kedro_datasets.tracking.MetricsDataset

class kedro_datasets.tracking.MetricsDataset(*, filepath, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]

MetricsDataset saves data to a JSON file using an underlying filesystem (e.g.: local, S3, GCS). It uses native json to handle the JSON file. The MetricsDataset is part of Kedro Experiment Tracking. The dataset is write-only, it is versioned by default and only takes metrics of numeric values.

Example usage for the YAML API:

cars:
  type: tracking.MetricsDataset
  filepath: data/09_tracking/cars.json

Example usage for the Python API:

from kedro_datasets.tracking import MetricsDataset

data = {"col1": 1, "col2": 0.23, "col3": 0.002}

dataset = MetricsDataset(filepath=tmp_path / "test.json")
dataset.save(data)

Attributes

DEFAULT_FS_ARGS

DEFAULT_SAVE_ARGS

versioned

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 data by delegation to the provided load method.

preview()

Load the Metrics tracking dataset used in Kedro-viz experiment tracking

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)

Converts all values in the data from a MetricsDataset to float to make sure they are numeric values which can be displayed in Kedro Viz and then saves the dataset.
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 JSONDataset 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 All defaults are preserved, except mode, which is set to w when saving.

  • 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()[source]

Loads data by delegation to the provided load method.

Return type:

NoReturn

Returns:

Data returned by the provided load method.

Raises:

DatasetError – When underlying load method raises error.

preview()[source]

Load the Metrics tracking dataset used in Kedro-viz experiment tracking

Return type:

NewType(MetricsTrackingPreview, dict)

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)[source]

Converts all values in the data from a MetricsDataset to float to make sure they are numeric values which can be displayed in Kedro Viz and then saves the dataset.

Return type:

None

versioned = True