def sample(self, n_samples):
ts = TimeSeries()
ts.y = self.model.sample(n_samples)[0]
ts.x = range(len(ts.y))
out_path = "{}/plots/{}/sample.png".format(script_dir,
self.__class__.__name__)
plot_procedures.plot_ts(ts, out_path, compress=True)
def get_ts_per_name(traceroute_type, ts_traceroute, dt_start, dt_end):
update_ip_name = False
ip_name = traceroute_exploratory_prints.get_ip_name(ts_traceroute.y)
if not ip_name:
update_ip_name = True
name_ts = {}
for i in xrange(len(ts_traceroute.y)):
traceroute = ts_traceroute.y[i]
if update_ip_name:
ip_name = traceroute_exploratory_prints.get_ip_name([traceroute])
ignore_traceroute = False
for hop in traceroute:
curr_name = None
for name in hop["names"]:
if name != u"##":
curr_name = name
if curr_name:
for name in hop["names"]:
if name == u"##":
ignore_traceroute = True
if not ignore_traceroute:
name = get_name(hop["names"][0], ip_name, traceroute_type)
for j in xrange(1, len(hop["names"])):
curr_name = get_name(hop["names"][j], ip_name,
traceroute_type)
if name != curr_name:
ignore_traceroute = True
if ignore_traceroute:
continue
for hop in traceroute:
name = hop["names"][0]
if name != "##":
name = get_name(name, ip_name, traceroute_type)
if name not in name_ts:
name_ts[name] = TimeSeries(dt_start=dt_start,
dt_end=dt_end)
sum_latency = 0
cnt_latency = 0
for latency in hop["times"]:
if type(latency) == int:
sum_latency += latency / 1000.0
cnt_latency += 1
if cnt_latency > 0:
mean_latency = sum_latency / cnt_latency
else:
continue
name_ts[name].dt_start = ts_traceroute.dt_start
name_ts[name].dt_end = ts_traceroute.dt_end
name_ts[name].x.append(ts_traceroute.x[i])
name_ts[name].y.append(mean_latency)
return name_ts
def simulate():
ts_len = 1000
l1 = np.random.normal(1, 0.2, ts_len)
l2 = np.random.normal(5, 0.2, ts_len)
l = np.append(l1, l2)
ts = TimeSeries(compressed=True)
ts.x = range(1, len(l) + 1)
ts.y = l
return ts
def mean_per_hour(in_path, server, mac):
ts = TimeSeries(in_path,
"loss",
dt_start=dt_start,
dt_end=dt_end,
ts_type="hourly")
ts.compress()
plot_procedures.plot_ts(ts,
"./plots/ts/ts_{}_{}.png".format(server, mac),
ylim=[-0.05, 1.05],
ylabel="Loss Fraction",
xlabel="day/month")
def print_name_ips(dt_start, dt_end):
# TODO: Probably deprecated
dt_dir = utils.get_dt_dir(dt_start, dt_end)
str_dt = utils.get_str_dt(dt_start, dt_end)
name_ip = {}
for server, mac, in_path in utils.iter_server_mac(dt_dir, True):
ts = TimeSeries(in_path=in_path,
metric="traceroute",
dt_start=dt_start,
dt_end=dt_end)
for traceroute in ts.y:
if traceroute:
for hop in traceroute:
for name, ip in izip(hop["names"], hop["ips"]):
if name not in name_ip:
name_ip[name] = set()
name_ip[name].add(ip)
out_path = "{}/prints/{}/not_filtered/name_ips.csv".format(
script_dir, str_dt)
with open(out_path, "w") as f:
f.write("name,ips\n")
for name in sorted(name_ip.keys()):
f.write("{},{}\n".format(name, sorted(list(name_ip[name]))))
def print_names_per_mac(dt_start, dt_end, mac_node):
# TODO: Probably deprecated
dt_dir = utils.get_dt_dir(dt_start, dt_end)
str_dt = utils.get_str_dt(dt_start, dt_end)
out_path = ("{}/prints/{}/not_filtered/names_per_mac.csv".format(
script_dir, str_dt))
with open(out_path, "w") as f:
f.write("server,node,mac,names\n")
for server, mac, in_path in utils.iter_server_mac(dt_dir, True):
ts = TimeSeries(in_path=in_path,
metric="traceroute",
dt_start=dt_start,
dt_end=dt_end)
names = set()
for traceroute in ts.y:
if traceroute:
