mlrl.boosting.cython.learner_classification module

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

class mlrl.boosting.cython.learner_classification.AutomaticBinaryPredictorMixin

Bases: ABC

Allows to configure a rule learner to automatically decide for a predictor for predicting whether individual labels are relevant or irrelevant.

abstractmethod use_automatic_binary_predictor()

Configures the rule learner to automatically decide for a predictor for predicting whether individual labels are relevant or irrelevant.

class mlrl.boosting.cython.learner_classification.AutomaticDefaultRuleMixin

Bases: ABC

Allows to configure a rule learner to automatically decide whether a default rule should be induced or not.

abstractmethod use_automatic_default_rule()

Configures the rule learner to automatically decide whether a default rule should be induced or not.

class mlrl.boosting.cython.learner_classification.AutomaticLabelBinningMixin

Bases: ABC

Allows to configure a rule learner to automatically decide whether a method for the assignment of labels to bins should be used or not.

abstractmethod use_automatic_label_binning()

Configures the rule learner to automatically decide whether a method for the assignment of labels to bins should be used or not.

class mlrl.boosting.cython.learner_classification.AutomaticPartitionSamplingMixin

Bases: ABC

Allows to configure a rule learner to automatically decide whether a holdout set should be used or not.

abstractmethod use_automatic_partition_sampling()

Configures the rule learner to automatically decide whether a holdout set should be used or not.

class mlrl.boosting.cython.learner_classification.AutomaticProbabilityPredictorMixin

Bases: ABC

Allows to configure a rule learner to automatically decide for a predictor for predicting probability estimates.

abstractmethod use_automatic_probability_predictor()

Configures the rule learner to automatically decide for a predictor for predicting probability estimates.

class mlrl.boosting.cython.learner_classification.AutomaticStatisticsMixin

Bases: ABC

Allows to configure a rule learner to automatically decide whether a dense or sparse representation of gradients and Hessians should be used.

abstractmethod use_automatic_statistics()

Configures the rule learner to automatically decide whether a dense or sparse representation of gradients and Hessians should be used.

class mlrl.boosting.cython.learner_classification.DecomposableLogisticLossMixin

Bases: ABC

Allows to configure a rule learner to use a loss function that implements a multivariate variant of the logistic loss that is decomposable.

abstractmethod use_decomposable_logistic_loss()

Configures the rule learner to use a loss function that implements a multivariate variant of the logistic loss that is decomposable.

class mlrl.boosting.cython.learner_classification.DecomposableSquaredHingeLossMixin

Bases: ABC

Allows to configure a rule learner to use a loss function that implements a multivariate variant of the squared hinge loss that is decomposable.

abstractmethod use_decomposable_squared_hinge_loss()

Configures the rule learner to use a loss function that implements a multivariate variant of the squared hinge loss that is decomposable.

class mlrl.boosting.cython.learner_classification.DenseStatisticsMixin

Bases: ABC

Allows to configure a rule learner to use a dense representation of gradients and Hessians.

abstractmethod use_dense_statistics()

Configures the rule learner to use a dense representation of gradients and Hessians.

class mlrl.boosting.cython.learner_classification.EqualWidthLabelBinningMixin

Bases: ABC

Allows to configure a rule learner to use a method for the assignment of labels to bins.

abstractmethod use_equal_width_label_binning() → EqualWidthLabelBinningConfig

Configures the rule learner to use a method for the assignment of labels to bins in a way such that each bin contains labels for which the predicted score is expected to belong to the same value range.

Returns:

A EqualWidthLabelBinningConfig that allows further configuration of the method for the assignment of labels to bins

class mlrl.boosting.cython.learner_classification.ExampleWiseBinaryPredictorMixin

Bases: ABC

Allows to configure a rule learner to use a predictor that predicts known label vectors for given query examples by comparing the predicted scores or probability estimates to the label vectors encountered in the training data.

abstractmethod use_example_wise_binary_predictor() → ExampleWiseBinaryPredictorConfig

Configures the rule learner to use a predictor that predicts known label vectors for given query examples by comparing the predicted scores or probability estimates to the label vectors encountered in the training data.

Returns:

An ExampleWiseBinaryPredictorConfig that allows further configuration of the predictor

class mlrl.boosting.cython.learner_classification.GfmBinaryPredictorMixin

Bases: ABC

Allows to configure a rule learner to use a predictor that predicts whether individual labels of given query examples are relevant or irrelevant by discretizing the scores or probability estimates that are predicted for each label according to the general F-measure maximizer (GFM).

abstractmethod use_gfm_binary_predictor() → GfmBinaryPredictorConfig

Configures the rule learner to use a predictor that predicts whether individual labels of given query examples are relevant or irrelevant by discretizing the scores or probability estimates that are predicted for each label according to the general F-measure maximizer (GFM).

