Index

_

__init__() (lore_sa.bbox.AbstractBBox method) (lore_sa.dataset.Dataset method) (lore_sa.dataset.TabularDataset method) (lore_sa.discretizer.Discretizer method) (lore_sa.discretizer.RMEPDiscretizer method) (lore_sa.encoder_decoder.ColumnTransformerEnc method) (lore_sa.encoder_decoder.EncDec method) (lore_sa.explanation.Explanation method) (lore_sa.explanation.ExplanationEncoder method) (lore_sa.explanation.ImageExplanation method) (lore_sa.explanation.MultilabelExplanation method) (lore_sa.explanation.TextExplanation method) (lore_sa.neighgen.GeneticGenerator method) (lore_sa.neighgen.RandomGenerator method) (lore_sa.rule.Expression method) (lore_sa.rule.Rule method) (lore_sa.rule.RuleEncoder method) (lore_sa.surrogate.DecisionTreeSurrogate method) (lore_sa.surrogate.Surrogate method)

A

AbstractBBox (class in lore_sa.bbox)

B

best_fit_distribution() (in module lore_sa.util)

C

check_feasibility_of_falsified_conditions() (lore_sa.surrogate.DecisionTreeSurrogate method) check_generated() (lore_sa.neighgen.GeneticGenerator method) (lore_sa.neighgen.RandomGenerator method)

classifier (lore_sa.bbox.AbstractBBox attribute) ColumnTransformerEnc (class in lore_sa.encoder_decoder) compact_premises() (lore_sa.surrogate.DecisionTreeSurrogate method) consequences (lore_sa.rule.Rule attribute)

D

Dataset (class in lore_sa.dataset) dataset_descriptor (lore_sa.encoder_decoder.EncDec attribute) DecisionTreeSurrogate (class in lore_sa.surrogate) decode() (lore_sa.encoder_decoder.ColumnTransformerEnc method) (lore_sa.encoder_decoder.EncDec method), [1] decode_target_class() (lore_sa.encoder_decoder.ColumnTransformerEnc method) (lore_sa.encoder_decoder.EncDec method)

default() (lore_sa.explanation.ExplanationEncoder method) (lore_sa.rule.RuleEncoder method) descriptor (lore_sa.dataset.TabularDataset attribute) df (lore_sa.dataset.TabularDataset attribute) Discretizer (class in lore_sa.discretizer)

E

eaSimple() (lore_sa.neighgen.GeneticGenerator method) EncDec (class in lore_sa.encoder_decoder) encode() (lore_sa.encoder_decoder.ColumnTransformerEnc method) (lore_sa.encoder_decoder.EncDec method), [1] (lore_sa.explanation.ExplanationEncoder method) (lore_sa.rule.RuleEncoder method) encode_target_class() (lore_sa.encoder_decoder.ColumnTransformerEnc method) (lore_sa.encoder_decoder.EncDec method)

encoded_descriptor (lore_sa.encoder_decoder.EncDec attribute) encoded_features (lore_sa.encoder_decoder.EncDec attribute) encoder (lore_sa.rule.Rule attribute) Explanation (class in lore_sa.explanation) ExplanationEncoder (class in lore_sa.explanation) Expression (class in lore_sa.rule)

F

fidelity (lore_sa.surrogate.Surrogate attribute)

from_csv() (lore_sa.dataset.TabularDataset class method) from_dict() (lore_sa.dataset.TabularDataset class method)

G

generate() (lore_sa.neighgen.GeneticGenerator method) (lore_sa.neighgen.RandomGenerator method) generate_synthetic_instance() (lore_sa.neighgen.GeneticGenerator method) (lore_sa.neighgen.RandomGenerator method) GeneticGenerator (class in lore_sa.neighgen) get_class_values() (lore_sa.dataset.Dataset method) (lore_sa.dataset.TabularDataset method) get_counterfactual_rules() (lore_sa.surrogate.DecisionTreeSurrogate method) (lore_sa.surrogate.Surrogate method), [1]

get_encoded_features() (lore_sa.encoder_decoder.ColumnTransformerEnc method) (lore_sa.encoder_decoder.EncDec method), [1] get_encoded_intervals() (lore_sa.encoder_decoder.ColumnTransformerEnc method) (lore_sa.encoder_decoder.EncDec method), [1] get_falsified_conditions() (lore_sa.surrogate.DecisionTreeSurrogate method) get_feature_name() (lore_sa.dataset.Dataset method) (lore_sa.dataset.TabularDataset method) get_rule() (lore_sa.surrogate.DecisionTreeSurrogate method) (lore_sa.surrogate.Surrogate method), [1]

I

ImageExplanation (class in lore_sa.explanation) is_leaf() (lore_sa.surrogate.DecisionTreeSurrogate method)

iterencode() (lore_sa.explanation.ExplanationEncoder method) (lore_sa.rule.RuleEncoder method)

J

json2explanation() (in module lore_sa.explanation)

json2expression() (in module lore_sa.rule) json2rule() (in module lore_sa.rule)

K

kind (lore_sa.surrogate.Surrogate attribute)

L

lore_sa.dataset.utils module

lore_sa.util module

M

mate() (lore_sa.neighgen.GeneticGenerator method) model() (lore_sa.bbox.AbstractBBox method), [1] module lore_sa.dataset.utils lore_sa.util

MultilabelExplanation (class in lore_sa.explanation)

O

operator (lore_sa.rule.Expression attribute)

operator2string() (lore_sa.rule.Expression method)

P

population_fitness_equal() (lore_sa.neighgen.GeneticGenerator method) predict() (lore_sa.bbox.AbstractBBox method), [1] predict_proba() (lore_sa.bbox.AbstractBBox method), [1] premises (lore_sa.rule.Rule attribute)

prepare_bank_dataset() (in module lore_sa.dataset.utils) preprocessing (lore_sa.surrogate.Surrogate attribute) prune_duplicate_leaves() (lore_sa.surrogate.DecisionTreeSurrogate method) prune_index() (lore_sa.surrogate.DecisionTreeSurrogate method)

R

RandomGenerator (class in lore_sa.neighgen) record_init() (lore_sa.neighgen.GeneticGenerator method)

RMEPDiscretizer (class in lore_sa.discretizer) Rule (class in lore_sa.rule) RuleEncoder (class in lore_sa.rule)

S

set_class_name() (lore_sa.dataset.Dataset method) (lore_sa.dataset.TabularDataset method) set_target_label() (lore_sa.dataset.Dataset method) (lore_sa.dataset.TabularDataset method)

sigmoid() (in module lore_sa.util) Surrogate (class in lore_sa.surrogate)

T

TabularDataset (class in lore_sa.dataset) TextExplanation (class in lore_sa.explanation)

to_dict() (lore_sa.rule.Expression method) train() (lore_sa.surrogate.DecisionTreeSurrogate method) (lore_sa.surrogate.Surrogate method), [1]

U

update_descriptor() (lore_sa.dataset.Dataset method) (lore_sa.dataset.TabularDataset method)

V

value (lore_sa.rule.Expression attribute)

variable (lore_sa.rule.Expression attribute) vector2dict() (in module lore_sa.util)