def test_plot_optimization_history(direction: str) -> None:
# Test with no studies.
figure = plot_edf(create_study(direction=direction))
assert len(figure.data) == 0
# Test with no trials.
figure = plot_edf(create_study(direction=direction))
assert len(figure.data) == 0
figure = plot_edf(
[create_study(direction=direction),
create_study(direction=direction)])
assert len(figure.data) == 0
# Test with a study.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf(study0)
assert len(figure.data) == 1
# Test with two studies.
study1 = create_study(direction=direction)
study1.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf([study0, study1])
assert len(figure.data) == 2
def test_plot_optimization_history(direction: str) -> None:
# Test with no studies.
figure = plot_edf([])
assert len(figure.data) == 0
# Test with no trials.
figure = plot_edf(create_study(direction=direction))
assert len(figure.data) == 0
figure = plot_edf(
[create_study(direction=direction),
create_study(direction=direction)])
assert len(figure.data) == 0
# Test with a study.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf(study0)
_validate_edf_values(figure.data[0]["y"])
assert len(figure.data) == 1
assert figure.layout.xaxis.title.text == "Objective Value"
# Test with two studies.
study1 = create_study(direction=direction)
study1.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf([study0, study1])
for points in figure.data:
_validate_edf_values(points["y"])
assert len(figure.data) == 2
figure = plot_edf((study0, study1))
for points in figure.data:
_validate_edf_values(points["y"])
assert len(figure.data) == 2
# Test with a customized target value.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
with pytest.warns(UserWarning):
figure = plot_edf(study0, target=lambda t: t.params["x"])
_validate_edf_values(figure.data[0]["y"])
assert len(figure.data) == 1
# Test with a customized target name.
study0 = create_study(direction=direction)
study0.optimize(lambda t: t.suggest_float("x", 0, 5), n_trials=10)
figure = plot_edf(study0, target_name="Target Name")
_validate_edf_values(figure.data[0]["y"])
assert len(figure.data) == 1
assert figure.layout.xaxis.title.text == "Target Name"
def test_inconsistent_number_of_trial_values() -> None:
studies: List[Study] = []
n_studies = 5
for i in range(n_studies):
study = prepare_study_with_trials()
if i % 2 == 0:
study.add_trial(create_trial(value=1.0))
studies.append(study)
figure = plot_edf(studies)
assert len(figure.data) == n_studies
def draw_results(study):
# 优化历史
plt.figure()
fig = pv.plot_optimization_history(study)
fig.write_image("./output/opt_his.png")
plt.close()
# 等高线图
plt.figure()
fig = pv.plot_contour(study)
fig.write_image("./output/opt_contour.png")
plt.close()
# 经验分布图
plt.figure()
fig = pv.plot_edf(study)
fig.write_image("./output/opt_edf.png")
plt.close()
# 高维参数
plt.figure()
fig = pv.plot_parallel_coordinate(study)
fig.write_image("./output/opt_coordinate.png")
plt.close()
# Visualize high-dimensional parameter relationships. See :func:`~optuna.visualization.plot_parallel_coordinate` for the details. plot_parallel_coordinate(study) ################################################################################################### # Select parameters to visualize. plot_parallel_coordinate(study, params=["bagging_freq", "bagging_fraction"]) ################################################################################################### # Visualize hyperparameter relationships. See :func:`~optuna.visualization.plot_contour` for the details. plot_contour(study) ################################################################################################### # Select parameters to visualize. plot_contour(study, params=["bagging_freq", "bagging_fraction"]) ################################################################################################### # Visualize individual hyperparameters as slice plot. See :func:`~optuna.visualization.plot_slice` for the details. plot_slice(study) ################################################################################################### # Select parameters to visualize. plot_slice(study, params=["bagging_freq", "bagging_fraction"]) ################################################################################################### # Visualize parameter importances. See :func:`~optuna.visualization.plot_param_importances` for the details. plot_param_importances(study) ################################################################################################### # Visualize empirical distribution function. See :func:`~optuna.visualization.plot_edf` for the details. plot_edf(study)
def test_target_is_none_and_study_is_multi_obj() -> None:
