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regression_tree

RegressionTree(*, dot_graph_export_path=None, criterion='squared_error', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, ccp_alpha=0.0, monotonic_cst=None, callbacks=None)

Bases: SupervisedLearner

Wrapper class for sklearn's DecisionTreeRegressor.

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html

Initialize the regression tree learner.

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html

Parameters:

Name Type Description Default
dot_graph_export_path None | str

Path to export the decision tree graph in Graphviz DOT format.

 None
criterion str

Function to measure the quality of a split.

 'squared_error'
splitter str

Strategy used to choose the split at each node.

 'best'
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 | None

Number of features to consider when looking for the best split.

 None
random_state int | None

Controls the randomness of the estimator.

 None
max_leaf_nodes int | None

Grow a tree 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
ccp_alpha float

Complexity parameter used for Minimal Cost-Complexity Pruning.

 0.0
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/regression_tree.py 
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def __init__(
    self,
    *,
    dot_graph_export_path: None | str = None,
    criterion: str = "squared_error",
    splitter: str = "best",
    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 | None = None,
    random_state: int | None = None,
    max_leaf_nodes: int | None = None,
    min_impurity_decrease: float = 0.0,
    ccp_alpha: float = 0.0,
    monotonic_cst: NDArray | None = None,
    callbacks: list[LearnerCallback] | LearnerCallback | None = None,
) -> None:
    """Initialize the regression tree learner.

    Reference: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html

    Args:
        dot_graph_export_path: Path to export the decision tree graph
            in Graphviz DOT format.
        criterion: Function to measure the quality of a split.
        splitter: Strategy used to choose the split at each node.
        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.
        random_state: Controls the randomness of the estimator.
        max_leaf_nodes: Grow a tree 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.
        ccp_alpha: Complexity parameter used for Minimal Cost-Complexity
            Pruning.
        monotonic_cst: Monotonicity constraints.
        callbacks: Optional callbacks for progress feedback. Use `None`
            for silent learning.
    """
    self.regressor = DecisionTreeRegressor(
        criterion=criterion,
        splitter=splitter,
        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,
        random_state=random_state or get_seed(),
        ccp_alpha=ccp_alpha,
        monotonic_cst=monotonic_cst,
    )
    self.dot_graph_export_path = dot_graph_export_path
    self.callback_manager = create_callback_manager(callbacks)