RegressionTask

Task

DashAI.back.tasks.RegressionTask

Abstract base task for continuous-output (regression) problems in DashAI.

Regression tasks predict one or more continuous numeric values from input features. This base class constrains output columns to Float or Integer types and accepts Float, Integer, and Categorical input types. Unlike classification tasks, regression does not require a Categorical output and num_labels always returns None.

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: str) -> int | None

Defined on SupervisedTask

Return the number of unique labels in the output column for supervised tasks.

Parameters

dataset : DashAIDataset: Dataset used for training
output_column : str: Output column

Returns

int | None: Number of unique labels or None if not applicable

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.

process_predictions(self, dataset: 'DashAIDataset', predictions: 'ndarray', output_column: str)

Defined on SupervisedTask

Process the predictions. Return predictions unchanged unless a supervised task specializes them.

Parameters

dataset : DashAIDataset: Dataset used for training
predictions : np.ndarray: Predictions from the model
output_column : str: Output column

Returns

Processed predictions

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.

Methods​

Methods