def test_get_data_dir(mock_os_path_dirname):
"""Tests function ``get_data_dir()``."""
from dirty_cat.datasets.utils import get_data_dir
expected_return_value_default = Path("/user/directory/data")
mock_os_path_dirname.return_value = "/user/directory/"
assert get_data_dir() == expected_return_value_default
expected_return_value_custom = expected_return_value_default / "tests"
assert get_data_dir("tests") == expected_return_value_custom
def test_fetch_dataset():
from dirty_cat.datasets import fetching
utils.MockResponse.set_with_content_length(True)
datadir = os.path.join(datasets_utils.get_data_dir(),
'testdata')
try:
urlinfo = fetching.DatasetInfo(name='testdata',
urlinfos=(fetching.UrlInfo(
url='http://foo/data',
filenames=('data',),
uncompress=False,
encoding='utf-8'),),
main_file='data',
source='http://foo/')
fetching.fetch_dataset(urlinfo, show_progress=False)
assert os.path.exists(os.path.join(datadir, 'data'))
shutil.rmtree(os.path.join(datadir))
# test with zipped data
utils.MockResponse.set_to_zipfile(True)
utils.MockResponse.set_with_content_length(False)
urlinfo = fetching.DatasetInfo(name='testdata',
urlinfos=(fetching.UrlInfo(
url='http://foo/data.zip',
filenames=('unzipped_data.txt',),
uncompress=True,
encoding='utf-8'),),
main_file='unzipped_data.txt',
source='http://foo/')
fetching.fetch_dataset(urlinfo, show_progress=False)
assert os.path.exists(os.path.join(datadir, 'unzipped_data.txt'))
finally:
if os.path.exists(datadir):
shutil.rmtree(datadir)
def test_convert_file_to_utf8(monkeypatch):
from dirty_cat.datasets import fetching
datadir = os.path.join(datasets_utils.get_data_dir(),
'testdata')
try:
# Specify some content encoded in latin-1, and make sure the final file
# contains the same content, but in utf-8. Here, '\xe9' in latin-1 is
# '\xc3\xa9' in utf-8
with monkeypatch.context() as m:
m.setattr(utils.MockResponse, "zipresult", False)
m.setattr(utils.MockResponse, "_file_contents", b'\xe9')
dataset_info = fetching.DatasetInfo(
name='testdata',
urlinfos=(fetching.UrlInfo(
url='http://foo/data',
filenames=('data',),
uncompress=False,
encoding='latin-1'),),
main_file='data',
source='http://foo/')
info = fetching.fetch_dataset(dataset_info, show_progress=False)
with open(info['path'], 'rb') as f:
content = f.read()
assert content == b'\xc3\xa9'
os.unlink(info['path'])
m.setattr(utils.MockResponse, "zipresult", True)
dataset_info_with_zipfile = fetching.DatasetInfo(
name='testdata',
urlinfos=(fetching.UrlInfo(
url='http://foo/data.zip',
filenames=('unzipped_data.txt',),
uncompress=True,
encoding='latin-1'),),
main_file='unzipped_data.txt',
source='http://foo/')
info_unzipped = fetching.fetch_dataset(
dataset_info_with_zipfile, show_progress=False)
with open(info_unzipped['path'], 'rb') as f:
content_unzipped = f.read()
assert content_unzipped == b'\xc3\xa9'
finally:
if os.path.exists(datadir):
shutil.rmtree(datadir)
def test_fetch_file_overwrite():
utils.MockResponse.set_with_content_length(True)
utils.MockResponse.set_to_zipfile(False)
test_dir = datasets_utils.get_data_dir(name='test')
from dirty_cat.datasets import fetching
try:
# test that filename is a md5 hash of the url if
# the url ends with /
fil = fetching._fetch_file(url='http://foo/', data_dir=test_dir,
overwrite=True, uncompress=False,
show_progress=False)
assert os.path.basename(fil) == datasets_utils.md5_hash('/')
os.remove(fil)
# overwrite non-exiting file.
fil = fetching._fetch_file(url='http://foo/testdata', data_dir=test_dir,
overwrite=True, uncompress=False,
show_progress=False)
# check if data_dir is actually used
assert os.path.dirname(fil) == test_dir
assert os.path.exists(fil)
with open(fil, 'r') as fp:
assert fp.read() == ' '
# Modify content
with open(fil, 'w') as fp:
fp.write('some content')
# Don't overwrite existing file.
fil = fetching._fetch_file(url='http://foo/testdata', data_dir=test_dir,
overwrite=False, uncompress=False,
show_progress=False)
assert os.path.exists(fil)
with open(fil, 'r') as fp:
assert fp.read() == 'some content'
