ClusteringScatterExplorer
2-D scatter plot of clustering results coloured by cluster label.
Projects the selected numeric feature columns onto a 2-D plane using either PCA or t-SNE, then colours each point by its assigned cluster label.
When the converter report contains cluster centres (K-Means, Faiss K-Means,
Gaussian Mixture), the centres are projected through the same PCA reducer
and rendered as distinct × markers — only available with PCA because
t-SNE does not support out-of-sample transforms.
Noise points produced by density-based algorithms (DBSCAN / HDBSCAN,
label -1) receive the dedicated label "Noise" and a neutral colour.
Select at least two numeric columns that were used to fit the clustering algorithm. The cluster label column is added automatically from the converter report and must not be included in the selection.
Parameters
- reduction_method : string, default=
pca - Dimensionality reduction method used to project the selected features onto two dimensions. 'pca' is fast and deterministic; 'tsne' can better reveal non-linear cluster structure but is slower and stochastic.
Methods
get_results(self, exploration_path: str, options: Dict[str, Any]) -> Dict[str, Any]
ClusteringScatterExplorerLoad the saved scatter plot and return it for the frontend.
Parameters
- exploration_path : str
- Path to the JSON file saved by
save_notebook. - options : Dict[str, Any]
- Rendering options from the frontend (unused).
Returns
- Dict[str, Any]
- Dictionary with keys
"data"(JSON-serialised Plotly figure),"type"("plotly_json"), and"config"(empty dict).
launch_exploration(self, dataset: 'DashAIDataset', explorer_info: DashAI.back.dependencies.database.models.Explorer) -> Any
ClusteringScatterExplorerProject features to 2D and build a scatter plot coloured by cluster.
Parameters
- dataset : DashAIDataset
- Dataset with the selected feature columns and the cluster label column (injected by
prepare_dataset). - explorer_info : Explorer
- Explorer record with column descriptors and optional custom title.
Returns
- plotly.graph_objects.Figure
- An interactive 2-D scatter plot figure.
prepare_dataset(self, loaded_dataset: 'DashAIDataset', columns: List[Dict[str, Any]]) -> 'DashAIDataset'
ClusteringScatterExplorerExtend column selection to include the cluster label column.
Parameters
- loaded_dataset : DashAIDataset
- The full dataset.
- columns : List[Dict[str, Any]]
- Feature column descriptors selected by the user (a subset of the columns used during the last Clustering converter fit).
Returns
- DashAIDataset
- Dataset restricted to the selected feature columns plus the cluster label column.
save_notebook(self, notebook_info: DashAI.back.dependencies.database.models.Notebook, explorer_info: DashAI.back.dependencies.database.models.Explorer, save_path: 'Path', result: Any) -> str
ClusteringScatterExplorerSave the scatter plot figure to disk as a JSON file.
Parameters
- notebook_info : Notebook
- The notebook database record (unused).
- explorer_info : Explorer
- The explorer record used for filename generation.
- save_path : Path
- Directory where the file will be saved.
- result : Any
- The Plotly figure returned by
launch_exploration.
Returns
- str
- The path of the saved JSON file as a POSIX string.
get_credential(self, name: str)
ConfigObjectResolve 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]
BaseExplorerGet metadata for the explorer, used by the DashAI frontend.
Returns
- Dict[str, Any]
- Dictionary containing display name, description, image preview path, category, icon, color, allowed semantic types, allowed dtypes, input cardinality constraints, and any optional capability flags declared by the explorer.
get_schema(cls) -> dict
ConfigObjectGenerates the component related Json Schema.
Returns
- dict
- Dictionary representing the Json Schema of the component.
set_context(self, context: Dict[str, Any]) -> None
BaseExplorerSet optional runtime context for explorers that need extra inputs.
validate_and_transform(self, raw_data: dict) -> dict
ConfigObjectIt 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.
validate_columns(cls, explorer_info: DashAI.back.dependencies.database.models.Explorer, column_spec: Dict[str, Dict[str, str]]) -> bool
BaseExplorerValidate that the selected columns satisfy the explorer's constraints.
Parameters
- explorer_info : Explorer
- The database record for the explorer instance, including the selected columns.
- column_spec : Dict[str, Dict[str, str]]
- A mapping from column name to a dict with at least
"type"(semantic type name) and"dtype"(dtype string).
Returns
- bool
- True if all column constraints are satisfied, False otherwise.
validate_parameters(cls, params: Dict[str, Any]) -> bool
BaseExplorerValidate explorer parameters against the explorer's schema.
Parameters
- params : Dict[str, Any]
- The configuration parameters to validate.
Returns
- BaseExplorerSchema
- The validated and parsed schema instance. Subclasses that override this method may return a bool to indicate pass/fail without returning the model instance.