301 lines
10 KiB
Python
301 lines
10 KiB
Python
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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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 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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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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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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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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datas = []
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data_names = sorted([f for f in os.listdir(unzipped_path) if f.startswith("data")])
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for f in data_names:
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with open(unzipped_path / f, "rb") as file:
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datas.append(pickle.load(file, encoding="bytes"))
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tests = []
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test_names = sorted([f for f in os.listdir(unzipped_path) if f.startswith("test")])
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for f in test_names:
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with open(unzipped_path / f, "rb") as file:
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tests.append(pickle.load(file, encoding="bytes"))
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with open(unzipped_path / "batches.meta", "rb") as file:
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meta = pickle.load(file, encoding="bytes")
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return Bunch(
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train=Bunch(
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data=np.concatenate([d[b"data"] for d in datas], axis=0),
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labels=np.concatenate([d[b"labels"] for d in datas]),
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),
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test=Bunch(
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data=np.concatenate([d[b"data"] for d in tests], axis=0),
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labels=np.concatenate([d[b"labels"] for d in tests]),
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),
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label_names=[cs.decode("utf-8") for cs in meta[b"label_names"]],
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)
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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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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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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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with open(unzipped_path / "train", "rb") as file:
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train_d = pickle.load(file, encoding="bytes")
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with open(unzipped_path / "test", "rb") as file:
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test_d = pickle.load(file, encoding="bytes")
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with open(unzipped_path / "meta", "rb") as file:
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meta_d = pickle.load(file, encoding="bytes")
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train_bunch = Bunch(
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data=train_d[b"data"],
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fine_labels=np.array(train_d[b"fine_labels"]),
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coarse_labels=np.array(train_d[b"coarse_labels"]),
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)
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test_bunch = Bunch(
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data=test_d[b"data"],
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fine_labels=np.array(test_d[b"fine_labels"]),
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coarse_labels=np.array(test_d[b"coarse_labels"]),
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)
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return Bunch(
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train=train_bunch,
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test=test_bunch,
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fine_label_names=meta_d[b"fine_label_names"],
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coarse_label_names=meta_d[b"coarse_label_names"],
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)
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class DatasetSample:
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def __init__(
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self,
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train: LabelledCollection,
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validation: LabelledCollection,
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test: LabelledCollection,
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):
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self.train = train
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self.validation = validation
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self.test = test
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@property
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def train_prev(self):
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return self.train.prevalence()
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@property
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def validation_prev(self):
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return self.validation.prevalence()
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@property
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def prevs(self):
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return {"train": self.train_prev, "validation": self.validation_prev}
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class Dataset:
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def __init__(self, name, n_prevalences=9, prevs=None, target=None):
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self._name = name
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self._target = target
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self.prevs = None
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self.n_prevs = n_prevalences
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if prevs is not None:
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prevs = np.unique([p for p in prevs if p > 0.0 and p < 1.0])
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if prevs.shape[0] > 0:
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self.prevs = np.sort(prevs)
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self.n_prevs = self.prevs.shape[0]
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def __spambase(self):
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return qp.datasets.fetch_UCIDataset("spambase", verbose=False).train_test
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# provare min_df=5
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def __imdb(self):
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return qp.datasets.fetch_reviews("imdb", tfidf=True, min_df=3).train_test
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def __rcv1(self):
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n_train = 23149
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available_targets = ["CCAT", "GCAT", "MCAT"]
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if self._target is None or self._target not in available_targets:
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raise ValueError(f"Invalid target {self._target}")
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dataset = fetch_rcv1()
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target_index = np.where(dataset.target_names == self._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):
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dataset = fetch_cifar10()
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available_targets: list = dataset.label_names
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if self._target is None or self._target not in available_targets:
