def conduct_BOCPD(n_trials, n_samples, dataset, changepoints,
tolerance_delay): # BOCPD
# hyperparameter tuning
objective_BOCPD = partial(_objective_BOCPD,
train=dataset[0],
changepoints=changepoints,
tolerance_delay=tolerance_delay)
study = optuna.create_study()
study.optimize(objective_BOCPD, n_trials=n_trials, n_jobs=-1)
opt_lam = study.best_params['lam']
opt_alpha = study.best_params['alpha']
opt_beta = study.best_params['beta']
opt_kappa = study.best_params['kappa']
opt_mu = study.best_params['mu']
# optimal threshold
h = partial(bocpd.constant_hazard, opt_lam)
lik = bocpd.StudentT(opt_alpha, opt_beta, opt_kappa, opt_mu)
retrospective = bocpd.Retrospective(hazard_func=h, likelihood_func=lik)
opt_threshold = calc_opt_threshold(train=dataset[0],
changepoints=changepoints,
tolerance_delay=tolerance_delay,
retrospective=retrospective)
# calculate metrics
calc_metrics = partial(_calc_metrics,
dataset=dataset,
changepoints=changepoints,
tolerance_delay=tolerance_delay,
threshold=opt_threshold,
retrospective=retrospective)
p = Pool(multi.cpu_count() - 1)
args = list(range(1, n_samples))
res = np.array(p.map(calc_metrics, args))
p.close()
# result
print("F1 score: ", np.mean(res[:, 0]), "±", np.std(res[:, 0]))
print("precision: ", np.mean(res[:, 1]), "±", np.std(res[:, 1]))
print("recall: ", np.mean(res[:, 2]), "±", np.std(res[:, 2]))
row = pd.DataFrame({
"method": ["BOCPD"],
"F1_score_mean": np.mean(res[:, 0]),
"F1_score_std": np.std(res[:, 0]),
"precision_mean": np.mean(res[:, 1]),
"precision_std": np.std(res[:, 1]),
"recall_mean": np.mean(res[:, 2]),
"recall_std": np.std(res[:, 2])
})
return row
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)
F1_score, _, _, _ = calc_F1_score(scores, changepoints, tolerance_delay)
return -F1_score
def _objective_BOCPD(trial, train, changepoints, tolerance_delay):
lam = trial.suggest_int('lam', 2, 100)
alpha = trial.suggest_uniform('alpha', 1e-8, 10)
beta = trial.suggest_uniform('beta', 1e-8, 0.001)
kappa = trial.suggest_uniform('kappa', 1e-8, 0.001)
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 = retrospective.calc_scores(train)
AUC = calc_AUC(scores, changepoints, tolerance_delay, both=both)
return -AUC
train=X,
changepoints=changepoints,
tolerance_delay=tolerance_delay)
study = optuna.create_study()
study.optimize(objective_BOCPD, n_trials=n_trials, n_jobs=-1)
# calculate scores
opt_lam = study.best_params['lam']
opt_alpha = study.best_params['alpha']
opt_beta = study.best_params['beta']
opt_kappa = study.best_params['kappa']
#opt_mu = study.best_params['mu']
opt_mu = 0
h = partial(bocpd.constant_hazard, opt_lam)
lik = bocpd.StudentT(opt_alpha, opt_beta, opt_kappa, opt_mu)
retrospective = bocpd.Retrospective(hazard_func=h, likelihood_func=lik)
bocpd_scores = np.zeros((8, 26450))
for i in range(8):
bocpd_scores[i] = retrospective.calc_scores(X[i])
bocpd_scores = np.nanmax(bocpd_scores, axis=0)
# CF
def _objective_CF(trial, train, changepoints, tolerance_delay):
# hyperparameters
r = trial.suggest_uniform('r', 0.01, 0.99)
def test_main():
np.random.seed(0)
tolerance_delay = 100
transition_period = 200
data, changepoints = one_mean_changing(transition_period=transition_period)
name = "ChangeFinder"
retrospective = changefinder.Retrospective(r=0.1, order=5, smooth=5)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "BOCPD"
h = partial(bocpd.constant_hazard, 1000)
lik = bocpd.StudentT(0.5, 5, 5, 0)
retrospective = bocpd.Retrospective(hazard_func=h, likelihood_func=lik)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "SDMDL_0th"
nml_gaussian = partial(sdmdl_nml.nml_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
complexity_gaussian = partial(sdmdl_nml.complexity_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
retrospective = sdmdl.Retrospective(h=100, encoding_func=nml_gaussian,
complexity_func=complexity_gaussian, order=0)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "SDMDL_1st"
nml_gaussian = partial(sdmdl_nml.nml_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