ip_name = get_ip_name(traceroute)
for hop in traceroute:
for name in hop["names"]:
names.add(get_name(name, ip_name))
node = mac_node.get(mac)
f.write("{},{},{},\"{}\"\n".format(server, node, mac,
sorted(list(names))))
utils.sort_csv_file(out_path, ["server", "node"])
def print_macs_per_name(dt_start, dt_end, mac_node):
# TODO: Probably deprecated
dt_dir = utils.get_dt_dir(dt_start, dt_end)
str_dt = utils.get_str_dt(dt_start, dt_end)
name_macs = {}
for server, mac, in_path in utils.iter_server_mac(dt_dir, True):
ts = TimeSeries(in_path=in_path,
metric="traceroute",
dt_start=dt_start,
dt_end=dt_end)
for traceroute in ts.y:
if traceroute:
ip_name = get_ip_name(traceroute)
for hop in traceroute:
for name in hop["names"]:
name = get_name(name, ip_name)
if name not in name_macs:
name_macs[name] = set()
name_macs[name].add((server, mac_node.get(mac), mac))
out_path = ("{}/prints/{}/not_filtered/macs_per_name.csv".format(
script_dir, str_dt))
with open(out_path, "w") as f:
f.write("name,macs\n")
names = sorted(name_macs.keys())
for name in names:
f.write("{},\"{}\"\n".format(name, sorted(list(name_macs[name]))))
def unpack_row(self, row):
dt_start = datetime.datetime.strptime(row["dt_start"], "%Y-%m-%d")
dt_end = datetime.datetime.strptime(row["dt_end"], "%Y-%m-%d")
in_path = utils.get_in_path(row["server"], row["mac"], dt_start,
dt_end)
ts = TimeSeries(in_path, "loss", dt_start, dt_end)
return row["server"], row["mac"], dt_start, dt_end, ts
def plot_single(self, row, cmp_class_args, dataset, out_path):
ts_preprocessed = cp_utils.get_ts(row, self.preprocess_args,
self.metric)
pred = self.predict(ts_preprocessed)
correct = cp_utils.from_str_to_int_list(row["change_points_ids"])
pred.sort()
correct.sort()
conf = cmp_class.conf_mat(correct, pred, ts_preprocessed,
cmp_class.match_id, **cmp_class_args)
print "pred={}".format(pred)
print "correct={}".format(correct)
print "conf={}".format(conf)
# if is an unsupervised problem, plot the predicted cps in the ts
cp_to_print = pred
if "unsupervised" in dataset:
pred, correct = correct, pred
in_path, dt_start, dt_end = cp_utils.unpack_pandas_row(row)
out_path = "{}.png".format(out_path)
ts_raw = TimeSeries(in_path, self.metric, dt_start, dt_end)
self.plot(ts_preprocessed, ts_raw, correct, pred, conf, out_path)
out_path = "{}.csv".format(out_path)
self.print_cp(ts_preprocessed, cp_to_print, out_path)
def mean_per_hour_in_a_day(in_path, server, mac):
ts = TimeSeries(in_path, "loss", dt_start=dt_start, dt_end=dt_end)
hour_samples, hour_cnt = [], []
for hour in xrange(24):
hour_samples.append([])
hour_cnt.append(0)
for i in xrange(len(ts.x)):
hour_samples[ts.x[i].hour].append(ts.y[i])
hour_cnt[ts.x[i].hour] += 1
hour_list, mean_list, var_list = [], [], []
for hour in xrange(24):
if len(hour_samples) > 0:
hour_list.append(hour)
mean_list.append(np.mean(hour_samples[hour]))
var_list.append(np.var(hour_samples[hour]))
hour_list = np.asarray(hour_list)
mean_list = np.asarray(mean_list)
var_list = np.asarray(var_list)
print "hour_cnt={}".format(hour_cnt)
plt.clf()
matplotlib.rcParams.update({'font.size': 30})
plt.gcf().set_size_inches(16, 15)
plt.grid()
plt.ylabel("Mean Loss Fraction")
plt.xlabel("Hour")
plt.xticks(range(0, 24, 2), rotation=45)
plt.plot(hour_list, mean_list, marker="o")
plt.fill_between(hour_list,
mean_list - var_list,
mean_list + var_list,
alpha=0.3)
plt.savefig("./plots/mean_per_hour_in_a_day/mean_per_hour_in_a_day_{}_{}."
