.. WARNING: this file is generated from 'doc-sources/reference/lincs.yml'. MANUAL EDITS WILL BE LOST.
.. module:: lincs
The ``lincs`` package
=====================
This is the main module for the *lincs* library.
It contains general information (version, GPU availability, *etc.*) and items of general usage (*e.g.* the exception for invalid data).
.. data:: __version__
:type: str
The version of *lincs*, as a string in `Version Specifier <https://packaging.python.org/en/latest/specifications/version-specifiers/>`_ format.
.. data:: has_gpu
:type: bool
``True`` if *lincs* was built with CUDA support.
.. exception:: DataValidationException
Raised by constructors when called with invalid data. ``ex.args[0]`` gives a human-readable description of the error.
.. exception:: LearningFailureException
Raised by learning algorithms when they can't reach their objective.
.. class:: UniformRandomBitsGenerator
Random number generator.
.. method:: __call__() -> int
Generate the next pseudo-random integer.
.. module:: lincs.classification
The ``lincs.classification`` module
-----------------------------------
This module contains everything related to classification.
.. class:: Criterion
A classification criterion, to be used in a classification :py:class:`Problem`.
.. method:: __init__(name: str, values: RealValues)
Constructor for real-valued criterion.
.. method:: __init__(name: str, values: IntegerValues)
:noindex:
Constructor for integer-valued criterion.
.. method:: __init__(name: str, values: EnumeratedValues)
:noindex:
Constructor for criterion with enumerated values.
.. property:: name
:type: str
The name of the criterion.
.. class:: ValueType
The different types of values for a criterion.
.. property:: real
:classmethod:
:type: lincs.classification.Criterion.ValueType
Real values.
.. property:: integer
:classmethod:
:type: lincs.classification.Criterion.ValueType
Integer values.
.. property:: enumerated
:classmethod:
:type: lincs.classification.Criterion.ValueType
Enumerated values.
.. property:: value_type
:type: ValueType
The type of values for this criterion.
.. property:: is_real
:type: bool
``True`` if the criterion is real-valued.
.. property:: is_integer
:type: bool
``True`` if the criterion is integer-valued.
.. property:: is_enumerated
:type: bool
``True`` if the criterion takes enumerated values.
.. class:: PreferenceDirection
What values are preferred for a criterion.
.. property:: increasing
:classmethod:
:type: lincs.classification.Criterion.PreferenceDirection
For criteria where higher numerical values are known to be better.
.. property:: decreasing
:classmethod:
:type: lincs.classification.Criterion.PreferenceDirection
For criteria where lower numerical values are known to be better.
.. property:: single_peaked
:classmethod:
:type: lincs.classification.Criterion.PreferenceDirection
For criteria where intermediate numerical values are known to be better.
.. property:: isotone
:classmethod:
:type: lincs.classification.Criterion.PreferenceDirection
Synonym for ``increasing``.
.. property:: antitone
:classmethod:
:type: lincs.classification.Criterion.PreferenceDirection
Synonym for ``decreasing``.
.. class:: RealValues
Descriptor of the real values allowed for a criterion.
.. method:: __init__(preference_direction: PreferenceDirection, min_value: float, max_value: float)
Parameters map exactly to attributes with identical names.
.. property:: min_value
:type: float
The minimum value allowed for this criterion.
.. property:: max_value
:type: float
The maximum value allowed for this criterion.
.. property:: preference_direction
:type: PreferenceDirection
The preference direction for this criterion.
.. property:: is_increasing
:type: bool
``True`` if the criterion has increasing preference direction.
.. property:: is_decreasing
:type: bool
``True`` if the criterion has decreasing preference direction.
.. property:: is_single_peaked
:type: bool
``True`` if the criterion has single-peaked preference direction.
.. property:: real_values
:type: RealValues
Descriptor of the real values allowed for this criterion, accessible if ``is_real``.
.. class:: IntegerValues
Descriptor of the integer values allowed for a criterion.
.. method:: __init__(preference_direction: PreferenceDirection, min_value: int, max_value: int)
Parameters map exactly to attributes with identical names.
.. property:: min_value
:type: float
The minimum value allowed for this criterion.
