queue_time_predict/main.py at main · SUSTech-HPCLab/queue_time_predict · GitHub

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
# from model.LSTMModel import LSTMModel
# from model.LSTMModelClassfier import LSTMModelClassfier
# from model.sklearn_linear_model import SklearnRegressionModel
# from model.sklearn_logistic_regression import SklearnLogisticRegressionModel
# from model.DecisionTreeClassifier import DecisionTreeClassifier
# from model.DecisionTreeRegressor import DecisionTreeRegressor
# from model.GradientBoostingClassifier import GB_Classifier
# from model.GradientBoostingRegressor import GB_Regressor
from preprocess.preprocess_theta import PreprocessorTheta
from preprocess.Preprocessor_mira import PreprocessorMira, RawSample
from preprocess.preprocess_taiyi import PreprocessorTaiyi
# from sample.sample import sample_save, sample_load, to_sample_list
# from sample.class_labeling import Labeler

if __name__ == '__main__':
    # preprocess
    preprocessor = PreprocessorMira()
    raw_list = preprocessor.load('data/local/Taiyi/taiyi_raw_samples.txt')
    # raw_list = preprocessor.index(raw_list)
    # preprocessor.save(raw_list, 'data/local/mira/RawSample_saved.txt')
    # raw_list = preprocessor.preprocess('data/local/Taiyi/taiyi_raw_samples.txt')
    tmp = []
    for i in raw_list:
        tmp.append(RawSample(i.request_ts, i.start_ts, i.end_ts, i.node_num, i.requested_sec, i.queue_name))

    for i in tmp:
        print(i)
    # preprocessor.index(tmp)

    # raw_list = preprocessor.load('data/local/mira/RawSample_saved.txt')
    # raw_list = preprocessor.load('data/local/mira/RawSample_saved.txt')
    # generate sample
    # sample_list = to_sample_list(raw_list)
    # sample_save(sample_list, 'data/local/mira/sample_saveT10000.txt')
    # sample_list = sample_load('data/local/mira/sample_saveT1500.txt')

    # # labelin
    # AAE = list()
    # PPE = list()
    # labeler = Labeler(k=1)
    # sample_list = labeler.label_samples(sample_list)
    #
    # # # run model
    # model = DecisionTreeRegressor(sample_list, labeler, 7, 1, raw_list)
    #
    # # 修改聚类，取消聚类，并以queue_name作为分类标准。
    # # model.label_queue_name()
    #
    # model.create_dataset()
    # model.train()
    # model.test()
    #
    # # # use model
    # # print(model.predict(sample_list[0]))