Plotter.attributes_obs_vs_pred

Plotter.attributes_obs_vs_pred(block: str, attributes: List[str] = None, normalize: bool = False, per_column: bool = False, downsamp: int = 1, n_cols: int = 1, datamodule: DataModule | None = None) → Figure | None[source]

Plots the observed vs predicted values for all the selected attributes (= DataObject’s) in the given data block of the datamodule. (InputML or OutputML).

Parameters:

  • block (str) – Name of the data block to be plotted. The block must be available the datamodule.

  • attributes (List[str], optional, default=None) – List of attributes (= names of DataObject’s) to be plotted. If None, all attributes of the block are plotted.

  • normalize (bool, optional, default=False) – If True, the data is plotted normalized, while False returns the plots it in its original domain

  • per_column (bool, optional, default=False) – If True, multi-dimensional attributes are plotted as separate traces and subplots, otherwise all data is flattened to a single trace.

  • downsamp (int, optional, default=1) – Down-sampling factor for the data.

  • n_cols (int, optional, default=1) – Number of columns in the plot.

  • datamodule (DataModule, optional, default=None) – A DataModule object used for evaluation. If None, the datamodule provided when creating the plotter is used.

Returns:

Optional[plotly.graph_objects.Figure] – Plotly figure object, if self.output is None, otherwise None.

Notes

The plot is generated using the validation set of the datamodule, and is only supported for non-categorical attributes.