ClusteringHeatmapExplorer
Annotated heatmap of mean feature values per cluster.
Reads the per-cluster feature profiles stored in the Clustering
converter report and renders an annotated heatmap where rows correspond
to clusters and columns to the numeric features used during fitting.
Each cell shows the raw mean value of a feature within a cluster as a number annotation. The cell colour can optionally be z-score normalised (default), which maps how far each cluster mean is from the global feature mean in standard deviations — making features with very different scales visually comparable and highlighting which clusters are the most distinctive for each feature.
A diverging red–blue colour scale is used: red cells indicate values well above the global mean, blue cells indicate values well below it.
No column selection is required — all data comes from the converter report.
Parameters
- normalize : boolean, default=
True - When enabled, cell colours represent z-score standardised values (how many standard deviations each cluster mean is from the global mean), making clusters with very different feature scales directly comparable. Raw mean values are always shown as cell annotations. When disabled, the colour scale uses raw means.
Methods
get_results(self, exploration_path: str, options: Dict[str, Any]) -> Dict[str, Any]
ClusteringHeatmapExplorerLoad the saved heatmap 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
ClusteringHeatmapExplorerBuild an annotated heatmap of mean feature values per cluster.
Parameters
- _dataset : DashAIDataset
- Not used. All data is read from the converter report.
- explorer_info : Explorer
- Explorer record, used for the optional custom title.
Returns
- plotly.graph_objects.Figure
- An annotated heatmap figure.
prepare_dataset(self, loaded_dataset: 'DashAIDataset', _columns: List[Dict[str, Any]]) -> 'DashAIDataset'
ClusteringHeatmapExplorerReturn the dataset unchanged.
Parameters
- loaded_dataset : DashAIDataset
- The full dataset.
- _columns : List[Dict[str, Any]]
- Ignored. Present for API compatibility.
Returns
- DashAIDataset
- The dataset as received, unmodified.
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
ClusteringHeatmapExplorerSave the heatmap 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.