"png".format(server, mac))
def add_cp_ids():
"""
write to in_path a new column: the change points ids (index of change
points when points are sorted by measure datetime)
"""
in_path = "{}/data_web_system.csv".format(script_dir)
df = pd.read_csv(in_path)
if "change_points_ids" not in df:
cp_ids = []
for idx, row in df.iterrows():
dt_start = dt_procedures.from_strdt_to_dt(row["dt_start"])
dt_end = dt_procedures.from_strdt_to_dt(row["dt_end"])
dt_dir = utils.get_dt_dir(dt_start, dt_end)
in_path = "{}/input/{}/{}/{}.csv".format(base_dir, dt_dir,
row["server"], row["mac"])
ts = TimeSeries(in_path, "loss", dt_start=dt_start, dt_end=dt_end)
if not ts.x:
cp_ids.append("")
continue
if str(row["change_points"]) != "\'\'":
l_dt = map(dt_procedures.from_js_strdt_to_dt,
row["change_points"].split(","))
l_id = from_dt_to_id(in_path, "loss", dt_start, dt_end, l_dt)
cp_ids.append(",".join(map(str, l_id)))
else:
cp_ids.append("")
df["change_points_ids"] = cp_ids
df.to_csv("{}/data_web_system.csv".format(script_dir), index=False)
def acf(in_path, server, mac):
ts = TimeSeries(in_path,
"loss",
dt_start=dt_start,
dt_end=dt_end,
ts_type="hourly")
lags, acf, cnt_pairs = ts.get_acf(84)
print "cnt_pairs={}".format(cnt_pairs)
plt.clf()
matplotlib.rcParams.update({'font.size': 30})
plt.gcf().set_size_inches(16, 15)
plt.grid()
plt.xticks(range(0, 85, 12), rotation=45)
plt.xlabel("Lag (hours)")
plt.ylabel("ACF")
plt.plot(lags, acf, marker="o")
plt.savefig("./plots/acf/acf_{}_{}.png".format(server, mac))
def plot(dt_start, dt_end, metric):
dt_dir = utils.get_dt_dir(dt_start, dt_end)
str_dt = utils.get_str_dt(dt_start, dt_end)
utils.create_dirs([
"{}/{}".format(script_dir, str_dt),
"{}/{}/{}".format(script_dir, str_dt, metric)
])
for server, mac, in_path in utils.iter_server_mac(dt_dir, True):
out_file_name = utils.get_out_file_name(server, mac, dt_start, dt_end)
out_path = "{}/{}/{}/{}.png".format(script_dir, str_dt, metric,
out_file_name)
# comparison between not filtered and filtered
ts = TimeSeries(in_path, metric, dt_start, dt_end)
ts_filter = TimeSeries(in_path, metric, dt_start, dt_end)
ts_filter.percentile_filter(win_len=5, p=0.5)
# if len(ts_filter.y) > 100:
# plot_procedures.plot_stl_decomposition(ts_filter,
# "median_filtered",
# out_path)
# comparison between with cross traffic and without
# ts = TimeSeries(in_path, metric, dt_start, dt_end)
# ts.percentile_filter(win_len=13, p=0.5)
# ts_filter = TimeSeries(in_path, metric, dt_start, dt_end,
# cross_traffic_thresh=0)
# ts_filter.percentile_filter(win_len=13, p=0.5)
# plot_procedures.plot_ts_share_x(ts, ts_filter, out_path,
# compress=True,
# plot_type2="scatter",
# title1="raw",
# title2="median filtered",
# default_ylabel=True,
# xlabel="$i$")
ylabel = plot_procedures.get_default_ylabel(ts)
plot_procedures.plot_ts(ts_filter,
out_path,
ylabel=ylabel,
compress=False,
title="median filtered")
