def _calc_metrics_draw_figure(name, data, changepoints, tolerance_delay, retrospective):
# Calculation of AUC
print(name)
_retrospective = deepcopy(retrospective)
scores = _retrospective.calc_scores(data)
AUC = calc_AUC(scores, changepoints, tolerance_delay, both=True)
print("AUC: ", AUC)
# Draw a figure
output_path = "./figs/"
os.makedirs(output_path, exist_ok=True)
fontsize = 18
plt.clf()
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(20, 10))
axes[0].plot(data, label="Raw Data")
axes[0].set_ylabel('Value', fontsize=fontsize)
axes[0].set_ylim(np.nanmin(data) - 5, np.nanmax(data) + 5)
axes[0].legend(loc='upper left', fontsize=fontsize)
axes[1].plot(scores, label=name, color="blue")
axes[1].set_ylabel('Score', fontsize=fontsize)
axes[1].legend(loc='upper left', fontsize=fontsize)
axes[1].set_ylim(np.nanmin(scores), np.nanmax(scores))
plt.subplots_adjust(left=0.1, right=0.95, bottom=0.1, top=0.95)
fig.savefig(output_path + name + ".png")
def _objective_FW2S_MDL(trial, train, changepoints, tolerance_delay,
params): # FW2S_MDL
nml_gaussian = partial(sdmdl_nml.nml_gaussian,
mu_max=params["mu_max"],
div_min=params["div_min"],
div_max=params["div_max"])
complexity_gaussian = partial(sdmdl_nml.complexity_gaussian,
mu_max=params["mu_max"],
div_min=params["div_min"],
div_max=params["div_max"])
window_size_1 = trial.suggest_int('window_size_1', 10, 500)
window_size_2 = trial.suggest_int('window_size_2', 10, 500)
retrospective_first = sdmdl.Retrospective(
h=window_size_1,
encoding_func=nml_gaussian,
complexity_func=complexity_gaussian,
order=0)
retrospective_second = sdmdl.Retrospective(
h=window_size_2,
encoding_func=nml_gaussian,
complexity_func=complexity_gaussian,
order=0)
retrospective = fw2s_mdl.Retrospective(retrospective_first,
retrospective_second)
scores = retrospective.calc_scores(train)
AUC = calc_AUC(scores, changepoints, tolerance_delay)
return -AUC
def _objective_CF(trial, train, changepoints, tolerance_delay): # CF
# hyperparameters
r = trial.suggest_uniform('r', 0.01, 0.99)
order = trial.suggest_int('order', 1, 20)
smooth = trial.suggest_int('smooth', 3, 20)
retrospective = changefinder.Retrospective(r=r, order=order, smooth=smooth)
scores = retrospective.calc_scores(train)
AUC = calc_AUC(scores, changepoints, tolerance_delay)
return -AUC
def _objective_BOCPD(trial, train, changepoints, tolerance_delay): # BOCPD
lam = trial.suggest_int('lam', 2, 1000)
alpha = trial.suggest_uniform('alpha', 0.01, 10)
beta = trial.suggest_uniform('beta', 0.01, 10)
kappa = trial.suggest_uniform('kappa', 0.01, 10)
mu = trial.suggest_uniform('mu', 0.01, 10)
h = partial(bocpd.constant_hazard, lam)
lik = bocpd.StudentT(alpha, beta, kappa, mu)
retrospective = bocpd.Retrospective(hazard_func=h, likelihood_func=lik)
scores = retrospective.calc_scores(train)
AUC = calc_AUC(scores, changepoints, tolerance_delay)
return -AUC
def _objective_SDMDL(trial, train, changepoints, tolerance_delay, params):
nml_gaussian = partial(sdmdl_nml.nml_gaussian, mu_max=params[
"mu_max"], div_min=params["div_min"], div_max=params["div_max"])
complexity_gaussian = partial(sdmdl_nml.complexity_gaussian, mu_max=params[
"mu_max"], div_min=params["div_min"], div_max=params["div_max"])
window_size = trial.suggest_int('window_size', 5, 60)