Returns:

A GfmBinaryPredictorConfig that allows further configuration of the predictor

class mlrl.boosting.cython.learner_classification.IsotonicJointProbabilityCalibrationMixin

Bases: ABC

Allows to configure a rule learner to calibrate joint probabilities via isotonic regression.

abstractmethod use_isotonic_joint_probability_calibration() → IsotonicJointProbabilityCalibratorConfig

Configures the rule learner to calibrate joint probabilities via isotonic regression.

Returns:

An IsotonicJointProbabilityCalibratorConfig that allows further configuration of the calibrator

class mlrl.boosting.cython.learner_classification.IsotonicMarginalProbabilityCalibrationMixin

Bases: ABC

Allows to configure a rule learner to calibrate marginal probabilities via isotonic regression.

abstractmethod use_isotonic_marginal_probability_calibration() → IsotonicMarginalProbabilityCalibratorConfig

Configures the rule learner to calibrate marginal probabilities via isotonic regression.

Returns:

An IsotonicMarginalProbabilityCalibratorConfig that allows further configuration of the calibrator

class mlrl.boosting.cython.learner_classification.MarginalizedProbabilityPredictorMixin

Bases: ABC

Allows to configure a rule learner to use predictor for predicting probability estimates by summing up the scores that are provided by individual rules of an existing rule-based model and comparing the aggregated score vector to the known label vectors according to a certain distance measure.

abstractmethod use_marginalized_probability_predictor() → MarginalizedProbabilityPredictorConfig

Configures the rule learner to use a predictor for predicting probability estimates by summing up the scores that are provided by individual rules of an existing rule-based model and comparing the aggregated score vector to the known label vectors according to a certain distance measure. The probability for an individual label calculates as the sum of the distances that have been obtained for all label vectors, where the respective label is specified to be relevant, divided by the total sum of all distances.

Returns:

A MarginalizedProbabilityPredictorConfig that allows further configuration of the predictor

class mlrl.boosting.cython.learner_classification.NoDefaultRuleMixin

Bases: ABC

Allows to configure a rule learner to not induce a default rule.

abstractmethod use_no_default_rule()

Configures the rule learner to not induce a default rule.

class mlrl.boosting.cython.learner_classification.NoLabelBinningMixin

Bases: ABC

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

abstractmethod use_no_label_binning()

Configures the rule learner to not use any method for the assignment of labels to bins.

class mlrl.boosting.cython.learner_classification.NonDecomposableLogisticLossMixin

Bases: ABC

Allows to configure a rule learner to use a loss function that implements a multivariate variant of the logistic loss that is non-decomposable.

abstractmethod use_non_decomposable_logistic_loss()

Configures the rule learner to use a loss function that implements a multivariate variant of the logistic loss that is non-decomposable.

class mlrl.boosting.cython.learner_classification.NonDecomposableSquaredHingeLossMixin

Bases: ABC

Allows to configure a rule learner to use a loss function that implements a multivariate variant of the squared hinge loss that is non-decomposable.

abstractmethod use_non_decomposable_squared_hinge_loss()

Configures the rule learner to use a loss function that implements a multivariate variant of the squared hinge loss that is non-decomposable.

class mlrl.boosting.cython.learner_classification.OutputWiseBinaryPredictorMixin

Bases: ABC

Allows to configure a predictor that predicts whether individual labels of given query examples are relevant or irrelevant by discretizing the scores or probability estimates that are predicted for each label individually.

abstractmethod use_output_wise_binary_predictor() → OutputWiseBinaryPredictorConfig

Configures the rule learner to use a predictor that predicts whether individual labels of given query examples are relevant or irrelevant by discretizing the scores or probability estimates that are predicted for each label individually.

Returns:

A OutputWiseBinaryPredictorConfig that allows further configuration of the predictor

class mlrl.boosting.cython.learner_classification.OutputWiseProbabilityPredictorMixin

Bases: ABC

Allows to configure a rule learner to use a predictor that predicts label-wise probabilities for given query examples by transforming the individual scores that are predicted for each label into probabilities.

abstractmethod use_output_wise_probability_predictor() → OutputWiseProbabilityPredictorConfig

Configures the rule learner to use a predictor that predicts label-wise probabilities for given query examples by transforming the individual scores that are predicted for each label into probabilities.

Returns:

A OutputWiseProbabilityPredictorConfig that allows further configuration of the predictor

class mlrl.boosting.cython.learner_classification.SparseStatisticsMixin

Bases: ABC

Allows to configure a rule learner to use a sparse representation of gradients and Hessians, if possible.

abstractmethod use_sparse_statistics()

Configures the rule learner to use a sparse representation of gradients and Hessians, if possible.