.. _auto_doe_adaptive_infill: *ADAPTIVE INFILL POINT GENERATOR* ================================= Reads an existing design and/or a trained ml_learn_auto model and proposes the next batch of points to run, balancing exploitation (committee-disagreement uncertainty) against exploration (distance to existing points). Reports current out-of-fold accuracy versus a target and estimates how many more points are needed via a learning-curve fit or confidence-interval-width formula. Supports accuracy and optimization (Expected-Improvement) modes; composes with the Sampling Point Generator and Sampling Domain Reducer. When to use ----------- Tagged: ``doe``, ``adaptive sampling``, ``active learning``, ``infill``, ``sequential``, ``surrogate``, ``optimization loop``, ``accuracy``. Inputs ------ .. list-table:: :header-rows: 1 :widths: 20 20 20 20 20 20 * - Label - ID - Type - Default - Required - Description * - Design Dataset - source - dataset - — - - Existing design: input columns plus response columns. Provide this, or a trained model via mfile/mathmodel. * - Model File - mfile - text - — - - Trained model handle: .pkl path or per-target CSV manifest. Feature names, targets and bounds are recovered via ml_predict_info. * - Math Model - mathmodel - text - — - - Saved math-model id; resolved to its .pkl model file. * - Variables - variables - dataset - — - - Variable definitions with name/min/max/type. If absent, recovered from the model schema or inferred from the dataset column ranges. * - Inputs - inputs - text - — - - Comma-separated input (feature) column names. Defaults to all non-target columns of the dataset, or the model's registered inputs. * - Targets - targets - text - — - - Comma-separated response (target) column names. Defaults to the model's registered targets. * - Mode - mode - select - accuracy - - accuracy = raise global surrogate accuracy; optimization = Expected-Improvement infill toward a better optimum; diagnose = model-free design-adequacy check (no surrogate run). * - Adequacy Model Order - adequacy_formula - select - linear - - Model order assumed when mode = diagnose; sets the coefficient count used for the adequacy floor. * - Objective - objective - select - minimize - - Optimization direction (used when mode = optimization). * - Number Of Points - num_points - scalar - 5 - - How many new points to propose this round. * - Target Accuracy - target_accuracy - scalar - 0.9 - - Desired out-of-fold R2 (CoP). Drives target_met and recommended_more. * - Sizing Method - sizing_method - select - learning_curve - - How recommended_more is estimated: learning-curve extrapolation or confidence-interval width. * - Target CI Width - target_ci_width - scalar - — - - Desired confidence-interval half-width (used when sizing_method = ci_width). * - Confidence Level - confidence_level - scalar - 0.95 - - Confidence level for the CI-width sizing method. * - Candidate Method - candidate_method - select - latin-hypercube - - How the dense candidate pool is generated before scoring. * - Candidate Pool Size - candidate_pool_size - scalar - 2000 - - Number of candidates generated and scored before selection. * - Exploration Weight - exploration_weight - scalar - 0.5 - - Weight w in score = w*local + (1-w)*global. Lower favors exploitation (model-weak regions). * - Regression Types - regression_types - text - linear_regression,rfr_regression,gp_regression - - Committee members trained by ml_learn_auto; their prediction spread is the model-agnostic uncertainty signal. * - CV Folds - cv_folds - scalar - 5 - - Folds for the out-of-fold accuracy estimate. * - Constraints - constraints - dataset - — - - Optional constraints (variable, condition, target) filtering proposed points; same format as the Sampling Point Generator. Outputs ------- .. list-table:: :header-rows: 1 :widths: 20 20 20 20 * - Label - ID - Type - Description * - Experiments - experiments - dataset - Proposed new design points to run, one row each with the input variable columns and a score. * - Current Accuracy - current_accuracy - scalar - Best out-of-fold CoP/R2 of the current design. * - Accuracy Metrics - accuracy_metrics - dataset - Per-target CoP / RMSE / MAE for the current design. * - Target Met - target_met - scalar - yes/no — whether current accuracy meets target_accuracy. * - Recommended More Points - recommended_more - scalar - Estimated additional points to reach the target (learning-curve or CI-width). An estimate, not a guarantee. * - Learning Curve - learning_curve - dataset - Sub-sample sizes vs accuracy plus fitted parameters and asymptote; empty when sizing_method = ci_width. * - Infill Scores - infill_scores - dataset - Per-candidate local/global/total score breakdown for transparency. * - Convergence - convergence - json - Summary: current, target, met, delta, recommended_more, sizing_method, asymptote, plateaued, global_term_basis. * - Design Adequacy - adequacy - json - mode = diagnose: model-free adequacy {n_samples, n_inputs, formula, coefficients, rank_floor, recommended, residual_dof, sample_to_coeff_ratio, deficit, status}. * - Adequacy Status - status - scalar - mode = diagnose: under-determined / minimal / ok. * - Adequacy Message - message - scalar - mode = diagnose: human-readable adequacy verdict and recommended sample count. Disciplines ----------- - ai_ml.prognosis - design_exploration.doe - design_exploration.optimization - design_exploration.sensitivity .. raw:: html

Auto-generated from platform schema. Worker id: doe_adaptive_infill. Schema hash: 771e6223956b. Hand-curated docs in workerexamples/ override this page when present.