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raise ValueError(f"Invalid target {self._target}")
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target_index = available_targets.index(self._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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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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return all_train, test
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def __cifar100(self):
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dataset = fetch_cifar100()
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available_targets: list = dataset.coarse_label_names
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if self._target is None or self._target not in available_targets:
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raise ValueError(f"Invalid target {self._target}")
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target_index = available_targets.index(self._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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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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return all_train, test
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def __train_test(self) -> 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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}[self._name]()
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return all_train, test
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def get_raw(self) -> DatasetSample:
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all_train, test = self.__train_test()
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train, val = all_train.split_stratified(
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train_prop=TRAIN_VAL_PROP, random_state=env._R_SEED
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)
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return DatasetSample(train, val, test)
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def get(self) -> List[DatasetSample]:
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all_train, test = self.__train_test()
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# resample all_train set to have (0.5, 0.5) prevalence
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at_positives = np.sum(all_train.y)
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all_train = all_train.sampling(
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min(at_positives, len(all_train) - at_positives) * 2,
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0.5,
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random_state=env._R_SEED,
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)
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# sample prevalences
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if self.prevs is not None:
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prevs = self.prevs
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else:
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prevs = np.linspace(0.0, 1.0, num=self.n_prevs + 1, endpoint=False)[1:]
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at_size = min(math.floor(len(all_train) * 0.5 / p) for p in prevs)
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datasets = []
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for p in 1.0 - prevs:
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all_train_sampled = all_train.sampling(at_size, p, random_state=env._R_SEED)
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train, validation = all_train_sampled.split_stratified(
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train_prop=TRAIN_VAL_PROP, random_state=env._R_SEED
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)
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datasets.append(DatasetSample(train, validation, test))
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return datasets
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def __call__(self):
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return self.get()
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@property
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def name(self):
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match (self._name, self.n_prevs):
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case (("rcv1" | "cifar10" | "cifar100"), 9):
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return f"{self._name}_{self._target}"
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case (("rcv1" | "cifar10" | "cifar100"), _):
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return f"{self._name}_{self._target}_{self.n_prevs}prevs"
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case (_, 9):
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return f"{self._name}"
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case (_, _):
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return f"{self._name}_{self.n_prevs}prevs"
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# >>> fetch_rcv1().target_names
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# array(['C11', 'C12', 'C13', 'C14', 'C15', 'C151', 'C1511', 'C152', 'C16',
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# 'C17', 'C171', 'C172', 'C173', 'C174', 'C18', 'C181', 'C182',
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# 'C183', 'C21', 'C22', 'C23', 'C24', 'C31', 'C311', 'C312', 'C313',
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# 'C32', 'C33', 'C331', 'C34', 'C41', 'C411', 'C42', 'CCAT', 'E11',
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# 'E12', 'E121', 'E13', 'E131', 'E132', 'E14', 'E141', 'E142',
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# 'E143', 'E21', 'E211', 'E212', 'E31', 'E311', 'E312', 'E313',
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# 'E41', 'E411', 'E51', 'E511', 'E512', 'E513', 'E61', 'E71', 'ECAT',
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# 'G15', 'G151', 'G152', 'G153', 'G154', 'G155', 'G156', 'G157',
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# 'G158', 'G159', 'GCAT', 'GCRIM', 'GDEF', 'GDIP', 'GDIS', 'GENT',
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# 'GENV', 'GFAS', 'GHEA', 'GJOB', 'GMIL', 'GOBIT', 'GODD', 'GPOL',
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# 'GPRO', 'GREL', 'GSCI', 'GSPO', 'GTOUR', 'GVIO', 'GVOTE', 'GWEA',
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# 'GWELF', 'M11', 'M12', 'M13', 'M131', 'M132', 'M14', 'M141',
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# 'M142', 'M143', 'MCAT'], dtype=object)
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def rcv1_info():
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dataset = fetch_rcv1()
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n_train = 23149
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targets = []
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for target in range(103):
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train_t_prev = np.average(dataset.target[:n_train, target].toarray().flatten())
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test_t_prev = np.average(dataset.target[n_train:, target].toarray().flatten())
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targets.append(
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(
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dataset.target_names[target],
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{
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"train": (1.0 - train_t_prev, train_t_prev),
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"test": (1.0 - test_t_prev, test_t_prev),
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},
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)
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)
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targets.sort(key=lambda t: t[1]["train"][1])
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for n, d in targets:
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print(f"{n}:")
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for k, (fp, tp) in d.items():
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print(f"\t{k}: {fp:.4f}, {tp:.4f}")
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if __name__ == "__main__":
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fetch_cifar100()
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