mlrl.seco.cython.learner_seco module

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

class mlrl.seco.cython.learner_seco.SeCoClassifier

Bases: ClassificationRuleLearner

The multi-label SeCo algorithm for classification problems.

class mlrl.seco.cython.learner_seco.SeCoClassifierConfig

Bases: RuleLearnerConfig, RNGMixin, CoverageStoppingCriterionMixin, SingleOutputHeadMixin, PartialHeadMixin, NoLiftFunctionMixin, PeakLiftFunctionMixin, KlnLiftFunctionMixin, AccuracyHeuristicMixin, AccuracyPruningHeuristicMixin, FMeasureHeuristicMixin, FMeasurePruningHeuristicMixin, MEstimateHeuristicMixin, MEstimatePruningHeuristicMixin, LaplaceHeuristicMixin, LaplacePruningHeuristicMixin, PrecisionHeuristicMixin, PrecisionPruningHeuristicMixin, RecallHeuristicMixin, RecallPruningHeuristicMixin, WraHeuristicMixin, WraPruningHeuristicMixin, OutputWiseBinaryPredictionMixin, SequentialRuleModelAssemblageMixin, DefaultRuleMixin, GreedyTopDownRuleInductionMixin, BeamSearchTopDownRuleInductionMixin, NoFeatureBinningMixin, EqualWidthFeatureBinningMixin, EqualFrequencyFeatureBinningMixin, NoOutputSamplingMixin, RoundRobinOutputSamplingMixin, OutputSamplingWithoutReplacementMixin, NoInstanceSamplingMixin, InstanceSamplingWithReplacementMixin, InstanceSamplingWithoutReplacementMixin, OutputWiseStratifiedInstanceSamplingMixin, ExampleWiseStratifiedInstanceSamplingMixin, NoFeatureSamplingMixin, FeatureSamplingWithoutReplacementMixin, NoPartitionSamplingMixin, RandomBiPartitionSamplingMixin, OutputWiseStratifiedBiPartitionSamplingMixin, ExampleWiseStratifiedBiPartitionSamplingMixin, NoRulePruningMixin, IrepRulePruningMixin, NoParallelRuleRefinementMixin, ParallelRuleRefinementMixin, NoParallelStatisticUpdateMixin, ParallelStatisticUpdateMixin, NoParallelPredictionMixin, ParallelPredictionMixin, NoSimdMixin, SimdMixin, NoSizeStoppingCriterionMixin, SizeStoppingCriterionMixin, NoTimeStoppingCriterionMixin, TimeStoppingCriterionMixin, NoSequentialPostOptimizationMixin, SequentialPostOptimizationMixin

Allows to configure the multi-label SeCo algorithm.

use_accuracy_heuristic()

Configures the rule learner to use the “Accuracy” heuristic for learning rules.

use_accuracy_pruning_heuristic()

Configures the rule learner to use the “Accuracy” heuristic for pruning rules.

use_beam_search_top_down_rule_induction() → BeamSearchTopDownRuleInductionConfig

Configures the algorithm to use a top-down beam search for the induction of individual rules.

Returns:

A BeamSearchTopDownRuleInductionConfig that allows further configuration of the algorithm for the induction of individual rules

use_coverage_stopping_criterion() → CoverageStoppingCriterionConfig

Configures the rule learner to use a stopping criterion that stops the induction of rules as soon as a certain fraction of the available training examples and labels is covered.

Returns:

A CoverageStoppingCriterionConfig that allows further configuration of the stopping criterion

use_default_rule()

Configures the rule learner to induce a default rule.

use_equal_frequency_feature_binning() → EqualFrequencyFeatureBinningConfig

Configures the rule learner to use a method for the assignment of numerical feature values to bins, such that each bin contains approximately the same number of values.

Returns:

An EqualFrequencyFeatureBinningConfig that allows further configuration of the method for the assignment of numerical feature values to bins

use_equal_width_feature_binning() → EqualWidthFeatureBinningConfig

Configures the rule learner to use a method for the assignment of numerical feature values to bins, such that each bin contains values from equally sized value ranges.

Returns:

An EqualWidthFeatureBinningConfig that allows further configuration of the method for the assignment of numerical feature values to bins

use_example_wise_stratified_bi_partition_sampling() → ExampleWiseStratifiedBiPartitionSamplingConfig

Configures the rule learner to partition the available training examples into a training set and a holdout set using stratification, where distinct label vectors are treated as individual classes

Returns:

An ExampleWiseStratifiedBiPartitionSamplingConfig that allows further configuration of the method for partitioning the available training examples into a training and a holdout set

use_example_wise_stratified_instance_sampling() → ExampleWiseStratifiedInstanceSamplingConfig

Configures the rule learner to sample from the available training examples using stratification, where distinct label vectors are treated as individual classes, whenever a new rule should be learned.