def plot(self, server, mac, dt_start, dt_end, ts, hidden_state_path):
ts_hidden_state_path = TimeSeries()
ts_hidden_state_path.x = copy.deepcopy(ts.x)
ts_hidden_state_path.y = copy.deepcopy(hidden_state_path)
out_file_name = utils.get_out_file_name(server, mac, dt_start, dt_end)
out_path = "{}/plots/{}/{}.png".format(script_dir,
self.__class__.__name__,
out_file_name)
plot_procedures.plot_ts_share_x(
ts,
ts_hidden_state_path,
out_path,
compress=True,
title1="raw time series",
title2="best hidden state path",
plot_type2="scatter",
yticks2=range(self.model.n_components),
ylim2=[-0.5, self.model.n_components - 0.5])
def plot_latencies_traceroute(dt_start, dt_end, preprocess_args):
str_dt = utils.get_str_dt(dt_start, dt_end)
in_path = "{}/prints/{}/filtered/traceroute_per_mac.csv".format(script_dir,
str_dt)
df = pd.read_csv(in_path)
for _, row, in df.iterrows():
if row["valid_cnt_samples"]:
in_path = utils.get_in_path(row["server"], row["mac"], dt_start,
dt_end)
ts_traceroute = TimeSeries(in_path=in_path, metric="traceroute",
dt_start=dt_start, dt_end=dt_end)
for traceroute_type in unsupervised_utils.iter_traceroute_types():
valid_traceroute_field, traceroute_field = \
cp_utils.get_traceroute_fields(traceroute_type)
if row[valid_traceroute_field]:
traceroute = ast.literal_eval(row[traceroute_field])
name_ts = get_ts_per_name(traceroute_type, ts_traceroute,
dt_start, dt_end)
dir_path = ("{}/plots/paths/{}/{}/{}/{}".
format(script_dir, str_dt, "latency",
traceroute_type, row["server"]))
traceroute_path = "/".join(map(str,
list(reversed(traceroute))))
dir_path = "{}/{}".format(dir_path, traceroute_path)
utils.create_dirs(["{}/traceroute_latencies/".
format(dir_path),
"{}/traceroute_latencies/{}".
format(dir_path, row["mac"])])
for i in range(len(traceroute) - 1):
name = traceroute[i][0][0]
traceroute_path = "hop{}_{}".format(str(i).zfill(2),
name)
out_path = ("{}/traceroute_latencies/{}/{}.png".
format(dir_path, row["mac"],
traceroute_path))
ts_preprocessed = name_ts[name].copy()
cp_utils.preprocess(ts_preprocessed, preprocess_args)
# plot_procedures.plot_ts_share_x(
# name_ts[name],
# ts_preprocessed,
# out_path,
# plot_type2="scatter",
# title1="raw",
# title2="median filtered",
# default_ylabel=True)
ts_preprocessed.metric = "latency"
plot_procedures.plot_ts(ts_preprocessed, out_path,
title="median filtered")
def print_cps(dt_start, dt_end, dir_model, metric, preprocess_args):
str_dt = utils.get_str_dt(dt_start, dt_end)
utils.create_dirs(["{}/prints/".format(script_dir),
"{}/prints/{}".format(script_dir, str_dt),
"{}/prints/{}/filtered".format(script_dir, str_dt),
"{}/prints/{}/filtered/{}".format(script_dir, str_dt,
metric)])
out_path = "{}/prints/{}/filtered/{}/cps_per_mac.csv".format(script_dir,
str_dt,
metric)
with open(out_path, "w") as f:
f.write("server,mac,cp_dts,type_cps,seg_means\n")
in_path_dir = ("{}/change_point/models/{}/plots/unsupervised/{}/{}".
