diff --git a/exec/execution_times/aude.cpp b/exec/execution_times/aude.cpp
index 0e40c09d93204762f3996c04aeb534cb2fd7aa9f..3fe050e8a851f029b7e829568b19b78f024b4ede 100755
--- a/exec/execution_times/aude.cpp
+++ b/exec/execution_times/aude.cpp
@@ -19,7 +19,7 @@ double eval(T && ls) {
}
int main() {
- std::ofstream data_log("output/aude_analysis.csv");
+ std::ofstream data_log("output/time_aude.csv");
data_log << std::fixed << std::setprecision(6);
data_log << "budget,budget_percent,base_ECA,max_delta_ECA,pl_eca_2_ECA,pl_"
"eca_2_obj,pl_eca_2_variables,pl_2_constraints,pl_2_entries,pl_"
diff --git a/exec/execution_times/biorevaix.cpp b/exec/execution_times/biorevaix.cpp
index e141409074b5565b3e9c75d494648c8f683f6f8f..b1d8742b08f60c6451d7ee5d3f9d64452d88f491 100755
--- a/exec/execution_times/biorevaix.cpp
+++ b/exec/execution_times/biorevaix.cpp
@@ -82,7 +82,7 @@ Instance make_instance(const double dist_coef, const double restoration_coef) {
}
int main() {
- std::ofstream data_log("output/biorevaix_analysis.csv");
+ std::ofstream data_log("output/time_biorevaix.csv");
data_log << std::fixed << std::setprecision(6);
data_log << "budget,budget_percent,base_ECA,max_delta_ECA,pl_eca_2_ECA,pl_"
"eca_2_obj,pl_eca_2_variables,pl_2_constraints,pl_2_entries,pl_"
diff --git a/exec/execution_times/contraction_benefits.cpp b/exec/execution_times/contraction_benefits.cpp
index 5a15961eabb08b7c0acd8ab955f4f786412cf9e8..6e66eade43648e89bebfbcf1a98573bcb6948198 100755
--- a/exec/execution_times/contraction_benefits.cpp
+++ b/exec/execution_times/contraction_benefits.cpp
@@ -28,7 +28,7 @@ void parse_quebec(MutableLandscape & landscape, double pow, double median) {
std::vector<MutableLandscape::Node> node_correspondance;
- io::CSVReader<3> patches("data/quebec_leam_v3/raw/sommets_leam_v3.csv");
+ io::CSVReader<3> patches("datas/Montreal_hare/sommets_leam_v3.csv");
patches.read_header(io::ignore_extra_column, "area", "xcoord", "ycoord");
double area, xcoord, ycoord;
while(patches.read_row(area, xcoord, ycoord)) {
@@ -37,7 +37,7 @@ void parse_quebec(MutableLandscape & landscape, double pow, double median) {
node_correspondance.push_back(u);
}
- io::CSVReader<3> links("data/quebec_leam_v3/raw/aretes_leam_v3.csv");
+ io::CSVReader<3> links("datas/Montreal_hare/aretes_leam_v3.csv");
links.read_header(io::ignore_extra_column, "from", "to", "Dist");
int from, to;
double Dist;
@@ -88,26 +88,16 @@ MutableLandscape * make_landscape(const MutableLandscape & landscape,
}
int main() {
- std::ofstream data_log("output/data.log");
+ std::ofstream data_log("output/contraction_benefits.csv");
data_log << std::fixed << std::setprecision(6);
- data_log << "center "
- << "percent_arcs "
- << "nb_vars "
- << "nb_constraints "
- << "nb_elems "
- << "nb_vars_contract "
- << "nb_constraints_contract "
- << "nb_elems_contract " << std::endl;
+ data_log << "center,percent_arcs,nb_vars,nb_constraints,nb_elems,nb_vars_"
+ "contract,nb_constraints_contract,nb_elems_contract"
+ << std::endl;
MutableLandscape landscape;
parse_quebec(landscape, 1, 2300);
const MutableLandscape::Graph & graph = landscape.getNetwork();