Returns:

An ExampleWiseStratifiedInstanceSamplingConfig that allows further configuration of the method for sampling instances

use_f_measure_heuristic() → FMeasureConfig

Configures the rule learner to use the “F-Measure” heuristic for learning rules.

Returns:

A FMeasureConfig that allows further configuration of the heuristic

use_f_measure_pruning_heuristic() → FMeasureConfig

Configures the rule learner to use the “F-Measure” heuristic for pruning rules.

Returns:

A FMeasureConfig that allows further configuration of the heuristic

use_feature_sampling_without_replacement() → FeatureSamplingWithoutReplacementConfig

Configures the rule learner to sample from the available features with replacement whenever a rule should be refined.

Returns:

A FeatureSamplingWithoutReplacementConfig that allows further configuration of the method for sampling features

use_greedy_top_down_rule_induction() → GreedyTopDownRuleInductionConfig

Configures the algorithm to use a greedy top-down search for the induction of individual rules.

Returns:

A GreedyTopDownRuleInductionConfig that allows further configuration of the algorithm for the induction of individual rules

use_instance_sampling_with_replacement() → InstanceSamplingWithReplacementConfig

Configures the rule learner to sample from the available training examples with replacement whenever a new rule should be learned.

Returns:

An InstanceSamplingWithReplacementConfig that allows further configuration of the method for sampling instances

use_instance_sampling_without_replacement() → InstanceSamplingWithoutReplacementConfig

Configures the rule learner to sample from the available training examples without replacement whenever a new rule should be learned.

Returns:

An InstanceSamplingWithoutReplacementConfig that allows further configuration of the method for sampling instances

use_irep_rule_pruning()

Configures the rule learner to prune individual rules by following the principles of “incremental reduced error pruning” (IREP).

use_kln_lift_function() → KlnLiftFunctionConfig

Configures the rule learner to use a lift function that monotonously increases according to the natural logarithm of the number of labels for which a rule predicts.

Returns:

A KlnLiftFunctionConfig that allows further configuration of the lift function

use_laplace_heuristic()

Configures the rule learner to use the “Laplace” heuristic for learning rules.

use_laplace_pruning_heuristic()

Configures the rule learner to use the “Laplace” heuristic for pruning rules.

use_m_estimate_heuristic() → MEstimateConfig

Configures the rule learner to use the “M-Estimate” heuristic for learning rules.

Returns:

A MEstimateConfig that allows further configuration of the heuristic

use_m_estimate_pruning_heuristic() → MEstimateConfig

Configures the rule learner to use the “M-Estimate” heuristic for pruning rules.

Returns:

A MEstimateConfig that allows further configuration of the heuristic

use_no_feature_binning()

Configures the rule learner to not use any method for the assignment of numerical feature values to bins.

use_no_feature_sampling()

Configures the rule learner to not sample from the available features whenever a rule should be refined.

use_no_instance_sampling()

Configures the rule learner to not sample from the available training examples whenever a new rule should be learned.

use_no_lift_function()

Configures the rule learner to not use a lift function.

use_no_output_sampling()

Configures the rule learner to not sample from the available outputs whenever a new rule should be learned.

use_no_parallel_prediction()

Configures the rule learner to not use any multi-threading to predict for several query examples in parallel.

use_no_parallel_rule_refinement()

Configures the rule learner to not use any multi-threading for the parallel refinement of rules.

use_no_parallel_statistic_update()

Configures the rule learner to not use any multi-threading for the parallel update of statistics.

use_no_partition_sampling()

Configures the rule learner to not partition the available training examples into a training set and a holdout set.

use_no_rule_pruning()

Configures the rule learner to not prune individual rules.

use_no_sequential_post_optimization()

Configures the rule learner to not use a post-optimization method that optimizes each rule in a model by relearning it in the context of the other rules.

use_no_simd_operations()

Configures the rule learner to not use any single instruction, multiple data (SIMD) operations.

use_no_size_stopping_criterion()

Configures the rule learner to not use a stopping criterion that ensures that the number of induced rules does not exceed a certain maximum.

use_no_time_stopping_criterion()

Configures the rule learner to not use a stopping criterion that ensures that a certain time limit is not exceeded.

use_output_sampling_without_replacement() → OutputSamplingWithoutReplacementConfig

Configures the rule learner to sample from the available outputs with replacement whenever a new rule should be learned.