format(base_dir, dir_model, str_dt, metric))
cnt = 0
for file_name in os.listdir(in_path_dir):
if ".csv" in file_name:
cnt += 1
print "cnt={}".format(cnt)
server = file_name.split("server")[1].split("_")[0]
mac = file_name.split("mac")[1].split("_")[0]
dt_cps = []
id_cps = []
df = pd.read_csv("{}/{}".format(in_path_dir, file_name))
for idx, row in df.iterrows():
dt_cps.append(row["dt"])
id_cps.append(row["dt_id"])
in_path = utils.get_in_path(server, mac, dt_start, dt_end)
ts = TimeSeries(in_path, metric, dt_start, dt_end)
cp_utils.preprocess(ts, preprocess_args)
seg_means = []
type_cps = []
if id_cps:
mean1 = np.mean(ts.y[0:id_cps[0]])
seg_means.append(mean1)
for i in range(1, len(id_cps)):
mean2 = np.mean(ts.y[id_cps[i - 1]:id_cps[i]])
seg_means.append(mean2)
update_type_cps(type_cps, mean1, mean2, metric)
mean1 = mean2
mean2 = np.mean(ts.y[id_cps[-1]:-1])
seg_means.append(mean2)
update_type_cps(type_cps, mean1, mean2, metric)
f.write("{},{},\"{}\",\"{}\",\"{}\"\n".format(server, mac,
dt_cps, type_cps,
seg_means))
def write_all_samples_to_file(targets, in_dir):
samples = []
for target in targets:
server, mac = target[0], target[1]
in_path = "{}/{}/{}.csv".format(in_dir, server, mac)
ts = TimeSeries(in_path, "loss", dt_start=dt_start, dt_end=dt_end)
samples = samples + ts.y
print "server={}, mac={}".format(server, mac)
with open("./plots/distribution/samples.csv", "w") as f:
f.write("samples\n")
for x in samples:
f.write("{}\n".format(x))
def plot_per_node(dt_start, dt_end, metric, only_unique_traceroute):
dt_dir = utils.get_dt_dir(dt_start, dt_end)
str_dt = utils.get_str_dt(dt_start, dt_end)
utils.create_dirs([
"{}/plots/".format(script_dir), "{}/plots/nodes".format(script_dir),
"{}/plots/nodes/{}".format(script_dir, str_dt),
"{}/plots/nodes/{}/{}".format(script_dir, str_dt, metric)
])
valid_nodes = read_input.get_valid_nodes()
mac_node = read_input.get_mac_node()
macs_unique_traceroute = read_input.get_macs_traceroute_filter(
dt_start, dt_end, "filtered")
for server, mac, in_path in utils.iter_server_mac(dt_dir, True):
if only_unique_traceroute and (mac not in macs_unique_traceroute):
continue
if mac_node[mac] in valid_nodes:
utils.create_dirs([
"{}/plots/nodes/{}/{}/{}".format(script_dir, str_dt, metric,
mac_node[mac])
])
out_file_name = utils.get_out_file_name(server, mac, dt_start,
dt_end)
out_path = ("{}/plots/nodes/{}/{}/{}/{}.png".format(
script_dir, str_dt, metric, mac_node[mac], out_file_name))
ts = TimeSeries(in_path, metric, dt_start, dt_end)
ts_filter = TimeSeries(in_path, metric, dt_start, dt_end)
ts_filter.percentile_filter(win_len=13, p=0.5)
plot_procedures.plot_ts_share_x(ts,
ts_filter,
out_path,
compress=False,
plot_type2="scatter")
def from_dt_to_id(in_path, metric, dt_start, dt_end, l_dt):
"""
return ids associated with dts in l_dt list
"""
ts = TimeSeries(in_path, metric, dt_start=dt_start, dt_end=dt_end)
dt_id = {}
for i in xrange(len(ts.x)):
dt_id[ts.x[i]] = i
l_id = []
for dt in l_dt:
l_id.append(dt_id[dt])
return l_id
def print_traceroute_per_mac(dt_start, dt_end):
dt_dir = utils.get_dt_dir(dt_start, dt_end)
str_dt = utils.get_str_dt(dt_start, dt_end)
out_path = ("{}/prints/{}/not_filtered/traceroute_per_mac.csv".format(
script_dir, str_dt))
with open(out_path, "w") as f:
f.write("server,mac,"
"valid_traceroute_compress_embratel,"
"traceroute_compress_embratel,"
"valid_traceroute_compress_embratel_without_last_hop_embratel,"
"traceroute_compress_embratel_without_last_hop_embratel,"
"valid_traceroute_without_embratel,"
