openml/datasets/functions.py
def fork_dataset(data_id: int) -> int:
    """
     Creates a new dataset version, with the authenticated user as the new owner.
     The forked dataset can have distinct dataset meta-data,
     but the actual data itself is shared with the original version.

     This API is intended for use when a user is unable to edit the critical fields of a dataset
     through the edit_dataset API.
     (Critical fields are default_target_attribute, ignore_attribute, row_id_attribute.)

     Specifically, this happens when the user is:
            1. Not the owner of the dataset.
            2. User is the owner of the dataset, but the dataset has tasks.

     In these two cases the only way to edit critical fields is:
            1. STEP 1: Fork the dataset using fork_dataset API
            2. STEP 2: Call edit_dataset API on the forked version.


    Parameters
    ----------
    data_id : int
        id of the dataset to be forked

    Returns
    -------
    Dataset id of the forked dataset

    """
    if not isinstance(data_id, int):
        raise TypeError("`data_id` must be of type `int`, not {}.".format(type(data_id)))
    # compose data fork parameters
    form_data = {"data_id": data_id}
    result_xml = openml._api_calls._perform_api_call("data/fork", "post", data=form_data)
    result = xmltodict.parse(result_xml)
    data_id = result["oml:data_fork"]["oml:id"]
    return int(data_id)

     Creates a new dataset version, with the authenticated user as the new owner.
     The forked dataset can have distinct dataset meta-data,
     but the actual data itself is shared with the original version.

     This API is intended for use when a user is unable to edit the critical fields of a dataset
     through the edit_dataset API.
     (Critical fields are default_target_attribute, ignore_attribute, row_id_attribute.)

     Specifically, this happens when the user is:
            1. Not the owner of the dataset.
            2. User is the owner of the dataset, but the dataset has tasks.

     In these two cases the only way to edit critical fields is:
            1. STEP 1: Fork the dataset using fork_dataset API
            2. STEP 2: Call edit_dataset API on the forked version.


    Parameters
    ----------
    data_id : int
        id of the dataset to be forked

    Returns
    -------
    Dataset id of the forked dataset

    
Creates a new dataset version, with the authenticated user as the new owner.
The forked dataset can have distinct dataset meta-data,
but the actual data itself is shared with the original version.

This API is intended for use when a user is unable to edit the critical fields of a dataset
through the edit_dataset API.

Specifically, this happens when the user is:
1. Not the owner of the dataset.
2. User is the owner of the dataset, but the dataset has tasks.

In these two cases the only way to edit critical fields is:
1. STEP 1: Fork the dataset using fork_dataset API
2. STEP 2: Call edit_dataset API on the forked version.

Parameters

data_id : int
id of the dataset to be forked

Returns

Dataset id of the forked dataset
Creates a new dataset version with the authenticated user as the new owner . The forked dataset can have distinct dataset meta - data but the actual data itself is shared with the original version . This API is intended for use when a user is unable to edit the critical fields of a dataset through the edit_dataset API . Specifically this happens when the user is : 1 . Not the owner of the dataset . 2 . User is the owner of the dataset but the dataset has tasks . In these two cases the only way to edit critical fields is : 1 . STEP 1 : Fork the dataset using fork_dataset API 2 . STEP 2 : Call edit_dataset API on the forked version . Parameters data_id : int id of the dataset to be forked Returns Dataset id of the forked dataset
def fork_dataset(data_id: int) -> int:
    if not isinstance(data_id, int):
        raise TypeError("`data_id` must be of type `int`, not {}.".format(type(data_id)))
    form_data = {"data_id": data_id}
    result_xml = openml._api_calls._perform_api_call("data/fork", "post", data=form_data)
    result = xmltodict.parse(result_xml)
    data_id = result["oml:data_fork"]["oml:id"]
    return int(data_id)
def fork_dataset ( data_id : int ) -> int : if not isinstance ( data_id , int ) : raise TypeError ( "`data_id` must be of type `int`, not {}." . format ( type ( data_id ) ) ) form_data = { "data_id" : data_id } result_xml = openml . _api_calls . _perform_api_call ( "data/fork" , "post" , data = form_data ) result = xmltodict . parse ( result_xml ) data_id = result [ "oml:data_fork" ] [ "oml:id" ] return int ( data_id )
Creates a new dataset version, with the authenticated user as the new owner.
Creates a new dataset version with the authenticated user as the new owner .
"""
     Creates a new dataset version, with the authenticated user as the new owner.
     The forked dataset can have distinct dataset meta-data,
     but the actual data itself is shared with the original version.

     This API is intended for use when a user is unable to edit the critical fields of a dataset
     through the edit_dataset API.
     (Critical fields are default_target_attribute, ignore_attribute, row_id_attribute.)

     Specifically, this happens when the user is:
            1. Not the owner of the dataset.
            2. User is the owner of the dataset, but the dataset has tasks.

     In these two cases the only way to edit critical fields is:
            1. STEP 1: Fork the dataset using fork_dataset API
            2. STEP 2: Call edit_dataset API on the forked version.


    Parameters
    ----------
    data_id : int
        id of the dataset to be forked

    Returns
    -------
    Dataset id of the forked dataset

    """ # compose data fork parameters
data_id
int