.. property:: max_value
:type: float
The maximum value allowed for this criterion.
.. property:: preference_direction
:type: PreferenceDirection
The preference direction for this criterion.
.. property:: is_increasing
:type: bool
``True`` if the criterion has increasing preference direction.
.. property:: is_decreasing
:type: bool
``True`` if the criterion has decreasing preference direction.
.. property:: is_single_peaked
:type: bool
``True`` if the criterion has single-peaked preference direction.
.. property:: integer_values
:type: IntegerValues
Descriptor of the integer values allowed for this criterion, accessible if ``is_integer``.
.. class:: EnumeratedValues
Descriptor of the enumerated values allowed for a criterion.
.. method:: __init__(ordered_values: list[str])
Parameters map exactly to attributes with identical names.
.. method:: get_value_rank(value: str) -> int
Get the rank of a given value.
.. property:: ordered_values
:type: list[str]
The values for this criterion, from the worst to the best.
.. property:: enumerated_values
:type: EnumeratedValues
Descriptor of the enumerated values allowed for this criterion, accessible if ``is_enumerated``.
.. class:: Category
A category of a classification :py:class:`Problem`.
.. method:: __init__(name: str)
Parameters map exactly to attributes with identical names.
.. property:: name
:type: str
The name of this category.
.. class:: Problem
A classification problem, with criteria and categories.
.. method:: __init__(criteria: list[Criterion], ordered_categories: list[Category])
Parameters map exactly to attributes with identical names.
.. property:: criteria
:type: list[Criterion]
The criteria of this problem.
.. property:: ordered_categories
:type: list[Category]
The categories of this problem, from the worst to the best.
.. method:: dump(out: object)
Dump the problem to the provided ``.write``-supporting file-like object, in YAML format.
.. method:: load(in: object) -> Problem
:staticmethod:
Load a problem from the provided ``.read``-supporting file-like object, in YAML format.
.. data:: JSON_SCHEMA
:type: str
The JSON schema defining the format used by ``dump`` and ``load``, as a string.
.. class:: AcceptedValues
The values accepted by a model for a criterion.
.. method:: __init__(values: RealThresholds)
Constructor for thresholds on a real-valued criterion.
.. method:: __init__(values: IntegerThresholds)
:noindex:
Constructor for thresholds on an integer-valued criterion.
.. method:: __init__(values: EnumeratedThresholds)
:noindex:
Constructor for thresholds on an enumerated criterion.
.. method:: __init__(values: RealIntervals)
:noindex:
Constructor for intervals on a real-valued criterion.
.. method:: __init__(values: IntegerIntervals)
:noindex:
Constructor for intervals on an integer-valued criterion.
.. property:: value_type
:type: ValueType
The type of values for the corresponding criterion.
.. property:: is_real
:type: bool
``True`` if the corresponding criterion is real-valued.
.. property:: is_integer
:type: bool
``True`` if the corresponding criterion is integer-valued.
.. property:: is_enumerated
:type: bool
``True`` if the corresponding criterion takes enumerated values.
.. class:: Kind
The different kinds of descriptors for accepted values.
.. property:: thresholds
:classmethod:
:type: lincs.classification.AcceptedValues.Kind
A threshold for each category.
.. property:: intervals
:classmethod:
:type: lincs.classification.AcceptedValues.Kind
An interval for each category.
.. property:: kind
:type: AcceptedValues.Kind
The kind of descriptor for these accepted values.
.. property:: is_thresholds
:type: bool
``True`` if the descriptor is a set of thresholds.
.. property:: is_intervals
:type: bool
``True`` if the descriptor is a set of intervals.
.. class:: RealThresholds
Descriptor for thresholds for an real-valued criterion.
.. method:: __init__(thresholds: list[Optional[float]])
Parameters map exactly to attributes with identical names.
.. property:: thresholds
:type: list[Optional[float]]
The thresholds for this descriptor.
.. property:: real_thresholds
:type: RealThresholds
Descriptor of the real thresholds, accessible if ``is_real and is_thresholds``.
.. class:: IntegerThresholds
Descriptor for thresholds for an integer-valued criterion.
.. method:: __init__(thresholds: list[Optional[int]])
Parameters map exactly to attributes with identical names.
