Clustering

Converter

DashAI.back.converters.clustering.Clustering

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Apply a clustering algorithm to numeric columns and append a cluster label.

Numeric columns in scope are passed to the selected algorithm, which assigns each row to a cluster without requiring a target column. The resulting label is stored in a new column appended to the original dataset.

After fitting, a JSON-serializable report is available via get_report() with per-cluster metrics, sizes, feature profiles, and algorithm-specific attributes (e.g. centroids for K-Means, linkage data for Agglomerative).

Parameters

algorithm : string, default=KMeansClustering

Clustering algorithm to apply.

algorithm_params : object

Parameters for the selected clustering algorithm.

output_column_name : string, default=cluster

Name of the output column to store the cluster labels.

Methods

fit(self, x: "'DashAIDataset'", y: "Union['DashAIDataset', None]" = None) -> "'Clustering'"

Defined on Clustering

Fit the selected clustering algorithm and store execution metadata.

Parameters

x : DashAIDataset

Input dataset containing the scoped feature columns.

y : DashAIDataset, optional

Ignored. Present for API compatibility. Defaults to None.

Returns

Clustering

The fitted converter instance (self).

get_output_type(self, column_name: 'str' = None) -> 'DashAIDataType'

Defined on Clustering

Return the DashAI type produced by the cluster label column.

Parameters

column_name : str, optional

Not used. Defaults to None.

Returns

DashAIDataType

An Integer type backed by pyarrow.int64().

get_report(self) -> 'Dict[str, object] | None'

Defined on Clustering

Return the report produced after the last fit, or None.

get_schema(cls) -> 'dict'

Defined on Clustering

transform(self, x: "'DashAIDataset'", y: "Union['DashAIDataset', None]" = None) -> "'DashAIDataset'"

Defined on Clustering

Append the cluster labels computed during fit to the dataset.

Parameters

x : DashAIDataset

Input dataset to transform. Must have the same number of rows as the dataset used in fit.

y : DashAIDataset, optional

Ignored. Present for API compatibility. Defaults to None.

Returns

DashAIDataset

The original dataset with a new cluster label column appended.

build_report(self, data: 'Dict[str, Any]') -> 'Dict[str, Any]'

Defined on BaseConverter

Wrap converter-specific data with the producer converter name.

get_credential(self, name: str)

Defined on ConfigObject

Resolve a registered credential component by name.

Parameters

name : str

Credential component class name (e.g. "HuggingFaceCredential").

Returns

BaseCredential

An instance of the requested credential component.

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

Defined on BaseConverter

Get metadata for the converter, used by the DashAI frontend.

Parameters

cls : type

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

Returns

Dict[str, Any]

Dictionary containing display name, short description, image preview path, category, icon, color, and whether the converter is supervised.

infer_output_columns(self, inputs: 'List[StateItem]') -> 'StructureDelta'

Defined on BaseConverter

Estimate what this converter does to its scope, without any data.

Parameters

inputs : list of ColumnItem | BlockItem

The dataset state items in this converter's scope.

Returns

StructureDelta

The scope items that survive (maybe retyped) and the new items.

rows_to_keep(self, x: "'DashAIDataset'") -> 'List[int]'

Defined on BaseConverter

Positions of the rows of x that survive this converter.

Parameters

x : DashAIDataset

The converter's scope columns of one split.

Returns

list of int

0-based positions of the kept rows, in order.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict

A dictionary with the data provided by the user to initialize the model.

Returns

dict

A validated dictionary with the necessary objects.