Package index
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gp3mlgp3ml-package - gp3ml: Governance-First Predictive Modelling for 'Gazepoint' Research
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audit_gazepoint_ml_leakage() - Audit leakage between predictive-analysis partitions
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print(<gazepoint_ml_leakage_audit>) - Print a Gazepoint ML leakage audit
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write_gazepoint_ml_leakage_audit_csv() - Write a Gazepoint ML leakage-audit table to CSV
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create_gazepoint_feature_manifest() - Create a Gazepoint feature-provenance manifest
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validate_gazepoint_feature_manifest() - Validate a Gazepoint feature-provenance manifest
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print(<gazepoint_feature_manifest_validation>) - Print feature-manifest validation
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write_gazepoint_feature_manifest_csv() - Write a Gazepoint feature manifest or validation table to CSV
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split_gazepoint_ml_data() - Create a deterministic group-aware Gazepoint holdout split
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validate_gazepoint_ml_split() - Validate a group-aware Gazepoint holdout split
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print(<gazepoint_ml_split>) - Print a group-aware Gazepoint split
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print(<gazepoint_ml_split_validation>) - Print group-aware split validation
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write_gazepoint_ml_split_csv() - Write group-aware split tables to CSV
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create_gazepoint_group_folds() - Create deterministic group-aware Gazepoint resampling folds
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validate_gazepoint_group_folds() - Validate group-aware Gazepoint resampling folds
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audit_gazepoint_group_folds() - Aggregate leakage audits across group-aware folds
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print(<gazepoint_group_folds>) - Print group-aware Gazepoint resampling folds
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print(<gazepoint_group_folds_validation>) - Print group-aware fold validation
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print(<gazepoint_group_folds_audit>) - Print aggregated group-fold leakage auditing
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write_gazepoint_group_folds_csv() - Write group-aware resampling tables to CSV
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diagnose_gazepoint_group_folds() - Diagnose group-aware Gazepoint resampling folds
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validate_gazepoint_fold_diagnostics() - Validate Gazepoint fold diagnostics
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print(<gazepoint_fold_diagnostics>) - Print Gazepoint fold diagnostics
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print(<gazepoint_fold_diagnostics_validation>) - Print Gazepoint fold-diagnostics validation
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write_gazepoint_fold_diagnostics_csv() - Write Gazepoint fold diagnostics to CSV files
Repository-aware fold evaluation
Fit fold-local preprocessing and models across materialized grouped folds.
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evaluate_gazepoint_group_folds()print(<gp3ml_resample_evaluation>) - Evaluate a governed model specification across materialized grouped folds
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collect_gazepoint_fold_predictions() - Collect predictions from a grouped-fold evaluation
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summarize_gazepoint_resample_performance()print(<gp3ml_resample_performance_summary>) - Summarize repeated grouped-resampling performance
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validate_gazepoint_resample_evaluation()print(<gp3ml_resample_evaluation_validation>) - Validate a grouped-fold evaluation result
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write_gazepoint_resample_evaluation() - Write grouped-fold evaluation tables
Governed comparison and tuning
Materialize, evaluate, compare, and review explicit model candidates.
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create_gazepoint_tuning_grid()print(<gp3ml_tuning_grid>) - Create an explicit governed tuning grid
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tune_gazepoint_model()print(<gp3ml_model_tuning>) - Evaluate every governed candidate on the same grouped folds
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compare_gazepoint_models() - Compare governed model candidates without selecting a winner
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select_gazepoint_model()print(<gp3ml_model_selection>) - Select a governed candidate using an explicit metric and direction
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validate_gazepoint_model_tuning()print(<gp3ml_model_tuning_validation>) - Validate governed tuning results
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write_gazepoint_model_tuning() - Write governed tuning and selection tables
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create_gazepoint_nested_folds()print(<gp3ml_nested_folds>) - Create nested grouped resampling from mature outer folds
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audit_gazepoint_nested_resampling()print(<gp3ml_nested_resampling_audit>) - Audit nested grouped resampling for outer-assessment leakage
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validate_gazepoint_nested_folds()print(<gp3ml_nested_folds_validation>) - Validate nested grouped folds
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evaluate_gazepoint_nested_resampling()print(<gp3ml_nested_evaluation>) - Evaluate nested grouped resampling with inner governed tuning
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validate_gazepoint_nested_evaluation()print(<gp3ml_nested_evaluation_validation>) - Validate a nested evaluation
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write_gazepoint_nested_evaluation() - Write nested-resampling evaluation tables
Target-aligned uncertainty
Record observation, cluster, fold, and repeat uncertainty without relabelling units.