- // StdMutableLandscapeParser::get().write(*landscape, "output",
- // "out_landscape");
-
- MyContractionAlgorithm alg2;
-
RandomChooser<MutableLandscape::Node> node_chooser;
for(MutableLandscape::NodeIt u(graph); u != lemon::INVALID; ++u)
node_chooser.add(u, 1);
@@ -116,9 +106,9 @@ int main() {
const int n_to_test[] = {500};
Solvers::PL_ECA_2 * pl_eca_2 = new Solvers::PL_ECA_2();
- (*pl_eca_2).setLogLevel(0);
+ (*pl_eca_2).setLogLevel(0).setTimeout(0);
Solvers::PL_ECA_3 * pl_eca_3 = new Solvers::PL_ECA_3();
- (*pl_eca_3).setLogLevel(0).setTimeout(3600);
+ (*pl_eca_3).setLogLevel(0).setTimeout(0);
for(int i = 0; i < nb_tests; i++) {
MutableLandscape::Node center = node_chooser.pick();
@@ -134,20 +124,21 @@ int main() {
std::cout << "nodes : 500" << std::endl
<< "arcs : " << m << std::endl;
- RandomChooser<MutableLandscape::Arc> arc_chooser;
+ RandomChooser<MutableLandscape::Arc> arc_chooser(i);
for(MutableLandscape::ArcIt a(sub_graph); a != lemon::INVALID; ++a)
arc_chooser.add(a, 1);
RestorationPlan<MutableLandscape> plan(*sub_landscape);
for(int percent_arcs = 0; percent_arcs <= 100; percent_arcs += 5) {
+ plan.initElementIDs();
Solution solution2 = pl_eca_2->solve(*sub_landscape, plan, 0);
Solution solution3 = pl_eca_3->solve(*sub_landscape, plan, 0);
- data_log << graph.id(center) << " " << percent_arcs << " "
- << solution2.nb_vars << " " << solution2.nb_constraints
- << " " << solution2.nb_elems << " "
- << solution3.nb_vars << " " << solution3.nb_constraints
- << " " << solution3.nb_elems << std::endl;
+ data_log << graph.id(center) << "," << percent_arcs << ","
+ << solution2.nb_vars << "," << solution2.nb_constraints
+ << "," << solution2.nb_elems << ","
+ << solution3.nb_vars << "," << solution3.nb_constraints
+ << "," << solution3.nb_elems << std::endl;
if(percent_arcs == 100) break;
for(int i = 0; i < (m / 20); ++i) {
diff --git a/exec/execution_times/marseille.cpp b/exec/execution_times/marseille.cpp
index 9988541bacaf81acb370188c230e13a88e38c014..43f504b7fa7ece2f690c806ff8dfef79ba753310 100755
--- a/exec/execution_times/marseille.cpp
+++ b/exec/execution_times/marseille.cpp
@@ -19,7 +19,7 @@ double eval(T && ls) {
}
int main() {
- std::ofstream data_log("output/marseille_analysis.csv");
+ std::ofstream data_log("output/time_marseille.csv");
data_log << std::fixed << std::setprecision(6);
data_log << "budget,budget_percent,base_ECA,max_delta_ECA,pl_eca_2_ECA,pl_"
"eca_2_obj,pl_eca_2_variables,pl_2_constraints,pl_2_entries,pl_"
diff --git a/exec/execution_times/quebec.cpp b/exec/execution_times/quebec.cpp
index 768980fe3bc934357edbd645caacfa562fc525af..6e88288e6b6033e167c9421ab7ba585acadd7667 100755
--- a/exec/execution_times/quebec.cpp
+++ b/exec/execution_times/quebec.cpp
@@ -19,7 +19,7 @@ double eval(T && ls) {
}
int main() {
- std::ofstream data_log("output/quebec_analysis.csv");
+ std::ofstream data_log("output/time_quebec.csv");
data_log << std::fixed << std::setprecision(6);
data_log << "budget,budget_percent,base_ECA,max_delta_ECA,pl_eca_2_ECA,pl_"
"eca_2_obj,pl_eca_2_variables,pl_2_constraints,pl_2_entries,pl_"
