def __init__(
self,
dataset_manager: DatasetManager,
cache_dir: Optional[str] = None,
cache_credentials: Optional[Union[str, dict]] = None,
):
self._dataset_manager = dataset_manager
self._cache_manager = CacheManager(self._dataset_manager, cache_dir,
cache_credentials)
def test_cache_dataset(use_cache_dir, use_split, tmpdir):
dataset_manager = PandasDatasetManager(backend=LocalTestBackend())
cache_dir = os.path.join(tmpdir, "cache") if use_cache_dir else None
manager = CacheManager(dataset_manager, cache_dir=cache_dir)
config = {
"input_features": [sequence_feature(reduce_output="sum")],
"output_features":
[category_feature(vocab_size=2, reduce_input="sum")],
"combiner": {
"type": "concat",
"output_size": 14
},
"preprocessing": {},
}
def touch(basename):
path = os.path.join(tmpdir, f"{basename}.csv")
Path(path).touch()
return path
dataset = training_set = test_set = validation_set = None
if not use_split:
dataset = touch("dataset")
cache_key = manager.get_cache_key(dataset, config)
else:
training_set = touch("train")
test_set = touch("test")
validation_set = touch("validation")
cache_key = manager.get_cache_key(training_set, config)
training_set_metadata = {
CHECKSUM: cache_key,
}
cache = manager.get_dataset_cache(config, dataset, training_set, test_set,
validation_set)
cache_map = cache.cache_map
assert len(cache_map) == 4
train_path = os.path.join(
cache_dir, alphanum(cache_key)) if use_cache_dir else os.path.join(
tmpdir, "dataset")
test_path = val_path = train_path
if use_split and not use_cache_dir:
train_path = os.path.join(tmpdir, "train")
test_path = os.path.join(tmpdir, "test")
val_path = os.path.join(tmpdir, "validation")
assert cache_map[META] == f"{train_path}.meta.json"
assert cache_map[TRAINING] == f"{train_path}.{TRAINING_PREPROC_FILE_NAME}"
assert cache_map[TEST] == f"{test_path}.test.hdf5"
assert cache_map[VALIDATION] == f"{val_path}.validation.hdf5"
for cache_path in cache_map.values():
assert not os.path.exists(cache_path)
training_set = pd.DataFrame()
test_set = pd.DataFrame()
validation_set = pd.DataFrame()
if use_cache_dir:
os.makedirs(cache_dir)
cache.put(training_set, test_set, validation_set, training_set_metadata)
for cache_path in cache_map.values():
assert os.path.exists(cache_path)
cache.delete()
for cache_path in cache_map.values():
assert not os.path.exists(cache_path)
def test_cache_dataset(use_cache_dir, use_split, tmpdir):
dataset_manager = PandasDatasetManager(backend=LocalTestBackend())
cache_dir = os.path.join(tmpdir, 'cache') if use_cache_dir else None
manager = CacheManager(dataset_manager, cache_dir=cache_dir)
config = {
'input_features': [sequence_feature(reduce_output='sum')],
'output_features': [category_feature(vocab_size=2, reduce_input='sum')],
'combiner': {'type': 'concat', 'fc_size': 14},
'preprocessing': {},
}
def touch(basename):
path = os.path.join(tmpdir, f'{basename}.csv')
Path(path).touch()
return path
dataset = training_set = test_set = validation_set = None
if not use_split:
dataset = touch('dataset')
cache_key = manager.get_cache_key(dataset, config)
else:
training_set = touch('train')
test_set = touch('test')
validation_set = touch('validation')
cache_key = manager.get_cache_key(training_set, config)
training_set_metadata = {
CHECKSUM: cache_key,
}
cache = manager.get_dataset_cache(
config, dataset, training_set, test_set, validation_set
)
cache_map = cache.cache_map
assert len(cache_map) == 4
train_path = os.path.join(cache_dir, alphanum(cache_key)) if \
use_cache_dir else \
os.path.join(tmpdir, 'dataset')
test_path = val_path = train_path
if use_split and not use_cache_dir:
train_path = os.path.join(tmpdir, 'train')
test_path = os.path.join(tmpdir, 'test')
