Rules
Contains classes representing rules and rulesets.
- class rulekit.rules.BaseRule(java_object)
Base class representing single rule.
- get_covering_information() dict
Returns information about rule covering
- Returns:
covering_data – Dictionary containing covering information.
- Return type:
dict
- print_stats()
Prints rule statistics as formatted text.
- property pvalue: float
Rule significance.
- property stats: RuleStatistics
Rule statistics.
- property weight: float
Rule weight
- property weighted_N: float
Number of negatives in the training set (accounting weights).
- property weighted_P: float
Number of positives in the training set (accounting weights).
- property weighted_n: float
Number of negatives covered by the rule (accounting weights).
- property weighted_p: float
Number of positives covered by the rule (accounting weights).
- class rulekit.rules.ClassificationRule(java_object)
Class representing classification rule
- property decision_class: str
Decision class of the rule
- class rulekit.rules.InductionParameters(java_object)
Induction parameters.
- class rulekit.rules.RegressionRule(java_object)
Class representing regression rule
- property conclusion_value: float
Value from the rule’s conclusion
- class rulekit.rules.RuleSet(java_object)
Class representing ruleset.
- calculate_avg_rule_coverage() float
- Returns:
count – Average rule coverage.
- Return type:
float
- calculate_avg_rule_precision() float
- Returns:
count – Average rule precision.
- Return type:
float
- calculate_avg_rule_quality() float
- Returns:
count – Average rule quality.
- Return type:
float
- calculate_conditions_count() float
- Returns:
count – Number of conditions.
- Return type:
float
- calculate_induced_conditions_count() float
- Returns:
count – Number of induced conditions.
- Return type:
float
- calculate_significance(alpha: float) dict
- Parameters:
alpha (float) –
- Returns:
count – Significance of the rule set.
- Return type:
float
- calculate_significance_fdr(alpha: float) dict
- Returns:
count – Significance of the rule set with false discovery rate correction. Dictionary contains two fields: fraction (fraction of rules significant at assumed level) and p (average p-value of all rules).
- Return type:
dict
- calculate_significance_fwer(alpha: float) dict
- Returns:
count – Significance of the rule set with familiy-wise error rate correction. Dictionary contains two fields: fraction (fraction of rules significant at assumed level) and p (average p-value of all rules).
- Return type:
dict
- property growing_time: float
Time of growing in seconds
- property is_voting: bool
Value indicating whether rules are voting.
- property parameters: object
Parameters used during rule set induction.
- property pruning_time: float
Time of pruning in seconds
- property rules: list[T]
List of rules objects.
- property stats: RuleSetStatistics
Rule set statistics.
- property total_time: float
Time of constructing the rule set in seconds