retrospective = sdmdl.Retrospective(h=window_size, encoding_func=nml_gaussian,
complexity_func=complexity_gaussian, order=params["order"])
scores = retrospective.calc_scores(train)
n = len(scores)
scores[2 * window_size - 1: n] = scores[window_size - 1: n - window_size]
scores[0:2 * window_size] = np.nan
AUC = calc_AUC(scores, changepoints, tolerance_delay, both=both)
return -AUC
def _objective_CF(trial, train, changepoints, tolerance_delay):
# hyperparameters
r = trial.suggest_uniform('r', 0.01, 0.99)
order = trial.suggest_int('order', 1, 20)
smooth = trial.suggest_int('smooth', 3, 20)
retrospective = changefinder.Retrospective(r=r, order=order, smooth=smooth)
scores = np.zeros((8, 26450))
for i in range(8):
scores[i] = retrospective.calc_scores(train[i])
scores = np.nanmax(scores, axis=0)
AUC = calc_AUC(scores, changepoints, tolerance_delay, both=both)
return -AUC
def _objective_FW2S_MDL(trial, train, changepoints, tolerance_delay, params):
nml_multgaussian = partial(sdmdl_nml.nml_multgaussian,
R=params["R"],
lambda_min=params["lambda_min"])
complexity_multgaussian = partial(sdmdl_nml.complexity_multgaussian,
m=8,
R=params["R"],
lambda_min=params["lambda_min"])
nml_gaussian = partial(sdmdl_nml.nml_gaussian,
mu_max=params["mu_max"],
div_min=params["div_min"],
div_max=params["div_max"])
complexity_gaussian = partial(sdmdl_nml.complexity_gaussian,
mu_max=params["mu_max"],
div_min=params["div_min"],
div_max=params["div_max"])
delta_0 = trial.suggest_uniform('delta_0', 0.001, 0.499)
window_size_1 = trial.suggest_int('window_size_1', 100, 300)
window_size_2 = trial.suggest_int('window_size_2', 100, 300)
retrospective_first = sdmdl.Retrospective(
h=window_size_1,
encoding_func=nml_multgaussian,
complexity_func=complexity_multgaussian,
delta_0=delta_0,
order=0)
retrospective_second = sdmdl.Retrospective(
h=window_size_2,
encoding_func=nml_gaussian,
complexity_func=complexity_gaussian,
order=0)
retrospective = fw2s_mdl.Retrospective(retrospective_first,
retrospective_second)
scores = retrospective.calc_scores(train)
n = len(scores)
scores[2 * (window_size_1 +
window_size_2):n] = scores[window_size_1 + window_size_2:n -
(window_size_1 + window_size_2)]
scores[0:2 * (window_size_1 + window_size_2)] = np.nan
AUC = calc_AUC(scores, changepoints, tolerance_delay, both=both)
return -AUC
def _objective_SDMDL(trial, train, changepoints, tolerance_delay,
params): # S-MDL
nml_gaussian = partial(sdmdl_nml.nml_gaussian,
mu_max=params["mu_max"],
div_min=params["div_min"],
div_max=params["div_max"])
complexity_gaussian = partial(sdmdl_nml.complexity_gaussian,
mu_max=params["mu_max"],
div_min=params["div_min"],
div_max=params["div_max"])
window_size = trial.suggest_int('window_size', 10, 500)
retrospective = sdmdl.Retrospective(h=window_size,
encoding_func=nml_gaussian,
complexity_func=complexity_gaussian,
order=params["order"])
scores = retrospective.calc_scores(train)
AUC = calc_AUC(scores, changepoints, tolerance_delay)
return -AUC
def _objective_BOCPD(trial, train, changepoints, tolerance_delay):
lam = trial.suggest_int('lam', 100, 1000)
alpha = trial.suggest_uniform('alpha', 1e-8, 10)
beta = trial.suggest_uniform('beta', 1e-8, 0.01)
kappa = trial.suggest_uniform('kappa', 1e-8, 0.01)
mu = 0
#mu = trial.suggest_uniform('mu', -1, 1)
