def temporal_kernel(self):
kernel = White(variance=self.model_config['noise_inner'])
m_inds = list(range(self.m))
# Initialize a non-linear kernels over inputs
if self.model_config['input_nonlinear']:
scales = [self.model_config['scale']
] * self.m if self.model_config[
'scale_tie'] else self.model_config['scale']
if self.model_config['rq']:
kernel += RationalQuadratic(active_dims=m_inds,
variance=1.0,
lengthscales=scales,
alpha=1e-2)
else:
kernel += SquaredExponential(active_dims=m_inds,
variance=1.0,
lengthscales=scales)
# Add a periodic kernel over inputs
# Decay?????
if self.model_config['per']:
scales = [self.model_config['per_scale']] * self.m
periods = [self.model_config['per_period']] * self.m
base_kernel = SquaredExponential(active_dims=m_inds,
variance=1.0,
lengthscales=scales)
kernel += Periodic(base_kernel, period=periods)
# Add a linear kernel over inputs
if self.model_config['input_linear']:
variances = [self.model_config['input_linear_scale']] * self.m
kernel += LinearKernel(active_dims=m_inds, variance=variances)
return kernel
def _kernels_generator(self):
def _determine_indicies(m, pi, markov):
# Build in the Markov structure: juggle with the indices of the outputs.
p_last = pi - 1 # Index of last output that is given as input.
p_start = 0 if markov is None else max(p_last - (markov - 1), 0)
p_num = p_last - p_start + 1
# Determine the indices corresponding to the outputs and inputs.
m_inds = list(range(m))
p_inds = list(range(m + p_start, m + p_last + 1))
return m_inds, p_inds, p_num
kernels = []
for pi in range(self.num_outputs):
m_inds, p_inds, p_num = _determine_indicies(
self.m, pi, self.model_config['markov'])
# Construct inner-layers noise kernel
kernel = White(variance=self.model_config['noise_inner'])
# Initialize a non-linear kernels over inputs
#if pi==0:
scales = [self.model_config['scale']
] * self.m if self.model_config[
'scale_tie'] else self.model_config['scale']
if self.model_config['rq']:
kernel += RationalQuadratic(active_dims=m_inds,
variance=1.0,
lengthscales=scales,
alpha=1e-2)
else:
kernel += SquaredExponential(active_dims=m_inds,
variance=1.0,
lengthscales=scales)
# Add a periodic kernel over inputs
# Decay?????
if self.model_config['per']:
scales = [self.model_config['per_scale']] * self.m
periods = [self.model_config['per_period']] * self.m
base_kernel = SquaredExponential(active_dims=m_inds,
variance=1.0,
lengthscales=scales)
kernel += Periodic(base_kernel, period=periods)
# Add a linear kernel over inputs
if self.model_config['input_linear']:
variances = [self.model_config['input_linear_scale']] * self.m
kernel += LinearKernel(active_dims=m_inds, variance=variances)
# Add a linear kernel over outputs
if self.model_config['linear'] and pi > 0:
variances = [self.model_config['linear_scale']] * p_num
kernel += LinearKernel(active_dims=p_inds, variance=variances)
# Add a non-linear kernel over outputs
if self.model_config['nonlinear'] and pi > 0:
if self.model_config['nonlinear_dependent']:
active_dims = m_inds.extend(p_inds)
scales = [self.model_config['scale']] * self.m
scales.extend([self.model_config['nonlinear_scale']] *
p_num)
else:
active_dims = p_inds
scales = [self.model_config['nonlinear_scale']] * p_num
if self.model_config['rq']:
kernel += RationalQuadratic(active_dims=active_dims,
variance=1.0,
lengthscales=scales,
alpha=1e-2)
else:
kernel += SquaredExponential(active_dims=active_dims,
variance=1.0,
lengthscales=scales)
kernels.append(kernel)
return kernels
def run_gp_optim(target_column: str, split_perc: float, imputation: str,
featureset: str):
"""
Run whole GPR 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
"""
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
kernels = []
base_kernels = [
SquaredExponential(),
Matern52(),
White(),
RationalQuadratic(),
Polynomial()
]
for kern in base_kernels:
if isinstance(kern, IsotropicStationary):
base_kernels.append(Periodic(kern, period=seasonal_periods))
TrainHelper.extend_kernel_combinations(kernels=kernels,
base_kernels=base_kernels)
param_grid = {
'dataset': datasets,
'imputation': [imputation],
'featureset': [featureset],
'dim_reduction': ['None', 'pca'],
'kernel': kernels,
'mean_function': [None, gpflow.mean_functions.Constant()],
'noise_variance': [0.01, 1, 10, 100],
'optimizer': [gpflow.optimizers.Scipy()],
'standardize_x': [False, True],
'standardize_y': [False, True],
'osa': [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']
# deepcopy to prevent impact of previous optimizations
kernel = gpflow.utilities.deepcopy(params_lst[i]['kernel'])
mean_fct = gpflow.utilities.deepcopy(params_lst[i]['mean_function'])
noise_var = params_lst[i]['noise_variance']
optimizer = gpflow.utilities.deepcopy(params_lst[i]['optimizer'])
stand_x = params_lst[i]['standardize_x']
stand_y = params_lst[i]['standardize_y']
one_step_ahead = params_lst[i]['osa']
# 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
kernel_string, mean_fct_string, optimizer_string = get_docresults_strings(
kernel=kernel, mean_function=mean_fct, optimizer=optimizer)
sum_dict = None
try:
for train, test in train_test_list:
model = ModelsGPR.GaussianProcessRegressionGPFlow(
target_column=target_column,
seasonal_periods=seasonal_periods,
kernel=kernel,
mean_function=mean_fct,
noise_variance=noise_var,
optimizer=optimizer,
standardize_x=stand_x,
standardize_y=stand_y,
one_step_ahead=one_step_ahead)
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,
'kernel':
kernel_string,
'mean_function':
mean_fct_string,
'noise_variance':
noise_var,
'optimizer':
optimizer_string,
'standardize_x':
stand_x,
'standardize_y':
stand_y,
'one_step_ahead':
one_step_ahead,
'optim_mod_params':
model.model.parameters
}
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,
'kernel':
kernel_string,
'mean_function':
mean_fct_string,
'noise_variance':
noise_var,
'optimizer':
optimizer_string,
'standardize_x':
stand_x,
'standardize_y':
stand_y,
'one_step_ahead':
one_step_ahead,
'optim_mod_params':
'failed'
}
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='gpr',
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.')