complexity_gaussian = partial(sdmdl_nml.complexity_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
retrospective = sdmdl.Retrospective(h=100, encoding_func=nml_gaussian,
complexity_func=complexity_gaussian, order=1)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "SDMDL_2nd"
nml_gaussian = partial(sdmdl_nml.nml_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
complexity_gaussian = partial(sdmdl_nml.complexity_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
retrospective = sdmdl.Retrospective(h=100, encoding_func=nml_gaussian,
complexity_func=complexity_gaussian, order=2)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "Hierarchical_SCAW1_0th"
lnml_gaussian = partial(hsdmdl1_nml.lnml_gaussian)
retrospective = hsdmdl1.Retrospective(encoding_func=lnml_gaussian, d=2, min_datapoints=5, delta_0=0.05,
delta_1=0.05, delta_2=0.05, how_to_drop='all', order=0, reliability=True)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "Hierarchical_SCAW1_1st"
lnml_gaussian = partial(hsdmdl1_nml.lnml_gaussian)
retrospective = hsdmdl1.Retrospective(encoding_func=lnml_gaussian, d=2, min_datapoints=5, delta_0=0.05,
delta_1=0.05, delta_2=0.05, how_to_drop='all', order=1, reliability=True)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "Hierarchical_SCAW1_2nd"
lnml_gaussian = partial(hsdmdl1_nml.lnml_gaussian)
retrospective = hsdmdl1.Retrospective(encoding_func=lnml_gaussian, d=2, min_datapoints=5, delta_0=0.05,
delta_1=0.05, delta_2=0.05, how_to_drop='all', order=2, reliability=True)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "Hierarchical_SCAW2_0th"
nml_gaussian = partial(hsdmdl2_nml.nml_gaussian)
retrospective = hsdmdl2.Retrospective(encoding_func=nml_gaussian, d=2, M=5, min_datapoints=5, delta_0=0.05,
delta_1=0.05, delta_2=0.05, how_to_drop='all', order=0, reliability=True)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "Hierarchical_SCAW2_1st"
nml_gaussian = partial(hsdmdl2_nml.nml_gaussian)
retrospective = hsdmdl2.Retrospective(encoding_func=nml_gaussian, d=2, M=5, min_datapoints=5, delta_0=0.05,
delta_1=0.05, delta_2=0.05, how_to_drop='all', order=1, reliability=True)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "Hierarchical_SCAW2_2nd"
nml_gaussian = partial(hsdmdl2_nml.nml_gaussian)
retrospective = hsdmdl2.Retrospective(encoding_func=nml_gaussian, d=2, M=5, min_datapoints=5, delta_0=0.05,
delta_1=0.05, delta_2=0.05, how_to_drop='all', order=2, reliability=True)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "FW2S_MDL"
nml_gaussian = partial(sdmdl_nml.nml_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
complexity_gaussian = partial(sdmdl_nml.complexity_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
retrospective_first = sdmdl.Retrospective(h=100, encoding_func=nml_gaussian,
complexity_func=complexity_gaussian, order=0)
nml_gaussian = partial(sdmdl_nml.nml_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
complexity_gaussian = partial(sdmdl_nml.complexity_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
retrospective_second = sdmdl.Retrospective(h=100, encoding_func=nml_gaussian,
complexity_func=complexity_gaussian, order=0)
retrospective = fw2s_mdl.Retrospective(
retrospective_first, retrospective_second)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)
name = "AW2S_MDL"
nml_gaussian = partial(sdmdl_nml.nml_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
complexity_gaussian = partial(sdmdl_nml.complexity_gaussian, mu_max=1e8,
div_min=1e-8, div_max=1e8)
retrospective_first = sdmdl.Retrospective(h=100, encoding_func=nml_gaussian,
complexity_func=complexity_gaussian, order=0)
lnml_gaussian = partial(hsdmdl2_nml.lnml_gaussian, sigma_given=0.3)
retrospective_second = hsdmdl2.Retrospective(encoding_func=lnml_gaussian, d=2, M=5, min_datapoints=5, delta_0=0.05,
delta_1=0.05, delta_2=0.05, how_to_drop='all', order=0, reliability=True)
retrospective = aw2s_mdl.Retrospective(
retrospective_first, retrospective_second)
_calc_metrics_draw_figure(
name, data, changepoints, tolerance_delay=tolerance_delay, retrospective=retrospective)