def run_sarimax_optim(target_column: str, split_perc: float, imputation: str,
featureset: str, univariate: bool):
"""
Run whole (S)ARIMA(X) optimization loop
:param target_column: target variable for predictions
:param split_perc: percentage of samples to use for train set
:param imputation: imputation method for missing values
:param featureset: featureset to use
:param univariate: use univariate version SARIMA as well
"""
config = configparser.ConfigParser()
config.read('Configs/dataset_specific_config.ini')
# get optim parameters
base_dir, seasonal_periods, split_perc, init_train_len, test_len, resample_weekly = \
TrainHelper.get_optimization_run_parameters(config=config, target_column=target_column, split_perc=split_perc)
# load datasets
datasets = TrainHelper.load_datasets(config=config,
target_column=target_column)
# prepare parameter grid
param_grid = {
'dataset': datasets,
'imputation': [imputation],
'featureset': [featureset],
'dim_reduction': ['None', 'pca'],
'p': [0, 1, 2, 3],
'd': [0, 1],
'q': [0, 1, 2, 3],
'P': [0, 1, 2, 3],
'D': [0, 1],
'Q': [0, 1, 2, 3],
'osa': [True],
'transf': [False, 'log', 'pw'],
'exog': [True],
'wi': [True]
}
if univariate:
param_grid['exog'] = [False, True]
# random sample from parameter grid
params_lst = TrainHelper.random_sample_parameter_grid(
param_grid=param_grid, sample_share=0.2)
doc_results = None
best_rmse = 5000000.0
best_mape = 5000000.0
best_smape = 5000000.0
dataset_last_name = 'Dummy'
imputation_last = 'Dummy'
dim_reduction_last = 'Dummy'
featureset_last = 'Dummy'
for i in tqdm(range(len(params_lst))):
warnings.simplefilter('ignore')
dataset = params_lst[i]['dataset']
imputation = params_lst[i]['imputation']
featureset = params_lst[i]['featureset']
dim_reduction = None if params_lst[i][
'dim_reduction'] == 'None' else params_lst[i]['dim_reduction']
p = params_lst[i]['p']
d = params_lst[i]['d']
q = params_lst[i]['q']
P = params_lst[i]['P']
D = params_lst[i]['D']
Q = params_lst[i]['Q']
one_step_ahead = params_lst[i]['osa']
transf = params_lst[i]['transf']
power, log = TrainHelper.get_pw_l_for_transf(transf=transf)
use_exog = params_lst[i]['exog']
with_interc = params_lst[i]['wi']
order = [p, d, q]
seasonal_order = [P, D, Q, seasonal_periods]
# dim_reduction only done without NaNs
if imputation is None and dim_reduction is not None:
continue
# dim_reduction does not make sense for few features
if featureset == 'none' and dim_reduction is not None:
continue
if not ((dataset.name == dataset_last_name) and
(imputation == imputation_last) and
(dim_reduction == dim_reduction_last) and
(featureset == featureset_last)):
if resample_weekly and 'weekly' not in dataset.name:
dataset.name = dataset.name + '_weekly'
print(dataset.name + ' ' +
str('None' if imputation is None else imputation) + ' ' +
str('None' if dim_reduction is None else dim_reduction) +
' ' + featureset + ' ' + target_column)
train_test_list = TrainHelper.get_ready_train_test_lst(
dataset=dataset,
config=config,
init_train_len=init_train_len,
test_len=test_len,
split_perc=split_perc,
imputation=imputation,
target_column=target_column,
dimensionality_reduction=dim_reduction,
featureset=featureset)
if dataset.name != dataset_last_name:
best_rmse = 5000000.0
best_mape = 5000000.0
best_smape = 5000000.0
dataset_last_name = dataset.name
imputation_last = imputation
dim_reduction_last = dim_reduction
featureset_last = featureset
sum_dict = None
try:
for train, test in train_test_list:
model = ModelsARIMA.ARIMA(target_column=target_column,
order=order,
seasonal_order=seasonal_order,
one_step_ahead=one_step_ahead,
power_transf=power,
log=log,
use_exog=use_exog,