study = create_study(directions=["minimize", "minimize"])
with pytest.raises(ValueError):
plot_edf(study)
def ml_mlp_mul_ms(station_name="종로구"):
print("Start Multivariate MLP Mean Seasonality Decomposition (MSE) Model")
targets = ["PM10", "PM25"]
# targets = ["SO2", "CO", "O3", "NO2", "PM10", "PM25",
# "temp", "u", "v", "pres", "humid", "prep", "snow"]
# 24*14 = 336
#sample_size = 336
sample_size = 48
output_size = 24
# If you want to debug, fast_dev_run = True and n_trials should be small number
fast_dev_run = False
n_trials = 128
# fast_dev_run = True
# n_trials = 1
# Hyper parameter
epoch_size = 500
batch_size = 64
learning_rate = 1e-3
# Blocked Cross Validation
# neglect small overlap between train_dates and valid_dates
# 11y = ((2y, 0.5y), (2y, 0.5y), (2y, 0.5y), (2.5y, 1y))
train_dates = [(dt.datetime(2008, 1, 4, 1).astimezone(SEOULTZ),
dt.datetime(2009, 12, 31, 23).astimezone(SEOULTZ)),
(dt.datetime(2010, 7, 1, 0).astimezone(SEOULTZ),
dt.datetime(2012, 6, 30, 23).astimezone(SEOULTZ)),
(dt.datetime(2013, 1, 1, 0).astimezone(SEOULTZ),
dt.datetime(2014, 12, 31, 23).astimezone(SEOULTZ)),
(dt.datetime(2015, 7, 1, 0).astimezone(SEOULTZ),
dt.datetime(2017, 12, 31, 23).astimezone(SEOULTZ))]
valid_dates = [(dt.datetime(2010, 1, 1, 0).astimezone(SEOULTZ),
dt.datetime(2010, 6, 30, 23).astimezone(SEOULTZ)),
(dt.datetime(2012, 7, 1, 0).astimezone(SEOULTZ),
dt.datetime(2012, 12, 31, 23).astimezone(SEOULTZ)),
(dt.datetime(2015, 1, 1, 0).astimezone(SEOULTZ),
dt.datetime(2015, 6, 30, 23).astimezone(SEOULTZ)),
(dt.datetime(2018, 1, 1, 0).astimezone(SEOULTZ),
dt.datetime(2018, 12, 31, 23).astimezone(SEOULTZ))]
train_valid_fdate = dt.datetime(2008, 1, 3, 1).astimezone(SEOULTZ)
train_valid_tdate = dt.datetime(2018, 12, 31, 23).astimezone(SEOULTZ)
# Debug
if fast_dev_run:
train_dates = [(dt.datetime(2015, 7, 1, 0).astimezone(SEOULTZ),
dt.datetime(2017, 12, 31, 23).astimezone(SEOULTZ))]
valid_dates = [(dt.datetime(2018, 1, 1, 0).astimezone(SEOULTZ),
dt.datetime(2018, 12, 31, 23).astimezone(SEOULTZ))]
train_valid_fdate = dt.datetime(2015, 7, 1, 0).astimezone(SEOULTZ)
train_valid_tdate = dt.datetime(2018, 12, 31, 23).astimezone(SEOULTZ)
test_fdate = dt.datetime(2019, 1, 1, 0).astimezone(SEOULTZ)
test_tdate = dt.datetime(2020, 10, 31, 23).astimezone(SEOULTZ)
# check date range assumption
assert len(train_dates) == len(valid_dates)
for i, (td, vd) in enumerate(zip(train_dates, valid_dates)):
assert vd[0] > td[1]
assert test_fdate > train_dates[-1][1]
assert test_fdate > valid_dates[-1][1]
train_features = [
"SO2", "CO", "NO2", "PM10", "PM25", "temp", "wind_spd", "wind_cdir",
"wind_sdir", "pres", "humid", "prep"
]
train_features_periodic = [
"SO2", "CO", "NO2", "PM10", "PM25", "temp", "wind_spd", "wind_cdir",
"wind_sdir", "pres", "humid"
]
train_features_nonperiodic = ["prep"]
for target in targets:
print("Training " + target + "...")
output_dir = Path(
f"/mnt/data/MLPMSMultivariate/{station_name}/{target}/")
Path.mkdir(output_dir, parents=True, exist_ok=True)
model_dir = output_dir / "models"
Path.mkdir(model_dir, parents=True, exist_ok=True)
log_dir = output_dir / "log"
Path.mkdir(log_dir, parents=True, exist_ok=True)
_df_h = data.load_imputed(HOURLY_DATA_PATH)
df_h = _df_h.query('stationCode == "' +
str(SEOUL_STATIONS[station_name]) + '"')
if station_name == '종로구' and \
not Path("/input/python/input_jongno_imputed_hourly_pandas.csv").is_file():
# load imputed result
df_h.to_csv("/input/python/input_jongno_imputed_hourly_pandas.csv")
# construct dataset for seasonality
print("Construct Train/Validation Sets...", flush=True)
train_valid_dataset = construct_dataset(train_valid_fdate,
train_valid_tdate,
filepath=HOURLY_DATA_PATH,
station_name=station_name,
target=target,
sample_size=sample_size,
output_size=output_size,
transform=False)
# compute seasonality
train_valid_dataset.preprocess()
