datasets added and adapted to refactoring
This commit is contained in:
Lorenzo Volpi
parent
d4b0212a92
commit
9d2a6dcdec
178
quacc/dataset.py
178
quacc/dataset.py
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@ -1,29 +1,31 @@
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import itertools
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import math
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import os
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import pickle
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import tarfile
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from typing import List, Tuple
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from typing import List
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import numpy as np
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import quapy as qp
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from quapy.data.base import LabelledCollection
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from sklearn.conftest import fetch_rcv1
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from quapy.data.datasets import fetch_lequa2022, fetch_UCIMulticlassLabelledCollection
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from sklearn.datasets import fetch_20newsgroups, fetch_rcv1
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.utils import Bunch
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from quacc import utils
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from quacc.environment import env
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from quacc.legacy.environment import env
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from quacc.utils import commons
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from quacc.utils.commons import save_json_file
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TRAIN_VAL_PROP = 0.5
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def fetch_cifar10() -> Bunch:
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URL = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
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data_home = utils.get_quacc_home()
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data_home = commons.get_quacc_home()
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unzipped_path = data_home / "cifar-10-batches-py"
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if not unzipped_path.exists():
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downloaded_path = data_home / URL.split("/")[-1]
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utils.download_file(URL, downloaded_path)
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commons.download_file(URL, downloaded_path)
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with tarfile.open(downloaded_path) as f:
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f.extractall(data_home)
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os.remove(downloaded_path)
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@ -58,11 +60,11 @@ def fetch_cifar10() -> Bunch:
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def fetch_cifar100():
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URL = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
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data_home = utils.get_quacc_home()
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data_home = commons.get_quacc_home()
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unzipped_path = data_home / "cifar-100-python"
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if not unzipped_path.exists():
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downloaded_path = data_home / URL.split("/")[-1]
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utils.download_file(URL, downloaded_path)
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commons.download_file(URL, downloaded_path)
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with tarfile.open(downloaded_path) as f:
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f.extractall(data_home)
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os.remove(downloaded_path)
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@ -96,6 +98,23 @@ def fetch_cifar100():
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)
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def save_dataset_stats(path, test_prot, L, V):
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test_prevs = [Ui.prevalence() for Ui in test_prot()]
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shifts = [qp.error.ae(L.prevalence(), Ui_prev) for Ui_prev in test_prevs]
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info = {
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"n_classes": L.n_classes,
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"n_train": len(L),
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"n_val": len(V),
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"train_prev": L.prevalence().tolist(),
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"val_prev": V.prevalence().tolist(),
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"test_prevs": [x.tolist() for x in test_prevs],
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"shifts": [x.tolist() for x in shifts],
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"sample_size": test_prot.sample_size,
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"num_samples": test_prot.total(),
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}
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save_json_file(path, info)
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class DatasetSample:
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def __init__(
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self,
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@ -121,49 +140,69 @@ class DatasetSample:
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class DatasetProvider:
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def __spambase(self, **kwargs):
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return qp.datasets.fetch_UCIDataset("spambase", verbose=False).train_test
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@classmethod
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def _split_train(cls, train: LabelledCollection):
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return train.split_stratified(0.5, random_state=0)
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# provare min_df=5
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def __imdb(self, **kwargs):
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return qp.datasets.fetch_reviews("imdb", tfidf=True, min_df=3).train_test
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@classmethod
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def _split_whole(cls, dataset: LabelledCollection):
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train, U = dataset.split_stratified(train_prop=0.66, random_state=0)
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T, V = train.split_stratified(train_prop=0.5, random_state=0)
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return T, V, U
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@classmethod
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def spambase(cls):
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train, U = qp.datasets.fetch_UCIDataset("spambase", verbose=False).train_test
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T, V = cls._split_train(train)
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return T, V, U
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@classmethod
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def imdb(cls):
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train, U = qp.datasets.fetch_reviews(
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"imdb", tfidf=True, min_df=10, pickle=True
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).train_test
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T, V = cls._split_train(train)
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return T, V, U
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@classmethod
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def rcv1(cls, target):
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training = fetch_rcv1(subset="train")
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test = fetch_rcv1(subset="test")
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def __rcv1(self, target, **kwargs):
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n_train = 23149
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available_targets = ["CCAT", "GCAT", "MCAT"]
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if target is None or target not in available_targets:
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raise ValueError(f"Invalid target {target}")
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class_names = training.target_names.tolist()
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class_idx = class_names.index(target)
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tr_labels = training.target[:, class_idx].toarray().flatten()
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te_labels = test.target[:, class_idx].toarray().flatten()
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tr = LabelledCollection(training.data, tr_labels)
