"""``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 versioned by default
and only takes metrics of numeric values.
"""
import json
from typing import NoReturn
from kedro.io.core import DatasetError, get_filepath_str
from kedro_datasets._typing import MetricsTrackingPreview
from kedro_datasets.json import json_dataset
[docs]
class MetricsDataset(json_dataset.JSONDataset):
"""``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 <https://docs.kedro.org/en/stable/data/\
data_catalog_yaml_examples.html>`_:
.. code-block:: yaml
cars:
type: tracking.MetricsDataset
filepath: data/09_tracking/cars.json
Example usage for the
`Python API <https://docs.kedro.org/en/stable/data/\
advanced_data_catalog_usage.html>`_:
.. code-block:: pycon
>>> 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)
"""
versioned = True
[docs]
def load(self) -> NoReturn:
raise DatasetError(f"Loading not supported for '{self.__class__.__name__}'")
[docs]
def save(self, data: dict[str, float]) -> None:
"""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.
"""
try:
for key, value in data.items():
data[key] = float(value)
except ValueError as exc:
raise DatasetError(
f"The MetricsDataset expects only numeric values. {exc}"
) from exc
save_path = get_filepath_str(self._get_save_path(), self._protocol)
with self._fs.open(save_path, **self._fs_open_args_save) as fs_file:
json.dump(data, fs_file, **self._save_args)
self._invalidate_cache()
[docs]
def preview(self) -> MetricsTrackingPreview: # type: ignore[override]
"Load the Metrics tracking dataset used in Kedro-viz experiment tracking"
load_path = get_filepath_str(self._get_load_path(), self._protocol)
with self._fs.open(load_path, **self._fs_open_args_load) as fs_file:
return json.load(fs_file)
