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"""``PickleLocalDataSet`` loads and saves a Python object to a
local pickle file. The underlying functionality is
supported by the ``pickle`` and ``joblib`` libraries, so it supports
all allowed options for loading and saving pickle files.
"""
import copy
import pickle
from pathlib import Path
from typing import Any, Dict
from kedro.io.core import AbstractVersionedDataSet, DataSetError, Version
try:
import joblib
except ImportError:
joblib = None
[docs]class PickleLocalDataSet(AbstractVersionedDataSet):
"""``PickleLocalDataSet`` loads and saves a Python object to a
local pickle file. The underlying functionality is
supported by the pickle and joblib libraries, so it supports
all allowed options for loading and saving pickle files.
Example:
::
>>> from kedro.io import PickleLocalDataSet
>>> import pandas as pd
>>>
>>> dummy_data = pd.DataFrame({'col1': [1, 2],
>>> 'col2': [4, 5],
>>> 'col3': [5, 6]})
>>> data_set = PickleLocalDataSet(filepath="data.pkl",
>>> backend='pickle',
>>> load_args=None,
>>> save_args=None)
>>> data_set.save(dummy_data)
>>> reloaded = data_set.load()
"""
DEFAULT_LOAD_ARGS = {} # type: Dict[str, Any]
DEFAULT_SAVE_ARGS = {} # type: Dict[str, Any]
BACKENDS = {"pickle": pickle, "joblib": joblib}
# pylint: disable=too-many-arguments
[docs] def __init__(
self,
filepath: str,
backend: str = "pickle",
load_args: Dict[str, Any] = None,
save_args: Dict[str, Any] = None,
version: Version = None,
) -> None:
"""Creates a new instance of ``PickleLocalDataSet`` pointing to a
concrete filepath. ``PickleLocalDataSet`` can use two backends to
serialise objects to disk:
pickle.dump: https://docs.python.org/3/library/pickle.html#pickle.dump
joblib.dump: https://pythonhosted.org/joblib/generated/joblib.dump.html
and it can use two backends to load serialised objects into memory:
pickle.load: https://docs.python.org/3/library/pickle.html#pickle.load
joblib.load: https://pythonhosted.org/joblib/generated/joblib.load.html
Joblib tends to exhibit better performance in case objects store NumPy
arrays:
http://gael-varoquaux.info/programming/new_low-overhead_persistence_in_joblib_for_big_data.html.
Args:
filepath: path to a pkl file.
backend: backend to use, must be one of ['pickle', 'joblib'].
load_args: Options for loading pickle files. Refer to the help
file of ``pickle.load`` or ``joblib.load`` for options.
save_args: Options for saving pickle files. Refer to the help
file of ``pickle.dump`` or ``joblib.dump`` for options.
version: If specified, should be an instance of
``kedro.io.core.Version``. If its ``load`` attribute is
None, the latest version will be loaded. If its ``save``
attribute is None, save version will be autogenerated.
Raises:
ValueError: If 'backend' is not one of ['pickle', 'joblib'].
ImportError: If 'backend' could not be imported.
"""
super().__init__(Path(filepath), version)
if backend not in ["pickle", "joblib"]:
raise ValueError(
"backend should be one of ['pickle', 'joblib'], got %s" % backend
)
if backend == "joblib" and joblib is None:
raise ImportError(
"selected backend 'joblib' could not be "
"imported. Make sure it is installed."
)
self._backend = backend
# Handle default load and save arguments
self._load_args = copy.deepcopy(self.DEFAULT_LOAD_ARGS)
if load_args is not None:
self._load_args.update(load_args)
self._save_args = copy.deepcopy(self.DEFAULT_SAVE_ARGS)
if save_args is not None:
self._save_args.update(save_args)
def _load(self) -> Any:
load_path = Path(self._get_load_path())
with load_path.open("rb") as local_file:
result = self.BACKENDS[self._backend].load(local_file, **self._load_args)
return result
def _save(self, data: Any) -> None:
save_path = Path(self._get_save_path())
save_path.parent.mkdir(parents=True, exist_ok=True)
with save_path.open("wb") as local_file:
try:
self.BACKENDS[self._backend].dump(data, local_file, **self._save_args)
except Exception: # pylint: disable=broad-except
# Checks if the error is due to serialisation or not
try:
self.BACKENDS[self._backend].dumps(data)
except Exception:
raise DataSetError(
"{} cannot be serialized. {} can only be used with "
"serializable data".format(
str(data.__class__), str(self.__class__.__name__)
)
)
else:
raise # pragma: no cover
def _describe(self) -> Dict[str, Any]:
return dict(
filepath=self._filepath,
backend=self._backend,
load_args=self._load_args,
save_args=self._save_args,
version=self._version,
)
def _exists(self) -> bool:
path = self._get_load_path()
return Path(path).is_file()