mlrl.common.cython.learner module¶

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

class mlrl.common.cython.learner.BeamSearchTopDownRuleInductionMixin¶

Bases: ABC

Allows to configure a rule learner to use a top-down beam search.

abstractmethod 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

class mlrl.common.cython.learner.DefaultRuleMixin¶

Bases: ABC

Allows to configure a rule learner to induce a default rule.

abstractmethod use_default_rule()¶: Configures the rule learner to induce a default rule.

class mlrl.common.cython.learner.EqualFrequencyFeatureBinningMixin¶

Bases: ABC

Allows to configure a rule learner to use equal-frequency feature binning.

abstractmethod 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

class mlrl.common.cython.learner.EqualWidthFeatureBinningMixin¶

Bases: ABC

Allows to configure a rule learner to use equal-width feature binning.

abstractmethod 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

class mlrl.common.cython.learner.ExampleWiseStratifiedBiPartitionSamplingMixin¶

Bases: ABC

Allows to configure a 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.

abstractmethod 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

class mlrl.common.cython.learner.ExampleWiseStratifiedInstanceSamplingMixin¶

Bases: ABC

Allows to configure a rule learner to use example-wise stratified instance sampling.

abstractmethod 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

class mlrl.common.cython.learner.FeatureSamplingWithoutReplacementMixin¶

Bases: ABC

Allows to configure a rule learner to use feature sampling without replacement.

abstractmethod 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

class mlrl.common.cython.learner.GreedyTopDownRuleInductionMixin¶

Bases: ABC

Allows to configure a rule learner to use a greedy top-down search for the induction of individual rules.

abstractmethod 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

class mlrl.common.cython.learner.InstanceSamplingWithReplacementMixin¶

Bases: ABC

Defines an interface for all classes that allow to configure a rule learner to use instance sampling with replacement.

abstractmethod 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

class mlrl.common.cython.learner.InstanceSamplingWithoutReplacementMixin¶

Bases: ABC

Defines an interface for all classes that allow to configure a rule learner to use instance sampling without replacement.

abstractmethod 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

class mlrl.common.cython.learner.IrepRulePruningMixin¶

Bases: ABC

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

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

class mlrl.common.cython.learner.NoFeatureBinningMixin¶

Bases: ABC

Allows to configure a rule learner to not use any method for the assignment of numerical features values to bins.

abstractmethod use_no_feature_binning()¶: Configures the rule learner to not use any method for the assignment of numerical feature values to bins.

class mlrl.common.cython.learner.NoFeatureSamplingMixin¶

Bases: ABC

Allows to configure a rule learner to not use feature sampling.

abstractmethod use_no_feature_sampling()¶: Configures the rule learner to not sample from the available features whenever a rule should be refined.

class mlrl.common.cython.learner.NoGlobalPruningMixin¶

Bases: ABC

Allows to configure a rule learner to not use global pruning.

abstractmethod use_no_global_pruning()¶: Configures the rule learner to not use global pruning.

class mlrl.common.cython.learner.NoInstanceSamplingMixin¶

Bases: ABC

Defines an interface for all classes that allow to configure a rule learner to not use instance sampling.

abstractmethod use_no_instance_sampling()¶: Configures the rule learner to not sample from the available training examples whenever a new rule should be learned.

class mlrl.common.cython.learner.NoJointProbabilityCalibrationMixin¶

Bases: ABC

Allows to configure a rule learner to not calibrate joint probabilities.

abstractmethod use_no_joint_probability_calibration()¶: Configures the rule learner to not calibrate joint probabilities.

class mlrl.common.cython.learner.NoMarginalProbabilityCalibrationMixin¶

Bases: ABC

Allows to configure a rule learner to not calibrate marginal probabilities.

abstractmethod use_no_marginal_probability_calibration()¶: Configures the rule learner to not calibrate marginal probabilities.

class mlrl.common.cython.learner.NoOutputSamplingMixin¶

Bases: ABC

Allows to configure a rule learner to not use output sampling.

abstractmethod use_no_output_sampling()¶: Configures the rule learner to not sample from the available outputs whenever a new rule should be learned.

class mlrl.common.cython.learner.NoParallelPredictionMixin¶

Bases: ABC

Allows to configure a rule learner to not use any multi-threading for prediction.

abstractmethod use_no_parallel_prediction()¶: Configures the rule learner to not use any multi-threading to predict for several query examples in parallel.

class mlrl.common.cython.learner.NoParallelRuleRefinementMixin¶

Bases: ABC

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

abstractmethod use_no_parallel_rule_refinement()¶: Configures the rule learner to not use any multi-threading for the parallel refinement of rules.

class mlrl.common.cython.learner.NoParallelStatisticUpdateMixin¶

Bases: ABC

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

abstractmethod use_no_parallel_statistic_update()¶: Configures the rule learner to not use any multi-threading for the parallel update of statistics.

