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 Sampling:
#
# @abstractmethod
# def load(cls, path: str, loader_func: callable, classes=None): ...
#
# @abstractmethod
# @property
# def __len__(self): ...
#
# @abstractmethod
# @property
# def prevalence(self): ...
#
# @abstractmethod
# @property
# def n_classes(self):
#
# @property
# def binary(self):
# return self.n_classes == 2
#
# 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 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):
return LabelledCollection(*loader_func(path), 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