#Import required libraries
import numpy as np
import pandas as pd
import sklearn
#When using Jupyter notebook, display plot result in notebook
import matplotlib.pyplot as plt
%matplotlib inline

df_result_dense = pd.read_csv('result-dense.csv')
df_result_dense

	Unnamed: 0	labels	k-means
0	0	7	2
1	1	2	5
2	2	1	9
3	3	0	3
4	4	4	7
...	...	...	...
9995	9995	2	5
9996	9996	3	0
9997	9997	4	7
9998	9998	5	4
9999	9999	6	6

10000 rows × 3 columns

def relabel(ans, labels):
    df = pd.DataFrame()
    df['ans'] = ans
    df['labels'] = labels
    relabel(df, 'ans', 'labels')

def relabel(df, ans, label):
    #Relabeling closest to ans
    # df[ans]Correct answer, df[labels]Expects to have a cluster label in
    labels = df[label].unique()
    label_dic = {}
    for i in labels:
        counts = df[df[label] == i][ans].value_counts()
        label_dic[i] = counts.index[0]
    display(label_dic)
    return list(pd.Series(df[label]).replace(label_dic))

relabel_k_means = relabel(df_result_dense, 'labels', 'k-means')
df_result_dense['relabel_k_means'] = relabel_k_means

{2: 7, 5: 2, 9: 1, 3: 0, 7: 4, 1: 9, 4: 5, 8: 8, 6: 6, 0: 3}

from sklearn.metrics import accuracy_score
print(accuracy_score(df_result_dense['labels'],df_result_dense['k-means']))
print(accuracy_score(df_result_dense['labels'],df_result_dense['relabel_k_means']))

0.1841
0.9309

ans = df_result_dense['labels']
labels = df_result_dense['k-means']
relabels = df_result_dense['relabel_k_means']

def eval_cluster(ans, labels, relabels):
    import seaborn as sns
    from sklearn.metrics import confusion_matrix

    plt.title('no-relabel')
    sns.heatmap(confusion_matrix(ans, labels), annot=True, fmt='d')
    plt.show()

    from sklearn.metrics import normalized_mutual_info_score
    print("nmi: " + str(normalized_mutual_info_score(ans, labels)))
    from sklearn.metrics.cluster import adjusted_rand_score
    print("ari: " + str(adjusted_rand_score(ans, labels)))

    plt.title('relabel')
    sns.heatmap(confusion_matrix(ans, relabels), annot=True, fmt='d')
    plt.show()
    print("acc: " + str(accuracy_score(ans, relabels)))

eval_cluster(ans, labels, relabels)

nmi: 0.8804532777228216
ari: 0.8405114317316403

acc: 0.9309

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