.. property:: thresholds
:type: list[Optional[int]]
The thresholds for this descriptor.
.. property:: integer_thresholds
:type: IntegerThresholds
Descriptor of the integer thresholds, accessible if ``is_integer and is_thresholds``.
.. class:: EnumeratedThresholds
Descriptor for thresholds for a criterion taking enumerated values.
.. method:: __init__(thresholds: list[Optional[str]])
Parameters map exactly to attributes with identical names.
.. property:: thresholds
:type: list[Optional[str]]
The thresholds for this descriptor.
.. property:: enumerated_thresholds
:type: EnumeratedThresholds
Descriptor of the enumerated thresholds, accessible if ``is_enumerated and is_thresholds``.
.. class:: RealIntervals
Descriptor for intervals for an real-valued criterion.
.. method:: __init__(intervals: list[Optional[tuple[float, float]]])
Parameters map exactly to attributes with identical names.
.. property:: intervals
:type: list[Optional[tuple[float, float]]]
The intervals for this descriptor.
.. property:: real_intervals
:type: RealIntervals
Descriptor of the real intervals, accessible if ``is_real and is_intervals``.
.. class:: IntegerIntervals
Descriptor for intervals for an integer-valued criterion.
.. method:: __init__(intervals: list[Optional[tuple[int, int]]])
Parameters map exactly to attributes with identical names.
.. property:: intervals
:type: list[Optional[tuple[int, int]]]
The intervals for this descriptor.
.. property:: integer_intervals
:type: IntegerIntervals
Descriptor of the integer intervals, accessible if ``is_integer and is_intervals``.
.. class:: SufficientCoalitions
The coalitions of sufficient criteria to accept an alternative in a category.
.. method:: __init__(weights: Weights)
Constructor for sufficient coalitions defined by weights.
.. method:: __init__(roots: Roots)
:noindex:
Constructor for sufficient coalitions defined by roots.
.. class:: Kind
The different kinds of descriptors for sufficient coalitions.
.. property:: weights
:classmethod:
:type: lincs.classification.SufficientCoalitions.Kind
For sufficient coalitions described by criterion weights.
.. property:: roots
:classmethod:
:type: lincs.classification.SufficientCoalitions.Kind
For sufficient coalitions described by the roots of their upset.
.. property:: kind
:type: SufficientCoalitions.Kind
The kind of descriptor for these sufficient coalitions.
.. property:: is_weights
:type: bool
``True`` if the descriptor is a set of weights.
.. property:: is_roots
:type: bool
``True`` if the descriptor is a set of roots.
.. class:: Weights
Descriptor for sufficient coalitions defined by weights.
.. method:: __init__(criterion_weights: list[float])
Parameters map exactly to attributes with identical names.
.. property:: criterion_weights
:type: list[float]
The weights for each criterion.
.. property:: weights
:type: Weights
Descriptor of the weights, accessible if ``is_weights``.
.. class:: Roots
Descriptor for sufficient coalitions defined by roots.
.. method:: __init__(problem: Problem, upset_roots: list[list[int]])
Parameters map exactly to attributes with identical names.
.. property:: upset_roots
:type: list[list[int]]
The roots of the upset of sufficient coalitions.
.. property:: roots
:type: Roots
Descriptor of the roots, accessible if ``is_roots``.
.. class:: Model
An NCS classification model.
.. method:: __init__(problem: Problem, accepted_values: list[AcceptedValues], sufficient_coalitions: list[SufficientCoalitions])
The :py:class:`Model` being initialized must correspond to the given :py:class:`Problem`. Other parameters map exactly to attributes with identical names.
.. method:: check_consistency_with(problem: Problem)
Raise :py:class:`DataValidationException` if the model is not consistent with the provided problem.
.. property:: accepted_values
:type: list[AcceptedValues]
The accepted values for each criterion.
.. property:: sufficient_coalitions
:type: list[SufficientCoalitions]
The sufficient coalitions for each category.
.. method:: dump(problem: Problem, out: object)
Dump the model to the provided ``.write``-supporting file-like object, in YAML format.
.. method:: load(problem: Problem, in: object) -> Model
:staticmethod:
Load a model for the provided ``Problem``, from the provided ``.read``-supporting file-like object, in YAML format.
