def settingSE8():
d = 2
n = 20
T = 10000
print("Setting 8 SE started at " + str(datetime.datetime.now()),
flush=True)
exp = Experiment()
exp.initialize(timesteps=T, n_runs=n)
#problem
exp.add_problem('ExperimentSetting8-v0', name='VIII')
exp.add_method('RealAR', {
'p': 16,
'optimizer': RealOGD(hyperparameters={'lr': 10.0})
},
name='ARMA-OGD')
exp.add_method(
'RealAR', {
'p': 64,
'optimizer': RealONS(hyperparameters={
'eps': 0.1,
'eta': 0.1
})
},
name='ARMA-ONS')
exp.add_method('ArimaAR', {
'p': 16,
'd': d,
'optimizer': RealOGD(hyperparameters={'lr': 10.0})
},
name='ARIMA-OGD')
exp.add_method(
'ArimaAR', {
'p': 16,
'd': d,
'optimizer': RealONS(hyperparameters={
'eps': 10.0,
'eta': 1.0
})
},
name='ARIMA-ONS')
exp.add_method('HedgeAR', {'p': 16, 'c': -1}, name='Hedge-FTRL')
exp.add_method('HedgeARSE', {'p': 16, 'c': -1}, name='Hedge-ONS')
print("Setting 8 SE finished at " + str(datetime.datetime.now()),
flush=True)
exp.scoreboard(n_digits=10)
exp.graph(save_as=store_directory + "papersetting8SE.pdf",
avg_regret=True,
size=15,
start_time=100,
dpi=100,
save_csv_path=store_directory + 'papersetting8SE')
def settingAE4():
ar_dyn = np.array([0.6, -0.5, 0.4, -0.4, 0.3])
ma_dyn = np.array([0.3, -0.2])
mean, noise_magnitude = 0, 0.3
d = 2
n = 20
T = 10000
print("Setting ae 4 started at " + str(datetime.datetime.now()),
flush=True)
exp = Experiment()
exp.initialize(metrics=['ae'], timesteps=T, n_runs=n)
#problem
exp.add_problem('ExperimentSetting4-v0', {
'p': ar_dyn,
'q': ma_dyn,
'c': mean,
'noise_magnitude': noise_magnitude
},
name='IV')
exp.add_method('RealAR', {
'p': 16,
'optimizer': RealOGD(loss=ae, hyperparameters={'lr': 0.1})
},
name='ARMA-OGD')
exp.add_method('ArimaAR', {
'p': 16,
'd': d,
'optimizer': RealOGD(loss=ae, hyperparameters={'lr': 1.0})
},
name='ARIMA-OGD')
exp.add_method('HedgeAR', {
'p': 16,
'c': -1,
'loss': ae
},
name='Hedge-FTRL')
print("Setting ae 4 finished at " + str(datetime.datetime.now()),
flush=True)
exp.scoreboard(n_digits=10, metric='ae')
exp.graph(save_as=store_directory + "papersetting4_AE.pdf",
avg_regret=True,
size=15,
start_time=100,
metric='ae',
dpi=100,
save_csv_path=store_directory + 'papersetting4_AE')
def settingSE1():
ar_dyn = np.array([0.6, -0.5, 0.4, -0.4, 0.3])
ma_dyn = np.array([0.3, -0.2])
mean, noise_magnitude = 0, 0.3
n = 20
T = 10000
print("Setting 1 SE started at " + str(datetime.datetime.now()),
flush=True)
exp = Experiment()
exp.initialize(timesteps=T, n_runs=n)
#problem
exp.add_problem('ARMA-v0', {
'p': ar_dyn,
'q': ma_dyn,
'c': mean,
'noise_magnitude': noise_magnitude
},
name='I')
exp.add_method(
'RealAR', {
'p': 8,
'optimizer': RealONS(hyperparameters={
'eps': 1.0,
'eta': 1.0
})
},
name='ARMA-ONS')
exp.add_method('RealAR', {
'p': 8,
'optimizer': RealOGD(hyperparameters={'lr': 10})
},
name='ARMA-OGD')
exp.add_method('HedgeAR', {'p': 16, 'c': -1}, name='Hedge-FTRL')
exp.add_method('HedgeARSE', {'p': 16, 'c': -1}, name='Hedge-ONS')
print("Setting 1 SE finished at " + str(datetime.datetime.now()),
flush=True)
exp.scoreboard(n_digits=10)
exp.graph(save_as=store_directory + "papersetting1SE.pdf",
avg_regret=True,
size=15,
start_time=100,
dpi=100,
save_csv_path=store_directory + 'papersetting1SE')
def settingAE8():
d = 2
n = 20
T = 10000
print("Setting ae 8 started at " + str(datetime.datetime.now()),
flush=True)
exp = Experiment()
exp.initialize(metrics=['ae'], timesteps=T, n_runs=n)
#problem
exp.add_problem('ExperimentSetting8-v0', name='VIII')
exp.add_method('RealAR', {
'p': 8,
'optimizer': RealOGD(loss=ae, hyperparameters={'lr': 10.0})
},
name='ARMA-OGD')
exp.add_method('ArimaAR', {
'p': 16,
'd': d,
'optimizer': RealOGD(loss=ae, hyperparameters={'lr': 0.1})
},
name='ARIMA-OGD')
exp.add_method('HedgeAR', {
'p': 16,
'c': -1,
'loss': ae
},
name='Hedge-FTRL')
print("Setting ae 8 finished at " + str(datetime.datetime.now()),
flush=True)
exp.scoreboard(n_digits=10, metric='ae')
exp.graph(save_as=store_directory + "papersetting8_AE.pdf",
avg_regret=True,
size=15,
start_time=100,
dpi=100,
metric='ae',
save_csv_path=store_directory + "papersetting8_MA")