def _from_config(config, version):
if VERSIONED_FLAG_KEY in config:
raise ValueError(
"Cached datasets should specify that they are versioned in the "
"`CachedDataSet`, not in the wrapped dataset.")
if version:
config[VERSIONED_FLAG_KEY] = True
return AbstractDataSet.from_config("_cached", config, version.load,
version.save)
return AbstractDataSet.from_config("_cached", config)
def _init_dataset(self):
if not getattr(self, "_ready", None):
self._ready = True
self.dataset_name = self.dataset_name or self._dataset_name
_dataset = self.dataset
if isinstance(self.dataset, str):
dataset_dict = dataset_dicts.get(
self.dataset, {"type": "pickle.PickleDataSet"})
dataset_dict["filepath"] = self.filepath = (
self.filepath or tempfile.gettempdir() + "/" +
self.dataset_name + "." + self.dataset)
_dataset = dataset_dict
if isinstance(_dataset, dict):
self._dataset = AbstractDataSet.from_config(
self._dataset_name, _dataset)
elif isinstance(_dataset, AbstractDataSet):
self._dataset = _dataset
else:
raise ValueError(
"The argument type of `dataset` should be either a dict/YAML "
"representation of the dataset, or the actual dataset object."
)
_filepath = getattr(self._dataset, "_filepath", None)
if _filepath:
self.filepath = str(_filepath)
if self.caching and (not self._running_parallel):
self._cache = MemoryDataSet(copy_mode=self.copy_mode)
def from_config(
cls: Type,
catalog: Optional[Dict[str, Dict[str, Any]]],
credentials: Dict[str, Dict[str, Any]] = None,
load_versions: Dict[str, str] = None,
save_version: str = None,
journal: Journal = None,
) -> "DataCatalog":
"""Create a ``DataCatalog`` instance from configuration. This is a
factory method used to provide developers with a way to instantiate
``DataCatalog`` with configuration parsed from configuration files.
Args:
catalog: A dictionary whose keys are the data set names and
the values are dictionaries with the constructor arguments
for classes implementing ``AbstractDataSet``. The data set
class to be loaded is specified with the key ``type`` and their
fully qualified class name. All ``kedro.io`` data set can be
specified by their class name only, i.e. their module name
can be omitted.
credentials: A dictionary containing credentials for different
data sets. Use the ``credentials`` key in a ``AbstractDataSet``
to refer to the appropriate credentials as shown in the example
below.
load_versions: A mapping between dataset names and versions
to load. Has no effect on data sets without enabled versioning.
save_version: Version string to be used for ``save`` operations
by all data sets with enabled versioning. It must: a) be a
case-insensitive string that conforms with operating system
filename limitations, b) always return the latest version when
sorted in lexicographical order.
journal: Instance of Journal.
Returns:
An instantiated ``DataCatalog`` containing all specified
data sets, created and ready to use.
Raises:
DataSetError: When the method fails to create any of the data
sets from their config.
DataSetNotFoundError: When `load_versions` refers to a dataset that doesn't
exist in the catalog.
Example:
::
>>> config = {
>>> "cars": {
>>> "type": "pandas.CSVDataSet",
>>> "filepath": "cars.csv",
>>> "save_args": {
>>> "index": False
>>> }
>>> },
>>> "boats": {
>>> "type": "pandas.CSVDataSet",
>>> "filepath": "s3://aws-bucket-name/boats.csv",
>>> "credentials": "boats_credentials"
>>> "save_args": {
>>> "index": False
>>> }
>>> }
>>> }
>>>
>>> credentials = {
>>> "boats_credentials": {
>>> "client_kwargs": {
>>> "aws_access_key_id": "<your key id>",
>>> "aws_secret_access_key": "<your secret>"
>>> }
>>> }
>>> }
>>>
>>> catalog = DataCatalog.from_config(config, credentials)
>>>
>>> df = catalog.load("cars")
>>> catalog.save("boats", df)
"""
data_sets = {}
catalog = copy.deepcopy(catalog) or {}
credentials = copy.deepcopy(credentials) or {}
run_id = journal.run_id if journal else None
save_version = save_version or run_id or generate_timestamp()
load_versions = copy.deepcopy(load_versions) or {}
missing_keys = load_versions.keys() - catalog.keys()
if missing_keys:
raise DataSetNotFoundError(
f"`load_versions` keys [{', '.join(sorted(missing_keys))}] "
f"are not found in the catalog.")