.. data:: JSON_SCHEMA
:type: str
The JSON schema defining the format used by ``dump`` and ``load``, as a string.
.. class:: Performance
The performance of an alternative on a criterion.
.. method:: __init__(performance: Real)
Constructor for a real-valued performance.
.. method:: __init__(performance: Integer)
:noindex:
Constructor for an integer-valued performance.
.. method:: __init__(performance: Enumerated)
:noindex:
Constructor for an enumerated performance.
.. property:: value_type
:type: ValueType
The type of values for the corresponding criterion.
.. property:: is_real
:type: bool
``True`` if the corresponding criterion is real-valued.
.. property:: is_integer
:type: bool
``True`` if the corresponding criterion is integer-valued.
.. property:: is_enumerated
:type: bool
``True`` if the corresponding criterion takes enumerated values.
.. class:: Real
A performance for a real-valued criterion.
.. method:: __init__(value: float)
Parameters map exactly to attributes with identical names.
.. property:: value
:type: float
The numerical value of the real performance.
.. property:: real
:type: Real
The real performance, accessible if ``is_real``.
.. class:: Integer
A performance for an integer-valued criterion.
.. method:: __init__(value: int)
Parameters map exactly to attributes with identical names.
.. property:: value
:type: int
The numerical value of the integer performance.
.. property:: integer
:type: Integer
The integer performance, accessible if ``is_integer``.
.. class:: Enumerated
A performance for a criterion taking enumerated values.
.. method:: __init__(value: str)
Parameters map exactly to attributes with identical names.
.. property:: value
:type: str
The string value of the enumerated performance.
.. property:: enumerated
:type: Enumerated
The enumerated performance, accessible if ``is_enumerated``.
.. class:: Alternative
An alternative, with its performance on each criterion, maybe classified.
.. method:: __init__(name: str, profile: list[Performance], category_index: Optional[int]=None)
Parameters map exactly to attributes with identical names.
.. property:: name
:type: str
The name of the alternative.
.. property:: profile
:type: list[Performance]
The performance profile of the alternative.
.. property:: category_index
:type: Optional[int]
The index of the category of the alternative, if it is classified.
.. class:: Alternatives
A set of alternatives, maybe classified.
.. method:: __init__(problem: Problem, alternatives: list[Alternative])
The :py:class:`Alternatives` being initialized must correspond to the given :py:class:`Problem`. Other parameters map exactly to attributes with identical names.
.. method:: check_consistency_with(problem: Problem)
Raise :py:class:`DataValidationException` if these alternatives are not consistent with the provided problem.
.. property:: alternatives
:type: list[Alternative]
The :py:class:`Alternative` objects in this set.
.. method:: dump(problem: Problem, out: object)
Dump the set of alternatives to the provided ``.write``-supporting file-like object, in CSV format.
.. method:: load(problem: Problem, in: object) -> Alternatives
:staticmethod:
Load a set of alternatives (classified or not) from the provided ``.read``-supporting file-like object, in CSV format.
.. function:: generate_problem(criteria_count: int, categories_count: int, random_seed: int, normalized_min_max: bool=True, allowed_preference_directions: list[PreferenceDirection]=[PreferenceDirection.increasing], allowed_value_types: list[ValueType]=[ValueType.real]) -> Problem
Generate a :py:class:`Problem` with ``criteria_count`` criteria and ``categories_count`` categories.
.. function:: generate_mrsort_model(problem: Problem, random_seed: int, fixed_weights_sum: Optional[float]=None) -> Model
Generate an MR-Sort model for the provided :py:class:`Problem`.
.. exception:: BalancedAlternativesGenerationException
Raised by ``generate_alternatives`` when it fails to find alternatives to balance the categories.
.. function:: generate_alternatives(problem: Problem, model: Model, alternatives_count: int, random_seed: int, max_imbalance: Optional[float]=None) -> Alternatives
Generate a set of ``alternatives_count`` pseudo-random alternatives for the provided :py:class:`Problem`, classified according to the provided :py:class:`Model`.
.. function:: misclassify_alternatives(problem: Problem, alternatives: Alternatives, count: int, random_seed: int)
Misclassify ``count`` alternatives from the provided :py:class:`Alternatives`.