diff --git a/exec/quality_of_solutions/aude.cpp b/exec/quality_of_solutions/aude.cpp
index e8cafd26b710d4cdb8d4105b54ee7359bdc4a4d4..2b7f6bcf48c31e0d2c5c7bfe2776104bb23a0cbf 100755
--- a/exec/quality_of_solutions/aude.cpp
+++ b/exec/quality_of_solutions/aude.cpp
@@ -23,7 +23,7 @@ double eval(T && ls) {
}
int main() {
- std::ofstream data_log("output/aude_analysis.csv");
+ std::ofstream data_log("output/qos_aude.csv");
data_log << std::fixed << std::setprecision(6);
data_log
<< "median,restored_prob,budget,budget_percent,base_ECA,max_delta_"
diff --git a/exec/quality_of_solutions/biorevaix.cpp b/exec/quality_of_solutions/biorevaix.cpp
index ee02e9465faaf2fd972411c25d6f3be24969dd35..26ca60f2c087d52ebce09c955a1cf016d75c4294 100755
--- a/exec/quality_of_solutions/biorevaix.cpp
+++ b/exec/quality_of_solutions/biorevaix.cpp
@@ -84,7 +84,7 @@ Instance make_instance(const double dist_coef, const double restoration_coef) {
}
int main() {
- std::ofstream data_log("output/biorevaix_analysis.csv");
+ std::ofstream data_log("output/qos_biorevaix.csv");
data_log << std::fixed << std::setprecision(6);
data_log
<< "dist_coef,restoration_coef,budget,budget_percent,base_ECA,max_"
diff --git a/exec/quality_of_solutions/marseille.cpp b/exec/quality_of_solutions/marseille.cpp
index 23efe3828c25404fe249ebd2f85d21d3fda936f2..a40492112b43a596a710f2b2106ea38790c3ca6b 100755
--- a/exec/quality_of_solutions/marseille.cpp
+++ b/exec/quality_of_solutions/marseille.cpp
@@ -23,7 +23,7 @@ double eval(T && ls) {
}
int main() {
- std::ofstream data_log("output/marseille_analysis.csv");
+ std::ofstream data_log("output/qos_marseille.csv");
data_log << std::fixed << std::setprecision(6);
data_log << "median,budget,budget_percent,base_ECA,max_delta_ECA,bogo_avg_"
"delta_ECA,naive_inc_delta_ECA,naive_dec_delta_ECA,glutton_inc_"
diff --git a/exec/quality_of_solutions/quebec.cpp b/exec/quality_of_solutions/quebec.cpp
index e0b5096959db9aff4a0ca997fa09be5b0d12a8ab..51d31c3df432001fb74f38a85116e6882b5b548a 100755
--- a/exec/quality_of_solutions/quebec.cpp
+++ b/exec/quality_of_solutions/quebec.cpp
@@ -23,7 +23,7 @@ double eval(T && ls) {
}
int main() {
- std::ofstream data_log("output/quebec_analysis.csv");
+ std::ofstream data_log("output/qos_quebec.csv");
data_log << std::fixed << std::setprecision(6);
data_log
<< "median,budget,budget_percent,base_ECA,max_delta_"
diff --git a/plot_scripts/contraction_benefits.py b/plot_scripts/contraction_benefits.py
new file mode 100755
index 0000000000000000000000000000000000000000..789e09d86ad4bc77e7e510365e20ce89943cfffd
--- /dev/null
+++ b/plot_scripts/contraction_benefits.py
@@ -0,0 +1,67 @@
+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/contraction_benefits.csv', ",")
+
+
+def substract(a, b):
+ return [x-y for x,y in zip(a,b)]
+def divide(a, b):
+ return [x/y if y > 0 else 1 for x,y in zip(a,b)]
+
+
+x_datas = np.array(range(0,105,5))
+
+def get_datas(name, linstyle, maker_size, t):
+ return ((name, (linstyle,maker_size)), (x_datas,
+ np.array([statistics.mean([100*(1-float(row["nb_{}_contract".format(t)])/float(row["nb_{}".format(t)])) for row in rows if int(row["percent_arcs"]) == percent]) for percent in x_datas]) ))
