Merge remote-tracking branch 'origin/master'

AGV_quantum/process_results.py

@@ -106,7 +106,8 @@ def analyze_constraints(lp, sample):
             result[f"eq_{num_eq}"] = expr <= lp.b_ub[i]
             num_eq += 1
 
-    return result, sum(x is False for x in result.values())
+    not_feas = list(result.values()).count(False)
+    return result, not_feas
 
 
 def process_result(sampleset: list):

README.md

@@ -4,13 +4,14 @@
 
 In directory ```examples``` there are AGVs scheduling problems. In order of increasing size of the problem these are:
 
-- example_tiny.py         - 2 AGVs reduced size
-- example_smallest.py     - 2 AGVs, 4 zones
-- example_small.py        - 4 AGVs, 4 zones
-- example_medium_small.py - 6 AGVs, 7 zones
-- example_medium.py       - 7 AGVs, 7 zones
-- example_large.py        - 12 AGVs, 7 zones
-- example_largest.py      - 15 AGVs, 7 zones
+- example_tiny.py          - 2 AGVs reduced size
+- example_smallest.py      - 2 AGVs, 4 zones
+- example_small.py         - 4 AGVs, 4 zones
+- example_medium_small.py  - 6 AGVs, 7 zones
+- example_medium.py        - 7 AGVs, 7 zones
+- example_large.py         - 12 AGVs, 7 zones
+- example_largest.py       - 15 AGVs, 7 zones
+- example_largest_ever.py  - 21 AGVs, 7 zones
 
 
 ## Usage 

check_sol.py

@@ -65,8 +65,8 @@ def csv_write_hist(file_name, hist, key1 = "value", key2 = "count"):
 cwd = os.getcwd()
 train_diagram = False
 
-#count = "_10"
-count = "_1"
+count = "_10"
+count = ""
 
 if args.example == "tiny":
     sol_folder = Path("annealing_results/tiny_2_AGV")
@@ -111,7 +111,15 @@ def csv_write_hist(file_name, hist, key1 = "value", key2 = "count"):
         lp.to_bqm_qubo_ising(p)
         sampleset = lp.interpreter(sampleset)
         solutions = get_results(sampleset, lp)
-        #print(solutions)
+        print(solutions[0]['energy'])
+
+        constraints = len(solutions[0]['feas_constraints'][0])
+        not_feas = solutions[0]['feas_constraints'][1]
+
+        print("feasible", solutions[0]['feasible'])
+        print("n.o. constraints", constraints)
+        print("broken feas", not_feas)
+        print("precentage broken", not_feas/constraints)
 
 
     elif hybrid == "cqm":

make_statistics.py

@@ -9,8 +9,6 @@
 
 
 
-n_vars = {2:16, 4:36, }
-
 
 def read_single_file(size, k, solver):
 
@@ -81,7 +79,7 @@ def read_smapleset(size, k, solver):
 
 def print_info(key, sizes):
     print(key)
-    no_qbits = []
+    no_vars = []
     means = []
     stds = []
     for size in sizes:
@@ -96,19 +94,19 @@ def print_info(key, sizes):
         m = np.mean(series/k)
         s = np.std(series/k)
 
-        no_qbits.append(vars)
+        no_vars.append(vars)
         means.append(m)
         stds.append(s)
 
 
         print(vars, m, m-s, m+s)
 
-    return no_qbits, means, stds
+    return no_vars, means, stds
 
 
 def print_obj(sizes, examples, optimum):
 
-    no_qbits = []
+    no_vars = []
     means = []
     stds = []
 
@@ -123,19 +121,19 @@ def print_obj(sizes, examples, optimum):
         print(vars, m,m-s, m+s)
 
 
-        no_qbits.append(vars)
+        no_vars.append(vars)
         means.append(m)
         stds.append(s)
 
         print(obj)
         print(np.mean(obj), np.std(obj))
 
-    return no_qbits, means, stds
+    return no_vars, means, stds
 
 
 def print_feas(sizes, examples):
 
-    no_qbits = []
+    no_vars = []
     means = []
     stds = []
 
