Revise LHS sampling code

autumn/projects/sm_covid2/common_school/runner_tools.py

@@ -75,24 +75,6 @@
     Functions related to model calibration
 """
 
-def sample_with_lhs(n_samples, bcm):
-
-    # sample using LHS in the right dimension
-    lhs_sampled_params = [p for p in bcm.priors if p != "random_process.delta_values"]  
-    d = len(lhs_sampled_params)
-    sampler = qmc.LatinHypercube(d=d)
-    regular_sample = sampler.random(n=n_samples)
-
-    # scale the data cube to match parameter bounds
-    l_bounds = [bcm.priors[p].bounds()[0] for p in lhs_sampled_params]
-    u_bounds = [bcm.priors[p].bounds()[1] for p in lhs_sampled_params]
-    sample = qmc.scale(regular_sample, l_bounds, u_bounds)
-
-    sample_as_dicts = [{p: sample[i][j] for j, p in enumerate(lhs_sampled_params)} for i in range(n_samples)]
-
-    return sample_as_dicts
-
-
 def optimise_model_fit(bcm, num_workers: int = 8, warmup_iterations: int = 0, search_iterations: int = 5000, suggested_start: dict = None, opt_class=ng.optimizers.CMA):
 
     # Build optimizer
@@ -251,7 +233,8 @@ def run_full_analysis(
     # Sample optimisation starting points with LHS
     if logger:
         logger.info("Perform LHS sampling")
-    sample_as_dicts = sample_with_lhs(run_config['n_opti_searches'], bcm)
+    sample_as_dicts = bcm.sample.lhs(run_config['n_opti_searches'], out_type="list_of_dicts")
+    sample_as_dicts = [{p: val for p, val in d.items() if p != 'random_process.delta_values'} for d in sample_as_dicts] # remove random process from LHS sample
 
     # Store starting points
     with open(out_path / "LHS_init_points.yml", "w") as f: