Updated output messages

PyCTBN/PyCTBN/estimators/structure_constraint_based_estimator.py

@@ -225,13 +225,14 @@ class StructureConstraintBasedEstimator(StructureEstimator):
         #with multiprocessing.Pool(processes=cpu_count) as pool:
         #with get_context("spawn").Pool(processes=cpu_count) as pool:
         if disable_multiprocessing:
-            print("DISABILITATO")
+            print("DISABLED")
             cpu_count = 1
             list_edges_partial = [ctpc_algo(n,total_vars_numb) for n in self._nodes]
         else:
-            if processes_number is not None and cpu_count < processes_number:
+            if processes_number is not None and cpu_count > processes_number:
                 cpu_count = processes_number
 
+            print(f"CPU COUNT: {cpu_count}")
             with concurrent.futures.ProcessPoolExecutor(max_workers=cpu_count) as executor:
                 list_edges_partial = executor.map(ctpc_algo,
                                                                  self._nodes,

PyCTBN/PyCTBN/estimators/structure_score_based_estimator.py

@@ -96,12 +96,11 @@ class StructureScoreBasedEstimator(StructureEstimator):
 
 
         'get the number of CPU'
-        cpu_count = multiprocessing.cpu_count()
-        print(f"CPU COUNT: {cpu_count}")
+        cpu_count = multiprocessing.cpu_count()        
 
         if disable_multiprocessing:
             cpu_count = 1
-        elif processes_number is not None and cpu_count < processes_number:
+        elif processes_number is not None and cpu_count > processes_number:
             cpu_count = processes_number
 
         
@@ -111,6 +110,8 @@ class StructureScoreBasedEstimator(StructureEstimator):
         #with get_context("spawn").Pool(processes=cpu_count) as pool:
         #with multiprocessing.Pool(processes=cpu_count) as pool:
 
+        print(f"CPU COUNT: {cpu_count}")
+
         'Estimate the best parents for each node'
         if disable_multiprocessing:
             list_edges_partial = [estimate_parents(n,max_parents,iterations_number,patience,tabu_length,tabu_rules_duration,optimizer) for n in self._nodes]