feat: Uncertainty Sampling

README.md

@@ -6,7 +6,7 @@
 ![build](https://img.shields.io/github/actions/workflow/status/eckelsjd/amisc/deploy.yml?logo=github)
 ![docs](https://img.shields.io/github/actions/workflow/status/eckelsjd/amisc/docs.yml?logo=materialformkdocs&logoColor=%2523cccccc&label=docs)
 ![tests](https://img.shields.io/github/actions/workflow/status/eckelsjd/amisc/tests.yml?logo=github&logoColor=%2523cccccc&label=tests)
-![Code Coverage](https://img.shields.io/badge/coverage-89%25-yellowgreen?logo=codecov)
+![Code Coverage](https://img.shields.io/badge/coverage-86%25-yellowgreen?logo=codecov)
 [![Algorithm description](https://img.shields.io/badge/DOI-10.1002/nme.6958-blue)](https://doi.org/10.1002/nme.6958)
 
 Efficient framework for building surrogates of multidisciplinary systems using the adaptive multi-index stochastic collocation ([AMISC](https://onlinelibrary.wiley.com/doi/full/10.1002/nme.6958))  technique.

src/amisc/component.py

@@ -40,9 +40,9 @@
 from pydantic import BaseModel, ConfigDict, ValidationInfo, field_validator
 from typing_extensions import TypedDict
 
-from amisc.interpolator import Interpolator, InterpolatorState, Lagrange
+from amisc.interpolator import GPR, Interpolator, InterpolatorState, Lagrange
 from amisc.serialize import PickleSerializable, Serializable, StringSerializable, YamlSerializable
-from amisc.training import SparseGrid, TrainingData
+from amisc.training import SparseGrid, TrainingData, UncertaintySampling
 from amisc.typing import COORDS_STR_ID, LATENT_STR_ID, Dataset, MultiIndex
 from amisc.utils import (
     _get_yaml_path,
@@ -1213,8 +1213,17 @@ def activate_index(self, alpha: MultiIndex, beta: MultiIndex, model_dir: str | P
                 # Training data is the same for all surrogate fidelity indices, given constant data fidelity
                 design_list.append([])
                 continue
-
-            design_coords, design_pts = self.training_data.refine(a, b[:len(self.data_fidelity)],
+            if isinstance(self.interpolator, GPR) and isinstance(self.training_data, UncertaintySampling):
+                states = list(self.misc_states)
+                match_state = [tup for tup in states if tup[0] == a]
+                if match_state:
+                    best_state = max(match_state, key=lambda x: sum(x[1]))[2].regressor.named_steps['gpr']
+                else: 
+                    best_state = None
+                design_coords, design_pts = self.training_data.refine(a, b[:len(self.data_fidelity)],
+                                                                  domains,best_state, weight_fcns)
+            else:
+                design_coords, design_pts = self.training_data.refine(a, b[:len(self.data_fidelity)],
                                                                   domains, weight_fcns)
             design_pts, fc = to_model_dataset(design_pts, self.inputs, del_latent=True, **field_coords)
 
@@ -1301,7 +1310,6 @@ def activate_index(self, alpha: MultiIndex, beta: MultiIndex, model_dir: str | P
             # Only for initial index which didn't come from the candidate set
             self.update_misc_coeff(IndexSet(s), index_set='test')
         self.active_set.update(s)
-
         self.update_misc_coeff(neighbors, index_set='test')  # neighbors will only ever pass through here once
         self.candidate_set.update(neighbors)