Client represents a higher level interface to datasets API.
abeja.datasets.
Client
(organization_id: typing.Union[str, NoneType] = None, credential: typing.Union[typing.Dict[str, str], NoneType] = None, timeout: typing.Union[int, NoneType] = None) → None¶A High-Level client for Dataset API
from abeja.datasets import Client
client = Client()
abeja.datasets.dataset.
Dataset
(api: abeja.datasets.api.client.APIClient, organization_id: str, dataset_id: str, name: typing.Union[str, NoneType] = None, type: typing.Union[str, NoneType] = None, props: typing.Union[dict, NoneType] = None, total_count: typing.Union[int, NoneType] = None, created_at: typing.Union[str, NoneType] = None, updated_at: typing.Union[str, NoneType] = None, **kwargs) → None¶a model class for a dataset
dataset_items
¶Get dataset Items object
dataset = client.get_dataset(dataset_id='1410805969256')
dataset_items = dataset.dataset_items
DatasetItem
objectabeja.datasets.dataset.
Datasets
(api: abeja.datasets.api.client.APIClient, organization_id: str) → None¶a class for handling datasets
create
(name: str, type: str, props: dict) → abeja.datasets.dataset.Dataset¶create a dataset
API reference: POST /organizations/<organization_id>/datasets/
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)
Dataset
objectdelete
(dataset_id: str) → abeja.datasets.dataset.Dataset¶delete a dataset
response = datasets.delete(dataset_id='1377232365920')
Dataset
objectabeja.datasets.dataset_item.
DatasetItem
(api: abeja.datasets.api.client.APIClient, organization_id: str, dataset_id: str, dataset_item_id: str, **kwargs) → None¶a model class for DatasetItem
asdict
()¶abeja.datasets.dataset_item.
DatasetItems
(api: abeja.datasets.api.client.APIClient, organization_id: str, dataset_id: str) → None¶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) → abeja.datasets.dataset_item.DatasetItem¶Update a datset item in bulk.
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)
DatasetItem
objectcreate
(source_data: typing.List[dict], attributes: dict) → abeja.datasets.dataset_item.DatasetItem¶create a item in dataset
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)
DatasetItem
objectdelete
(dataset_item_id: str) → abeja.datasets.dataset_item.DatasetItem¶Delete a datset item.
response = dataset_items.delete(dataset_item_id=0)
DatasetItem
objectget
(dataset_item_id: str) → abeja.datasets.dataset_item.DatasetItem¶get a item in dataset
response = dataset_items.get(dataset_item_id=0)
DatasetItem
objectlist
(next_page_token: typing.Union[str, NoneType] = None, limit: typing.Union[int, NoneType] = None, prefetch: bool = False) → abeja.datasets.dataset_item.DatasetItemIterator¶generate all dataset_items in a dataset
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)
ABEJA_STORAGE_DIR_PATH
or current
directory by default. [optional]DatasetItemIterator
objectupdate
(dataset_item_id: str, attributes: dict) → abeja.datasets.dataset_item.DatasetItem¶Update a datset item.
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)
DatasetItem
object