ForecastVsActual
Forecast and truth as two lines over the observation index.
An error scalar says how far off the forecast is on average; this says where. A forecast that tracks well early and drifts later shows a model whose error compounds over the horizon, while a line that runs parallel but offset reveals a level bias that RMSE folds into the rest of the error. Row order is time order for a forecasting run, because the task sorts its rows by date before anything downstream reads them.
Methods
compute(self, y_true, y_pred, class_names: Optional[List[str]] = None) -> List[DashAI.back.core.artifacts.Artifact]
ForecastVsActualBuild the forecast against actual line plot.
Parameters
- y_true : ndarray
- Ground truth values of the series.
- y_pred : ndarray
- The model's forecast for the same points.
- class_names : Optional[List[str]]
- Unused; always None for forecasting.
Returns
- List[Artifact]
- A single figure holding the truth and the forecast lines.
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]
BaseReportGet metadata values for the current report.
Returns
- Dict[str, Any]
- UI metadata, including whether the report needs a model that outputs class probabilities.
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.