Feature/structured specs

libensemble/alloc_funcs/fast_alloc.py

@@ -7,7 +7,7 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
     to evaluate in the simulation function. The fields in ``sim_specs['in']``
     are given. If all entries in `H` have been given a be evaluated, a worker
     is told to call the generator function, provided this wouldn't result in
-    more than ``gen_specs['num_active_gen']`` active generators.
+    more than ``gen_specs['user']['num_active_gen']`` active generators.
 
     :See:
         ``/libensemble/tests/regression_tests/test_fast_alloc.py``
@@ -23,7 +23,7 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
             sim_work(Work, i, sim_specs['in'], [persis_info['next_to_give']], [])
             persis_info['next_to_give'] += 1
 
-        elif gen_count < gen_specs.get('num_active_gens', gen_count+1):
+        elif gen_count < gen_specs['user'].get('num_active_gens', gen_count+1):
 
             # Give gen work
             persis_info['total_gen_calls'] += 1

libensemble/alloc_funcs/fast_alloc_and_pausing.py

@@ -9,16 +9,16 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
     to evaluate in the simulation function. The fields in ``sim_specs['in']``
     are given. If all entries in `H` have been given a be evaluated, a worker
     is told to call the generator function, provided this wouldn't result in
-    more than ``gen_specs['num_active_gen']`` active generators. Also allows
+    more than ``gen_specs['user']['num_active_gen']`` active generators. Also allows
     for a 'batch_mode'.
 
     When there are multiple objective components, this allocation function
     does not evaluate further components for some point in the following
     scenarios:
 
-    alloc_specs['stop_on_NaNs']: True --- after a NaN has been found in returned in some
+    alloc_specs['user']['stop_on_NaNs']: True --- after a NaN has been found in returned in some
         objective component
-    allocated['stop_partial_fvec_eval']: True --- after the value returned from
+    alloc_specs['user']['stop_partial_fvec_eval']: True --- after the value returned from
         combine_component_func is larger than a known upper bound on the objective.
 
     :See:
@@ -46,14 +46,14 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
         if len(persis_info['need_to_give']):
             # If 'stop_on_NaN' is true and any f_i is a NaN, then pause
             # evaluations of other f_i, corresponding to the same pt_id
-            if 'stop_on_NaNs' in alloc_specs and alloc_specs['stop_on_NaNs']:
+            if alloc_specs['user'].get('stop_on_NaNs'):
                 pt_ids_to_pause.update(H['pt_id'][np.isnan(H['f_i'])])
 
             # If 'stop_partial_fvec_eval' is true, pause entries in H if a
             # partial combine_component_func evaluation is # worse than the
             # best, known, complete evaluation (and the point is not a
             # local_pt).
-            if 'stop_partial_fvec_eval' in alloc_specs and alloc_specs['stop_partial_fvec_eval']:
+            if alloc_specs['user'].get('stop_partial_fvec_eval'):
                 pt_ids = set(persis_info['pt_ids']) - persis_info['has_nan'] - persis_info['complete']
                 pt_ids = np.array(list(pt_ids))
                 partial_fvals = np.zeros(len(pt_ids))
@@ -71,11 +71,11 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
 
                     if np.all(H['returned'][a1]):
                         persis_info['complete'].add(pt_id)
-                        persis_info['best_complete_val'] = min(persis_info['best_complete_val'], gen_specs['combine_component_func'](H['f_i'][a1]))
+                        persis_info['best_complete_val'] = min(persis_info['best_complete_val'], gen_specs['user']['combine_component_func'](H['f_i'][a1]))
                     else:
                         # Ensure combine_component_func calculates partial fevals correctly
                         # with H['f_i'] = 0 for non-returned point
-                        partial_fvals[j] = gen_specs['combine_component_func'](H['f_i'][a1])
+                        partial_fvals[j] = gen_specs['user']['combine_component_func'](H['f_i'][a1])
 
                 if len(persis_info['complete']) and len(pt_ids) > 1:
 
@@ -102,13 +102,13 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
                 i, idle_workers = idle_workers[0], idle_workers[1:]
                 sim_work(Work, i, sim_specs['in'], [next_row], [])
 
-        elif gen_count < gen_specs.get('num_active_gens', gen_count+1):
+        elif gen_count < gen_specs['user'].get('num_active_gens', gen_count+1):
             lw = persis_info['last_worker']
 
             last_size = persis_info.get('last_size')
             if len(H):
                 # Don't give gen instances in batch mode if points are unfinished
-                if (gen_specs.get('batch_mode')
+                if (gen_specs['user'].get('batch_mode')
                     and not all(np.logical_or(H['returned'][last_size:],
                                               H['paused'][last_size:]))):
                     break
@@ -131,7 +131,7 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
 
             persis_info['last_worker'] = i
 
-        elif gen_count >= gen_specs.get('num_active_gens', gen_count+1):
+        elif gen_count >= gen_specs['user'].get('num_active_gens', gen_count+1):
             idle_workers = []
 
     return Work, persis_info

libensemble/alloc_funcs/fast_alloc_to_aposmm.py

@@ -9,7 +9,7 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
     to evaluate in the simulation function. The fields in ``sim_specs['in']``
     are given. If all entries in `H` have been given a be evaluated, a worker
     is told to call the generator function, provided this wouldn't result in
-    more than ``gen_specs['num_active_gen']`` active generators. Also allows
+    more than ``gen_specs['user']['num_active_gen']`` active generators. Also allows
     for a 'batch_mode'.
 
