test_ascm_theta_realdata_exp3.py

import time
import matplotlib.pyplot as plt

import accelerated_sequence_clustering as ascm
import pandas as pd
import numpy as np
import time
import matplotlib.pyplot as plt
import glob
import os
import sys
def __main__(argv):

    input_path=os.getcwd()
    folder='datasets_real'
    files_path = glob.glob(input_path+'/'+folder+'/*.csv')  
    files_path.sort()
    THETA=[1, 2, 4, 8, 16, 32, 64]
    max=10
    k_set=[3,5,10,20]
    columns=['dataset_name', 'method','k', 'theta','max','SSE','Total_time']
    all_results=pd.DataFrame()
    
    for file_path in files_path:
        
        results=pd.DataFrame(columns=columns)
        
        names= file_path.split('/')
        dataset_name=os.path.basename(names[-1])
        if dataset_name.endswith('.csv'):
            dataset_name = dataset_name[:-4] 
        
        data=pd.read_csv(file_path)
        a_2d=data.to_numpy()
        if len(data>50000):
            a_2d=a_2d[:50000]
               
        print(f'ASC method outputs for {dataset_name}....')
        for k in k_set: 
            print(f'cluster={k}')
            for theta in THETA:
                start_a = time.time()
                sizes, SSE_a = ascm.accelerated_sequence_clustering_approximated3_2d(a_2d, k, max, theta)
                end_a = time.time()
                total_time_a= end_a-start_a
                
                asc_current= {'dataset_name':dataset_name, 'method':'asc','k':k, 'theta':theta,'max':max,
                              'SSE':SSE_a, 'Total_time':total_time_a}
                results= results.append(asc_current, ignore_index=True)
                all_results= all_results.append(asc_current, ignore_index=True)

        
        results[columns].to_csv(input_path+f'/{dataset_name}_exp3.csv')
        
        
    all_results[columns].to_csv(input_path+f'/{folder}_exp3.csv')   
    print('Finished.')
if __name__ == "__main__":
    __main__(sys.argv)