.. class:: LearnUcncsByMaxSatByCoalitionsUsingEvalmaxsat
The "max-SAT by coalitions" approach to learn Uc-NCS models.
.. method:: __init__(problem: Problem, learning_set: Alternatives, nb_minimize_threads: int=0, timeout_fast_minimize: int=60, coef_minimize_time: int=2)
Constructor.
.. method:: perform() -> Model
Actually perform the learning and return the learned model.
.. class:: LearnUcncsByMaxSatBySeparationUsingEvalmaxsat
The "max-SAT by separation" approach to learn Uc-NCS models.
.. method:: __init__(problem: Problem, learning_set: Alternatives, nb_minimize_threads: int=0, timeout_fast_minimize: int=60, coef_minimize_time: int=2)
Constructor.
.. method:: perform() -> Model
Actually perform the learning and return the learned model.
.. class:: LearnUcncsBySatByCoalitionsUsingMinisat
The "SAT by coalitions" approach to learn Uc-NCS models.
.. method:: __init__(problem: Problem, learning_set: Alternatives)
Constructor.
.. method:: perform() -> Model
Actually perform the learning and return the learned model.
.. class:: LearnUcncsBySatBySeparationUsingMinisat
The "SAT by separation" approach to learn Uc-NCS models.
.. method:: __init__(problem: Problem, learning_set: Alternatives)
Constructor.
.. method:: perform() -> Model
Actually perform the learning and return the learned model.
.. class:: PreprocessedLearningSet
A representation of a learning set with its data normalized as ranks (unsigned integers).
.. method:: __init__(problem: Problem, learning_set: Alternatives)
Constructor, pre-processing the learning set into a simpler form for learning.
.. property:: criteria_count
:type: int
Number of criteria in the :py:class:`Problem`.
.. property:: categories_count
:type: int
Number of categories in the :py:class:`Problem`.
.. property:: boundaries_count
:type: int
Number of boundaries in the :py:class:`Problem`, *i.e* ``categories_count - 1``.
.. property:: alternatives_count
:type: int
Number of alternatives in the ``learning_set``.
.. property:: single_peaked
:type: list[bool]
Indexed by ``[criterion_index]``. Whether each criterion is single-peaked or not.
.. property:: values_counts
:type: list[int]
Indexed by ``[criterion_index]``. Number of different values for each criterion, in the ``learning_set`` and min and max values for numerical criteria.
.. property:: performance_ranks
:type: list[list[int]]
Indexed by ``[criterion_index][alternative_index]``. Rank of each alternative in the ``learning_set`` for each criterion.
.. property:: assignments
:type: list[int]
Indexed by ``[alternative_index]``. Category index of each alternative in the ``learning_set``.
.. class:: LearnMrsortByWeightsProfilesBreed
The approach described in Olivier Sobrie's PhD thesis to learn MR-Sort models.
.. method:: __init__(preprocessed_learning_set: PreprocessedLearningSet, models_being_learned: ModelsBeingLearned, profiles_initialization_strategy: ProfilesInitializationStrategy, weights_optimization_strategy: WeightsOptimizationStrategy, profiles_improvement_strategy: ProfilesImprovementStrategy, breeding_strategy: BreedingStrategy, termination_strategy: TerminationStrategy, observers: list[Observer]=[])
Constructor accepting the strategies to use for each step of the learning.
.. class:: ModelsBeingLearned
Data shared by all the strategies used in this learning.
.. method:: __init__(preprocessed_learning_set: PreprocessedLearningSet, models_count: int, random_seed: int)
Constructor, allocating but not initializing data about models about to be learned.
.. property:: models_count
:type: int
The number of in-progress models for this learning.
.. property:: random_generators
:type: list[UniformRandomBitsGenerator]
Indexed by ``[model_index]``. Random number generators associated to each in-progress model.
.. property:: iteration_index
:type: int
The index of the current iteration of the WPB algorithm.
.. property:: model_indexes
:type: list[int]
Indexed by ``0`` to ``models_count - 1``. Indexes of in-progress models ordered by increasing accuracy.