# For Block Cross Validation..
# load dataset in given range dates and transform using scaler from train_valid_set
# all dataset are saved in tuple
print("Construct Training Sets...", flush=True)
train_datasets = tuple(
construct_dataset(td[0],
td[1],
scaler_X=train_valid_dataset.scaler_X,
scaler_Y=train_valid_dataset.scaler_Y,
filepath=HOURLY_DATA_PATH,
station_name=station_name,
target=target,
sample_size=sample_size,
output_size=output_size,
features=train_features,
features_periodic=train_features_periodic,
features_nonperiodic=train_features_nonperiodic,
transform=True) for td in train_dates)
print("Construct Validation Sets...", flush=True)
valid_datasets = tuple(
construct_dataset(vd[0],
vd[1],
scaler_X=train_valid_dataset.scaler_X,
scaler_Y=train_valid_dataset.scaler_Y,
filepath=HOURLY_DATA_PATH,
station_name=station_name,
target=target,
sample_size=sample_size,
output_size=output_size,
features=train_features,
features_periodic=train_features_periodic,
features_nonperiodic=train_features_nonperiodic,
transform=True) for vd in valid_dates)
# just single test set
print("Construct Test Sets...", flush=True)
test_dataset = construct_dataset(
test_fdate,
test_tdate,
scaler_X=train_valid_dataset.scaler_X,
scaler_Y=train_valid_dataset.scaler_Y,
filepath=HOURLY_DATA_PATH,
station_name=station_name,
target=target,
sample_size=sample_size,
output_size=output_size,
features=train_features,
features_periodic=train_features_periodic,
features_nonperiodic=train_features_nonperiodic,
transform=True)
# convert tuple of datasets to ConcatDataset
train_dataset = ConcatDataset(train_datasets)
val_dataset = ConcatDataset(valid_datasets)
# num_layer == number of hidden layer
hparams = Namespace(num_layers=1,
layer_size=128,
learning_rate=learning_rate,
batch_size=batch_size)
def objective(trial):
model = BaseMLPModel(
trial=trial,
hparams=hparams,
input_size=sample_size * len(train_features),
sample_size=sample_size,
output_size=output_size,
station_name=station_name,
target=target,
features=train_features,
features_periodic=train_features_periodic,
features_nonperiodic=train_features_nonperiodic,
train_dataset=train_dataset,
val_dataset=val_dataset,
test_dataset=test_dataset,
scaler_X=train_valid_dataset.scaler_X,
scaler_Y=train_valid_dataset.scaler_Y,
output_dir=output_dir)
# most basic trainer, uses good defaults
trainer = Trainer(gpus=1 if torch.cuda.is_available() else None,
precision=32,
min_epochs=1,
max_epochs=20,
default_root_dir=output_dir,
fast_dev_run=fast_dev_run,
logger=True,
checkpoint_callback=False,
callbacks=[
PyTorchLightningPruningCallback(
trial, monitor="valid/MSE")
])
trainer.fit(model)
# Don't Log
# hyperparameters = model.hparams
# trainer.logger.log_hyperparams(hyperparameters)
return trainer.callback_metrics.get("valid/MSE")
if n_trials > 1:
study = optuna.create_study(direction="minimize")
study.enqueue_trial({
'sigma': 1.3,
'num_layers': 4,
'layer_size': 8,
'learning_rate': learning_rate,
'batch_size': batch_size
})
study.enqueue_trial({
'sigma': 1.3,
'num_layers': 4,
'layer_size': 32,
'learning_rate': learning_rate,
'batch_size': batch_size
})
study.enqueue_trial({
'sigma': 1.3,
'num_layers': 4,
'layer_size': 64,
'learning_rate': learning_rate,
'batch_size': batch_size
})
study.enqueue_trial({
'sigma': 1.3,
'num_layers': 4,
'layer_size': 32,
'learning_rate': learning_rate,
'batch_size': batch_size
})
study.enqueue_trial({
'sigma': 1.3,
'num_layers': 8,
'layer_size': 32,
'learning_rate': learning_rate,
'batch_size': batch_size
})
study.enqueue_trial({
'sigma': 1.3,
'num_layers': 12,
'layer_size': 32,
'learning_rate': learning_rate,
'batch_size': batch_size
})
study.enqueue_trial({
'sigma': 0.7,
'num_layers': 4,
'layer_size': 32,
'learning_rate': learning_rate,
'batch_size': batch_size
})
study.enqueue_trial({
'sigma': 2.0,
'num_layers': 4,
'layer_size': 32,
'learning_rate': learning_rate,
'batch_size': batch_size
})
# timeout = 3600*36 = 36h
study.optimize(objective, n_trials=n_trials, timeout=3600 * 36)
trial = study.best_trial