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bootstrap_gazepoint_metrics_by_unit()print(<gp3ml_target_uncertainty>) - Generalization-target-aligned bootstrap uncertainty
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summarize_gazepoint_resample_uncertainty()print(<gp3ml_resample_uncertainty>) - Summarize uncertainty across folds or repeats
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validate_gazepoint_target_uncertainty()print(<gp3ml_uncertainty_validation>) - Validate target-aligned uncertainty metadata
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write_gazepoint_target_uncertainty() - Write target-aligned uncertainty tables
External validation and transportability
Declare independent data and report performance, drift, schema, and coverage.
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declare_gazepoint_external_dataset()print(<gp3ml_external_dataset_declaration>) - Declare an external dataset and its independence status
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evaluate_gazepoint_external_transportability()print(<gp3ml_transportability_report>) - Evaluate external transportability and validation status
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validate_gazepoint_transportability()print(<gp3ml_transportability_validation>) - Validate an external transportability report
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write_gazepoint_transportability_report() - Write an expanded transportability report
Synthetic workflows and release reporting
Deterministic demonstrations and release-ready governance records.
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simulate_gazepoint_governed_data() - Simulate governed synthetic Gazepoint-derived data
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create_gazepoint_synthetic_manifest() - Create a synthetic governed feature manifest
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create_gazepoint_synthetic_task() - Create one of the governed synthetic demonstration tasks
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create_gazepoint_release_model_card()print(<gp3ml_release_model_card>) - Create a release-ready governed model card
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write_gazepoint_release_model_card() - Write a release-ready governed model card
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create_gazepoint_release_evidence()print(<gp3ml_release_evidence>) - Create a release evidence manifest
Governed modelling core
Task governance, preprocessing, model fitting, performance, calibration, uncertainty, external validation, model cards, and reproducibility reporting.
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declare_gazepoint_task() - Declare a governed Gazepoint prediction task
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assert_gp3ml_use_case() - Assert that a task is within the permitted gp3ml scope
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validate_gazepoint_ml_roles() - Validate outcome, predictor, identifier, and grouping roles
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gp3ml_prohibited_uses() - Prohibited gp3ml uses
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fit_gazepoint_preprocessor() - Fit a fold-local preprocessing engine
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bake_gazepoint_preprocessor() - Apply a fitted preprocessing engine
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gp3ml_available_engines() - List available model engines
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integrate_black_box_model() - Integrate a controlled black-box model engine
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fit_gazepoint_model() - Fit a governed Gazepoint model
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train_gazepoint_classifier() - Generic governed binary-classifier training wrapper
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predict(<gp3ml_model>) - Predict from a gp3ml model
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fit_gazepoint_deep_model() - Fit an optional governed deep-learning model through keras3
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gazepoint_classification_metrics() - Binary classification metrics
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gazepoint_regression_metrics() - Regression metrics
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gazepoint_performance_metrics() - Task-aware performance metrics
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bootstrap_gazepoint_metrics() - Bootstrap uncertainty intervals for performance metrics
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fit_gazepoint_calibrator() - Fit a probability calibrator
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apply_gazepoint_calibrator() - Apply a fitted probability calibrator
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assess_gazepoint_calibration() - Calibration assessment with bootstrap uncertainty
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evaluate_external_validation() - Evaluate an independent external-validation dataset
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create_external_validation_report() - Create an external-validation report object
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write_external_validation_report() - Write an external-validation report
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create_gazepoint_model_card() - Create a governance-focused model card
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write_gazepoint_model_card() - Write a model card
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create_gazepoint_reproducibility_report() - Create a reproducibility report
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write_gazepoint_reproducibility_report() - Write a reproducibility report