"traceroute_without_embratel,"
"valid_traceroute,"
"traceroute\n")
for server, mac, in_path in utils.iter_server_mac(dt_dir, True):
ts_traceroute = TimeSeries(in_path=in_path,
metric="traceroute",
dt_start=dt_start,
dt_end=dt_end)
(valid_traceroute_compress_embratel,
traceroute_compress_embratel) = \
get_traceroute(ts_traceroute, True, True, True)
(valid_traceroute_compress_embratel_without_last_hop_embratel,
traceroute_compress_embratel_without_last_hop_embratel) = \
get_traceroute(ts_traceroute, True, True, False)
(valid_traceroute_without_embratel,
traceroute_without_embratel) = \
get_traceroute(ts_traceroute, False, False, False)
(valid_traceroute, traceroute) = \
get_traceroute(ts_traceroute, True, False, False)
l = "{},{}" + ",{},\"{}\"" * 4 + "\n"
l = l.format(
server, mac, valid_traceroute_compress_embratel,
traceroute_compress_embratel,
valid_traceroute_compress_embratel_without_last_hop_embratel,
traceroute_compress_embratel_without_last_hop_embratel,
valid_traceroute_without_embratel, traceroute_without_embratel,
valid_traceroute, traceroute)
f.write(l)
utils.sort_csv_file(out_path, ["server", "mac"])
def sliding_window(ts):
ts_dist = TimeSeries(compressed=True)
win_len = 100
for i in xrange(win_len, len(ts.y) - win_len + 1):
dist = cmp_win.hellinger_dist(ts.y[i - win_len:i],
ts.y[i:i + win_len],
bins=np.arange(0.02, 10.02, 0.02))
ts_dist.x.append(ts.x[i])
ts_dist.y.append(dist)
plot_procedures.plot_ts_share_x(ts,
ts_dist,
"./sliding_window_toy_example.png",
compress=True,
plot_type1="plot",
ylim1=[0, max(ts.y)],
ylabel1="$y_{i}$",
ylabel2="$H_{i}$",
xlabel="$i$")
def plot_per_name(dt_start, dt_end, metric, preprocess_args, plot_cps=True):
dt_dir = utils.get_dt_dir(dt_start, dt_end)
str_dt = utils.get_str_dt(dt_start, dt_end)
utils.create_dirs([
"{}/plots/".format(script_dir), "{}/plots/names".format(script_dir),
"{}/plots/names/{}".format(script_dir, str_dt),
"{}/plots/names/{}/{}".format(script_dir, str_dt, metric)
])
client_cps = unsupervised_utils.get_client_cps(plot_cps, str_dt, metric)
# avoid reploting
client_plotPath = {}
for traceroute_type in unsupervised_utils.iter_traceroute_types():
valid_traceroute_field, traceroute_field = \
cp_utils.get_traceroute_fields(traceroute_type)
utils.create_dirs([
"{}/plots/names/{}/{}/{}".format(script_dir, str_dt, metric,
traceroute_type)
])
df = pd.read_csv("{}/prints/{}/filtered/traceroute_per_mac.csv".format(
script_dir, str_dt))
cnt = 0
for idx, row in df.iterrows():
if row["valid_cnt_samples"] and row[valid_traceroute_field]:
print("cnt={}, traceroute_type={}, str_dt={}".format(
cnt, traceroute_type, str_dt))
cnt += 1
client = utils.get_client(row["server"], row["mac"])
for name in cp_utils.iter_names_traceroute_filtered(
ast.literal_eval(row[traceroute_field])):
utils.create_dirs([
"{}/plots/names/{}/{}/{}/{}".format(
script_dir, str_dt, metric, traceroute_type,
row["server"]),
"{}/plots/names/{}/{}/{}/{}/{}".format(
script_dir, str_dt, metric, traceroute_type,
row["server"], name)
])
out_file_name = utils.get_out_file_name(
row["server"], row["mac"], dt_start, dt_end)
out_path = ("{}/plots/names/{}/{}/{}/{}/{}/{}.png".format(
script_dir, str_dt, metric, traceroute_type,
row["server"], name, out_file_name))
# avoid reploting
if client in client_plotPath:
shutil.copyfile(client_plotPath[client], out_path)
else:
client_plotPath[client] = out_path
cp_dts = client_cps[client]
in_path = "{}/input/{}/{}/{}.csv".format(
base_dir, dt_dir, row["server"], row["mac"])
