Client¶

Client represents a higher level interface to datasets API.

class abeja.datasets.Client(organization_id: str | None = None, credential: Dict[str, str] | None = None, timeout: int | None = None)¶

A High-Level client for Dataset API

from abeja.datasets import Client

client = Client()

property datasets: Datasets¶

Get datasets object

Request syntax:

datasets = client.datasets

Returns:

Datasets

get_dataset(dataset_id: str) → Dataset¶

Get dataset for specific dataset_id

Request syntax:

response = client.get_dataset(dataset_id='1234567890123')

Params:

dataset_id (str): dataset id

Return type:

Dataset

Dataset¶

a model class for a dataset

Properties:

organization_id (str)
dataset_id (str)
name (str)
type (str)
props (dict)
total_count (int)
created_at (datetime)
updated_at (datetime)

property dataset_items: DatasetItems¶

Get dataset Items object

Request syntax:

dataset = client.get_dataset(dataset_id='1410805969256')
dataset_items = dataset.dataset_items

Returns:

DatasetItem object

Datasets¶

class abeja.datasets.dataset.Datasets(api: APIClient, organization_id: str)¶

a class for handling datasets

create(name: str, type: str, props: dict) → Dataset¶

create a dataset

API reference: POST /organizations/<organization_id>/datasets/

Request Syntax:

name = "test-dataset"
dataset_type = "classification"
props = {
    "categories": [
        {
            "labels": [
                {
                    "label_id": 1,
                    "label": "dog"
                },
                {
                    "label_id": 2,
                    "label": "cat"
                },
                {
                    "label_id": 3,
                    "label": "others"
                }
            ],
            "category_id": 1,
            "name": "cats_dogs"
        }
    ]
}
response = datasets.create(name, dataset_type, props)

Params:

name (str): dataset name
type (str): dataset types eg: classification, detection
props (dict): properties of dataset

Return type:

Dataset object

delete(dataset_id: str) → Dataset¶

delete a dataset

Request syntax:

response = datasets.delete(dataset_id='1377232365920')

Params:

dataset_id (str): dataset id

Response type:

Dataset object

get(dataset_id: str) → Dataset¶

get a dataset

Request syntax:

response = datasets.get(dataset_id=1410805969256)

Params:

dataset_id (str): dataset id

Return type:

Dataset object

list() → List[Dataset]¶

Get dataset list

Request syntax:

response = datasets.list()

Response type:

list of Dataset object

Dataset Item¶

class abeja.datasets.dataset_item.DatasetItem(api: APIClient, organization_id: str, dataset_id: str, dataset_item_id: str, **kwargs)¶

a model class for DatasetItem

Properties:

organization_id (str)
dataset_id (str)
dataset_item_id (int)
attributes (dict)
created_at (datetime)
updated_at (datetime)
source_data (list)

asdict()¶

Dataset Items¶

class abeja.datasets.dataset_item.DatasetItems(api: APIClient, organization_id: str, dataset_id: str)¶

a class for a dataset item

from abeja.datasets import Client

client = Client()
dataset = client.get_dataset(dataset_id='1410805969256')
dataset_items = dataset.dataset_items

bulk_update(bulk_attributes: dict) → DatasetItem¶

Update a datset item in bulk.

Request syntax:

bulk_attributes = [
    {
        "dataset_item_id": 1111111111111,
        "attributes": {
            "classification": [
                {
                    "category_id": 1,
                    "label_id": 1
                }
            ],
            "custom_format": {
                "anything": "something"
                   },
            "detection": [
                {
                    "category_id": 1,
                    "label_id": 2,
                    "rect": {
                        "xmin": 22,
                        "ymin": 145,
                        "xmax": 140,
                        "ymax": 220
                    }
                }
            ]
        }
    }
]

response = dataset_items.bulk_update(bulk_attributes=bulk_attributes)

Params:

bulk_attributes (dict): list of attributes.

Return type:

return the updated dataset item list DatasetItem object

create(source_data: List[dict], attributes: dict) → DatasetItem¶

create a item in dataset

Request syntax:

source_data = [
    {
        "data_type": "image/jpeg",
        "data_uri": "datalake://1200123803688/20170815T044617-f20dde80-1e3b-4496-bc06-1b63b026b872",
        "height": 500,
        "width": 200
    }
]

attributes = {
    "classification": [
        {
            "category_id": 1,
            "label_id": 1,
        }
    ],
    "detection": [
        {
            "category_id": 1,
            "label_id": 2,
            "rect": {
                "xmin": 22,
                "ymin": 145,
                "xmax": 140,
                "ymax": 220
            }
        }
    ]
    "custom": [
        {
            "anything": "something"
        }
    ]
}

response = dataset_items.create(source_data=source_data, attributes=attributes)

Params:

source_data (list): meta data annotated to source data.
attribute (dict): list of source data stored in external storage.

Return type:

DatasetItem object

delete(dataset_item_id: str) → DatasetItem¶

Delete a datset item.

Request syntax:

response = dataset_items.delete(dataset_item_id=0)

Params:

-dataset_item_id (int): dataset item id

Return type:

return the deleted dataset item DatasetItem object

get(dataset_item_id: str) → DatasetItem¶

get a item in dataset

Request syntax:

response = dataset_items.get(dataset_item_id=0)

Params:

dataset_item_id (int): dataset item id

Return type:

DatasetItem object

list(next_page_token: str | None = None, limit: int | None = None, prefetch: bool = False) → DatasetItemIterator¶

generate all dataset_items in a dataset

Request syntax:

dataset_item_iter = dataset_items.list()

# list all dataset items
dataset_items = list(dataset_item_iter)

# or get the first dataset item
dataset_item = next(dataset_item_iter)

Params:

next_page_token (str) : next page token to get the next items. [optional]
limit (int): limit of items. [optional]
prefetch (bool) : False by default. if True, download source_data of all dataset_item concurrently (therefore the order of dataset_items can be changed) and save them in the path specified in environment variable as ABEJA_STORAGE_DIR_PATH or current directory by default. [optional]

Return type:

DatasetItemIterator object

update(dataset_item_id: str, attributes: dict) → DatasetItem¶

Update a datset item.

Request syntax:

attributes = {
    "classification": [
        {
            "category_id": 1,
            "label_id": 1,
        }
    ],
    "detection": [
        {
            "category_id": 1,
            "label_id": 2,
            "rect": {
                "xmin": 22,
                "ymin": 145,
                "xmax": 140,
                "ymax": 220
            }
        }
    ]
    "custom": [
        {
            "anything": "something"
        }
    ]
}

response = dataset_items.update(dataset_item_id=0, attributes=attributes)

Params:

dataset_item_id (int): dataset item id
attribute (dict): list of source data stored in external storage.

Return type:

return the updated dataset item DatasetItem object

Table Of Contents

Client¶

Dataset¶

Datasets¶

Dataset Item¶

Dataset Items¶