time sclaing

exec/marseille_execution_time_scaling.cpp

@@ -10,10 +10,10 @@
 int main() {
     std::ofstream data_log("output/marseille_execution_time_scaling.csv");
     data_log << std::fixed << std::setprecision(6);
-    data_log << "wastelands "
-              << "local_inc "
-              << "greedy_dec "
-              << "MIP " << std::endl;
+    data_log << "wastelands,"
+              << "local_inc,"
+              << "greedy_dec,"
+              << "mip" << std::endl;
 
     std::vector<int> nbs_wastelands;
     for(int i = 10; i <= 200; i += 10) nbs_wastelands.push_back(i);

plot_scripts/marseille_execution_time_scaling.py

+import matplotlib.pyplot as plt
+import csv
+import numpy as np
+import statistics
+
+def readCSV(file_name, delimiter=','):
+    file = csv.DictReader(open(file_name), delimiter=delimiter)
+    return list([row for row in file])
+
+rows = readCSV('output/marseille_execution_time_scaling_tmp.csv')
+
+
+x_datas = [int(float(row["wastelands"])) for row in rows]
+
+def get_datas(name, linstyle, maker_size, t):
+    return ((name, (linstyle,maker_size)), (x_datas,
+        np.array([float(row[t])/1000 for row in rows]) ))
+
+datas = [
+    get_datas("local incremental", "s-",6, "local_inc"),
+    get_datas("greedy decremental", "o-",6, "greedy_dec"),
+    get_datas("MIP", "P-",6, "mip")
+]
+
+
+fig_size = plt.rcParams["figure.figsize"]
+fig_size[0] = 10
+fig_size[1] = 5
+plt.rcParams["figure.figsize"] = fig_size
+
+# plt.subplots_adjust(left=0.100, right=0.95, top=0.92, bottom=0.150)
+
+plt.rcParams.update({'font.size': 16})
+
+xmin = min(x_datas)
+xmax = max(x_datas)
+
+plt.xlim(xmin , xmax)
+
+ymin = 0
+ymax = max([float(row["mip"])/1000 for row in rows])
+yrange = ymax - ymin
+
+y_border_percent = 7.5
+y_bottom = ymin - y_border_percent * yrange / 100
+y_top = ymax + y_border_percent * yrange / 100
+
+plt.ylim(y_bottom, y_top)
+
+
+# plt.title("quebec-{}-{}-ECA value vs available budget.pdf".format(orig, median))
+plt.ylabel('computation time in seconds', rotation=90, fontweight ='bold')
+plt.xlabel("number of unbuilt lots", fontweight ='bold')
+
+for ((label,(linestyle,marker_size)),(xdatas,ydatas)) in datas:
+    plt.plot(xdatas, ydatas, linestyle, markersize=marker_size, label=label)
+    
+legend = plt.legend(loc='upper left', shadow=True, fontsize='medium')
+
+plt.tight_layout()
+plt.show()
+plt.savefig("output/marseille_execution_time_scaling_tmp.pdf", dpi=500)
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