     :See:
@@ -27,13 +27,13 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
             sim_work(Work, i, sim_specs['in'], [persis_info['next_to_give']], [])
             persis_info['next_to_give'] += 1
 
-        elif gen_count < gen_specs.get('num_active_gens', gen_count+1):
+        elif gen_count < gen_specs['user'].get('num_active_gens', gen_count+1):
             lw = persis_info['last_worker']
 
             last_size = persis_info.get('last_size')
             if len(H):
                 # Don't give gen instances in batch mode if points are unfinished
-                if (gen_specs.get('batch_mode')
+                if (gen_specs['user'].get('batch_mode')
                     and not all(np.logical_or(H['returned'][last_size:],
                                               H['paused'][last_size:]))):
                     break

libensemble/alloc_funcs/give_sim_work_first.py

@@ -7,10 +7,10 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
     """
     Decide what should be given to workers. This allocation function gives any
     available simulation work first, and only when all simulations are
-    completed or running does it start (at most ``gen_specs['num_active_gens']``)
+    completed or running does it start (at most ``gen_specs['user']['num_active_gens']``)
     generator instances.
 
-    Allows for a ``gen_specs['batch_mode']`` where no generation
+    Allows for a ``gen_specs['user']['batch_mode']`` where no generation
     work is given out unless all entries in ``H`` are returned.
 
     Allows for ``blocking`` of workers that are not active, for example, so
@@ -39,7 +39,7 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
             # Pick all high priority, oldest high priority, or just oldest point
             if 'priority' in H.dtype.fields:
                 priorities = H['priority'][~H['allocated']]
-                if gen_specs.get('give_all_with_same_priority'):
+                if gen_specs['user'].get('give_all_with_same_priority'):
                     q_inds = (priorities == np.max(priorities))
                 else:
                     q_inds = np.argmax(priorities)
@@ -68,12 +68,12 @@ def give_sim_work_first(W, H, sim_specs, gen_specs, alloc_specs, persis_info):
         else:
 
             # Allow at most num_active_gens active generator instances
-            if gen_count >= gen_specs.get('num_active_gens', gen_count+1):
+            if gen_count >= gen_specs['user'].get('num_active_gens', gen_count+1):
                 break
 
             # No gen instances in batch mode if workers still working
             still_working = ~H['returned']
-            if gen_specs.get('batch_mode') and np.any(still_working):
+            if gen_specs['user'].get('batch_mode') and np.any(still_working):
                 break
 
             # Give gen work

libensemble/alloc_funcs/inverse_bayes_allocf.py

@@ -32,7 +32,7 @@ def only_persistent_gens_for_inverse_bayes(W, H, sim_specs, gen_specs, alloc_spe
             inds_to_send_back = np.where(np.logical_and(inds_generated_by_i,
                                                         last_batch_inds))[0]
             if H['batch'][-1] > 0:
-                n = gen_specs['subbatch_size']*gen_specs['num_subbatches']
+                n = gen_specs['user']['subbatch_size']*gen_specs['user']['num_subbatches']
                 k = H['batch'][-1]
                 H['weight'][(n*(k-1)):(n*k)] = H['weight'][(n*k):(n*(k+1))]
 

libensemble/alloc_funcs/persistent_aposmm_alloc.py

@@ -23,7 +23,7 @@ def persistent_aposmm_alloc(W, H, sim_specs, gen_specs, alloc_specs, persis_info
 
     # If any persistent worker's calculated values have returned, give them back.
     for i in avail_worker_ids(W, persistent=True):
-        if persis_info.get('sample_done') or sum(H['returned']) >= gen_specs['initial_sample_size']:
+        if persis_info.get('sample_done') or sum(H['returned']) >= gen_specs['user']['initial_sample_size']:
             # Don't return if the initial sample is not complete
             persis_info['sample_done'] = True
 

libensemble/alloc_funcs/start_persistent_local_opt_gens.py

@@ -55,7 +55,7 @@ def start_persistent_local_opt_gens(W, H, sim_specs, gen_specs, alloc_specs, per
         # Find candidates to start local opt runs if a sample has been evaluated
         if np.any(np.logical_and(~H['local_pt'], H['returned'])):
             n, _, _, _, r_k, mu, nu = initialize_APOSMM(H, gen_specs)
-            update_history_dist(H, n, gen_specs, c_flag=False)
+            update_history_dist(H, n, gen_specs['user'], c_flag=False)
             starting_inds = decide_where_to_start_localopt(H, r_k, mu, nu)
         else:
             starting_inds = []