class_relabel_1D1.py

import pandas as pd


method = ['bbgp', 'gcmh', 'ks']
shape = ['full','tied', ]
kmeans = ['full']




for c in range(2,11):

    labels = pd.DataFrame()

    for m in method:
        for s in shape:
            PATH = './1d_cutoff/em_1d_' + m + '_' + s +'/em_' + str(c)+ '.csv'

            preLabel = pd.read_csv(PATH, ',')

            class_min = preLabel.groupby(['label'])[m.upper()].min()
            class_min = class_min.sort_values(ascending=True).tolist()

            j = 1
            for cut in class_min:
                preLabel.loc[preLabel[m.upper()] >= cut, 'label'] = (c+1)-j
                j += 1

                labels['pos'] = preLabel['2L_POS']
                labels[m+s] = preLabel['label']
        for k in kmeans:
            PATH = './1d_cutoff/kmeans_1d_' + m + '_' + k + '/Kmeans' + str(c) + '.csv'

            preLabel = pd.read_csv(PATH, ',')

            class_min = preLabel.groupby(['label'])[m.upper()].min()
            class_min = class_min.sort_values(ascending=True).tolist()

            j=1
            for cut in class_min:
                preLabel.loc[preLabel[m.upper()] >= cut, 'label'] = (c+1)-j
                j += 1

                labels['pos'] = preLabel['2L_POS']
                labels['Km' + m+k] = preLabel['label']
    print(c)
    labels.to_csv('./1d_cutoff/merging_em' + str(c) + '.csv', index= False)