+
+datas = [
+ get_datas("constraints", "s-",8, "constraints"),
+ get_datas("variables", "o-",8, "vars"),
+ get_datas("non-zero entries", "P-",8, "elems")
+]
+
+
+fig_size = plt.rcParams["figure.figsize"]
+fig_size[0] = 10
+fig_size[1] = 5
+plt.rcParams["figure.figsize"] = fig_size
+
+plt.subplots_adjust(left=0.125, right=0.95, top=0.92, bottom=0.13)
+
+plt.rcParams.update({'font.size': 16})
+
+xmin = min(x_datas)
+xmax = max(x_datas)
+
+plt.xlim(xmin , xmax)
+
+ymin = 0
+ymax = 100
+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('percentage of elements\nremoved by the preprocessing', rotation=90, fontweight ='bold')
+plt.xlabel("percentage of restored arcs", 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 right', shadow=True, fontsize='medium')
+
+
+plt.savefig("output/contraction_benefits.pdf", dpi=500)
\ No newline at end of file
diff --git a/plot_scripts/execution_times_tiled.py b/plot_scripts/execution_times_tiled.py
new file mode 100644
index 0000000000000000000000000000000000000000..319c180b98f8de8f06a06967291a2012e55ca098
--- /dev/null
+++ b/plot_scripts/execution_times_tiled.py
@@ -0,0 +1,48 @@
+import matplotlib.pyplot as plt
+import csv
+import numpy as np
+
+def readCSV(file_name, delimiter=','):
+ file = csv.DictReader(open(file_name), delimiter=delimiter)
+ return list([row for row in file])
+
+
+def plot(name, subplot, file_name):
+ subplot.set_title(name)
+
+ rows = readCSV('output/{}'.format(file_name))
+
+ def get_datas(column_name):
+ return ([float(row['budget_percent']) for row in rows], [float(row[column_name]) / 1000 for row in rows])
+
+ datas = [
+ (("mip", "v-"), get_datas('pl_2_time')),
+ (("mip preprocessed", "^-"), get_datas('pl_3_time'))
+ ]
+
+ for ((label,linestyle),(xdatas,ydatas)) in datas:
+ subplot.plot(xdatas, ydatas, linestyle, label=label, markersize=3.25)
+
+ subplot.set_yscale('log')
+
+
+fig, axs = plt.subplots(2, 2)
+plot("Aude", axs[0, 0], "time_aude.csv")
+plot("Montreal", axs[0, 1], "time_quebec_analysis.csv")
+plot("Aix", axs[1, 0], "time_biorevaix_analysis.csv")
+plot("Marseille", axs[1, 1], "time_marseille_analysis.csv")
+
+axs[0, 0].set(xlabel=None, ylabel='execution time (s)')
+axs[0, 1].set(xlabel=None, ylabel=None)
+axs[1, 0].set(xlabel='budget percent', ylabel='execution time (s)')
+axs[1, 1].set(xlabel='budget percent', ylabel=None)
+
+fig.subplots_adjust(bottom=0.2, top=0.91, wspace=0.2, hspace=0.35)
+axs.flatten()[-2].legend(loc='upper center', bbox_to_anchor=(1.05, -0.35), ncol=3)
+
+fig.set_size_inches(8,5)
+plt.rcParams.update({'font.size': 18})
+
+# plt.tight_layout()
+plt.savefig("figures/execution_times.pdf", dpi=500)
+plt.show()
\ No newline at end of file
diff --git a/plot_scripts/optimum_ratios_tiled.py b/plot_scripts/optimum_ratios_tiled.py
new file mode 100644
index 0000000000000000000000000000000000000000..20aac40aef56bff6c75930748ce9ba9f95332107
--- /dev/null
+++ b/plot_scripts/optimum_ratios_tiled.py
@@ -0,0 +1,54 @@
+import matplotlib.pyplot as plt
+import csv
+import numpy as np
+
+def readCSV(file_name, delimiter=','):
