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192 | """Functions related to the loading of the data."""
import collections.abc as c
import logging
import sys
import time
import typing as t
import requests
from datasets import DatasetDict, load_dataset
from datasets.exceptions import DatasetsError
from huggingface_hub.errors import HfHubHTTPError
from numpy.random import Generator
from .constants import SUPPORTED_FILE_FORMATS_FOR_LOCAL_DATASETS
from .exceptions import HuggingFaceHubDown, InvalidBenchmark
from .logging_utils import log, no_terminal_output
from .tasks import EUROPEAN_VALUES
from .utils import unscramble
if t.TYPE_CHECKING:
from datasets import Dataset
from .data_models import BenchmarkConfig, DatasetConfig
def load_data(
rng: Generator, dataset_config: "DatasetConfig", benchmark_config: "BenchmarkConfig"
) -> list["DatasetDict"]:
"""Load the raw bootstrapped datasets.
Args:
rng:
The random number generator to use.
dataset_config:
The configuration for the dataset.
benchmark_config:
The configuration for the benchmark.
Returns:
A list of bootstrapped datasets, one for each iteration.
Raises:
InvalidBenchmark:
If the dataset cannot be loaded.
HuggingFaceHubDown:
If the Hugging Face Hub is down.
"""
dataset = load_raw_data(
dataset_config=dataset_config, cache_dir=benchmark_config.cache_dir
)
if not benchmark_config.evaluate_test_split and "val" in dataset:
dataset["test"] = dataset["val"]
# Remove empty examples from the datasets
for text_feature in ["tokens", "text"]:
for split in dataset_config.splits:
if text_feature in dataset[split].features:
dataset = dataset.filter(lambda x: len(x[text_feature]) > 0)
# If we are testing then truncate the test set, unless we need the full set for
# evaluation
if hasattr(sys, "_called_from_test") and dataset_config.task != EUROPEAN_VALUES:
dataset["test"] = dataset["test"].select(range(1))
# Bootstrap the splits, if applicable
if dataset_config.bootstrap_samples:
bootstrapped_splits: dict[str, c.Sequence["Dataset"]] = dict()
for split in dataset_config.splits:
bootstrap_indices = rng.integers(
0,
len(dataset[split]),
size=(benchmark_config.num_iterations, len(dataset[split])),
)
bootstrapped_splits[split] = [
dataset[split].select(bootstrap_indices[idx])
for idx in range(benchmark_config.num_iterations)
]
datasets = [
DatasetDict(
{
split: bootstrapped_splits[split][idx]
for split in dataset_config.splits
}
)
for idx in range(benchmark_config.num_iterations)
]
else:
datasets = [dataset] * benchmark_config.num_iterations
return datasets
def load_raw_data(dataset_config: "DatasetConfig", cache_dir: str) -> "DatasetDict":
"""Load the raw dataset.
Args:
dataset_config:
The configuration for the dataset.
cache_dir:
The directory to cache the dataset.
Returns:
The dataset.
"""
# Case where the dataset source is a Hugging Face ID
if isinstance(dataset_config.source, str):
num_attempts = 5
for _ in range(num_attempts):
try:
with no_terminal_output():
dataset = load_dataset(
path=dataset_config.source.split("::")[0],
name=(
dataset_config.source.split("::")[1]
if "::" in dataset_config.source
else None
),
cache_dir=cache_dir,
token=unscramble("XbjeOLhwebEaSaDUMqqaPaPIhgOcyOfDpGnX_"),
)
break
except (
FileNotFoundError,
ConnectionError,
DatasetsError,
requests.ConnectionError,
requests.ReadTimeout,
) as e:
log(
f"Failed to load dataset {dataset_config.source!r}, due to "
f"the following error: {e}. Retrying...",
level=logging.DEBUG,
)
time.sleep(1)
continue
except HfHubHTTPError:
raise HuggingFaceHubDown()
else:
raise InvalidBenchmark(
f"Failed to load dataset {dataset_config.source!r} after "
f"{num_attempts} attempts. Run with verbose mode to see the individual "
"errors."
)
# Case where the dataset source is a dictionary with keys "train", "val" and "test",
# with the values pointing to local CSV files
else:
data_files = {
split: dataset_config.source[split]
for split in dataset_config.splits
if split in dataset_config.source
}
# Get the file extension and ensure that all files have the same extension
file_extensions = {
split: dataset_config.source[split].split(".")[-1]
for split in dataset_config.splits
if split in dataset_config.source
}
if len(set(file_extensions.values())) != 1:
raise InvalidBenchmark(
"All data files in a custom dataset must have the same file extension. "
f"Got the extensions {', '.join(file_extensions.values())} for the "
f"dataset {dataset_config.name!r}."
)
file_extension = list(file_extensions.values())[0]
# Check that the file extension is supported
if file_extension not in SUPPORTED_FILE_FORMATS_FOR_LOCAL_DATASETS:
raise InvalidBenchmark(
"Unsupported file extension for custom dataset. Supported file "
"extensions are "
f"{', '.join(SUPPORTED_FILE_FORMATS_FOR_LOCAL_DATASETS)}, but got "
f"{file_extension!r}."
)
# Load the dataset
with no_terminal_output():
dataset = load_dataset(
path=file_extension, data_files=data_files, cache_dir=cache_dir
)
assert isinstance(dataset, DatasetDict) # type: ignore[used-before-def]
missing_keys = [key for key in dataset_config.splits if key not in dataset]
if missing_keys:
raise InvalidBenchmark(
"The dataset is missing the following required splits: "
f"{', '.join(missing_keys)}"
)
return DatasetDict({key: dataset[key] for key in dataset_config.splits})
|