mlrl.boosting.cython.prediction module

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

class mlrl.boosting.cython.prediction.ExampleWiseBinaryPredictorConfig

Bases: object

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

is_based_on_probabilities() → bool

Returns whether binary predictions are derived from probability estimates rather than regression scores or not.

Returns:

True, if binary predictions are derived from probability estimates rather than regression scores, False otherwise

is_probability_calibration_model_used() → bool

Returns whether a model for the calibration of probabilities is used, if available, or not.

Returns:

True, if a model for the calibration of probabilities is used, if available, False otherwise

set_based_on_probabilities(based_on_probabilities: bool) → ExampleWiseBinaryPredictorConfig

Sets whether binary predictions should be derived from probability estimates rather than regression scores or not.

Parameters:

based_on_probabilities – True, if binary predictions should be derived from probability estimates rather than regression scores, False otherwise

Returns:

An ExampleWiseBinaryPredictorConfig that allows further configuration of the predictor

set_use_probability_calibration_model(use_probability_calibration_model: bool) → ExampleWiseBinaryPredictorConfig

Sets whether a model for the calibration of probabilities should be used, if available, or not.

Parameters:

use_probability_calibration_model – True, if a model for the calibration of probabilities should be used, if available, false otherwise

Returns:

An ExampleWiseBinaryPredictorConfig that allows further configuration of the predictor

class mlrl.boosting.cython.prediction.GfmBinaryPredictorConfig

Bases: object

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

is_probability_calibration_model_used() → bool

Returns whether a model for the calibration of probabilities is used, if available, or not.

Returns:

True, if a model for the calibration of probabilities is used, if available, False otherwise

set_use_probability_calibration_model(use_probability_calibration_model: bool) → GfmBinaryPredictorConfig

Sets whether a model for the calibration of probabilities should be used, if available, or not.

Parameters:

use_probability_calibration_model – True, if a model for the calibration of probabilities should be used, if available, false otherwise

Returns:

A GfmBinaryPredictorConfig that allows further configuration of the predictor

class mlrl.boosting.cython.prediction.LabelWiseBinaryPredictorConfig

Bases: object

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

is_based_on_probabilities() → bool

Returns whether binary predictions are derived from probability estimates rather than regression scores or not.

Returns:

True, if binary predictions are derived from probability estimates rather than regression scores, False otherwise

is_probability_calibration_model_used() → bool

Returns whether a model for the calibration of probabilities is used, if available, or not.

Returns:

True, if a model for the calibration of probabilities is used, if available, False otherwise

set_based_on_probabilities(based_on_probabilities: bool) → LabelWiseBinaryPredictorConfig

Sets whether binary predictions should be derived from probability estimates rather than regression scores or not.

Parameters:

based_on_probabilities – True, if binary predictions should be derived from probability estimates rather than regression scores, False otherwise

Returns:

A LabelWiseBinaryPredictorConfig that allows further configuration of the predictor

set_use_probability_calibration_model(use_probability_calibration_model: bool) → LabelWiseBinaryPredictorConfig

Sets whether a model for the calibration of probabilities should be used, if available, or not.

Parameters:

use_probability_calibration_model – True, if a model for the calibration of probabilities should be used, if available, false otherwise

Returns:

A LabelWiseBinaryPredictorConfig that allows further configuration of the predictor

class mlrl.boosting.cython.prediction.LabelWiseProbabilityPredictorConfig

Bases: object

Allows to configure a predictor that predicts label-wise probabilities for given query examples, which estimate the chance of individual labels to be relevant, by summing up the scores that are provided by individual rules of an existing rule-based model and transforming the aggregated scores into probabilities in [0, 1] according to a certain transformation function that is applied to each label individually.

is_probability_calibration_model_used() → bool

Returns whether a model for the calibration of probabilities is used, if available, or not.

Returns:

True, if a model for the calibration of probabilities is used, if available, False otherwise

set_use_probability_calibration_model(use_probability_calibration_model: bool) → LabelWiseProbabilityPredictorConfig

Sets whether a model for the calibration of probabilities should be used, if available, or not.

Parameters:

use_probability_calibration_model – True, if a model for the calibration of probabilities should be used, if available, false otherwise

Returns:

A LabelWiseProbabilityPredictorConfig that allows further configuration of the predictor

class mlrl.boosting.cython.prediction.MarginalizedProbabilityPredictorConfig

Bases: object

Allows to configure a predictor that predicts marginalized probabilities for given query examples, which estimate the chance of individual labels to be relevant, 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.

is_probability_calibration_model_used() → bool

Returns whether a model for the calibration of probabilities is used, if available, or not.

Returns:

True, if a model for the calibration of probabilities is used, if available, False otherwise

set_use_probability_calibration_model(use_probability_calibration_model: bool) → MarginalizedProbabilityPredictorConfig

Sets whether a model for the calibration of probabilities should be used, if available, or not.

Parameters:

use_probability_calibration_model – True, if a model for the calibration of probabilities should be used, if available, false otherwise

Returns:

A MarginalizedProbabilityPredictorConfig that allows further configuration of the predictor