rhino_health.lib.endpoints.dataset.dataset_dataclass#
Module Contents#
Classes#
Input arguments for adding a new Dataset |
|
- class rhino_health.lib.endpoints.dataset.dataset_dataclass.DatasetCreateInput(**data)#
Bases:
BaseDatasetInput arguments for adding a new Dataset
- csv_filesystem_location: str | None#
The location the Dataset data is located on-prem. The file should be a CSV.
- method: typing_extensions.Literal[DICOM, filesystem] = 'filesystem'#
What source are we importing imaging data from. Either a DICOM server, or the local file system
- is_data_deidentified: bool | None = False#
Is the data already deidentified?
- image_dicom_server: str | None#
The DICOM Server URL to import DICOM images from
- image_filesystem_location: str | None#
The on-prem Location to import DICOM images from
- file_base_path: str | None#
The location of non DICOM files listed in the dataset data CSV on-prem
- sync: bool | None = True#
Should we perform this import request synchronously.
- name: str#
The name of the Dataset
- description: str#
The description of the Dataset
- base_version_uid: str | None#
The original Dataset this Dataset is a new version of, if applicable
- project_uid: typing_extensions.Annotated[str, Field(alias='project')]#
The unique ID of the Project this Dataset belongs to.
- workgroup_uid: typing_extensions.Annotated[str, Field(alias='workgroup')]#
The unique ID of the Workgroup this Dataset belongs to .. warning workgroup_uid may change to primary_workgroup_uid in the future
- data_schema_uid: typing_extensions.Annotated[Any, Field(alias='data_schema')]#
The unique ID of the DataSchema this Dataset follows
- import_args()#
- class rhino_health.lib.endpoints.dataset.dataset_dataclass.Dataset(**data)#
- property primary_workgroup#
- property dataset_info#
Sanitized metadata information about the Dataset.
- property data_schema: DataSchema#
Return the DataSchema Dataclass associated with data_schema_uid
Warning
The result of this function is cached. Be careful calling this function after making changes. All dataclasses must already exist on the platform before making this call.
- Returns:
- data_schema: DataSchema
Dataclass representing the DataSchema
- property project: Project#
Return the Project Dataclass associated with project_uid
Warning
The result of this function is cached. Be careful calling this function after making changes. All dataclasses must already exist on the platform before making this call.
- Returns:
- project: Project
Dataclass representing the Project
- property workgroup: Workgroup#
Return the Workgroup Dataclass associated with workgroup_uid
Warning
The result of this function is cached. Be careful calling this function after making changes. All dataclasses must already exist on the platform before making this call.
- Returns:
- workgroup: Workgroup
Dataclass representing the Workgroup
- property creator: User#
Return the User Dataclass associated with creator_uid
Warning
The result of this function is cached. Be careful calling this function after making changes. All dataclasses must already exist on the platform before making this call.
- Returns:
- creator: User
Dataclass representing the User
- uid: str#
The unique ID of the Dataset
- version: int | None = 0#
Which revision this Dataset is
- num_cases: int#
The number of cases in the Dataset
- import_status: str#
The import status of the Dataset
- data_schema_uid: str#
- name: str#
The name of the Dataset
- description: str#
The description of the Dataset
- base_version_uid: str | None#
The original Dataset this Dataset is a new version of, if applicable
- creator_uid: str#
The UID of the creator of this dataclass on the system
- created_at: str#
When this dataclass was created on the system
- data_schema_name: str#
The data_schema name
- project_name: str#
The project name
- workgroup_name: str#
The workgroup name
- creator_name: str#
The creator name
- run_code(run_code, print_progress=True, **kwargs)#
Create and run code on this dataset using defaults that can be overridden
Warning
This function relies on a dataset’s metadata so make sure to create the input dataset first
Warning
This feature is under development and the interface may change
- run_code: str
The code that will run in the container
- print_progress: bool = True
Whether to print how long has elapsed since the start of the wait
- name: Optional[str] = “{dataset.name} (v.{dataset.version}) containerless code”
Model name - Uses the dataset name and version as part of the default (eg: when using a the first version of dataset named dataset_one the name will be dataset_one (v.1) containerless code)
- description: Optional[str] = “Python code run”
Model description
- container_image_uri: Optional[str] = {ENV_URL}/rhino-gc-workgroup-rhino-health:generic-python-runner”
Uri to container that should be run - ENV_URL is the environment ecr repo url
- input_data_schema_uid: Optional[str] = dataset.data_schema_uid
The data_schema used for the input dataset - By default uses the data_schema used to import the dataset
- output_data_schema_uid: Optional[str] = None (Auto generate data schema)
The data_schema used for the output dataset - By default generates a schema from the dataset_csv
- output_dataset_names_suffix: Optional[str] = “containerless code”
String that will be added to output dataset name
- timeout_seconds: Optional[int] = 600
Amount of time before timeout in seconds
- Returns:
- Tuple: (output_datasets, code_run)
output_datasets: List of Dataset Dataclasses code_run: A CodeRun object containing the run outcome
Examples
dataset.run_code(run_code = <df[‘BMI’] = df.Weight / (df.Height ** 2)>)
- get_metric(metric_configuration: rhino_health.lib.metrics.base_metric.BaseMetric)#
Queries on-prem and returns the result based on the METRIC_CONFIGURATION for this Dataset.
See also