from abc import abstractmethod from typing import List, Union import numpy as np from scipy.sparse import issparse from scipy.sparse import vstack from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold from quapy.functional import artificial_prevalence_sampling, strprev class LabelledCollection: ''' A LabelledCollection is a set of objects each with a label associated to it. ''' def __init__(self, instances, labels, classes_=None): """ :param instances: list of objects :param labels: list of labels, same length of instances :param classes_: optional, list of classes from which labels are taken. When used, must contain the set of values used in labels. """ if issparse(instances): self.instances = instances elif isinstance(instances, list) and len(instances) > 0 and isinstance(instances[0], str): # lists of strings occupy too much as ndarrays (although python-objects add a heavy overload) self.instances = np.asarray(instances, dtype=object) else: self.instances = np.asarray(instances) self.labels = np.asarray(labels) n_docs = len(self) if classes_ is None: self.classes_ = np.unique(self.labels) self.classes_.sort() else: self.classes_ = np.unique(np.asarray(classes_)) self.classes_.sort() if len(set(self.labels).difference(set(classes_))) > 0: raise ValueError(f'labels ({set(self.labels)}) contain values not included in classes_ ({set(classes_)})') self.index = {class_: np.arange(n_docs)[self.labels == class_] for class_ in self.classes_} @classmethod def load(cls, path: str, loader_func: callable, classes=None, **loader_kwargs): return LabelledCollection(*loader_func(path, **loader_kwargs), classes) 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[class_]) for class_ 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_ 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_]) index_sample = self.index[class_][ 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, classes_=self.classes_) def split_stratified(self, train_prop=0.6, random_state=None): # with temp_seed(42): tr_docs, te_docs, tr_labels, te_labels = \ train_test_split(self.instances, self.labels, train_size=train_prop, stratify=self.labels, random_state=random_state) 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 other is None: return self elif issparse(self.instances) and issparse(other.instances): join_instances = vstack([self.instances, other.instances]) elif isinstance(self.instances, list) and isinstance(other.instances, list): join_instances = self.instances + other.instances elif isinstance(self.instances, np.ndarray) and isinstance(other.instances, np.ndarray): join_instances = np.concatenate([self.instances, other.instances]) else: raise NotImplementedError('unsupported operation for collection types') labels = np.concatenate([self.labels, other.labels]) return LabelledCollection(join_instances, labels) @property def Xy(self): return self.instances, self.labels def stats(self, show=True): ninstances = len(self) instance_type = type(self.instances[0]) if instance_type == list: nfeats = len(self.instances[0]) elif instance_type == np.ndarray or issparse(self.instances): nfeats = self.instances.shape[1] else: nfeats = '?' stats_ = {'instances': ninstances, 'type': instance_type, 'features': nfeats, 'classes': self.classes_, 'prevs': strprev(self.prevalence())} if show: print(f'#instances={stats_["instances"]}, type={stats_["type"]}, #features={stats_["features"]}, ' f'#classes={stats_["classes"]}, prevs={stats_["prevs"]}') return stats_ def kFCV(self, nfolds=5, nrepeats=1, random_state=0): kf = RepeatedStratifiedKFold(n_splits=nfolds, n_repeats=nrepeats, random_state=random_state) for train_index, test_index in kf.split(*self.Xy): train = self.sampling_from_index(train_index) test = self.sampling_from_index(test_index) yield train, test class Dataset: def __init__(self, training: LabelledCollection, test: LabelledCollection, vocabulary: dict = None, name=''): assert set(training.classes_) == set(test.classes_), 'incompatible labels in training and test collections' self.training = training self.test = test self.vocabulary = vocabulary self.name = name @classmethod def SplitStratified(cls, collection: LabelledCollection, train_size=0.6): return Dataset(*collection.split_stratified(train_prop=train_size)) @property def classes_(self): return self.training.classes_ @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) @property def vocabulary_size(self): return len(self.vocabulary) def stats(self): tr_stats = self.training.stats(show=False) te_stats = self.test.stats(show=False) 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): 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 isbinary(data): if isinstance(data, Dataset) or isinstance(data, LabelledCollection): return data.binary return False