lore_sa.surrogate.DecisionTreeSurrogate

class lore_sa.surrogate.DecisionTreeSurrogate(kind=None, preprocessing=None)

__init__(kind=None, preprocessing=None)

Methods

__init__([kind, preprocessing])
apply_counterfactual(x, delta, dataset[, ...])
check_feasibility_of_falsified_conditions(...)	Check if a falsifield confition is in an unadmittible feature list :param delta: :param unadmittible_features: :return: True or False
compact_premises(premises_list)	Remove the same premises with different values of threashold
get_counterfactual_rules(x, class_name, ...)	param [Numpy Array] x instance encoded of the dataset
get_falsified_conditions(x_dict, crule)	Check the wrong conditions :param x_dict: :param crule: :return: list of falsified premises
get_rule(x, dataset[, encoder])	Extract the rules as the promises and consequences {p -> y}, starting from a Decision Tree
is_leaf(inner_tree, index)	Check whether node is leaf node
prune_duplicate_leaves(dt)	Remove leaves if both
prune_index(inner_tree, decisions[, index])	Start pruning from the bottom - if we start from the top, we might miss nodes that become leaves during pruning.
train(Z, Yb[, weights, class_values, ...])	param Z The training input samples

check_feasibility_of_falsified_conditions(delta, unadmittible_features: list)

Check if a falsifield confition is in an unadmittible feature list :param delta: :param unadmittible_features: :return: True or False

compact_premises(premises_list)

Remove the same premises with different values of threashold

Parameters

premises_list – List of Expressions that defines the premises

Returns

get_counterfactual_rules(x: array, class_name, feature_names, neighborhood_dataset: TabularDataset, features_map_inv=None, multi_label: bool = False, encoder: EncDec = None, filter_crules=None, constraints: dict = None, unadmittible_features: list = None)

Parameters

• x ([Numpy Array]) – instance encoded of the dataset

• neighborhood_dataset ([Numpy Array]) – Neighborhood instances

• dataset ([TabularDataset]) –

• features_map_inv –

• multi_label ([bool]) –

• encdec ([EncDec]) –

• filter_crules –

• constraints ([dict]) –

• unadmittible_features ([list]) – List of unadmittible features

Returns

get_falsified_conditions(x_dict: dict, crule: Rule)

Check the wrong conditions :param x_dict: :param crule: :return: list of falsified premises

get_rule(x: array, dataset: TabularDataset, encoder: EncDec = None)

Extract the rules as the promises and consequences {p -> y}, starting from a Decision Tree

>>> {( income > 90) -> grant),
    ( job = employer) -> grant)
}

Parameters

• x ([Numpy Array]) – instance encoded of the dataset to extract the rule

• dataset ([TabularDataset]) – Neighborhood instances

• encdec ([EncDec]) –

Return [Rule]

Rule objects

is_leaf(inner_tree, index)

Check whether node is leaf node

prune_duplicate_leaves(dt)

Remove leaves if both

prune_index(inner_tree, decisions, index=0)

Start pruning from the bottom - if we start from the top, we might miss nodes that become leaves during pruning. Do not use this directly - use prune_duplicate_leaves instead.

train(Z, Yb, weights=None, class_values=None, multi_label: bool = False, one_vs_rest: bool = False, cv=5, prune_tree: bool = False)

Parameters

• Z – The training input samples

• Yb – The target values (class labels) as integers or strings.

• weights – Sample weights.

• class_values –

• multi_label ([bool]) –

• one_vs_rest ([bool]) –

• cv ([int]) –

• prune_tree ([bool]) –

Returns