.. property:: weights
:type: list[list[int]]
Indexed by ``[model_index][criterion_index]``. The current MR-Sort weight of each criterion for each model.
.. property:: low_profile_ranks
:type: list[list[list[int]]]
Indexed by ``[model_index][boundary_index][criterion_index]``. The current rank of each low profile, for each model and criterion.
.. property:: high_profile_rank_indexes
:type: list[unsigned]
Indexed by ``[criterion_index]``. The index in ``high_profile_ranks``, for each single-peaked criterion.
.. property:: high_profile_ranks
:type: list[list[list[int]]]
Indexed by ``[model_index][boundary_index][high_profile_rank_indexes[criterion_index]]``. The current rank of each high profile, for each model and single-peaked criterion.
.. property:: accuracies
:type: list[int]
Indexed by ``[model_index]``. Accuracy of each in-progress model.
.. method:: get_best_accuracy() -> int
Return the accuracy of the best model so far.
.. method:: get_best_model() -> Model
Return the best model so far.
.. method:: get_model(model_index: int) -> Model
Return the model at the given index.
.. method:: recompute_accuracy(model_index: int)
Recompute the accuracy of the model at the given index.
.. class:: ProfilesInitializationStrategy
Abstract base class for profiles initialization strategies.
.. method:: initialize_profiles(model_indexes_begin: int, model_indexes_end: int)
Method to override. Should initialize all ``low_profile_ranks`` and ``high_profile_ranks`` of models at indexes in ``[model_indexes[i] for i in range(model_indexes_begin, model_indexes_end)]``.
.. class:: WeightsOptimizationStrategy
Abstract base class for weights optimization strategies.
.. method:: optimize_weights(model_indexes_begin: int, model_indexes_end: int)
Method to override. Should optimize ``weights`` of models at indexes in ``[model_indexes[i] for i in range(model_indexes_begin, model_indexes_end)]``.
.. class:: ProfilesImprovementStrategy
Abstract base class for profiles improvement strategies.
.. method:: improve_profiles(model_indexes_begin: int, model_indexes_end: int)
Method to override. Should improve ``low_profile_ranks`` and ``high_profile_ranks`` of models at indexes in ``[model_indexes[i] for i in range(model_indexes_begin, model_indexes_end)]``.
.. class:: BreedingStrategy
Abstract base class for breeding strategies.
.. method:: breed()
Method to override.
.. class:: TerminationStrategy
Abstract base class for termination strategies.
.. method:: terminate() -> bool
Method to override. Should return ``True`` if the learning should stop, ``False`` otherwise.
.. class:: Observer
Abstract base class for observation strategies.
.. method:: after_iteration()
Method to override. Called after each iteration. Should not change anything in the learning data.
.. method:: before_return()
Method to override. Called just before returning the learned model. Should not change anything in the learning data.
.. method:: perform() -> Model
Actually perform the learning and return the learned model.
.. class:: InitializeProfilesForProbabilisticMaximalDiscriminationPowerPerCriterion
The profiles initialization strategy described in Olivier Sobrie's PhD thesis.
.. method:: __init__(preprocessed_learning_set: PreprocessedLearningSet, models_being_learned: ModelsBeingLearned)
Constructor. Keeps a reference to the learning data.
.. method:: initialize_profiles(model_indexes_begin: int, model_indexes_end: int)
Overrides the base method.
.. class:: OptimizeWeightsUsingAlglib
The weights optimization strategy described in Olivier Sobrie's PhD thesis. The linear program is solved using AlgLib.
.. method:: __init__(preprocessed_learning_set: PreprocessedLearningSet, models_being_learned: ModelsBeingLearned)
Constructor. Keeps a reference to the learning data.
.. method:: optimize_weights(model_indexes_begin: int, model_indexes_end: int)
Overrides the base method.
.. class:: OptimizeWeightsUsingGlop
The weights optimization strategy described in Olivier Sobrie's PhD thesis. The linear program is solved using GLOP.
.. method:: __init__(preprocessed_learning_set: PreprocessedLearningSet, models_being_learned: ModelsBeingLearned)
Constructor. Keeps a reference to the learning data.
.. method:: optimize_weights(model_indexes_begin: int, model_indexes_end: int)
Overrides the base method.
