ScatterMatrixExplorer
Display pairwise scatter plots for all selected numeric columns.
Generates a grid of scatter plots (also known as a SPLOM, short for Scatter PLOt Matrix) where each cell shows the relationship between one pair of numeric columns. The diagonal cells can optionally show the distribution of a single variable. Colour and symbol encodings can be mapped to a grouping column to reveal class separation or cluster structure across all feature pairs at once.
This explorer is the standard first step for discovering linear and nonlinear pairwise correlations in tabular datasets before applying feature selection or dimensionality reduction.
Parameters
- color_group, default=
None - Column name or index used to group colored points.
- simbol_group, default=
None - Column name or index used to group point symbols.
Methods
get_results(self, exploration_path: str, options: Dict[str, Any]) -> Dict[str, Any]
ScatterMatrixExplorerLoad and return the saved scatter matrix 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-serialized Plotly figure),"type"("plotly_json"), and"config"(empty dict).
launch_exploration(self, dataset: 'DashAIDataset', explorer_info: DashAI.back.dependencies.database.models.Explorer)
ScatterMatrixExplorerGenerate a Plotly scatter matrix for N selected columns.
Parameters
- dataset : DashAIDataset
- The prepared dataset with at least two columns.
- explorer_info : Explorer
- Explorer record with column names and optional display name.
Returns
- plotly.graph_objects.Figure
- An interactive scatter matrix figure.
prepare_dataset(self, loaded_dataset: 'DashAIDataset', columns: List[Dict[str, Any]]) -> 'DashAIDataset'
ScatterMatrixExplorerExtend column selection to include optional color and symbol grouping columns.
Parameters
- loaded_dataset : DashAIDataset
- The full dataset.
- columns : List[Dict[str, Any]]
- Explicitly selected column descriptors.
Returns
- DashAIDataset
- Dataset containing the selected columns plus any optional grouping columns.
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
ScatterMatrixExplorerSave the scatter matrix figure to a JSON file on disk.
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_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, and input cardinality constraints.
get_schema(cls) -> dict
ConfigObjectGenerates the component related Json Schema.
Returns
- dict
- Dictionary representing the Json Schema of the component.
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.