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U = LabelledCollection(test.data, te_labels)
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T, V = cls._split_train(tr)
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return T, V, U
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@classmethod
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def cifar10(cls, target):
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dataset = fetch_cifar10()
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available_targets: list = dataset.label_names
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if target is None or target not in available_targets:
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raise ValueError(f"Invalid target {target}")
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dataset = fetch_rcv1()
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target_index = np.where(dataset.target_names == target)[0]
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all_train_d = dataset.data[:n_train, :]
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test_d = dataset.data[n_train:, :]
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labels = dataset.target[:, target_index].toarray().flatten()
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all_train_l, test_l = labels[:n_train], labels[n_train:]
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all_train = LabelledCollection(all_train_d, all_train_l, classes=[0, 1])
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test = LabelledCollection(test_d, test_l, classes=[0, 1])
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return all_train, test
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def __cifar10(self, target, **kwargs):
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dataset = fetch_cifar10()
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available_targets: list = dataset.label_names
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if target is None or self._target not in available_targets:
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raise ValueError(f"Invalid target {target}")
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target_index = available_targets.index(target)
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all_train_d = dataset.train.data
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all_train_l = (dataset.train.labels == target_index).astype(int)
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target_idx = available_targets.index(target)
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train_d = dataset.train.data
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train_l = (dataset.train.labels == target_idx).astype(int)
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test_d = dataset.test.data
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test_l = (dataset.test.labels == target_index).astype(int)
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all_train = LabelledCollection(all_train_d, all_train_l, classes=[0, 1])
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test = LabelledCollection(test_d, test_l, classes=[0, 1])
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test_l = (dataset.test.labels == target_idx).astype(int)
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train = LabelledCollection(train_d, train_l, classes=[0, 1])
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U = LabelledCollection(test_d, test_l, classes=[0, 1])
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T, V = cls._split_train(train)
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return all_train, test
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return T, V, U
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def __cifar100(self, target, **kwargs):
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@classmethod
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def cifar100(cls, target):
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dataset = fetch_cifar100()
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available_targets: list = dataset.coarse_label_names
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@ -171,31 +210,48 @@ class DatasetProvider:
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raise ValueError(f"Invalid target {target}")
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target_index = available_targets.index(target)
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all_train_d = dataset.train.data
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all_train_l = (dataset.train.coarse_labels == target_index).astype(int)
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train_d = dataset.train.data
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train_l = (dataset.train.coarse_labels == target_index).astype(int)
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test_d = dataset.test.data
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test_l = (dataset.test.coarse_labels == target_index).astype(int)
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all_train = LabelledCollection(all_train_d, all_train_l, classes=[0, 1])
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test = LabelledCollection(test_d, test_l, classes=[0, 1])
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train = LabelledCollection(train_d, train_l, classes=[0, 1])
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U = LabelledCollection(test_d, test_l, classes=[0, 1])
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T, V = cls._split_train(train)
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return all_train, test
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return T, V, U
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def __twitter_gasp(self, **kwargs):
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return qp.datasets.fetch_twitter("gasp", min_df=3).train_test
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@classmethod
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def twitter(cls, dataset_name):
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data = qp.datasets.fetch_twitter(dataset_name, min_df=3, pickle=True)
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T, V = cls._split_train(data.training)
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U = data.test
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return T, V, U
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def alltrain_test(
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self, name: str, target: str | None
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) -> Tuple[LabelledCollection, LabelledCollection]:
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all_train, test = {
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"spambase": self.__spambase,
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"imdb": self.__imdb,
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"rcv1": self.__rcv1,
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"cifar10": self.__cifar10,
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"cifar100": self.__cifar100,
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"twitter_gasp": self.__twitter_gasp,
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}[name](target=target)
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@classmethod
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def uci_multiclass(cls, dataset_name):
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dataset = fetch_UCIMulticlassLabelledCollection(dataset_name)
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return cls._split_whole(dataset)
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return all_train, test
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@classmethod
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def news20(cls):
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train = fetch_20newsgroups(
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subset="train", remove=("headers", "footers", "quotes")
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)
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test = fetch_20newsgroups(
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subset="test", remove=("headers", "footers", "quotes")
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)
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tfidf = TfidfVectorizer(min_df=5, sublinear_tf=True)
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Xtr = tfidf.fit_transform(train.data)
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Xte = tfidf.transform((test.data))
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train = LabelledCollection(instances=Xtr, labels=train.target)
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U = LabelledCollection(instances=Xte, labels=test.target)
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T, V = cls._split_train(train)
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return T, V, U
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@classmethod
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def t1b_lequa2022(cls):
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dataset, _, _ = fetch_lequa2022(task="T1B")
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return cls._split_whole(dataset)
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class Dataset(DatasetProvider):
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