layers = defaultdict(set) # type: Dict[str, Set[str]]
for ds_name, ds_config in catalog.items():
ds_layer = ds_config.pop("layer", None)
if ds_layer is not None:
layers[ds_layer].add(ds_name)
ds_config = _resolve_credentials(ds_config, credentials)
data_sets[ds_name] = AbstractDataSet.from_config(
ds_name, ds_config, load_versions.get(ds_name), save_version)
dataset_layers = layers or None
return cls(data_sets=data_sets, journal=journal, layers=dataset_layers)
def from_config(
cls: Type,
catalog: Optional[Dict[str, Dict[str, Any]]],
credentials: Dict[str, Dict[str, Any]] = None,
load_versions: Dict[str, str] = None,
save_version: str = None,
) -> "DataCatalog":
"""Create a ``DataCatalog`` instance from configuration. This is a
factory method used to provide developers with a way to instantiate
``DataCatalog`` with configuration parsed from configuration files.
Args:
catalog: A dictionary whose keys are the data set names and
the values are dictionaries with the constructor arguments
for classes implementing ``AbstractDataSet``. The data set
class to be loaded is specified with the key ``type`` and their
fully qualified class name. All ``kedro.io`` data set can be
specified by their class name only, i.e. their module name
can be omitted.
credentials: A dictionary containing credentials for different
data sets. Use the ``credentials`` key in a ``AbstractDataSet``
to refer to the appropriate credentials as shown in the example
below.
load_versions: A mapping between dataset names and versions
to load. Has no effect on data sets without enabled versioning.
save_version: Version string to be used for ``save`` operations
by all data sets with enabled versioning. It must: a) be a
case-insensitive string that conforms with operating system
filename limitations, b) always return the latest version when
sorted in lexicographical order.
Returns:
An instantiated ``DataCatalog`` containing all specified
data sets, created and ready to use.
Raises:
DataSetError: When the method fails to create any of the data
sets from their config.
Example:
::
>>> config = {
>>> "cars": {
>>> "type": "CSVLocalDataSet",
>>> "filepath": "cars.csv",
>>> "save_args": {
>>> "index": False
>>> }
>>> },
>>> "boats": {
>>> "type": "CSVS3DataSet",
>>> "filepath": "boats.csv",
>>> "bucket_name": "mck-147789798-bucket",
>>> "credentials": "boats_credentials"
>>> "save_args": {
>>> "index": False
>>> }
>>> }
>>> }
>>>
>>> credentials = {
>>> "boats_credentials": {
>>> "aws_access_key_id": "<your key id>",
>>> "aws_secret_access_key": "<your secret>"
>>> }
>>> }
>>>
>>> catalog = DataCatalog.from_config(config, credentials)
>>>
>>> df = catalog.load("cars")
>>> catalog.save("boats", df)
"""
data_sets = {}
catalog = copy.deepcopy(catalog) or {}
credentials = copy.deepcopy(credentials) or {}
save_version = save_version or generate_current_version()
load_versions = copy.deepcopy(load_versions) or {}
for ds_name, ds_config in catalog.items():
if "type" not in ds_config:
raise DataSetError("`type` is missing from DataSet '{}' "
"catalog configuration".format(ds_name))
if CREDENTIALS_KEY in ds_config:
ds_config[CREDENTIALS_KEY] = _get_credentials(
ds_config.pop(CREDENTIALS_KEY),
credentials # credentials name
)
data_sets[ds_name] = AbstractDataSet.from_config(
ds_name, ds_config, load_versions.get(ds_name), save_version)
return cls(data_sets=data_sets)
