Quick Start
***********

The following examples provide an overview of how to use ADS to work with the Data Labeling service.

List all the datasets in the compartment:

.. code-block:: python3

   from ads.data_labeling import DataLabeling
   dls = DataLabeling()
   dls.list_dataset()

With a labeled data set, the details of the labeling is called the export.  To generate the export and get the path to the metadata JSONL file, you can use ``export()`` with these parameters:

* ``dataset_id``: The OCID of the Data Labeling dataset to take a snapshot of.
* ``path``: The Object Storage path to store the generated snapshot.

.. code-block:: python3

   metadata_path = dls.export(
       dataset_id="<dataset_id>",
       path="oci://<bucket_name>@<namespace>/<prefix>"
   )


To load the labeled data into a Pandas dataframe, you can use ``LabeledDatasetReader`` object that has these parameters:

* ``materialize``: Load the contents of the dataset. This can be quite large. The default is `False`.
* ``path``: The metadata file path that can be local or object storage path.

.. code-block:: python3

   from ads.data_labeling import LabeledDatasetReader
   ds_reader = LabeledDatasetReader.from_export(
     path="<metadata_path>",
     materialize=True
   )
   df = ds_reader.read()

You can also read labeled datasets from the OCI Data Labeling Service into a Pandas dataframe using ``LabeledDatasetReader`` object by specifying
``dataset_id``:

.. code-block:: python3

   from ads.data_labeling import LabeledDatasetReader
   ds_reader = LabeledDatasetReader.from_DLS(
     dataset_id="<dataset_ocid>",
     materialize=True
   )
   df = ds_reader.read()

Alternatively, you can use the ``.read_labeled_data()`` method by either specifying ``path`` or ``dataset_id``.

This example loads a labeled dataset and returns a Pandas dataframe containing the content and the annotations:

.. code-block:: python3

   df = pd.DataFrame.ads.read_labeled_data(
       path="<metadata_path>",
       materialize=True
   )

The following example loads a labeled dataset from the OCI Data Labeling, and returns a Pandas dataframe containing the content and the annotations:

.. code-block:: python3

   df = pd.DataFrame.ads.read_labeled_data(
       dataset_id="<dataset_ocid>",
       materialize=True
   )