def run_sequential(dir_model, metric, preprocess_args):
for dt_start, dt_end in utils.iter_dt_range():
print_cps(dt_start, dt_end, dir_model, metric, preprocess_args)
unsupervised_utils.print_cps_per_name(dt_start, dt_end, metric,
"cps_per_mac.csv")
unsupervised_utils.print_cps_per_path(dt_start, dt_end, metric,
"cps_per_mac.csv")
def run_parallel(metric, eps_hours, min_fraction_of_clients):
dt_ranges = list(utils.iter_dt_range())
f_localize_events = functools.partial(
localize_events,
metric=metric,
eps_hours=eps_hours,
min_fraction_of_clients=min_fraction_of_clients)
utils.parallel_exec(f_localize_events, dt_ranges)
def run_sequential(metric, min_seg_len, filtered, hours_tol):
for dt_start, dt_end in utils.iter_dt_range():
print_empty_segs(dt_start,
dt_end,
metric,
min_seg_len,
filtered,
plot=False)
unsupervised_utils.print_per_name(dt_start, dt_end, metric,
"empty_segs_per_mac.csv")
unsupervised_utils.print_per_path(dt_start, dt_end, metric,
"empty_segs_per_mac.csv")
def run_parallel(metric, eps_hours):
dt_ranges = list(utils.iter_dt_range())
fp_voting = functools.partial(voting,
metric=metric,
in_dir="paths",
eps_hours=eps_hours)
utils.parallel_exec(fp_voting, dt_ranges)
fp_voting = functools.partial(voting,
metric=metric,
in_dir="names",
eps_hours=eps_hours)
utils.parallel_exec(fp_voting, dt_ranges)
def run_parallel(dir_model, metric, preprocess_args):
dt_ranges = list(utils.iter_dt_range())
fp_print_cps = functools.partial(print_cps,
dir_model=dir_model,
metric=metric,
preprocess_args=preprocess_args)
utils.parallel_exec(fp_print_cps, dt_ranges)
fp_print_per_name = functools.partial(unsupervised_utils.print_per_name,
metric=metric,
file_name="cps_per_mac.csv")
utils.parallel_exec(fp_print_per_name, dt_ranges)
fp_print_per_path = functools.partial(unsupervised_utils.print_per_path,
metric=metric,
file_name="cps_per_mac.csv")
utils.parallel_exec(fp_print_per_path, dt_ranges)
def run_parallel(metric, min_seg_len, filtered):
dt_ranges = list(utils.iter_dt_range())
fp_print_empty_segs = functools.partial(print_empty_segs,
metric=metric,
min_seg_len=min_seg_len,
filtered=filtered,
plot=False)
utils.parallel_exec(fp_print_empty_segs, dt_ranges)
fp_print_per_name = functools.partial(unsupervised_utils.print_per_name,
metric=metric,
file_name="empty_segs_per_mac.csv")
utils.parallel_exec(fp_print_per_name, dt_ranges)
fp_print_per_path = functools.partial(unsupervised_utils.print_per_path,
metric=metric,
file_name="empty_segs_per_mac.csv")
utils.parallel_exec(fp_print_per_path, dt_ranges)
def run_sequential(metric, only_unique_traceroute):
for dt_start, dt_end in utils.iter_dt_range():
plot_per_node(dt_start, dt_end, metric, only_unique_traceroute)
def run_sequential(metric, eps_hours, min_fraction_of_clients):
for dt_start, dt_end in utils.iter_dt_range():
localize_events(dt_start, dt_end, metric, eps_hours,
min_fraction_of_clients)
def run_sequential(metric, preprocess_args):
for dt_start, dt_end in utils.iter_dt_range():
plot_per_name(dt_start, dt_end, metric, preprocess_args)
def run_parallel(metric):
dt_ranges = list(utils.iter_dt_range())
f_plot_per_name = functools.partial(plot_per_name,
metric=metric,
preprocess_args=preprocess_args)
utils.parallel_exec(f_plot_per_name, dt_ranges)
def run_sequential():
for dt_start, dt_end in utils.iter_dt_range():
process_graphs(dt_start, dt_end)
def run_parallel():
dt_ranges = list(utils.iter_dt_range())
utils.parallel_exec(process_graphs, dt_ranges)
def run_parallel():
mac_node = read_input.get_mac_node()
dt_ranges = list(utils.iter_dt_range())
f_print_all = functools.partial(print_all, mac_node=mac_node)
utils.parallel_exec(f_print_all, dt_ranges)
def run_sequential():
mac_node = read_input.get_mac_node()
for dt_start, dt_end in utils.iter_dt_range():
print_all(dt_start, dt_end, mac_node)
def run_sequential():
for dt_start, dt_end in utils.iter_dt_range():
create_dataset_unsupervised(dt_start, dt_end)
def run_parallel():
dt_ranges = list(utils.iter_dt_range())
utils.parallel_exec(create_dataset_unsupervised, dt_ranges)
def run_parallel(metric, only_unique_traceroute):
dt_ranges = list(utils.iter_dt_range())
f_plot_per_node = \
functools.partial(plot_per_node, metric=metric,
only_unique_traceroute=only_unique_traceroute)
utils.parallel_exec(f_plot_per_node, dt_ranges)
def run_sequential(metric, eps_hours):
for dt_start, dt_end in utils.iter_dt_range():
match_cps_different_metrics(dt_start, dt_end, eps_hours)
def run_parallel(preprocess_args):
dt_ranges = list(utils.iter_dt_range())
fp = functools.partial(plot_latencies_traceroute,
preprocess_args=preprocess_args)
utils.parallel_exec(fp, dt_ranges)
def run_sequential(preprocess_args):
for dt_start, dt_end in utils.iter_dt_range():
plot_latencies_traceroute(dt_start, dt_end, preprocess_args)
def run_sequential(metric, eps_hours):
for dt_start, dt_end in utils.iter_dt_range():
voting(dt_start, dt_end, metric, "paths", eps_hours)
voting(dt_start, dt_end, metric, "names", eps_hours)