+ file = csv.DictReader(open(file_name), delimiter=delimiter)
+ return list([row for row in file])
+
+
+def plot(name, subplot, file_name):
+ rows = readCSV('output/{}'.format(file_name))
+
+ max_delta = [float(row['max_delta_ECA']) for row in rows]
+
+ def get_datas(solver_name):
+ return ([float(row['budget_percent']) for row in rows], [float(row[solver_name]) / float(row['opt_delta_ECA']) for row in rows])
+ # return ([float(row['budget_percent']) for row in rows], [(float(row[solver_name]) - float(row['bogo_avg_delta_ECA'])) / (float(row['opt_delta_ECA']) - float(row['bogo_avg_delta_ECA'])) for row in rows])
+
+ datas = [
+ (("static decremental", "v--"), get_datas('naive_dec_delta_ECA')),
+ (("static incremental", "^--"), get_datas('naive_inc_delta_ECA')),
+ (("decremental greedy", "v-"), get_datas('glutton_dec_delta_ECA')),
+ (("incremental greedy", "^-"), get_datas('glutton_inc_delta_ECA')),
+ (("optimal", "s-"), get_datas('opt_delta_ECA'))
+ ]
+
+ subplot.set_title(name)
+
+ for ((label,linestyle),(xdatas,ydatas)) in datas:
+ subplot.plot(xdatas, ydatas, linestyle, label=label, markersize=3.25)
+
+
+
+
+fig, axs = plt.subplots(2, 2)
+plot("Aude", axs[0, 0], "qos_aude.csv")
+plot("Montreal", axs[0, 1], "qos_quebec.csv")
+plot("BiorevAix", axs[1, 0], "qos_biorevaix.csv")
+plot("Marseille", axs[1, 1], "qos_marseille.csv")
+
+axs[0, 0].set(xlabel=None, ylabel='optimum ratio')
+axs[0, 1].set(xlabel=None, ylabel=None)
+axs[1, 0].set(xlabel='budget percent', ylabel='optimum ratio')
+axs[1, 1].set(xlabel='budget percent', ylabel=None)
+
+fig.subplots_adjust(bottom=0.3, wspace=0.2, hspace=0.35)
+axs.flatten()[-2].legend(loc='upper center', bbox_to_anchor=(1.05, -0.35), ncol=3)
+
+fig.set_size_inches(8,6)
+plt.rcParams.update({'font.size': 18})
+
+# plt.tight_layout()
+plt.savefig("figures/optimum_ratios.pdf", dpi=500)
+# plt.show()
\ No newline at end of file
diff --git a/plot_scripts/pl_sizes_table.py b/plot_scripts/pl_sizes_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..abcab2636c294e69057f13fb95060b7c88b8cbe9
--- /dev/null
+++ b/plot_scripts/pl_sizes_table.py
@@ -0,0 +1,59 @@
+import matplotlib.pyplot as plt
+import csv
+import numpy as np
+
+
+def readCSV(file_name, delimiter=','):
+ file = csv.DictReader(open(file_name), delimiter=delimiter)
+ return list([row for row in file])
+
+
+def get_datas(rows, column_name):
+ return [float(row[column_name]) for row in rows]
+
+
+def avg(arr):
+ if arr[0] != arr[0]:
+ return "--"
+ sum = 0
+ cpt = 0
+ for v in arr:
+ if v == v:
+ sum += v
+ cpt = cpt + 1
+ return int(sum / cpt / 10) / 100
+ # return int(sum(arr) / len(arr) / 10) / 100
+
+
+print("\\begin{tabular}{crrrrrrr}")
+
+print("\t\\multirow{2}{*}{case} & \\multicolumn{3}{c}{MIP} & \\multicolumn{4}{c}{preprocessed MIP}\\tabularnewline")
+
+print("\t\\cmidrule(lr){2-4}\\cmidrule(lr){5-8}")
+
+print("\t& \\#var & \\#const & time & \\#var & \\#const & p. time & time \\tabularnewline")
+
+print("\t\\hline")
+
+
+aude_rows = readCSV('output/time_aude.csv')