ts = TimeSeries(in_path, metric, dt_start, dt_end)
cp_utils.preprocess(ts, preprocess_args)
plot_procedures.plot_ts(ts,
out_path,
dt_axvline=cp_dts,
title="median filtered")
def process():
create_dirs(target_email)
cnt_points = 0
cps_per_ts_samples = []
middle_seg_len_samples = []
first_seg_len_samples = []
last_seg_len_samples = []
abs_mean_diff_consecutive_segs = []
hellinger_dist_consecutive_segs = []
cnt_rows = 0
df = pd.read_csv("./classifications.csv", sep=";")
for idx, row in df.iterrows():
if row["email"] == target_email:
cnt_rows += 1
print "cnt_rows={}".format(cnt_rows)
# print "row={}".format(row)
# get change points list
dt_cp_list = []
if row["change_points"] != "''":
for strdt in row["change_points"].split(","):
dt = dt_procedures.from_js_strdt_to_dt(strdt)
dt_cp_list.append(dt)
dt_cp_list.sort()
dt_start = dt_procedures.from_js_strdate_to_dt(row["date_start"])
dt_end = dt_procedures.from_js_strdate_to_dt(row["date_end"])
in_path = ("../../../input/{}_{}/{}/{}.csv"
"".format(dt_start.year,
str(dt_start.month).zfill(2), row["server"],
row["mac"]))
ts = TimeSeries(in_path=in_path,
metric="loss",
dt_start=dt_start,
dt_end=dt_end)
cnt_points += len(ts.y)
cps_per_ts_samples.append(len(dt_cp_list))
# get id_cp_list, first_seg_len, middle_seg_len, last_seg_len
id_cp_list = []
i, j, last_cp_i = 0, 0, -1
while (i < len(ts.x)) and (j < len(dt_cp_list)):
if ts.x[i] == dt_cp_list[j]:
id_cp_list.append(i)
if j == 0:
first_seg_len_samples.append(i + 1)
if j == len(dt_cp_list) - 1:
last_seg_len_samples.append(len(ts.x) - i - 1)
if last_cp_i != -1:
middle_seg_len_samples.append(i - last_cp_i)
last_cp_i = i
j += 1
i += 1
if (j != len(dt_cp_list)):
print "ERROR"
# get abs_mean_diff_consecutive_segs and
# hellinger_dist_consecutive_segments
segs_list = []
last_id_cp = -1
for i in xrange(len(id_cp_list)):
if (id_cp_list[i] == 0) or (id_cp_list[i] == len(ts.y) - 1):
continue
segs_list.append([last_id_cp + 1, id_cp_list[i]])
if i == len(id_cp_list) - 1:
segs_list.append([id_cp_list[i] + 1, len(ts.y) - 1])
last_id_cp = id_cp_list[i]
for i in xrange(1, len(segs_list)):
l1 = ts.y[segs_list[i - 1][0]:segs_list[i - 1][1] + 1]
l2 = ts.y[segs_list[i][0]:segs_list[i][1] + 1]
mean1 = np.mean(l1)
mean2 = np.mean(l2)
abs_mean_diff_consecutive_segs.append(abs(mean1 - mean2))
bins = np.arange(0.0, 1.02, 0.02)
hellinger_dist_consecutive_segs.append(
hellinger_dist(l1, l2, bins))
plot(row, dt_cp_list, ts, dt_start, dt_end)
def print_empty_segs(dt_start,
dt_end,
metric,
min_seg_len,
filtered,
plot=False):
dt_dir = utils.get_dt_dir(dt_start, dt_end)
str_dt = utils.get_str_dt(dt_start, dt_end)
utils.create_dirs([
"{}/prints/".format(script_dir),
"{}/prints/{}".format(script_dir, str_dt),
"{}/prints/{}/{}".format(script_dir, str_dt, filtered),
"{}/prints/{}/{}/{}".format(script_dir, str_dt, filtered, metric)
])
out_path = "{}/prints/{}/{}/{}/empty_segs_per_mac.csv".format(
script_dir, str_dt, filtered, metric)
with open(out_path, "w") as f:
f.write("server,mac,empty_segs\n")
target_macs = read_input.get_macs_traceroute_filter(
dt_start, dt_end, filtered)
for server, mac, in_path in utils.iter_server_mac(dt_dir, True):
if mac not in target_macs:
continue
ts = TimeSeries(in_path=in_path,
metric=metric,
dt_start=dt_start,
dt_end=dt_end)
axvline_dts = []
empty_segs = []
if len(ts.x) >= 2:
if is_empty_seg(dt_start, ts.x[0], min_seg_len):
axvline_dts.append(ts.x[0])
empty_segs.append([str(dt_start), str(ts.x[0])])
for i in xrange(1, len(ts.x)):
