Registry reference

Experiment components

The preferred way to set up an experiment is to use a YAML configuration. Use the [LR system selection helper](lrsystem_yaml.md) to learn how to use the YAML interface.

This page lists the components that may be needed to set up an experiment.

Experiment strategies

Registry section: experiment_strategies

• experiment_strategies.single_run Prepare Experiment consisting of a single run using configuration values.

• experiment_strategies.grid Prepare Experiment consisting of multiple runs using configuration values.

• experiment_strategies.optuna Prepare Experiment for optimizing configuration parameters.

Data strategies

Registry section: data_strategies

• data_strategies.binary_train_test_split Representation of a train/test split.

• data_strategies.binary_cross_validation Representation of a K-fold cross validation iterator over each train/test split fold.

• data_strategies.multiclass_train_test_split Representation of a multi-class train/test split.

• data_strategies.multiclass_cross_validation Representation of a K-fold cross validation iterator over train/test splits.

• data_strategies.predefined_train_test_split Splits data into a training set and a test set, according to pre-existing assignments in the data.

Data providers

Registry section: data_providers

• data_providers.synthesized_normal_binary

• data_providers.synthesized_normal_multiclass

• data_providers.glass LA-ICP-MS measurements of elemental concentration from floatglass.

• data_providers.parse_features_from_csv_file Read CSV data from file.

• data_providers.parse_features_from_csv_url

Metrics

Registry section: metric

• metric.cllr Calculate a log likelihood ratio cost (C_llr) for a series of log likelihood ratios.

• metric.cllr_min Estimate the discriminative power from a collection of log likelihood ratios.

• metric.cllr_cal Calculate the difference between the C_llr before and after isotonic calibration.

• metric.llr_lower_bound Provide corresponding lower bound for provided LLR data.

• metric.llr_upper_bound Provide corresponding upper bound for provided LLR data.

Output

Registry section: output

• output.metrics

• output.pav

• output.ece

• output.lr_histogram

• output.llr_interval

• output.llr_overestimation

• output.nbe

• output.invariance_delta_function

• output.tippett

• output.save_model

Hyperparameters

Registry section: hyperparameter_types

• hyperparameter_types.categorical

• hyperparameter_types.cluster

• hyperparameter_types.constant

• hyperparameter_types.float

• hyperparameter_types.folder Parse the parameters section of the configuration into a FolderHyperparameter object.

LR system components

You may choose to use either the Python API or a YAML configuration to set up an LR system.

• Use the Pracitioner’s Guide to learn how to use the Python API.

• Use the [LR system selection helper](lrsystem_yaml.md) to learn how to use the YAML interface.

This page lists the components that may be needed to define an LR system.

LR system architecture

Registry section: lrsystem_architecture

• lrsystem_architectures.specific_source

• lrsystem_architectures.score_based

• lrsystem_architectures.two_level

LR system modules

Registry section: modules

• modules.pipeline

• modules.logging_pipeline

• modules.bootstrap

• modules.standard_scaler Standardize features by removing the mean and scaling to unit variance.

• modules.probabilities_to_odds Converts a probability to odds.

• modules.probabilities_to_logodds Converts probability values to their log odds with base 10.

• modules.element_wise_difference Calculate the element-wise absolute difference between pairs.

• modules.manhattan_distance Calculate the Manhattan distance between pairs.

• modules.tee Parse configuration for allowing multiple tasks for given input.

• modules.csv_writer

• modules.logistic_regression Logistic Regression (aka logit, MaxEnt) classifier.

• modules.svm C-Support Vector Classification.

• modules.kde Calculate LR from a score, belonging to one of two distributions using KDE.

• modules.isotonic_calibrator Wrap SKlearn implementation to support infinite values.

• modules.static_bounder Bound LLRs to constant values.

• modules.elub_bounder Calculate the Emperical Upper and Lower Bounds for a given LR system.

• modules.iv_bounder Calculate Invariance Verification bounds for a given LR system.

• modules.n_source_bounder Bound LLRs based on the number of sources.

Pairing methods

Registry section: pairing

• pairing.instance_pairs Construct pairs from a set of instances.

• pairing.source_pairs Construct pairs of sources (i.e. classes) from an array of instances.