# Overwrite existing file.
# Overwrite existing file.
fil = fetching._fetch_file(url='http://foo/testdata', data_dir=test_dir,
overwrite=True, uncompress=False,
show_progress=False)
assert os.path.exists(fil)
with open(fil, 'r') as fp:
assert fp.read() == ' '
# modify content,
# change filename,add it in argument, and set overwrite to false
with open(fil, 'w') as fp:
fp.write('some content')
newf = 'moved_file'
os.rename(fil, os.path.join(test_dir, newf))
fetching._fetch_file(url='http://foo/testdata',
filenames=(newf,),
data_dir=test_dir,
overwrite=False, uncompress=False,
show_progress=False)
assert (
not os.path.exists(fil)) # it has been removed and should not have
with open(os.path.join(test_dir, newf), 'r') as fp:
assert fp.read() == 'some content'
# been downloaded again
os.remove(os.path.join(test_dir, newf))
# # create a zipfile with a file inside, remove the file, and
#
# zipd = os.path.join('testzip.zip')
# with contextlib.closing(zipfile.ZipFile())
# fetching._fetch_file(url='http://foo/', filenames=('test_filename',),
# data_dir=test_dir,
# overwrite=False, uncompress=False,
# show_progress=False)
# add wrong md5 sum file and catch ValueError
try:
fil = fetching._fetch_file(url='http://foo/testdata',
data_dir=test_dir,
overwrite=True, uncompress=False,
show_progress=False, md5sum='1')
raise ValueError # if no error raised in the previous line,
# it is bad:
# a wrong md5 sum should raise an error. So forcing the except chunk
# to happen anyway
except Exception as e:
assert isinstance(e, fetching.FileChangedError)
utils.MockResponse.set_with_content_length(False)
# write content if no content size
fil = fetching._fetch_file(url='http://foo/testdata', data_dir=test_dir,
overwrite=True, uncompress=False,
show_progress=False)
assert os.path.exists(fil)
os.remove(fil)
finally:
shutil.rmtree(test_dir)
def get_test_data_dir() -> Path:
from dirty_cat.datasets.utils import get_data_dir
return get_data_dir("tests")
def fetch_openml_dataset(
dataset_id: int,
data_directory: Path = get_data_dir(),
) -> Dict[str, Any]:
"""
Gets a dataset from OpenML (https://www.openml.org),
or from the disk if already downloaded.
Parameters
----------
dataset_id: int
The ID of the dataset to fetch.
data_directory: Path
Optional. A directory to save the data to.
By default, the dirty_cat data directory.
Returns
-------
Dict[str, Any]
A dictionary containing:
- ``description``: str
The description of the dataset,
as gathered from OpenML.
- ``source``: str
The dataset's URL from OpenML.
- ``path``: pathlib.Path
The local path leading to the dataset,
saved as a CSV file.
"""
# Make path absolute
data_directory = data_directory.resolve()
# Construct the path to the gzip file containing the details on a dataset.
details_gz_path = data_directory / DETAILS_DIRECTORY / f'{dataset_id}.gz'
features_gz_path = data_directory / FEATURES_DIRECTORY / f'{dataset_id}.gz'
if not details_gz_path.is_file() or not features_gz_path.is_file():
# If the details file or the features file don't exist,
# download the dataset.
warnings.warn(
f"Could not find the dataset {dataset_id} locally. "
"Downloading it from OpenML; this might take a while... "
"If it is interrupted, some files might be invalid/incomplete: "
"if on the following run, the fetching raises errors, you can try "
f"fixing this issue by deleting the directory {data_directory!r}.")
_download_and_write_openml_dataset(dataset_id=dataset_id,
data_directory=data_directory)
details = _get_details(details_gz_path)
# The file ID is required because the data file is named after this ID,
# and not after the dataset's.
file_id = details.file_id
csv_path = data_directory / f'{details.name}.csv'
data_gz_path = data_directory / DATA_DIRECTORY / f'{file_id}.gz'
if not data_gz_path.is_file():
# This is a double-check.
# If the data file does not exist, download the dataset.
_download_and_write_openml_dataset(dataset_id=dataset_id,
data_directory=data_directory)
if not csv_path.is_file():
# If the CSV file does not exist, use the dataset
# downloaded by ``fetch_openml()`` to construct it.
features = _get_features(features_gz_path)
_export_gz_data_to_csv(data_gz_path, csv_path, features)
url = openml_url.format(ID=dataset_id)
return {
"description": details.description,
"source": url,
"path": csv_path.resolve()
}