if is_empty_seg(ts.x[i - 1], ts.x[i], min_seg_len):
axvline_dts.append(ts.x[i - 1])
axvline_dts.append(ts.x[i])
empty_segs.append([str(ts.x[i - 1]), str(ts.x[i])])
if is_empty_seg(ts.x[-1], dt_end, min_seg_len):
axvline_dts.append(ts.x[i - 1])
empty_segs.append([str(ts.x[-1]), str(dt_end)])
f.write("{},{},\"{}\"\n".format(server, mac, empty_segs))
if plot:
utils.create_dirs([
"{}/plots/".format(script_dir),
"{}/plots/empty_segs".format(script_dir),
"{}/plots/empty_segs/{}".format(script_dir, str_dt),
"{}/plots/empty_segs/{}/{}".format(script_dir, str_dt,
metric)
])
out_file_name = utils.get_out_file_name(
server, mac, dt_start, dt_end)
out_path = ("{}/plots/empty_segs/{}/{}/{}.png".format(
script_dir, str_dt, metric, out_file_name))
plot_procedures.plot_ts(ts, out_path, dt_axvline=axvline_dts)
def print_traceroute_per_mac_filtered(dt_start,
dt_end,
min_fraction_of_samples=0.7):
str_dt = utils.get_str_dt(dt_start, dt_end)
out_path = ("{}/prints/{}/filtered/traceroute_per_mac.csv".format(
script_dir, str_dt))
with open(out_path, "w") as f:
l = ("server,mac,valid_cnt_samples,"
"valid_traceroute_compress_embratel,"
"traceroute_compress_embratel_filter,"
"valid_traceroute_compress_embratel_without_last_hop_embratel,"
"traceroute_compress_embratel_without_last_hop_embratel_filter,"
"valid_traceroute_without_embratel,"
"traceroute_without_embratel_filter,"
"valid_traceroute,"
"traceroute_filter\n")
f.write(l)
in_path = ("{}/prints/{}/not_filtered/traceroute_per_mac.csv".format(
script_dir, str_dt))
df = pd.read_csv(in_path)
for cnt, (idx, row) in enumerate(df.iterrows()):
print(
"print_traceroute_per_mac_filtered, str_dt={}, cnt={}".format(
str_dt, cnt))
traceroute_compress_embratel_filter = \
get_traceroute_filtered(
row["valid_traceroute_compress_embratel"],
row["traceroute_compress_embratel"],
row["server"])
traceroute_compress_embratel_without_last_hop_embratel_filter = \
get_traceroute_filtered(
row["valid_traceroute_compress_embratel_"
"without_last_hop_embratel"],
row["traceroute_compress_embratel_"
"without_last_hop_embratel"],
row["server"])
traceroute_without_embratel_filter = \
get_traceroute_filtered(
row["valid_traceroute_without_embratel"],
row["traceroute_without_embratel"],
row["server"])
traceroute_filter = \
get_traceroute_filtered(
row["valid_traceroute"],
row["traceroute"],
row["server"])
# check if client has the minimum number of samples. Since the
# metric is not specified at the moment, only check the presence of
# the measurement timestamp
in_path = utils.get_in_path(row["server"], row["mac"], dt_start,
dt_end)
ts = TimeSeries(in_path=in_path,
metric="dt",
dt_start=dt_start,
dt_end=dt_end)
delta_days = (dt_end - dt_start).days
fraction_of_samples = float(len(ts.y)) / (delta_days * 24.0 * 2.0)
if fraction_of_samples < min_fraction_of_samples:
valid_cnt_samples = False
else:
valid_cnt_samples = True
l = "{},{},{}" + ",{},\"{}\"" * 4 + "\n"
l = l.format(
row["server"], row["mac"], valid_cnt_samples,
row["valid_traceroute_compress_embratel"],
traceroute_compress_embratel_filter,
row["valid_traceroute_compress_embratel_"
"without_last_hop_embratel"],
traceroute_compress_embratel_without_last_hop_embratel_filter,
row["valid_traceroute_without_embratel"],
traceroute_without_embratel_filter, row["valid_traceroute"],
traceroute_filter)
f.write(l)
def get_ts(row, preprocess_args, metric):
in_path, dt_start, dt_end = unpack_pandas_row(row)
ts = TimeSeries(in_path, metric, dt_start, dt_end)
preprocess(ts, preprocess_args)
return ts
