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QuaPy/quapy/data/base.py

136 lines
4.8 KiB
Python

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) == 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 sampling(self, size, *prevs, shuffle=True):
index = self.sampling_index(size, *prevs, shuffle=shuffle)
return self.sampling_from_index(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 __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)