Prediction result containers.
Classes:
| Name | Description |
|---|---|
PredictionConfig | Configuration that produced a PredictionState. |
PredictionState | Prediction results with optional intervals. |
Classes¶
PredictionConfig¶
Configuration that produced a PredictionState.
Created by: model.predict(), build_prediction_state() Consumed by: dispatch_prediction_inference(), compute_prediction_bootstrap() Augmented by: Never
Attributes:
| Name | Type | Description |
|---|---|---|
allow_new_levels | bool | |
formula_spec | Any | |
newdata | ‘pl.DataFrame | None’ | |
pred_type | str | |
training_data | ‘pl.DataFrame | None’ | |
varying | str |
Attributes¶
allow_new_levels¶
allow_new_levels: bool = field(default=False, validator=(validators.instance_of(bool)))formula_spec¶
formula_spec: Any = field(default=None, repr=False)newdata¶
newdata: 'pl.DataFrame | None' = field(default=None, repr=False)pred_type¶
pred_type: str = field(validator=(validators.in_(('response', 'link'))))training_data¶
training_data: 'pl.DataFrame | None' = field(default=None, repr=False)varying¶
varying: str = field(default='exclude', validator=(validators.in_(('exclude', 'include'))))PredictionState¶
Prediction results with optional intervals.
Created by: build_prediction_state(), model.predict() Consumed by: build_predictions_dataframe(), model.predictions, plot_predict() Augmented by: attrs.evolve() after infer() adds intervals/CV outputs
Attributes:
| Name | Type | Description |
|---|---|---|
X_pred | ndarray | None | |
ci_lower | ndarray | None | |
ci_upper | ndarray | None | |
conf_level | float | None | |
config | PredictionConfig | None | |
cv_fitted | ndarray | None | |
cv_fold | ndarray | None | |
cv_residual | ndarray | None | |
fitted | ndarray | |
grid | ‘pl.DataFrame | None’ | |
has_cv | bool | Check if CV inference has been computed. |
has_inference | bool | Check if inference has been computed. |
interval_type | str | None | |
link | ndarray | None | |
se | ndarray | None |
Attributes¶
X_pred¶
X_pred: np.ndarray | None = field(default=None, validator=is_optional_ndarray, repr=False)ci_lower¶
ci_lower: np.ndarray | None = field(default=None, validator=is_optional_ndarray)ci_upper¶
ci_upper: np.ndarray | None = field(default=None, validator=is_optional_ndarray)conf_level¶
conf_level: float | None = field(default=None, converter=normalize_optional_conf_level, validator=is_optional_conf_level)config¶
config: PredictionConfig | None = field(default=None, repr=False, validator=(validators.optional(validators.instance_of(PredictionConfig))))cv_fitted¶
cv_fitted: np.ndarray | None = field(default=None, validator=is_optional_ndarray)cv_fold¶
cv_fold: np.ndarray | None = field(default=None, validator=is_optional_ndarray)cv_residual¶
cv_residual: np.ndarray | None = field(default=None, validator=is_optional_ndarray)fitted¶
fitted: np.ndarray = field(validator=is_ndarray)grid¶
grid: 'pl.DataFrame | None' = field(default=None, repr=False)has_cv¶
has_cv: boolCheck if CV inference has been computed.
has_inference¶
has_inference: boolCheck if inference has been computed.
interval_type¶
interval_type: str | None = field(default=None, validator=(validators.optional(is_choice_str(('confidence', 'prediction')))))link¶
link: np.ndarray | None = field(default=None, validator=is_optional_ndarray)se¶
se: np.ndarray | None = field(default=None, validator=is_optional_ndarray)