h = partial(bocpd.constant_hazard, lam)
lik = bocpd.StudentT(alpha, beta, kappa, mu)
retrospective = bocpd.Retrospective(hazard_func=h, likelihood_func=lik)
scores = np.zeros((8, 26450))
for i in range(8):
scores[i] = retrospective.calc_scores(train[i])
scores = np.nanmax(scores, axis=0)
AUC = calc_AUC(scores, changepoints, tolerance_delay, both=both)
return -AUC
n = len(fw2s_mdl_scores)
fw2s_mdl_scores[2 *
(opt_window_size_1 +
opt_window_size_2):n] = fw2s_mdl_scores[opt_window_size_1 +
opt_window_size_2:n -
(opt_window_size_1 +
opt_window_size_2)]
fw2s_mdl_scores[0:2 * (opt_window_size_1 + opt_window_size_2)] = np.nan
sdmdl_0_scores[np.isnan(sdmdl_0_scores)] = np.nanmin(sdmdl_0_scores)
sdmdl_1_scores[np.isnan(sdmdl_1_scores)] = np.nanmin(sdmdl_1_scores)
sdmdl_2_scores[np.isnan(sdmdl_2_scores)] = np.nanmin(sdmdl_2_scores)
fw2s_mdl_scores[np.isnan(fw2s_mdl_scores)] = np.nanmin(fw2s_mdl_scores)
print("SDMDL 0th: ",
calc_AUC(sdmdl_0_scores, changepoints, tolerance_delay, both=both))
print("SDMDL 1st: ",
calc_AUC(sdmdl_1_scores, changepoints, tolerance_delay, both=both))
print("SDMDL 2nd: ",
calc_AUC(sdmdl_2_scores, changepoints, tolerance_delay, both=both))
print("FW2S-MDL: ",
calc_AUC(fw2s_mdl_scores, changepoints, tolerance_delay, both=both))
print("ChangeFinder: ",
calc_AUC(cf_scores, changepoints, tolerance_delay, both=both))
print("BOCPD: ",
calc_AUC(bocpd_scores, changepoints, tolerance_delay, both=both))
df_result = pd.DataFrame()
row = pd.DataFrame({
"method: ": ["SDMDL 0th"],
"AUC: ":
def _calc_metrics(idx_data, dataset, changepoints, tolerance_delay,
retrospective):
_retrospective = deepcopy(retrospective)
scores = _retrospective.calc_scores(dataset[idx_data])
AUC = calc_AUC(scores, changepoints, tolerance_delay)
return AUC
complexity_func=complexity_gaussian, order=0)
retrospective = fw2s_mdl.Retrospective(
retrospective_first, retrospective_second)
fw2s_mdl_scores = retrospective.calc_scores(np.array(df['Rate']))
n = len(fw2s_mdl_scores)
fw2s_mdl_scores[2 * (opt_window_size_1 + opt_window_size_2): n] = fw2s_mdl_scores[opt_window_size_1 +
opt_window_size_2: n - (opt_window_size_1 + opt_window_size_2)]
fw2s_mdl_scores[0:2 * (opt_window_size_1 + opt_window_size_2)] = np.nan
sdmdl_0_scores[np.isnan(sdmdl_0_scores)] = np.nanmin(sdmdl_0_scores)
sdmdl_1_scores[np.isnan(sdmdl_1_scores)] = np.nanmin(sdmdl_1_scores)
sdmdl_2_scores[np.isnan(sdmdl_2_scores)] = np.nanmin(sdmdl_2_scores)
fw2s_mdl_scores[np.isnan(fw2s_mdl_scores)] = np.nanmin(fw2s_mdl_scores)
print("SDMDL 0th: ", calc_AUC(sdmdl_0_scores,
changepoints, tolerance_delay, both=both))
print("SDMDL 1st: ", calc_AUC(sdmdl_1_scores,
changepoints, tolerance_delay, both=both))
print("SDMDL 2nd: ", calc_AUC(sdmdl_2_scores,
changepoints, tolerance_delay, both=both))
print("FW2S-MDL: ", calc_AUC(fw2s_mdl_scores,
changepoints, tolerance_delay, both=both))
print("ChangeFinder: ", calc_AUC(
cf_scores, changepoints, tolerance_delay, both=both))
print("BOCPD: ", calc_AUC(bocpd_scores, changepoints, tolerance_delay, both=both))
df_result = pd.DataFrame()
row = pd.DataFrame({"method: ": ["SDMDL 0th"], "AUC: ": calc_AUC(
sdmdl_0_scores, changepoints, tolerance_delay, both=both)})
df_result = pd.concat([df_result, row], axis=0)
row = pd.DataFrame({"method: ": ["SDMDL 1st"], "AUC: ": calc_AUC(
