mlrl.common.config.parameters module¶

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

Provides utilities that ease the configuration of rule learning algorithms.

class mlrl.common.config.parameters.FeatureBinningParameter¶

Bases: NominalParameter

A parameter that allows to configure the strategy to be used for feature binning.

class mlrl.common.config.parameters.FeatureSamplingParameter¶

Bases: NominalParameter

A parameter that allows to configure the strategy to be used for feature sampling.

OPTION_NUM_RETAINED = 'num_retained'¶

class mlrl.common.config.parameters.FloatParameter(name: str, description: str, mixin: type)¶

Bases: NumericalParameter, ABC

An abstract base class for all parameters of a rule learning algorithm that allow to set a floating point value.

class mlrl.common.config.parameters.GlobalPruningParameter¶

Bases: NominalParameter

A parameter that allows to configure the strategy to be used for pruning entire rules.

AGGREGATION_FUNCTION_ARITHMETIC_MEAN = 'avg'¶

AGGREGATION_FUNCTION_MAX = 'max'¶

AGGREGATION_FUNCTION_MIN = 'min'¶

GLOBAL_PRUNING_POST = 'post-pruning'¶

GLOBAL_PRUNING_PRE = 'pre-pruning'¶

OPTION_AGGREGATION_FUNCTION = 'aggregation'¶

OPTION_INTERVAL = 'interval'¶

OPTION_MIN_IMPROVEMENT = 'min_improvement'¶

OPTION_MIN_RULES = 'min_rules'¶

OPTION_NUM_PAST = 'num_past'¶

OPTION_NUM_RECENT = 'num_recent'¶

OPTION_REMOVE_UNUSED_RULES = 'remove_unused_rules'¶

OPTION_STOP_INTERVAL = 'stop_interval'¶

OPTION_UPDATE_INTERVAL = 'update_interval'¶

class mlrl.common.config.parameters.InstanceSamplingParameter¶

Bases: NominalParameter

A parameter that allows to configure the strategy to be used for instance sampling.

class mlrl.common.config.parameters.IntParameter(name: str, description: str, mixin: type)¶

Bases: NumericalParameter, ABC

An abstract base class for all parameters of a rule learning algorithm that allow to set an integer value.

class mlrl.common.config.parameters.NominalParameter(name: str, description: str)¶

Bases: Parameter, ABC

An abstract base class for all nominal parameters of a rule learning algorithm that allow to set one out of a set of predefined values.

class Value(name: str, mixin: type, options: set[str] | None, description: str | None)¶

Bases: object

A value that can be set for a nominal parameter.

add_value(name: str, mixin: type, options: set[str] | None = None, description: str | None = None)¶

Adds a new value to the parameter.

Parameters:

name – The name of the value to be added
mixin – The type of the mixin that must be implemented by a rule learner to support the value
options – A set that contains the names of additional options that may be specified or None, if no additional options are available
description – A textual description of the value

Returns:

The parameter itself

as_argument(config_type: type) → Argument | None¶

Creates and returns an Argument from this parameter, if it is supported by a configuration of a specific type.

Parameters:: config_type – The type of the configuration
Returns:: The Argument that has been created or None, if it is not supported by a configuration of the given type

configure(config, value)¶

Configures a rule learner depending on this parameter.

Parameters:

config – The configuration to be modified
value – The value to be set

values: dict[str, Value]¶

class mlrl.common.config.parameters.NumericalParameter(name: str, description: str, mixin: type, numeric_type: type)¶

Bases: Parameter, ABC

An abstract base class for all parameters of a rule learning algorithm that allow to set a numerical value.

as_argument(config_type: type) → Argument | None¶

Creates and returns an Argument from this parameter, if it is supported by a configuration of a specific type.

Parameters:: config_type – The type of the configuration
Returns:: The Argument that has been created or None, if it is not supported by a configuration of the given type

configure(config, value)¶

Configures a rule learner depending on this parameter.

Parameters:

config – The configuration to be modified
value – The value to be set

class mlrl.common.config.parameters.OutputSamplingParameter¶

Bases: NominalParameter

A parameter that allows to configure the strategy to be used for output sampling.

OUTPUT_SAMPLING_ROUND_ROBIN = 'round-robin'¶

class mlrl.common.config.parameters.ParallelPredictionParameter¶

Bases: NominalParameter

A parameter that allows to configure whether predictions for different examples should be obtained in parallel or not.

class mlrl.common.config.parameters.ParallelRuleRefinementParameter¶

Bases: NominalParameter

A parameter that allows to configure whether potential refinements of rules should be searched for in parallel or not.

class mlrl.common.config.parameters.ParallelStatisticUpdateParameter¶

Bases: NominalParameter

A parameter that allows to configure whether the statistics for different examples should be updated in parallel or not.

class mlrl.common.config.parameters.Parameter(name: str, description: str)¶

Bases: ABC

An abstract base class for all parameters of a rule learning algorithm.

property argument_name: str¶: The name of a command line argument that corresponds to the parameter.

abstractmethod as_argument(config_type: type) → Argument | None¶

Creates and returns an Argument from this parameter, if it is supported by a configuration of a specific type.

Parameters:: config_type – The type of the configuration
Returns:: The Argument that has been created or None, if it is not supported by a configuration of the given type

abstractmethod configure(config, value)¶

Configures a rule learner depending on this parameter.

Parameters: