-
Notifications
You must be signed in to change notification settings - Fork 5
/
RunESOptim.py
executable file
·151 lines (141 loc) · 7.71 KB
/
RunESOptim.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
import os
import sys
import warnings
import configparser
import numpy as np
import pandas as pd
import sklearn
from tqdm import tqdm
from training import TrainHelper, ModelsES
from utils import MixedHelper
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.')
if __name__ == '__main__':
target_column = str(sys.argv[1])
split_perc = float(sys.argv[2])
# univariate method -> imputation after statistical features not needed for raw dataset without missing values
config = configparser.ConfigParser()
config.read('Configs/dataset_specific_config.ini')
imputations = [None]
if config[target_column]['univariate_imputation_needed']:
imputations = ['mean', 'iterative', 'knn']
for imputation in imputations:
new_pid = os.fork()
if new_pid == 0:
run_es_optim(target_column=target_column, split_perc=split_perc, imputation=imputation)
sys.exit()
else:
os.waitpid(new_pid, 0)
print('finished run with ' + str('None' if imputation is None else imputation))