with_intercept=with_interc)
cross_val_dict = model.train(train=train, cross_val_call=False)
eval_dict = model.evaluate(train=train, test=test)
eval_dict.update(cross_val_dict)
if sum_dict is None:
sum_dict = eval_dict
else:
for k, v in eval_dict.items():
sum_dict[k] += v
evaluation_dict = {
k: v / len(train_test_list)
for k, v in sum_dict.items()
}
params_dict = {
'dataset':
dataset.name,
'featureset':
featureset,
'imputation':
str('None' if imputation is None else imputation),
'dim_reduction':
str('None' if dim_reduction is None else dim_reduction),
'init_train_len':
init_train_len,
'test_len':
test_len,
'split_perc':
split_perc,
'order':
order,
'seasonal_order':
seasonal_order,
'one_step_ahead':
one_step_ahead,
'power_transform':
power,
'log_transform':
log,
'use_exog':
use_exog,
'with_intercept':
with_interc
}
save_dict = params_dict.copy()
save_dict.update(evaluation_dict)
if doc_results is None:
doc_results = pd.DataFrame(columns=save_dict.keys())
doc_results = doc_results.append(save_dict, ignore_index=True)
best_rmse, best_mape, best_smape = TrainHelper.print_best_vals(
evaluation_dict=evaluation_dict,
best_rmse=best_rmse,
best_mape=best_mape,
best_smape=best_smape,
run_number=i)
except KeyboardInterrupt:
print('Got interrupted')
break
except Exception as exc:
print(exc)
params_dict = {
'dataset':
'Failure',
'featureset':
featureset,
'imputation':
str('None' if imputation is None else imputation),
'dim_reduction':
str('None' if dim_reduction is None else dim_reduction),
'init_train_len':
init_train_len,
'test_len':
test_len,
'split_perc':
split_perc,
'order':
order,
'seasonal_order':
seasonal_order,
'one_step_ahead':
one_step_ahead,
'power_transform':
power,
'log_transform':
log,
'use_exog':
use_exog,
'with_intercept':
with_interc
}
save_dict = params_dict.copy()
save_dict.update(TrainHelper.get_failure_eval_dict())
if doc_results is None:
doc_results = pd.DataFrame(columns=save_dict.keys())
doc_results = doc_results.append(save_dict, ignore_index=True)
TrainHelper.save_csv_results(doc_results=doc_results,
save_dir=base_dir + 'OptimResults/',
company_model_desc='sarima-x',
target_column=target_column,
seasonal_periods=seasonal_periods,
datasets=datasets,
featuresets=param_grid['featureset'],
imputations=param_grid['imputation'],
split_perc=split_perc)
print('Optimization Done. Saved Results.')
def run_es_optim(target_column: str, split_perc: float, imputation: str):
"""
Run whole ES optimization loop
:param target_column: target variable for predictions
:param split_perc: percentage of samples to use for train set
:param imputation: imputation method for missing values
"""
config = configparser.ConfigParser()
config.read('Configs/dataset_specific_config.ini')
# get optim parameters
base_dir, seasonal_periods, split_perc, init_train_len, test_len, resample_weekly = \
TrainHelper.get_optimization_run_parameters(config=config, target_column=target_column, split_perc=split_perc)
# load datasets
datasets = TrainHelper.load_datasets(config=config,
target_column=target_column)
# prepare parameter grid
param_grid = {
'dataset': datasets,
'imputation': [imputation],
'trend': ['add', None],
'damp': [False, True],
'seasonality': ['add', 'mul', None],
'remove_bias': [False, True],
'brute': [False, True],
'osa': [True],
'transf': [False, 'log', 'pw']
}
# random sample from parameter grid
params_lst = sorted(list(
sklearn.model_selection.ParameterSampler(
param_distributions=param_grid,
n_iter=int(
1 * MixedHelper.get_product_len_dict(dictionary=param_grid)),
random_state=np.random.RandomState(42))),
key=lambda d: (d['dataset'].name, d['imputation']))
doc_results = None
best_rmse = 5000000.0