print(" Value: ", trial.value)
print(" Params: ")
for key, value in trial.params.items():
print(" {}: {}".format(key, value))
print("sample_size : ", sample_size)
print("output_size : ", output_size)
# plot optmization results
fig_cont1 = optv.plot_contour(study,
params=['num_layers', 'layer_size'])
fig_cont1.write_image(
str(output_dir / "contour_num_layers_layer_size.png"))
fig_cont1.write_image(
str(output_dir / "contour_num_layers_layer_size.svg"))
fig_edf = optv.plot_edf(study)
fig_edf.write_image(str(output_dir / "edf.png"))
fig_edf.write_image(str(output_dir / "edf.svg"))
fig_iv = optv.plot_intermediate_values(study)
fig_iv.write_image(str(output_dir / "intermediate_values.png"))
fig_iv.write_image(str(output_dir / "intermediate_values.svg"))
fig_his = optv.plot_optimization_history(study)
fig_his.write_image(str(output_dir / "opt_history.png"))
fig_his.write_image(str(output_dir / "opt_history.svg"))
fig_pcoord = optv.plot_parallel_coordinate(
study, params=['num_layers', 'layer_size'])
fig_pcoord.write_image(str(output_dir / "parallel_coord.png"))
fig_pcoord.write_image(str(output_dir / "parallel_coord.svg"))
fig_slice = optv.plot_slice(study,
params=['num_layers', 'layer_size'])
fig_slice.write_image(str(output_dir / "slice.png"))
fig_slice.write_image(str(output_dir / "slice.svg"))
# set hparams with optmized value
hparams.num_layers = trial.params['num_layers']
hparams.layer_size = trial.params['layer_size']
dict_hparams = copy.copy(vars(hparams))
dict_hparams["sample_size"] = sample_size
dict_hparams["output_size"] = output_size
with open(output_dir / 'hparams.json', 'w') as f:
print(dict_hparams, file=f)
with open(output_dir / 'hparams.csv', 'w') as f:
print(pd.DataFrame.from_dict(dict_hparams, orient='index'),
file=f)
model = BaseMLPModel(hparams=hparams,
input_size=sample_size * len(train_features),
sample_size=sample_size,
output_size=output_size,
station_name=station_name,
target=target,
features=train_features,
features_periodic=train_features_periodic,
features_nonperiodic=train_features_nonperiodic,
train_dataset=train_dataset,
val_dataset=val_dataset,
test_dataset=test_dataset,
scaler_X=train_valid_dataset.scaler_X,
scaler_Y=train_valid_dataset.scaler_Y,
output_dir=output_dir)
# record input
for i, _train_set in enumerate(train_datasets):
_train_set.to_csv(
model.data_dir /
("df_trainset_{0}_".format(str(i).zfill(2)) + target + ".csv"))
for i, _valid_set in enumerate(valid_datasets):
_valid_set.to_csv(
model.data_dir /
("df_validset_{0}_".format(str(i).zfill(2)) + target + ".csv"))
train_valid_dataset.to_csv(model.data_dir /
("df_trainvalidset_" + target + ".csv"))
test_dataset.to_csv(model.data_dir / ("df_testset_" + target + ".csv"))
checkpoint_callback = pl.callbacks.ModelCheckpoint(os.path.join(
model_dir, "train_{epoch}_{valid/MSE:.2f}"),
monitor="valid/MSE",
period=10)
early_stop_callback = EarlyStopping(monitor='valid/MSE',
min_delta=0.001,
patience=30,
verbose=True,
mode='min')
log_version = dt.date.today().strftime("%y%m%d-%H-%M")
loggers = [ \
TensorBoardLogger(log_dir, version=log_version),
CSVLogger(log_dir, version=log_version)]
# most basic trainer, uses good defaults
trainer = Trainer(gpus=1 if torch.cuda.is_available() else None,
precision=32,
min_epochs=1,
max_epochs=epoch_size,
default_root_dir=output_dir,
fast_dev_run=fast_dev_run,
logger=loggers,
log_every_n_steps=5,
flush_logs_every_n_steps=10,
callbacks=[early_stop_callback],
checkpoint_callback=checkpoint_callback)
trainer.fit(model)
# run test set
trainer.test(ckpt_path=None)
shutil.rmtree(model_dir)
def test_nonfinite_removed(value: int) -> None:
study = prepare_study_with_trials(value_for_first_trial=value)
figure = plot_edf(study)
assert all(np.isfinite(figure.data[0]["x"]))
def test_nonfinite_multiobjective(objective: int, value: int) -> None:
study = prepare_study_with_trials(n_objectives=2,
value_for_first_trial=value)
figure = plot_edf(study, target=lambda t: t.values[objective])
assert all(np.isfinite(figure.data[0]["x"]))