Returns:

An OutputSamplingWithoutReplacementConfig that allows further configuration of the sampling method

use_output_wise_binary_predictor()

Configures the rule learner to use predictor for predicting whether individual labels of given query examples are relevant or irrelevant by processing rules of an existing rule-based model in the order they have been learned. If a rule covers an example, its prediction is applied to each label individually.

use_output_wise_stratified_bi_partition_sampling() → OutputWiseStratifiedBiPartitionSamplingConfig

Configures the rule learner to partition the available training examples into a training set and a holdout set using stratification, such that for each label the proportion of relevant and irrelevant examples is maintained.

Returns:

An OutputWiseStratifiedBiPartitionSamplingConfig that allows further configuration of the method for partitioning the available training examples into a training and a holdout set

use_output_wise_stratified_instance_sampling() → OutputWiseStratifiedInstanceSamplingConfig

Configures the rule learner to sample from the available training examples using stratification, such that for each label the proportion of relevant and irrelevant examples is maintained, whenever a new rule should be learned.

Returns:

An OutputWiseStratifiedInstanceSamplingConfig that allows further configuration of the method for sampling instances

use_parallel_prediction() → ManualMultiThreadingConfig

Configures the rule learner to use multi-threading to predict for several query examples in parallel.

Returns:

A ManualMultiThreadingConfig that allows further configuration of the multi-threading behavior

use_parallel_rule_refinement() → ManualMultiThreadingConfig

Configures the rule learner to use multi-threading for the parallel refinement of rules.

Returns:

A ManualMultiThreadingConfig that allows further configuration of the multi-threading behavior

use_parallel_statistic_update() → ManualMultiThreadingConfig

Configures the rule learner to use multi-threading for the parallel update of statistics.

Returns:

A ManualMultiThreadingConfig that allows further configuration of the multi-threading behavior

use_partial_heads()

Configures the rule learner to induce rules with partial heads that predict for a subset of the available labels.

use_peak_lift_function() → PeakLiftFunctionConfig

Configures the rule learner to use a lift function that monotonously increases until a certain number of labels, where the maximum lift is reached, and monotonously decreases afterwards.

Returns:

A PeakLiftFunctionConfig that allows further configuration of the lift function

use_precision_heuristic()

Configures the rule learner to use the “Precision” heuristic for learning rules.

use_precision_pruning_heuristic()

Configures the rule learner to use the “Precision” heuristic for pruning rules.

use_random_bi_partition_sampling() → RandomBiPartitionSamplingConfig

Configures the rule learner to partition the available training examples into a training set and a holdout set by randomly splitting the training examples into two mutually exclusive sets.

Returns:

A RandomBiPartitionSamplingConfig that allows further configuration of the method for partitioning the available training examples into a training set and a holdout set

use_recall_heuristic()

Configures the rule learner to use the “Recall” heuristic for learning rules.

use_recall_pruning_heuristic()

Configures the rule learner to use the “Recall” heuristic for pruning rules.

use_rng() → RNGConfig

Configures the random number generators that are used by the rule learner.

Returns:

An RNGConfig that allows further configuration of the random number generators

use_round_robin_output_sampling()

Configures the rule learner to sample a one output at a time in a round-robin fashion whenever a new rule should be learned.

use_sequential_post_optimization() → SequentialPostOptimizationConfig

Configures the rule learner to use a post-optimization method that optimizes each rule in a model by relearning it in the context of the other rules.

Returns:

A SequentialPostOptimizationConfig that allows further configuration of the post-optimization method

use_sequential_rule_model_assemblage()

Configures the rule learner to use an algorithm that sequentially induces several rules, optionally starting with a default rule, that are added to a rule-based model.

use_simd_operations()

Configures the rule learner to use single instruction, multiple data (SIMD) operations.

use_single_output_heads()

Configures the rule learner to induce rules with single-output heads that predict for a single output.

use_size_stopping_criterion() → SizeStoppingCriterionConfig

Configures the rule learner to use a stopping criterion that ensures that the number of induced rules does not exceed a certain maximum.

Returns:

A SizeStoppingCriterionConfig that allows further configuration of the stopping criterion

use_time_stopping_criterion() → TimeStoppingCriterionConfig

Configures the rule learner to use a stopping criterion that ensures that a certain time limit is not exceeded.

Returns:

A TimeStoppingCriterionConfig that allows further configuration of the stopping criterion

use_wra_heuristic()

Configures the rule learner to use the “Weighted Relative Accuracy” heuristic for learning rules.

use_wra_pruning_heuristic()

Configures the rule learner to use the “Weighted Relative Accuracy” heuristic for pruning rules.