best_mape = 5000000.0
best_smape = 5000000.0
dataset_last_name = 'Dummy'
imputation_last = 'Dummy'
for i in tqdm(range(len(params_lst))):
warnings.simplefilter('ignore')
dataset = params_lst[i]['dataset']
imputation = params_lst[i]['imputation']
tr = params_lst[i]['trend']
damp = params_lst[i]['damp']
season = params_lst[i]['seasonality']
remo_bias = params_lst[i]['remove_bias']
brute = params_lst[i]['brute']
one_step_ahead = params_lst[i]['osa']
transf = params_lst[i]['transf']
power, log = TrainHelper.get_pw_l_for_transf(transf=transf)
if not ((dataset.name == dataset_last_name) and
(imputation == imputation_last)):
if resample_weekly and 'weekly' not in dataset.name:
dataset.name = dataset.name + '_weekly'
print(dataset.name + ' ' +
str('None' if imputation is None else imputation) + ' ' +
target_column)
train_test_list = TrainHelper.get_ready_train_test_lst(
dataset=dataset,
config=config,
init_train_len=init_train_len,
test_len=test_len,
split_perc=split_perc,
imputation=imputation,
target_column=target_column,
reset_index=True)
if dataset.name != dataset_last_name:
best_rmse = 5000000.0
best_mape = 5000000.0
best_smape = 5000000.0
dataset_last_name = dataset.name
imputation_last = imputation
sum_dict = None
try:
for train, test in train_test_list:
model = ModelsES.ExponentialSmoothing(
target_column=target_column,
trend=tr,
damped=damp,
seasonal=season,
seasonal_periods=seasonal_periods,
remove_bias=remo_bias,
use_brute=brute,
one_step_ahead=one_step_ahead,
power_transf=power,
log=log)
cross_val_dict = model.train(train=train, cross_val_call=False)
eval_dict = model.evaluate(train=train, test=test)
eval_dict.update(cross_val_dict)
if sum_dict is None:
sum_dict = eval_dict
else:
for k, v in eval_dict.items():
sum_dict[k] += v
evaluation_dict = {
k: v / len(train_test_list)
for k, v in sum_dict.items()
}
params_dict = {
'dataset': dataset.name,
'imputation':
str('None' if imputation is None else imputation),
'init_train_len': init_train_len,
'test_len': test_len,
'split_perc': split_perc,
'trend': tr,
'damped': damp,
'seasonal': season,
'seasonal_periods': seasonal_periods,
'remove_bias': remo_bias,
'use_brute': brute,
'one_step_ahead': one_step_ahead,
'power_transform': power,
'log': log
}
save_dict = params_dict.copy()
save_dict.update(evaluation_dict)
if doc_results is None:
doc_results = pd.DataFrame(columns=save_dict.keys())
doc_results = doc_results.append(save_dict, ignore_index=True)
best_rmse, best_mape, best_smape = TrainHelper.print_best_vals(
evaluation_dict=evaluation_dict,
best_rmse=best_rmse,
best_mape=best_mape,
best_smape=best_smape,
run_number=i)
except KeyboardInterrupt:
print('Got interrupted')
break
except Exception as exc:
print(exc)
params_dict = {
'dataset': 'Failure',
'imputation':
str('None' if imputation is None else imputation),
'init_train_len': init_train_len,
'test_len': test_len,
'split_perc': split_perc,
'trend': tr,
'damped': damp,
'seasonal': season,
'seasonal_periods': seasonal_periods,
'remove_bias': remo_bias,
'use_brute': brute,
'one_step_ahead': one_step_ahead,
'power_transform': power,
'log': log
}
save_dict = params_dict.copy()
save_dict.update(TrainHelper.get_failure_eval_dict())
if doc_results is None:
doc_results = pd.DataFrame(columns=save_dict.keys())
doc_results = doc_results.append(save_dict, ignore_index=True)
TrainHelper.save_csv_results(doc_results=doc_results,
save_dir=base_dir + 'OptimResults/',
company_model_desc='es',
target_column=target_column,
seasonal_periods=seasonal_periods,
datasets=datasets,
imputations=param_grid['imputation'],
split_perc=split_perc)
print('Optimization Done. Saved Results.')