Datasets
Dataset ¶
attachments
property
writable
¶
attachments
The attachments associated with the dataset.
Returns:
Type | Description |
---|---|
list[TFormattedAttachment] | None
|
The attachments associated with the dataset. |
derived_from
property
¶
derived_from
latest_version_id
property
writable
¶
latest_version_id
The id of the latest version of this dataset.
Returns:
Type | Description |
---|---|
str | None
|
The id of the latest version of this dataset. |
properties
property
writable
¶
properties
resource
property
¶
resource
clean
staticmethod
¶
clean(
resource,
name=None,
derived_from=None,
properties=None,
attachments=None,
)
Create a clean dataset.
Examples:
from vectice import Dataset, FileResource
dataset = Dataset.clean(
name="my clean dataset",
resource=FileResource(paths="clean_dataset.csv"),
)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
resource |
Resource
|
The resource for the clean dataset. |
required |
name |
str | None
|
The name of the dataset. |
None
|
derived_from |
list[TBaseDerivedFrom | Dataset] | TBaseDerivedFrom | Dataset | None
|
A list of datasets versions (or ids) from which this dataset is derived. |
None
|
properties |
dict[str, str | int] | list[Property] | Property | None
|
A dict, for example |
None
|
attachments |
str | list[str] | None
|
Path of a file that will be attached to the iteration along with the dataset. |
None
|
modeling
staticmethod
¶
modeling(
training_resource,
testing_resource,
validation_resource=None,
name=None,
properties=None,
attachments=None,
derived_from=None,
)
Create a modeling dataset.
Examples:
from vectice import Dataset, FileResource
dataset = Dataset.modeling(
name="my modeling dataset",
training_resource=FileResource(paths="training_dataset.csv"),
testing_resource=FileResource(paths="testing_dataset.csv"),
validation_resource=FileResource(paths="validation_dataset.csv"),
)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
training_resource |
Resource
|
The resource for the training set (for modeling datasets). |
required |
testing_resource |
Resource
|
The resource for the testing set (for modeling datasets). |
required |
validation_resource |
Resource | None
|
The resource for the validation set (optional, for modeling datasets). |
None
|
name |
str | None
|
The name of the dataset. |
None
|
properties |
dict[str, str | int] | list[Property] | Property | None
|
A dict, for example |
None
|
attachments |
str | list[str] | None
|
Path of a file that will be attached to the iteration along with the dataset. |
None
|
derived_from |
list[TBaseDerivedFrom | Dataset] | TBaseDerivedFrom | Dataset | None
|
A list of datasets versions (or ids) from which this dataset is derived. |
None
|
origin
staticmethod
¶
origin(
resource, name=None, properties=None, attachments=None
)
Create an origin dataset.
Examples:
from vectice import Dataset, FileResource
dataset = Dataset.origin(
name="my origin dataset",
resource=FileResource(paths="origin_dataset.csv"),
)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
resource |
Resource
|
The resource for the origin dataset. |
required |
name |
str | None
|
The name of the dataset. |
None
|
properties |
dict[str, str | int] | list[Property] | Property | None
|
A dict, for example |
None
|
attachments |
str | list[str] | None
|
Path of a file that will be attached to the iteration along with the dataset. |
None
|