+print("\tAude & {} & {} & {} & {} & {} & {} & {}\\tabularnewline".format(int(get_datas(aude_rows, "pl_2_variables")[0]), int(get_datas(aude_rows, "pl_2_constraints")[0]), avg(get_datas(aude_rows, "pl_2_time")), int(
+ get_datas(aude_rows, "pl_3_variables")[0]), int(get_datas(aude_rows, "pl_3_constraints")[0]), avg(get_datas(aude_rows, "pl_3_preprocessing_time")), avg(get_datas(aude_rows, "pl_3_time"))))
+
+
+quebec_rows = readCSV('output/time_quebec.csv')
+print("\tMontereal & {} & {} & {} & {} & {} & {} & {}\\tabularnewline".format(int(get_datas(quebec_rows, "pl_2_variables")[0]), int(get_datas(quebec_rows, "pl_2_constraints")[0]), avg(get_datas(quebec_rows, "pl_2_time")), int(
+ get_datas(quebec_rows, "pl_3_variables")[0]), int(get_datas(quebec_rows, "pl_3_constraints")[0]), avg(get_datas(quebec_rows, "pl_3_preprocessing_time")), avg(get_datas(quebec_rows, "pl_3_time"))))
+
+
+biorevaix_rows = readCSV('output/time_biorevaix.csv')
+print("\tAix & {} & {} & {} & {} & {} & {} & {}\\tabularnewline".format(int(get_datas(biorevaix_rows, "pl_2_variables")[0]), int(get_datas(biorevaix_rows, "pl_2_constraints")[0]), avg(get_datas(biorevaix_rows, "pl_2_time")), int(
+ get_datas(biorevaix_rows, "pl_3_variables")[0]), int(get_datas(biorevaix_rows, "pl_3_constraints")[0]), avg(get_datas(biorevaix_rows, "pl_3_preprocessing_time")), avg(get_datas(biorevaix_rows, "pl_3_time"))))
+
+
+marseille_rows = readCSV('output/time_marseille.csv')
+print("\tMarseille & {} & {} & {} & {} & {} & {} & {}\\tabularnewline".format(int(get_datas(marseille_rows, "pl_2_variables")[0]), int(get_datas(marseille_rows, "pl_2_constraints")[0]), avg(get_datas(marseille_rows, "pl_2_time")), int(
+ get_datas(marseille_rows, "pl_3_variables")[0]), int(get_datas(marseille_rows, "pl_3_constraints")[0]), avg(get_datas(marseille_rows, "pl_3_preprocessing_time")), avg(get_datas(marseille_rows, "pl_3_time"))))
+
+
+print("\\end{tabular}")
diff --git a/plot_scripts/table_optimum_ratios.py b/plot_scripts/table_optimum_ratios.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a682fa5205f3b281bf65080f5f5397db7b3bd74
--- /dev/null
+++ b/plot_scripts/table_optimum_ratios.py
@@ -0,0 +1,28 @@
+import matplotlib.pyplot as plt
+import csv
+import numpy as np
+import statistics as stats
+
+def readCSV(file_name, delimiter=','):
+ file = csv.DictReader(open(file_name), delimiter=delimiter)
+ return list([row for row in file])
+
+
+def plot(name, file_name):
+ rows = readCSV('output/{}'.format(file_name))
+
+ def get_datas(solver_name):
+ return [100 * float(row[solver_name]) / float(row['opt_delta_ECA']) for row in rows]
+
+ IL = get_datas('naive_inc_delta_ECA')
+ DL = get_datas('naive_dec_delta_ECA')
+ IG = get_datas('glutton_inc_delta_ECA')
+ DG = get_datas('glutton_dec_delta_ECA')
+
+ print("{} & {:.3g}~\\% & {:.3g}~\\% & {:.3g}~\\% & {:.3g}~\\% & {:.3g}~\\% & {:.3g}~\\% & {:.3g}~\\% & {:.3g}~\\%\\tabularnewline\n\\hline".format(name, min(IL), stats.mean(IL), min(DL), stats.mean(DL), min(IG), stats.mean(IG), min(DG), stats.mean(DG)))
+
+
+plot("Aude", "qos_aude.csv")
+plot("Montreal", "qos_quebec.csv")
+plot("Aix", "qos_biorevaix.csv")
+plot("Marseille", "qos_marseille.csv")
\ No newline at end of file