kedro_datasets_experimental.prophet.ProphetModelDataset

class kedro_datasets_experimental.prophet.ProphetModelDataset(*, filepath, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)

ProphetModelDataset loads/saves Facebook Prophet models to a JSON file using an underlying filesystem (e.g., local, S3, GCS). It uses Prophet’s built-in serialization to handle the JSON file.

Example usage for the YAML API:

model:
  type: custom_datasets.ProphetModelDataset
  filepath: gcs://your_bucket/model.json
  fs_args:
    project: my-project
  credentials: my_gcp_credentials

Example usage for the Python API:

 from kedro_datasets_experimental.prophet import ProphetModelDataset
 from prophet import Prophet
 import pandas as pd

 df = pd.DataFrame(
...     {"ds": ["2024-01-01", "2024-01-02", "2024-01-03"], "y": [100, 200, 300]}
... )

 model = Prophet()
 model.fit(df)
 dataset = ProphetModelDataset(filepath="path/to/model.json")
 dataset.save(model)
 reloaded_model = dataset.load()

Attributes

DEFAULT_FS_ARGS
DEFAULT_SAVE_ARGS

Methods

exists()	Checks whether a dataset's output already exists by calling the provided _exists() method.
from_config(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
load()	Loads a Prophet model from a JSON file.
preview()	Generate a preview of the JSON dataset with a specified number of items.
release()	Release any cached data.
resolve_load_version()	Compute the version the dataset should be loaded with.
resolve_save_version()	Compute the version the dataset should be saved with.
save(data)	Saves a Prophet model to a JSON file.
to_config()	Converts the dataset instance into a dictionary-based configuration for serialization.

DEFAULT_FS_ARGS: dict[str, Any] = {'open_args_save': {'mode': 'w'}}

DEFAULT_SAVE_ARGS: dict[str, Any] = {'indent': 2}

__init__(*, filepath, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)

Creates a new instance of ProphetModelDataset pointing to a concrete JSON file on a specific filesystem.

Parameters:

• filepath (str) – Filepath in POSIX format to a JSON file prefixed with a protocol like s3://. If prefix is not provided, file protocol (local filesystem) will be used. The prefix should be any protocol supported by fsspec. Note: http(s) doesn’t support versioning.

• save_args (Optional[dict[str, Any]]) – json options for saving JSON files (arguments passed into `json.dump). Here you can find all available arguments: https://docs.python.org/3/library/json.html All defaults are preserved, but “default_flow_style”, which is set to False.

• version (Optional[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.

• credentials (Optional[dict[str, Any]]) – Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {“token”: None}.

• fs_args (Optional[dict[str, Any]]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“project”: “my-project”} for GCSFileSystem), as well as to pass to the filesystem’s open method through nested keys open_args_load and open_args_save. Here you can find all available arguments for open: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open

• metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

exists()

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:

bool

Returns:

Flag indicating whether the output already exists.

Raises:

DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)

Create a dataset instance using the configuration provided.

Parameters:

• name (str) – Data set name.

• config (dict[str, Any]) – Data set config dictionary.

• load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

• save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()

Loads a Prophet model from a JSON file.

Returns:

A deserialized Prophet model.

Return type:

Prophet

preview()

Generate a preview of the JSON dataset with a specified number of items.

Return type:

NewType(JSONPreview, str)

Returns:

A string representing the JSON data for previewing.

release()

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

resolve_load_version()

Compute the version the dataset should be loaded with.

Return type:

Optional[str]

resolve_save_version()

Compute the version the dataset should be saved with.

Return type:

Optional[str]

save(data)

Saves a Prophet model to a JSON file.

Parameters:

data (Prophet) – The Prophet model instance to be serialized and saved.

Return type:

None

to_config()

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:

dict[str, Any]

Returns:

A dictionary containing the dataset’s type and initialization arguments.