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RunGPOptim.py
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RunGPOptim.py
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import configparser
import os
import sys
import warnings
import argparse
import itertools
import pandas as pd
from sklearn.gaussian_process.kernels import RBF, Matern, RationalQuadratic, ExpSineSquared, ConstantKernel, WhiteKernel
from tqdm import tqdm
from training import TrainHelper, ModelsGaussianProcessRegression
def run_gp_optim(company: str, target_column: str, split_perc: float, imputation: str, featureset: str):
"""
Run GPR offline optimization loop
:param company: prefix for data in case company data is also used
:param target_column: target column to use
:param split_perc: share of train data
:param imputation: imputation method
: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, company=company, target_column=target_column,
split_perc=split_perc)
# load datasets
datasets = TrainHelper.load_datasets(config=config, company=company, target_column=target_column)
# prepare parameter grid
kernels = []
base_kernels = [ConstantKernel(constant_value=1000, constant_value_bounds=(1e-5, 1e5)),
Matern(length_scale=1.0, length_scale_bounds=(1e-5, 1e5)),
ExpSineSquared(length_scale=1.0, periodicity=seasonal_periods,
length_scale_bounds=(1e-5, 1e5),
periodicity_bounds=(int(seasonal_periods * 0.8), int(seasonal_periods*1.2))),
RBF(length_scale=1.0, length_scale_bounds=(1e-5, 1e5)),
RationalQuadratic(length_scale=1.0, alpha=1.0,
length_scale_bounds=(1e-5, 1e5), alpha_bounds=(1e-5, 1e5)),
WhiteKernel(noise_level=1.0, noise_level_bounds=(1e-5, 1e5))]
TrainHelper.extend_kernel_combinations(kernels=kernels, base_kernels=base_kernels)
param_grid = {'dataset': [datasets[0]],
'imputation': [imputation],
'featureset': [featureset],
'dim_reduction': ['None', 'pca'],
'kernel': kernels,
'alpha': [1e-5, 1e-3, 1e-1, 1, 1e1, 1e3],
'n_restarts_optimizer': [0, 5, 10],
'standardize': [False, True],
'norm_y': [False, True],
'osa': [False]
}
# random sample from parameter grid
sample_share = 0.1
params_lst = TrainHelper.random_sample_parameter_grid(param_grid=param_grid, sample_share=sample_share)
doc_results = None
best_rmse = 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']
kernel = params_lst[i]['kernel']
alpha = params_lst[i]['alpha']
n_restarts_optimizer = params_lst[i]['n_restarts_optimizer']
stand = params_lst[i]['standardize']
norm_y = params_lst[i]['norm_y']
one_step_ahead = params_lst[i]['osa']
# dim_reduction can only be 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
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 = ModelsGaussianProcessRegression.GaussianProcessRegression(target_column=target_column,
seasonal_periods=seasonal_periods,
kernel=kernel,
alpha=alpha,
n_restarts_optimizer=
n_restarts_optimizer,
one_step_ahead=one_step_ahead,
standardize=stand,
normalize_y=norm_y)
cross_val_dict = model.train(train=train, cross_val_call=True)
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, 'alpha': alpha, 'n_restarts_optimizer': n_restarts_optimizer,
'standardize': stand, 'normalize_y': norm_y, 'one_step_ahead': one_step_ahead,
'optimized_kernel': model.model.kernel_}
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 = TrainHelper.print_best_vals(evaluation_dict=evaluation_dict, best_rmse=best_rmse, 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, 'alpha': alpha, 'n_restarts_optimizer': n_restarts_optimizer,
'standardize': stand, 'normalize_y': norm_y, 'one_step_ahead': one_step_ahead,
'optimized_kernel': '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=company+'-gp-sklearn_raw', 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.')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-company", "--company_name", type=str, default='General', help="specify company name")
parser.add_argument("-tc", "--target_column", type=str, default='PotTotal', help="specify target column")
parser.add_argument("-splitperc", "--split_percentage", type=float, default=0.8,
help="specify share of train set")
args = parser.parse_args()
company = args.company_name
target_column = args.target_column
split_perc = args.split_percentage
imputations = ['mean']
featuresets = ['full']
imp_feat_combis = list(itertools.product(*[imputations, featuresets]))
for (imputation, featureset) in imp_feat_combis:
new_pid = os.fork()
if new_pid == 0:
run_gp_optim(company=company, target_column=target_column, split_perc=split_perc,
imputation=imputation, featureset=featureset)
sys.exit()
else:
os.waitpid(new_pid, 0)
print('finished run with ' + featureset + ' ' + str('None' if imputation is None else imputation))