def plot(self, ts, ts_raw, correct, pred, conf, out_path):
_, ts_dist = self.slide(ts)
plot_procedures.plot_ts_share_x(ts,
ts_dist,
out_path,
compress=True,
title1="median filtered",
title2="mean distance sliding windows",
dt_axvline1=np.asarray(ts.x)[correct],
dt_axvline2=np.asarray(ts.x)[pred],
y_axhline2=[self.thresh],
xlabel="$i$",
ylabel2="$D_{i}$ (ms)",
ylabel1="RTT (ms)")
def plot(self, ts, ts_raw, correct, pred, conf, out_path):
ts_cp_prob = self.get_cp_prob(ts)
ylabel1 = plot_procedures.get_default_ylabel(ts)
plot_procedures.plot_ts_share_x(ts,
ts_cp_prob,
out_path,
compress=True,
title1="median filtered",
ylim2=[-0.02, 1.02],
yticks2=np.arange(0.0, 1.0 + 0.1, 0.1),
dt_axvline1=np.asarray(ts.x)[correct],
dt_axvline2=np.asarray(ts.x)[pred],
y_axhline2=[self.thresh],
title2="probabilities",
ylabel1=ylabel1,
ylabel2="p($i$ is change point)",
xlabel="$i$")
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 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_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 plot(self, ts, ts_raw, correct, pred, conf, out_path):
if "unsupervised" in out_path:
ylabel = plot_procedures.get_default_ylabel(ts)
plot_procedures.plot_ts(ts,
out_path,
dt_axvline=np.asarray(ts.x)[correct],
ylabel=ylabel,
xlabel="$i$",
compress=True,
title="median filtered")
else:
dt_axvline1 = np.asarray(ts.x)[correct]
dt_axvline2 = np.asarray(ts.x)[pred]
plot_procedures.plot_ts_share_x(
ts_raw,
ts,
out_path,
compress=False,
title1="correct",
dt_axvline1=dt_axvline1,
dt_axvline2=dt_axvline2,
title2="predicted. conf={}".format(conf),
plot_type2="scatter")