val_path = os.path.join(tmpdir, 'validation')
assert cache_map[META] == f'{train_path}.meta.json'
assert cache_map[TRAINING] == f'{train_path}.training.hdf5'
assert cache_map[TEST] == f'{test_path}.test.hdf5'
assert cache_map[VALIDATION] == f'{val_path}.validation.hdf5'
for cache_path in cache_map.values():
assert not os.path.exists(cache_path)
training_set = pd.DataFrame()
test_set = pd.DataFrame()
validation_set = pd.DataFrame()
if use_cache_dir:
os.makedirs(cache_dir)
cache.put(training_set, test_set, validation_set, training_set_metadata)
for cache_path in cache_map.values():
assert os.path.exists(cache_path)
cache.delete()
for cache_path in cache_map.values():
assert not os.path.exists(cache_path)
def __init__(self, cache_dir=None, cache_format=None):
self._dataset_manager = create_dataset_manager(self, cache_format)
self._cache_manager = CacheManager(self._dataset_manager, cache_dir)
def test_cache_dataset(use_cache_dir, use_split, use_df, tmpdir,
change_test_dir):
dataset_manager = PandasDatasetManager(backend=LocalTestBackend())
cache_dir = os.path.join(tmpdir, "cache") if use_cache_dir else None
creds_fname = os.path.join(tmpdir, "credentials.json")
creds = {
"s3": {
"client_kwargs": {
"endpoint_url": "http://localhost:9000",
"aws_access_key_id": "test",
"aws_secret_access_key": "test",
}
}
}
with open(creds_fname, "w") as f:
json.dump(creds, f)
manager = CacheManager(dataset_manager,
cache_dir=cache_dir,
cache_credentials=creds_fname)
assert manager.credentials == creds
config = {
"input_features": [sequence_feature(reduce_output="sum")],
"output_features":
[category_feature(vocab_size=2, reduce_input="sum")],
"combiner": {
"type": "concat",
"output_size": 14
},
"preprocessing": {},
}
def touch(basename):
path = os.path.join(tmpdir, f"{basename}.csv")
Path(path).touch()
return path
def create_dataset(name):
if use_df:
return CacheableDataframe(df=pd.DataFrame(),
name=name,
checksum=name)
else:
return wrap(touch(name))
dataset = training_set = test_set = validation_set = None
if not use_split:
dataset = create_dataset("dataset")
cache_key = manager.get_cache_key(dataset, config)
else:
training_set = create_dataset("train")
test_set = create_dataset("test")
validation_set = create_dataset("validation")
cache_key = manager.get_cache_key(training_set, config)
training_set_metadata = {
CHECKSUM: cache_key,
}
cache = manager.get_dataset_cache(config, dataset, training_set, test_set,
validation_set)
cache_map = cache.cache_map
assert len(cache_map) == 4
train_path = os.path.join(
cache_dir, alphanum(cache_key)) if use_cache_dir else os.path.join(
tmpdir, "dataset")
test_path = val_path = train_path
if use_split and not use_cache_dir:
train_path = os.path.join(tmpdir, "train")
test_path = os.path.join(tmpdir, "test")
val_path = os.path.join(tmpdir, "validation")
assert cache_map[META] == f"{train_path}.meta.json"
assert cache_map[TRAINING] == f"{train_path}.{TRAINING_PREPROC_FILE_NAME}"
assert cache_map[TEST] == f"{test_path}.test.hdf5"
assert cache_map[VALIDATION] == f"{val_path}.validation.hdf5"
for cache_path in cache_map.values():
assert not os.path.exists(cache_path)
training_set = pd.DataFrame()
test_set = pd.DataFrame()
validation_set = pd.DataFrame()
if use_cache_dir:
os.makedirs(cache_dir)
cache.put(training_set, test_set, validation_set, training_set_metadata)
for cache_path in cache_map.values():
assert os.path.exists(cache_path)
cache.delete()
for cache_path in cache_map.values():
assert not os.path.exists(cache_path)
def __init__(self, dataset_manager: DatasetManager, cache_dir: str = None):
self._dataset_manager = dataset_manager
self._cache_manager = CacheManager(self._dataset_manager, cache_dir)