class mlrl.common.cython.learner.NoPartitionSamplingMixin¶

Bases: ABC

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

abstractmethod use_no_partition_sampling()¶: Configures the rule learner to not partition the available training examples into a training set and a holdout set.

class mlrl.common.cython.learner.NoPostProcessorMixin¶

Bases: ABC

Allows to configure a rule learner to not use any post processor.

abstractmethod use_no_post_processor()¶: Configures the rule learner to not use any post-processor.

class mlrl.common.cython.learner.NoRulePruningMixin¶

Bases: ABC

Allows to configure a rule learner to not prune individual rules.

abstractmethod use_no_rule_pruning()¶: Configures the rule learner to not prune individual rules.

class mlrl.common.cython.learner.NoSequentialPostOptimizationMixin¶

Bases: ABC

Allows to configure a 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.

abstractmethod 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.

class mlrl.common.cython.learner.NoSizeStoppingCriterionMixin¶

Bases: ABC

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

abstractmethod 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.

class mlrl.common.cython.learner.NoTimeStoppingCriterionMixin¶

Bases: ABC

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

abstractmethod 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.

class mlrl.common.cython.learner.OutputSamplingWithoutReplacementMixin¶

Bases: ABC

Allows to configure a rule learner to use output sampling without replacement.

abstractmethod 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

class mlrl.common.cython.learner.OutputWiseStratifiedBiPartitionSamplingMixin¶

Bases: ABC

Allows to configure a 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.

abstractmethod 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

class mlrl.common.cython.learner.OutputWiseStratifiedInstanceSamplingMixin¶

Bases: ABC

Allows to configure a rule learner to use label-wise stratified instance sampling.

abstractmethod 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

class mlrl.common.cython.learner.ParallelPredictionMixin¶

Bases: ABC

Allows to configure a rule learner to use multi-threading to predict for several examples in parallel.

abstractmethod 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

class mlrl.common.cython.learner.ParallelRuleRefinementMixin¶

Bases: ABC

Allows to configure a rule learner to use multi-threading for the parallel refinement of rules.

abstractmethod 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

class mlrl.common.cython.learner.ParallelStatisticUpdateMixin¶

Bases: ABC

Allows to configure a rule learner to use multi-threading for the parallel update of statistics.

abstractmethod 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

class mlrl.common.cython.learner.PostPruningMixin¶

Bases: ABC

Allows to configure a rule learner to use a stopping criterion that keeps track of the number of rules in a model that perform best with respect to the examples in the training or holdout set according to a certain measure.

abstractmethod use_global_post_pruning() → PostPruningConfig¶: Configures the rule learner to use a stopping criterion that keeps track of the number of rules in a model that perform best with respect to the examples in the training or holdout set according to a certain measure.

class mlrl.common.cython.learner.PrePruningMixin¶

Bases: ABC

Allows to configure a rule learner to use a stopping criterion that stops the induction of rules as soon as the quality of a model’s predictions for the examples in the training or holdout set do not improve according to a certain measure.

abstractmethod use_global_pre_pruning() → PrePruningConfig¶

Configures the rule learner to use a stopping criterion that stops the induction of rules as soon as the quality of a model’s predictions for the examples in the training or holdout set do not improve according to a certain measure.

Returns:: A PrePruningConfig that allows further configuration of the stopping criterion

class mlrl.common.cython.learner.RNGMixin¶

Bases: ABC

Allows to configure the random number generators (RNGs) that are used by a rule learner.

abstractmethod 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

class mlrl.common.cython.learner.RandomBiPartitionSamplingMixin¶

Bases: ABC

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

abstractmethod 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

class mlrl.common.cython.learner.RoundRobinOutputSamplingMixin¶

Bases: ABC

Allows to configure a rule learner to sample one output at a time in a round-robin fashion.

abstractmethod 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.

class mlrl.common.cython.learner.RuleLearnerConfig¶: Bases: object

class mlrl.common.cython.learner.SequentialPostOptimizationMixin¶

Bases: ABC

Allows to configure a 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.

abstractmethod 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

class mlrl.common.cython.learner.SequentialRuleModelAssemblageMixin¶

Bases: ABC

Allows to configure a rule learner to use an algorithm that sequentially induces several rules.

abstractmethod 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.

class mlrl.common.cython.learner.SizeStoppingCriterionMixin¶

Bases: ABC

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

abstractmethod 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

class mlrl.common.cython.learner.TimeStoppingCriterionMixin¶

Bases: ABC

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

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

class mlrl.common.cython.learner.TrainingResult¶

Bases: object

Provides access to the results of fitting a rule learner to training data. It incorporates the model that has been trained, as well as additional information that is necessary for obtaining predictions for unseen data.

joint_probability_calibration_model¶

marginal_probability_calibration_model¶

num_outputs¶

output_space_info¶

rule_model¶