computation on CPLEX

results_KO_GLC/fast_plot_lim.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from matplotlib.lines import Line2D
+
+plt.rcParams.update(
+    {"text.usetex": True, "font.family": "serif", "font.size": 12}
+)
+
+
+def load_data(solver,case, lim = 0):
+    if lim == 0:
+        file = f"results_{solver}__{case}_Integer.pkl"
+    else:
+        file = f"results_{solver}_ilp_t_lim{lim}__{case}_Integer.pkl"
+    results = pd.read_pickle(rf'{file}')
+    return results
+
+markers = ["o", "x", "*", "^", "^", "d", "v", "s", "*", "^"]
+sizes = [40,60,60,40,40]
+
+def plotting_comparisons(c_solvers,cases, dict_times):
+    q_solver = "cqm"
+
+    for case in cases:
+        times = dict_times[case]
+        for solver in c_solvers:
+            fig, (ax1,ax2,ax3) = plt.subplots(3,figsize=(2.75, 5.5),sharex=True, tight_layout= True)
+            for t,m,s in zip(times,markers, sizes): 
+                file = f"results{t}__{q_solver}_{case}_Integer.pkl"
+                results1 = pd.read_pickle(rf'{file}')
+                N = results1["samples"]
+                x1 = [results1[i]["info"]["run_time"]/(1000000) for i in range(N)]
+                y1 = [results1[i]["objective"]*results1["d_max"] for i in range(N)]
+                x2 = [results1[i]["info"]["qpu_access_time"]/1000000 for i in range(N)]
+
+                instance = [i for i in range(N)]
+
+                colors1 = []
+                label2 = q_solver.upper() + " t\_min =" f" {t}"
+                for i in range(N):
+                    if results1[i]["feasible"] is True:
+                        colors1.append("green")
+                    else:
+                        colors1.append("red")
+                        # label2 = "_"
+
+
+                ax1.scatter(instance, y1, s=s, marker = m, c=colors1, alpha=0.5, label = f"{label2}")
+                ax2.scatter(instance, x1, s=s, marker = m, c=colors1, alpha=0.5, label = f"{label2}")
+                ax3.scatter(instance, x2, s=s, marker = m, c=colors1, alpha=0.5, label = f"{label2}")
+
+
+
+            results = load_data(solver,case)
+
+            int_vars = [results[i]["int_vars"] for i in range(N)]
+            order_vars = [results[i]["order_vars"] for i in range(N)]
+            constraints = [results[i]["constraints"] for i in range(N)]
+
+            print("mean n.o. int vars", np.mean(int_vars))
+            print("mean n.o. order vars", np.mean(order_vars))
+            print("mean n.o. constraints", np.mean(constraints))
+
+
+            results_l = load_data(solver,case, lim = 5.0)
+
+            x = [results[i]["comp_time_seconds"] for i in range(N)]
+            y = [results[i]["objective"] for i in range(N)]
+
+
+            x_l = [results_l[i]["comp_time_seconds"] for i in range(N)]
+            y_l = [results_l[i]["objective"] for i in range(N)]
+
+            colors = []
+            for i in range(N):
+                if results[i]["feasible"] is True:
+                    colors.append("green")
+                else:
+                    colors.append("red")
+
+            label1 = solver.replace("_CMD"," ")
+
+            ax1.set_title(f"line {case}")
+
+            ax1.plot(instance, y, c="blue", marker = "*", linestyle= ":",  alpha=0.5, label = f"{label1}")
+            ax1.scatter(instance, y, s=40, c=colors, marker = "*",  alpha=0.25)
+
+
+            ax1.plot(instance, y_l, c="black", marker = "s", linestyle= ":",  alpha=0.3, label = f"{label1}_lim")
+
+
+            if case == 1:
+                ax1.text(10,95, "a)", fontsize = 14)
+            elif case == 2:
+                ax1.text(10.5,480, "b)", fontsize = 14)
+            else:
+                ax1.text(10.5,80, "c)", fontsize = 14)
+
+            fig.subplots_adjust(bottom=0.2, left = 0.2)
+            # plt.xlabel("instance")
+            ax1.set_ylabel("objective [min]")
+            #ax1.set_ylim(bottom=-25)
+            ax3.set_ylim(bottom=-0.005, top = 0.1)
+            ax3.plot(instance, [0. for _ in instance], c="blue", marker = "*", linestyle= ":",  alpha=0.5, label = f"{label1}")
+            ax3.plot(instance, [0. for _ in instance], c="black", marker = "s", linestyle= ":",  alpha=0.2, label = f"{label1}_lim")
+
+            ax2.plot(instance, x_l, c="black", marker = "s", linestyle= ":",  alpha=0.3, label = f"{label1}_lim")
+            ax2.plot(instance, x, c="blue", marker = "*", linestyle= ":",  alpha=0.5, label = f"{label1}")
+            ax2.scatter(instance, x, s=40, c=colors, marker = "*",  alpha=0.25)
+            fig.subplots_adjust(bottom=0.2, left = 0.2)
+            ax3.set_xlabel("instance")
+            ax2.set_ylabel("comp. time [s]")
+            ax3.set_ylabel("QPU acess time [s]")
+
+            ax3.legend(ncol = 1, loc=2, fontsize=7)
+
+            #ax2.legend(ncol = 2)
+            #ax3.legend()
+
+
+            plt.savefig(f"{solver}_{q_solver}_compt_case{case}b_lim.pdf")
+
+if __name__ == "__main__":
+    cases = list(range(1,4))
+    c_solvers = [r"CPLEX_CMD"]
+    dict_times = {1: [5, 20], 2:[5, 20], 3: [5,20]}
+
+
+    plotting_comparisons(c_solvers,cases,dict_times)