@@ -149,25 +147,25 @@ def print_feas(sizes, examples):
         print(vars, m,m-s, m+s)
 
 
-        no_qbits.append(vars)
+        no_vars.append(vars)
         means.append(m)
         stds.append(s)
 
 
-    return no_qbits, means, stds
+    return no_vars, means, stds
 
 
 
 
-def csv_write(file_name, no_qbits, means, stds):
+def csv_write(file_name, no_vars, means, stds):
     with open(file_name, 'w', newline='', encoding="utf-8") as csvfile:
-        fieldnames = ["no_qbits", "mean", "mean-std", "mean+std"]
+        fieldnames = ["no_vars", "mean", "mean-std", "mean+std"]
 
         writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-        for i,v in enumerate(no_qbits):
+        for i,v in enumerate(no_vars):
             m = means[i]
             s = stds[i]
-            writer.writerow({"no_qbits": v, "mean": np.round(m, 4), "mean-std": np.round(m-s, 4), "mean+std": np.round(m+s, 4)})
+            writer.writerow({"no_vars": v, "mean": np.round(m, 4), "mean-std": np.round(m-s, 4), "mean+std": np.round(m+s, 4)})
 
 
 
@@ -181,37 +179,35 @@ def get_no_vars(size):
 
     #sizes = [2,4,6,7,12,15,21]
 
-    optimum = {2:4, 4: 8.2, 6: 3.22, 7: 4.25, 12: 9.175,  15: 10.975 }
+    optimum = {2:4, 4: 8.2, 6: 3.22, 7: 4.25, 12: 9.175,  15: 10.975, 21:  17.625}
 
     examples = {2:"smallest", 4:"small", 6:"medium_small", 7:"medium", 12:"large", 15:"largest", 21:"largest_ever"}
     sizes = examples.keys()
 
 
     key = 'qpu_access_time'
-    no_qbits, means, stds = print_info(key, sizes)
+    no_vars, means, stds = print_info(key, sizes)
 
     file = "article_plots/CQM_QPU_time.csv"
-    csv_write(file, no_qbits, means, stds)
+    csv_write(file, no_vars, means, stds)
 
     key = 'run_time'
-    no_qbits, means, stds = print_info(key, sizes)
+    no_vars, means, stds = print_info(key, sizes)
 
     file = "article_plots/time_CQM.csv"
-    csv_write(file, no_qbits, means, stds)
+    csv_write(file, no_vars, means, stds)
 
-    no_qbits, means, stds = print_feas(sizes, examples)
+    no_vars, means, stds = print_feas(sizes, examples)
 
     file = "article_plots/feasibility_CQM.csv"
-    csv_write(file, no_qbits, means, stds)
+    csv_write(file, no_vars, means, stds)
 
-    # TODO we do not have yet CPLEX largest ever
-    examples = {2:"smallest", 4:"small", 6:"medium_small", 7:"medium", 12:"large", 15:"largest"}
     sizes = examples.keys()
 
-    no_qbits, means, stds = print_obj(sizes, examples, optimum)
+    no_vars, means, stds = print_obj(sizes, examples, optimum)
 
     file = "article_plots/obj_CQM.csv"
-    csv_write(file, no_qbits, means, stds)
+    csv_write(file, no_vars, means, stds)
 
 
 

run_examples.py

@@ -106,14 +106,15 @@
 
         model.to_bqm_qubo_ising(p)
 
+        print("n.o. qubits", model._count_qubits())
+        print("n.o. quandratic couplings", model._count_quadratic_couplings())
+        print("n.o. linear fields", model._count_linear_fields())
+
         # check if results are saved
         is_file = os.path.isfile(os.path.join(save_path, f"new_{hybrid}_info{count}.pkl"))
         if is_file:
             print(".......... files exist ............")
-            if hybrid == "bqm":
-                print("n.o. qubits", model._count_qubits())
-                print("n.o. quandratic couplings", model._count_quadratic_couplings())
-                print("n.o. linear fields", model._count_linear_fields())
+
         else:
             model.to_cqm()
             cwd = os.getcwd()