mlrl.common.cython.rule_induction module¶

@author: Michael Rapp (michael.rapp.ml@gmail.com)

class mlrl.common.cython.rule_induction.BeamSearchTopDownRuleInductionConfig¶

Bases: object

Allows to configure an algorithm for the induction of individual rules that uses a top-down beam search.

are_features_resampled() → bool¶

Returns whether a new sample of the available features is created for each rule that is refined during the beam search or not.

Returns:: True, if a new sample is created for each rule, false otherwise

are_predictions_recalculated() → bool¶

Returns whether the predictions of rules are recalculated on all training examples, if some of the examples have zero weights, or not.

Returns:: True, if the predictions of rules are recalculated on all training examples, False otherwise

get_beam_width() → int¶

Returns the width that is used by the beam search.

Returns:: The width that is used by the beam search

get_max_conditions() → int¶

Returns the maximum number of conditions to be included in a rule’s body.

Returns:: The maximum number of conditions to be included in a rule’s body or 0, if the number of conditions is not restricted

get_max_head_refinements() → int¶

Returns the maximum number of times, the head of a rule may be refinement after a new condition has been added to its body.

Returns:: The maximum number of times, the head of a rule may be refined or 0, if the number of refinements is not restricted

get_min_coverage() → int¶

Returns the minimum number of training examples that must be covered by a rule.

Returns:: The minimum number of training examples that must be covered by a rule

get_min_support() → float¶

Returns the minimum support, i.e., the minimum fraction of the training examples that must be covered by a rule.

Returns:: The minimum support or 0, if the support of rules is not restricted

set_beam_width(beam_width: int) → BeamSearchTopDownRuleInductionConfig¶

Sets the width that should be used by the beam search.

Parameters:: beam_width – The width that should be used by the beam search. Must be at least 2
Returns:: A BeamSearchTopDownRuleInductionConfig that allows further configuration of the algorithm for the induction of individual rules

set_max_conditions(max_conditions: int) → BeamSearchTopDownRuleInductionConfig¶

Sets the maximum number of conditions to be included in a rule’s body.

Parameters:: max_conditions – The maximum number of conditions to be included in a rule’s body. Must be at least 2 or 0, if the number of conditions should not be restricted
Returns:: A BeamSearchTopDownRuleInductionConfig that allows further configuration of the algorithm for the induction of individual rules

set_max_head_refinements(max_head_refinements: int) → BeamSearchTopDownRuleInductionConfig¶

Sets the maximum number of times, the head of a rule may be refined after a new condition has been added to its body.

Parameters:: max_head_refinements – The maximum number of times, the head of a rule may be refined. Must be at least 1 or 0, if the number of refinements should not be restricted
Returns:: A BeamSearchTopDownRuleInductionConfig that allows further configuration of the algorithm for the induction of individual rules

set_min_coverage(min_coverage: int) → BeamSearchTopDownRuleInductionConfig¶

Sets the minimum number of training examples that must be covered by a rule.

Parameters:: min_coverage – The minimum number of training examples that must be covered by a rule. Must be at least 1
Returns:: A BeamSearchTopDownRuleInductionConfig that allows further configuration of the algorithm for the induction of individual rules

set_min_support(min_support: float) → BeamSearchTopDownRuleInductionConfig¶

Sets the minimum support, i.e., the minimum fraction of the training examples that must be covered by a rule.

Parameters:: min_support – The minimum support. Must be in [0, 1] or 0, if the support of rules should not be restricted
Returns:: A BeamSearchTopDownRuleInductionConfig that allows further configuration of the algorithm for the induction of individual rules

set_recalculate_predictions(recalculate_predictions: bool) → BeamSearchTopDownRuleInductionConfig¶

Sets whether the predictions of rules should be recalculated on all training examples, if some of the examples have zero weights, or not.

Parameters:: recalculate_predictions – True, if the predictions of rules should be recalculated on all training examples, False otherwise
Returns:: A BeamSearchTopDownRuleInductionConfig that allows further configuration of the algorithm for the induction of individual rules

set_resample_features(resample_features: bool) → BeamSearchTopDownRuleInductionConfig¶

Sets whether a new sample of the available features should be created for each rule that is refined during the beam search or not.

Parameters:: resample_features – True, if a new sample should be created for each rule, false otherwise
Returns:: A BeamSearchTopDownRuleInductionConfig that allows further configuration of the algorithm for the induction of individual rules

class mlrl.common.cython.rule_induction.GreedyTopDownRuleInductionConfig¶