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RunXGBOptim.py
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RunXGBOptim.py
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import configparser
import os
import sys
import warnings
import pandas as pd
from tqdm import tqdm
from training import TrainHelper, ModelXGBoost
def run_xgb_optim(target_column: str, split_perc: float, imputation: str, featureset: str):
"""
Run whole XGB 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
param_grid = {'dataset': datasets,
'imputation': [imputation],
'featureset': [featureset],
'dim_reduction': ['None', 'pca'],
'learning_rate': [0.05, 0.1, 0.3],
'max_depth': [3, 5, 10],
'subsample': [0.3, 0.7, 1],
'n_estimators': [10, 100, 1000],
'gamma': [0, 1, 10],
'alpha': [0, 0.1, 1, 10],
'reg_lambda': [0, 0.1, 1, 10],
'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']
learning_rate = params_lst[i]['learning_rate']
max_depth = params_lst[i]['max_depth']
subsample = params_lst[i]['subsample']
n_estimators = params_lst[i]['n_estimators']
gamma = params_lst[i]['gamma']
alpha = params_lst[i]['alpha']
reg_lambda = params_lst[i]['reg_lambda']
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
sum_dict = None
try:
for train, test in train_test_list:
model = ModelXGBoost.XGBoostRegression(target_column=target_column,
seasonal_periods=seasonal_periods,
learning_rate=learning_rate,
max_depth=max_depth, subsample=subsample,
n_estimators=n_estimators, gamma=gamma, alpha=alpha,
reg_lambda=reg_lambda, 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,
'learning_rate': learning_rate, 'max_depth': max_depth, 'subsample': subsample,
'n_estimators': n_estimators, 'gamma': gamma, 'alpha': alpha, 'lambda': reg_lambda,
'one_step_ahead': one_step_ahead}
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,
'learning_rate': learning_rate, 'max_depth': max_depth, 'subsample': subsample,
'n_estimators': n_estimators, 'gamma': gamma, 'alpha': alpha, 'lambda': reg_lambda,
'one_step_ahead': one_step_ahead}
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='xgb', 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__':
target_column = str(sys.argv[1])
split_perc = float(sys.argv[2])
imputations = [None, 'mean', 'iterative', 'knn']
featuresets = ['full', 'cal', 'stat', 'none']
imp_feat_combis = TrainHelper.get_imputation_featureset_combis(imputations=imputations, featuresets=featuresets,
target_column=target_column)
for (imputation, featureset) in imp_feat_combis:
new_pid = os.fork()
if new_pid == 0:
run_xgb_optim(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))