cart

A scenario discovery oriented implementation of CART.

It essentially is a wrapper around scikit-learn’s version of CART, with some scneario discovery specific functionality.

class ema_workbench.analysis.cart.CART(x: DataFrame, y: ndarray[tuple[Any, ...], dtype[_ScalarT]], mass_min: float = 0.05, mode: RuleInductionType = RuleInductionType.BINARY)

CART algorithm.

Can be used in a manner similar to PRIM. It provides access to the underlying tree, but it can also show the boxes described by the tree in a table or graph form similar to prim.

Parameters:

  • x (DataFrame)

  • y (1D ndarray)

  • mass_min (float, optional) – a value between 0 and 1 indicating the minimum fraction of data points in a terminal leaf. Defaults to 0.05, identical to prim.

  • mode ({BINARY, CLASSIFICATION, REGRESSION}) – indicates the mode in which CART is used. Binary indicates binary classification, classification is multiclass, and regression is regression.

boxes

list of DataFrame box lims

Type:

list

stats

list of dicts with stats

Type:

list

Notes

This class is a wrapper around scikit-learn’s CART algorithm. It provides an interface to CART that is more oriented towards scenario discovery, and shared some methods with PRIM

See also

prim

property boxes: list[DataFrame]

Return a list with the box limits for each terminal leaf.

Return type:

list with boxlims for each terminal leaf

build_tree() → None

Train CART on the data.

show_tree(mplfig: bool = True, format: str = 'png')

Return a png (defaults) or svg of the tree.

On Windows, graphviz needs to be installed with conda.

Parameters:

  • mplfig (bool, optional) – if true (default) returns a matplotlib figure with the tree, otherwise, it returns the output as bytes

  • format ({'png', 'svg'}, default 'png') – Gives a format of the output.

property stats: list[dict]

Returns list with the scenario discovery statistics for each terminal leaf.

Return type:

list with scenario discovery statistics for each terminal leaf