rmse_dict = {}
rmse_ratio_dict = {}
n_cps_detected_dict = {}
n_refits_dict = {}
rmse_base_dict = {}
# iterate over all seasonal lengths
for seas_len in seasons_list:
print('+++++++++++ Seasonal Length ' + str(seas_len) + ' +++++++++++')
# create base data
season_length = seas_len
X = TrainHelper.get_periodic_noisy_x(x_base=np.linspace(
-0.5 * math.pi, 1.5 * math.pi, season_length),
n_periods=n_periods)
Y = TrainHelper.noisy_sin(X)
data = pd.DataFrame(columns=['X', 'Y'])
data['X'] = X
data['Y'] = Y
train_ind = int(0.6 * data.shape[0])
train = data[0:train_ind]
# Train offline base model
target_column = 'Y'
kernel = ExpSineSquared()
alpha = 0.1
n_restarts_optimizer = 10
standardize = False
normalize_y = True
model_sine = ModelsGaussianProcessRegression.GaussianProcessRegression(
target_column=target_column,
seasonal_periods=season_length,
# CPD Params
cpd = args.change_point_detection
const_hazard_factor = args.constant_hazard_factor
const_hazard = args.constant_hazard
const_hazard = const_hazard_factor * season_length if const_hazard == 9999 else const_hazard
cf_r = args.changefinder_r
cf_order = args.changefinder_order
cf_smooth = args.changefinder_smooth
cf_thr_perc = args.changefinder_threshold_percentile
# get base data
X = TrainHelper.get_periodic_noisy_x(x_base=np.linspace(
-0.5 * math.pi, 1.5 * math.pi, season_length),
n_periods=n_periods,
noise=noise)
Y = TrainHelper.noisy_sin(X, noise=noise, offset=offset)
data = pd.DataFrame(columns=['X', 'Y'])
data['X'] = X
data['Y'] = Y
train_ind = int(0.6 * data.shape[0])
train = data[0:train_ind]
# Train offline base model
target_column = 'Y'
kernel = ExpSineSquared()
alpha = 0.1
n_restarts_optimizer = 10
standardize = False
normalize_y = True
model_sine = ModelsGaussianProcessRegression.GaussianProcessRegression(
target_column=target_column,