random_forest

RandomForestRegressorLearner(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None, callbacks=None)

Bases: SupervisedLearner

Wrapper class for sklearn's RandomForestRegressor.

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html

Initialize the random forest learner.

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html

Parameters:

Name	Type	Description	Default
n_estimators	int	Number of trees in the forest.	100
criterion	str	Function to measure the quality of a split.	'squared_error'
max_depth	int | None	Maximum depth of the tree.	None
min_samples_split	int	Minimum number of samples required to split an internal node.	2
min_samples_leaf	int	Minimum number of samples required to be at a leaf node.	1
min_weight_fraction_leaf	float	Minimum weighted fraction of the sum total of weights required to be at a leaf node.	0.0
max_features	float	Number of features to consider when looking for the best split.	1.0
max_leaf_nodes	int | None	Grow trees with max_leaf_nodes in best-first fashion.	None
min_impurity_decrease	float	A node will be split if this split induces a decrease of the impurity greater than or equal to this value.	0.0
bootstrap	bool	Whether bootstrap samples are used when building trees.	True
oob_score	bool	Whether to use out-of-bag samples to estimate the R^2 on unseen data.	False
n_jobs	int | None	Number of jobs to run in parallel.	None
random_state	int | None	Controls the randomness of the estimator.	None
verbose	int	Controls the verbosity when fitting and predicting.	0
warm_start	bool	When set to True, reuse the solution of the previous call to fit.	False
ccp_alpha	float	Complexity parameter used for Minimal Cost-Complexity Pruning.	0.0
max_samples	int | float | None	If bootstrap is True, the number of samples to draw from X to train each base estimator.	None
monotonic_cst	NDArray | None	Monotonicity constraints.	None
callbacks	list[LearnerCallback] | LearnerCallback | None	Optional callbacks for progress feedback. Use None for silent learning.	None

Source code in src/flowcean/sklearn/random_forest.py

def __init__(
    self,
    n_estimators: int = 100,
    *,
    criterion: str = "squared_error",
    max_depth: int | None = None,
    min_samples_split: int = 2,
    min_samples_leaf: int = 1,
    min_weight_fraction_leaf: float = 0.0,
    max_features: float = 1.0,
    max_leaf_nodes: int | None = None,
    min_impurity_decrease: float = 0.0,
    bootstrap: bool = True,
    oob_score: bool = False,
    n_jobs: int | None = None,
    random_state: int | None = None,
    verbose: int = 0,
    warm_start: bool = False,
    ccp_alpha: float = 0.0,
    max_samples: int | float | None = None,  # noqa: PYI041
    monotonic_cst: NDArray | None = None,
    callbacks: list[LearnerCallback] | LearnerCallback | None = None,
) -> None:
    """Initialize the random forest learner.

    Reference: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html

    Args:
        n_estimators: Number of trees in the forest.
        criterion: Function to measure the quality of a split.
        max_depth: Maximum depth of the tree.
        min_samples_split: Minimum number of samples required to split
            an internal node.
        min_samples_leaf: Minimum number of samples required to be at
            a leaf node.
        min_weight_fraction_leaf: Minimum weighted fraction of the sum
            total of weights required to be at a leaf node.
        max_features: Number of features to consider when looking for
            the best split.
        max_leaf_nodes: Grow trees with max_leaf_nodes in best-first
            fashion.
        min_impurity_decrease: A node will be split if this split
            induces a decrease of the impurity greater than or equal
            to this value.
        bootstrap: Whether bootstrap samples are used when building trees.
        oob_score: Whether to use out-of-bag samples to estimate the R^2
            on unseen data.
        n_jobs: Number of jobs to run in parallel.
        random_state: Controls the randomness of the estimator.
        verbose: Controls the verbosity when fitting and predicting.
        warm_start: When set to True, reuse the solution of the previous
            call to fit.
        ccp_alpha: Complexity parameter used for Minimal Cost-Complexity
            Pruning.
        max_samples: If bootstrap is True, the number of samples to draw
            from X to train each base estimator.
        monotonic_cst: Monotonicity constraints.
        callbacks: Optional callbacks for progress feedback. Use `None`
            for silent learning.
    """
    self.regressor = RandomForestRegressor(
        n_estimators=n_estimators,
        criterion=criterion,
        max_depth=max_depth,
        min_samples_split=min_samples_split,
        min_samples_leaf=min_samples_leaf,
        min_weight_fraction_leaf=min_weight_fraction_leaf,
        max_features=max_features,
        max_leaf_nodes=max_leaf_nodes,
        min_impurity_decrease=min_impurity_decrease,
        bootstrap=bootstrap,
        oob_score=oob_score,
        n_jobs=n_jobs,
        random_state=random_state or get_seed(),
        verbose=verbose,
        warm_start=warm_start,
        ccp_alpha=ccp_alpha,
        max_samples=max_samples,
        monotonic_cst=monotonic_cst,
    )
    self.callback_manager = create_callback_manager(callbacks)

learn(inputs, outputs)

Fit the random forest regressor on the given inputs and outputs.

Source code in src/flowcean/sklearn/random_forest.py

@override
def learn(
    self,
    inputs: pl.LazyFrame,
    outputs: pl.LazyFrame,
) -> Model:
    """Fit the random forest regressor on the given inputs and outputs."""
    dfs = pl.collect_all([inputs, outputs])
    collected_inputs = dfs[0]
    collected_outputs = dfs[1]

    # Notify callbacks that learning is starting
    context = {
        "n_estimators": self.regressor.n_estimators,
        "n_samples": len(collected_inputs),
        "n_features": len(collected_inputs.columns),
    }
    self.callback_manager.on_learning_start(self, context)

    try:
        # Fit the model (flatten outputs to 1D if single column)
        outputs_array = collected_outputs.to_numpy()
        if outputs_array.shape[1] == 1:
            outputs_array = outputs_array.ravel()
        self.regressor.fit(collected_inputs, outputs_array)
        logger.info("Using Random Forest Regressor")

        # Create the model
        model = SciKitModel(
            self.regressor,
            output_names=outputs.collect_schema().names(),
        )

        # Notify callbacks that learning is complete
        self.callback_manager.on_learning_end(self, model)
    except Exception as e:
        # Notify callbacks of the error
        self.callback_manager.on_learning_error(self, e)
        raise
    else:
        return model