def _find_nof_links(self):
min_links = 100000000
max_links = -100000000
min_nodes = 100000000
max_nodes = -100000000
for elem in self.data:
if "topology" in elem:
n_links = elem["topology"]["nof_links"]
n_nodes = elem["topology"]["nof_nodes"]
self.nof_links_for_topology[elem["topology"]["name"]] = n_links
self.nof_nodes_for_topology[elem["topology"]["name"]] = n_nodes
if elem["topology"]["name"] in ["isp"]:
break
if n_links < min_links:
min_links = n_links
if n_links > max_links:
max_links = n_links
if n_nodes < min_nodes:
min_nodes = n_nodes
if n_nodes > max_nodes:
max_nodes = n_nodes
log.info("Links: MIN {} MAX {}".format(min_links, max_links))
log.info("Nodes: MIN {} MAX {}".format(min_nodes, max_nodes))
self._print_links_nodes("Colt")
self._print_links_nodes("Uninett2010")
self._print_links_nodes("Kdl")
self._print_links_nodes("AS-3549")
def _run_scenarios(scenarios: list, data_file: str, log_file: str):
"""
:param scenarios: list[BasicScenario]
"""
log.initialize('ERROR', data_log_file=data_file, log_file=log_file, file_level='INFO')
np.random.seed()
for s in scenarios:
with log_context(str(s)):
try:
s.run()
except Exception:
log.error("Exception while running scenario %s", str(s), exc_info=True)
log.info("finished running scenarios")
def _plot_1bc(self):
data_list = []
nof_states = {}
for elem in self.data:
if "precision" in elem and elem["ctx"][0] == "Colt-trace":
data_list.append((elem["ctx"][1], elem["precision"]))
elif "finished" in elem and elem["ctx"][0] == "Colt-trace":
nof_states[elem["ctx"][1]] = elem["finished"]["num_explored"]
df = pd.DataFrame(data_list, columns=["rep", "precision"])
sph.new_figure(9, 6)
self._multi_trace_plot(df, nof_states)
sph.savefig(os.path.join(self.output_dir, "plot_1bi.pdf"))
time_data_list = []
for elem in self.data:
if "time-explore" in elem and elem["ctx"][0] == "isp-trace":
time_data_list.append(("ISP", elem["time-explore"]))
if len(time_data_list) == 0:
log.warning("skipping plot_1cii as ISP data is missing")
return
df = pd.DataFrame(time_data_list, columns=["net", "time"])
max_y = 120
log.info("Outliers:\n%s", str(df[df.time > max_y]))
nof_greater_max_y = df[df.time > max_y].count()["net"]
fig, ax = sph.new_figure(2, 6)
df.boxplot(column="time",
by="net",
ax=ax,
grid=False,
flierprops=self.flierprops)
ax.grid(b=True, which='major', axis='y', color='w')
ax.set_axisbelow(True)
plt.ylim([0, max_y])
plt.xlabel("")
plt.ylabel("time [s]")
plt.title("")
fig.suptitle("")
plt.gcf().text(0.4,
0.93,
"+ {}".format(nof_greater_max_y),
fontsize=12)
sph.savefig(os.path.join(self.output_dir, "plot_1cii.pdf"))
args = parser.parse_args()
if args.debug:
log.initialize('DEBUG')
elif args.quiet:
log.initialize('WARNING')
else:
log.initialize('INFO')
input_file = get_relative_to_working_directory(args.input_file)
query_file = None
if args.query:
query_file = get_relative_to_working_directory(args.query)
parser = InputParser(input_file, query_file)
problems = parser.get_problems()
for problem in problems:
problem.target_precision = args.precision
explorer = Explorer(problem)
sol = explorer.explore_all()
log.info("explored states: {}".format(sol.num_explored))
log.info("precision: {}".format(sol.p_explored.invert().val()))
p_low = sol.p_property.val()
p_up = sol.p_property.val() + sol.p_explored.invert().val()
print("P({}) ∈ [{:8.8f}, {:8.8f}]".format(
problem.property.get_human_readable(parser.name_resolver), p_low,
p_up))
"Xcongest-{}".format(n_flows),
as_3549_file,
1.0E-4,
10,
n_flows,
timeout_h=2.0))
"""
# real ISP configuration (for plot_1cii, under NDA)
isp_file = os.path.join(input_dir, "private", "isp.json")
runner.scenarios.append(RealScenario("isp", "trace", isp_file, 1.0E-4, 10, collect_precision=True))
"""
runner.run_all(args.processes)
if args.analyze:
log.initialize('INFO')
log.info("Generating plots for data directory '%s'", input_dir)
# collect experiment data
log.info("Collecting data...")
