61 lines
1.9 KiB
Python
Executable File
61 lines
1.9 KiB
Python
Executable File
#!/usr/bin/env python3
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import numpy as np
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import glob
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import os
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import pandas as pd
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DIR: str = os.path.dirname(os.path.realpath(__file__))
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IN_DIR: str = DIR + '/clustering'
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OUT_DIR: str = DIR + ''
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def intrapairs(path: str) -> set[set[str, str]]:
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df = pd.read_csv(path)
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clusters: list[list[str]] = df.groupby(
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'cluster').agg(list).iloc[:, 0].values
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intrapairs: set[set[str]] = set() # inner sets always contain 2 elements
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for cluster in clusters:
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for i, e1 in enumerate(cluster):
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for j in range(i + 1, len(cluster)):
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e2 = cluster[j]
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intrapairs.add(frozenset((e1, e2,)))
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return intrapairs
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def main():
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filelist = glob.glob(IN_DIR + '/*_groundtruth.csv')
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df_table = pd.DataFrame(columns=pd.MultiIndex.from_tuples([
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('KMeans', 'Precision'),
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('KMeans', 'Recall'),
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('Agglomerative', 'Precision'),
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('Agglomerative', 'Recall')]))
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df_table.index.name = 'Class Name'
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for f in filelist:
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clazz_name = os.path.basename(f)
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clazz_name = clazz_name[:clazz_name.rfind('_groundtruth.csv')]
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ground_pairs = intrapairs(f)
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for method in ['kmeans', 'hierarchical']:
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cluster_pairs = intrapairs(
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IN_DIR + '/' + clazz_name + '_' + method + '.csv')
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n_common = len(ground_pairs.intersection(cluster_pairs))
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precision = n_common / len(cluster_pairs)
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recall = n_common / len(ground_pairs)
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algo = 'KMeans' if method == 'kmeans' else 'Agglomerative'
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df_table.loc[clazz_name, [(algo, 'Precision'), (algo, 'Recall')]] = [
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str(round(precision * 100, 2)) + '%',
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str(round(recall * 100, 2)) + '%'
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]
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df_table.columns = [x[0] + ' ' + x[1] for x in df_table.columns]
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print(df_table.to_markdown())
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if __name__ == '__main__':
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main()
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