.. class:: ImproveProfilesWithAccuracyHeuristicOnCpu
The profiles improvement strategy described in Olivier Sobrie's PhD thesis. Run on the CPU.
.. method:: __init__(preprocessed_learning_set: PreprocessedLearningSet, models_being_learned: ModelsBeingLearned)
Constructor. Keeps a reference to the learning data.
.. method:: improve_profiles(model_indexes_begin: int, model_indexes_end: int)
Overrides the base method.
.. class:: ImproveProfilesWithAccuracyHeuristicOnGpu
The profiles improvement strategy described in Olivier Sobrie's PhD thesis. Run on the CUDA-capable GPU.
.. method:: __init__(preprocessed_learning_set: PreprocessedLearningSet, models_being_learned: ModelsBeingLearned)
Constructor. Keeps a reference to the learning data.
.. method:: improve_profiles(model_indexes_begin: int, model_indexes_end: int)
Overrides the base method.
.. class:: ReinitializeLeastAccurate
The breeding strategy described in Olivier Sobrie's PhD thesis: re-initializes ``count`` in-progress models.
.. method:: __init__(models_being_learned: ModelsBeingLearned, profiles_initialization_strategy: ProfilesInitializationStrategy, weights_optimization_strategy: WeightsOptimizationStrategy, count: int)
Constructor. Keeps references to the profiles initialization strategy and the learning data.
.. method:: breed()
Overrides the base method.
.. class:: TerminateAfterIterations
Termination strategy. Terminates the learning after a given number of iterations.
.. method:: __init__(models_being_learned: ModelsBeingLearned, max_iterations_count: int)
Constructor. Keeps a reference to the learning data.
.. method:: terminate() -> bool
Overrides the base method.
.. class:: TerminateAfterIterationsWithoutProgress
Termination strategy. Terminates the learning after a given number of iterations without progress.
.. method:: __init__(models_being_learned: ModelsBeingLearned, max_iterations_count: int)
Constructor. Keeps a reference to the learning data.
.. method:: terminate() -> bool
Overrides the base method.
.. class:: TerminateAfterSeconds
Termination strategy. Terminates the learning after a given duration.
.. method:: __init__(max_seconds: float)
Constructor.
.. method:: terminate() -> bool
Overrides the base method.
.. class:: TerminateAfterSecondsWithoutProgress
Termination strategy. Terminates the learning after a given duration without progress.
.. method:: __init__(models_being_learned: ModelsBeingLearned, max_seconds: float)
Constructor. Keeps a reference to the learning data.
.. method:: terminate() -> bool
Overrides the base method.
.. class:: TerminateAtAccuracy
Termination strategy. Terminates the learning when the best model reaches a given accuracy.
.. method:: __init__(models_being_learned: ModelsBeingLearned, target_accuracy: int)
Constructor. Keeps a reference to the learning data.
.. method:: terminate() -> bool
Overrides the base method.
.. class:: TerminateWhenAny
Termination strategy. Terminates the learning when one or more termination strategies decide to terminate.
.. method:: __init__(termination_strategies: list[TerminationStrategy])
Constructor. Keeps references to each termination strategies.
.. method:: terminate() -> bool
Overrides the base method.
.. class:: ClassificationResult
Return type for ``classify_alternatives``.
.. property:: changed
:type: int
Number of alternatives that were not in the same category before and after classification.
.. property:: unchanged
:type: int
Number of alternatives that were in the same category before and after classification.
.. function:: classify_alternatives(problem: Problem, model: Model, alternatives: Alternatives) -> ClassificationResult
Classify the provided :py:class:`Alternatives` according to the provided :py:class:`Model`.
.. function:: describe_model(problem: lincs.classification.Problem, model: lincs.classification.Model) -> Iterable[str]
Generate a human-readable description of a classification model.
.. function:: describe_problem(problem: lincs.classification.Problem) -> Iterable[str]
Generate a human-readable description of a classification problem.
.. function:: visualize_model(problem: lincs.classification.Problem, model: lincs.classification.Model, alternatives: Iterable[lincs.classification.Alternative], axes: matplotlib.axes._axes.Axes)
Create a visual representation of a classification model and classified alternatives, using Matplotlib.