ClusteringTask

Task

DashAI.back.tasks.ClusteringTask

---

Task for grouping samples into clusters without target labels.

Clustering tasks discover groups directly from numeric input features. Unlike supervised tasks, they do not require output columns and are evaluated with internal clustering metrics computed from the feature matrix and the cluster labels produced by the model.

Methods

get_metadata(cls) -> Dict[str, Any]

Defined on BaseTask

Return serialisable metadata for the current task.

Parameters

cls : type

The task class (injected automatically by Python for classmethods).

Returns

Dict[str, Any]

Dictionary with keys "inputs_types", "outputs_types", "inputs_cardinality", "outputs_cardinality", "inputs", "outputs" and "requires_target".

num_labels(self, dataset: 'DashAIDataset', output_column: Optional[str] = None) -> None

Defined on UnsupervisedTask

Return None: a task without a target has no labels to count.

Parameters

dataset : DashAIDataset

Dataset used for training.

output_column : str, optional

Unused: there is no output column.

Returns

None

prepare_for_task(self, dataset: Union[ForwardRef('DatasetDict'), ForwardRef('DashAIDataset')], input_columns: List[str], output_columns: Optional[List[str]] = None) -> 'DashAIDataset'

Defined on BaseTask

Prepare and validate a dataset for this task.

Parameters

dataset : DatasetDict or DashAIDataset

The dataset to prepare. If a DatasetDict is supplied it is converted to DashAIDataset automatically.

input_columns : list of str

Names of columns to use as model inputs.

output_columns : list of str, optional

Names of columns to use as model outputs/targets. Tasks without targets, such as clustering, can omit this argument.

Returns

DashAIDataset

The validated dataset, ready to be passed to a model.

process_manual_input(self, manual_input: List[dict], dataset_path: str) -> 'DashAIDataset'

Defined on BaseTask

Process manual input data into a DashAIDataset with type validation.

Parameters

manual_input : List[dict]

List of dictionaries representing manual input data.

dataset_path : str

Path to the training dataset (used to get column specs for validation)

Returns

DashAIDataset

Processed DashAIDataset from manual input.

validate_dataset_for_task(self, dataset: 'DashAIDataset', dataset_name: str, input_columns: List[str], output_columns: Optional[List[str]]) -> None

Defined on BaseTask

Validate a dataset for the current task.

Parameters

dataset : DashAIDataset

Dataset to be validated

dataset_name : str

Dataset name

input_columns : list of str

Names of the columns selected as inputs.

output_columns : list of str

Names of the columns selected as outputs.