data = []
for fname in os.listdir(input_dir):
if fname.startswith("experiment_data") and fname.endswith(".log"):
fpath = os.path.join(input_dir, fname)
with open(fpath, 'r') as f:
for line in f:
data.append(json.loads(line))
# analyze data, create plots
Analyzer(data, output_dir).analyze()
def _plot_1a(self):
data_list = []
prec_list = []
is_timeout = set()
max_time_explore = {}
for elem in self.data:
if "timeout_after_seconds" in elem and elem["ctx"][0].endswith(
"-default"):
is_timeout.add((self._get_topo_name(elem), elem["ctx"][1]))
elif "time-explore" in elem and elem["ctx"][0].endswith(
"-default"):
nof_links = self.nof_links_for_topology[self._get_topo_name(
elem)]
if self._get_topo_name(
elem) not in max_time_explore or max_time_explore[
self._get_topo_name(elem)] < elem["time-explore"]:
max_time_explore[self._get_topo_name(
elem)] = elem["time-explore"]
if nof_links <= 75:
range = "50--75"
elif nof_links <= 100:
range = "76--100"
elif nof_links <= 200:
range = "101--200"
else:
range = "$>$ 200"
timeout = (self._get_topo_name(elem),
elem["ctx"][1]) in is_timeout
data_list.append(
(elem["ctx"][0], nof_links, range, elem["ctx"][1],
elem["time-explore"], timeout))
elif "finished" in elem and elem["ctx"][0].endswith("-default"):
prec_list.append(
(elem["ctx"][0], elem["finished"]["precision"]))
df = pd.DataFrame(data_list,
columns=[
"experiment", "links", "range", "rep", "time",
"is_timeout"
])
df_prec = pd.DataFrame(prec_list, columns=["experiment", "precision"])
# count number of timeouts per topology
df_to = df[["experiment", "is_timeout"]].groupby("experiment").sum()
log.info("Number of timeouts:\n%s", str(df_to[df_to.is_timeout > 0]))
# compute the worst-case imprecision
log.info(
"Worst imprecision:\n%s",
str(df_prec[df_prec.precision > 1E-4].groupby("experiment").max()))
sph.new_figure(11, 5.5)
plt.axhline(60 * 60, c="gray", lw=1, label="1 h (timeout)")
df_max = df[["experiment", "time",
"links"]].groupby("experiment").max()
plt.plot("links",
"time",
"x",
data=df_max,
markersize=4,
mew=0.6,
label="maximum")
df_med = df[["experiment", "time",
"links"]].groupby("experiment").median()
plt.plot("links",
"time",
"+",
data=df_med,
markersize=4,
mew=0.6,
label="median")
plt.xlabel("links")
plt.ylabel("time [s]")
plt.legend(handletextpad=0.3)
plt.loglog()
sph.savefig(os.path.join(self.output_dir, "plot_1a.pdf"))
def _print_links_nodes(self, topo_name):
log.info("Topology {}: {} nodes, {} links".format(
topo_name, self.nof_nodes_for_topology[topo_name],
self.nof_links_for_topology[topo_name]))
def analyze(self):
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
log.info("Collecting topology statistics...")
self._find_nof_links()
log.info("Generating plot 1a...")
self._plot_1a()
log.info("Generating plot 1bc...")
self._plot_1bc()
log.info("Generating plot intro...")
self._plot_intro()
log.info("Generating plot 2...")
self._plot_2()
log.info("Generating plot 3...")
self._plot_3()
log.info("Generating plot 5...")
self._plot_5()