iaml.actionables.predictors.regressor.act_light_gbm_regressor¶

[STEP] LightGBM Regressor

Classes¶

ActLightGBMRegressor

[STEP] LightGBM Regressor

Module Contents¶

class iaml.actionables.predictors.regressor.act_light_gbm_regressor.ActLightGBMRegressor¶

Bases: iaml.predictor.Predictor

[STEP] LightGBM Regressor

name: str = 'LightGBM Regressor'¶: Step’s name.

refs: list[dict[str, Any]]¶: List of references for this step.

configuration¶: Configuration of this step (equivalent to hyperparameters for models).

model: lightgbm.LGBMRegressor = None¶

columns: list[str] = []¶

categorical_columns: list[str] = []¶

fit(dataset: iaml.dataset.Dataset)¶

Fits the step on the given dataset.

Parameters:: dataset (Dataset) – Features and labels.
Returns:: Fitted step.

predict(X)¶

Apply prediction model on DataFrame

Parameters:: X (pd.DataFrame) – DataFrame use to predict.
Returns:: Predicted values.

score(X, y=None, *args, **kwargs)¶

Mimic Scikitlearn API

Returns:: The model score

suitable(dataset: iaml.dataset.Dataset) → bool¶

Evaluates whether the step is suitable to run with the given dataset.

Parameters:: dataset (Dataset) – Dataset to evaluate on.
Returns:: Whether the step is suitable to run.

priorize(candidate: iaml.candidate.Candidate = None) → float¶

Evaluates the priority of the step within a pipeline. 0 means the execution of the step should not be prioritized, and 1 means it should be executed early in the pipeline.

Parameters:: candidate (Candidate, optional) – Candidate on which the priority should be evaluated.
Returns:: Value between 0 and 1.