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run.py
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run.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import cascading_clustering as cluster
import time
#***********************************CODE USAGE GUIDE***************************************
# Work for FSE 2018
#
# 1. How to run the code?
# Open a terminal, run the cascading clustering with "python run.py".
# Make sure that you have Python 3 and all required packages installed.
#
# 2. How to set the parameters?
# Replace the parameters in the following "para" according to your data
#
# Notes: multiprocessing is only used to read input files and save output files.
#******************************************************************************************
if __name__ == '__main__':
para = {
'seq_folder': 'seq_folder/', # folder of log sequence matrix file
'kpi_path': 'kpi_path', # the path of KPI file
'proc_num': 16, # number of processes when loading files and saving files
'sample_rate': 100, # sample rate for sampling, 100 represents 1% sample rate
'thre': 0.3, # threshold for clustering, and also used when matching the nearest sequence
'saveFile': False, # FLAG to decide whether saving output clusters, it costs a lot if turned on, default False
'output_path': 'output_path', # folder for saving output clusters of data
'rep_path': 'rep_path/', # path used for savinng all representatives (patterns).
}
kpipath = para['kpi_path']
t1 = time.time()
rawData, rawIndex, eveOccuMat = cluster.loading_all_data(para)
kpiList = cluster.load_kpi(kpipath)
corWeightList = cluster.get_corr_weight(eveOccuMat, kpiList)
weight_data, weightList = cluster.weighting(rawData, corWeightList)
finacluResult = cluster.cascading(para, rawData, rawIndex, weight_data)
t2 = time.time() - t1
print('the entire time usage is ', t2)