cart
A scenario discovery oriented implementation of CART. It essentially is a wrapper around scikit-learn’s version of CART.
- class ema_workbench.analysis.cart.CART(x, y, mass_min=0.05, mode=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
rtype: list with boxlims for each terminal leaf
- build_tree()
train CART on the data
- show_tree(mplfig=True, format='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
rtype: list with scenario discovery statistics for each terminal leaf
- ema_workbench.analysis.cart.setup_cart(results, classify, incl_unc=None, mass_min=0.05)
helper function for performing cart in combination with data generated by the workbench.
- Parameters:
results (tuple of DataFrame and dict with numpy arrays) – the return from
perform_experiments()
.classify (string, function or callable) – either a string denoting the outcome of interest to use or a function.
incl_unc (list of strings, optional) –
mass_min (float, optional) –
- Raises:
TypeError – if classify is not a string or a callable.