test = LabelledCollection.load(test_path, loader_func) return Dataset(training, test)