96 lines
2.9 KiB
Python
Executable File
96 lines
2.9 KiB
Python
Executable File
#!/usr/bin/env python3
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.metrics import silhouette_score
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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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import argparse
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from k_means import cluster_kmeans
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from hierarchical import cluster_hierarchical
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DIR: str = os.path.dirname(os.path.realpath(__file__))
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OUT_DIR: str = DIR + '/clustering'
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IN_DIR: str = DIR + '/feature_vectors'
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K_MAX: int = 65
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def clean_output():
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filelist = glob.glob(OUT_DIR + '/*_silhouette.csv')
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for f in filelist:
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os.remove(f)
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def validate(path: str, clazz_name: str, autorun: bool):
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df = pd.DataFrame(columns=['k_means', 'hierarchical'], dtype=float)
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# We bound the number of clusters by the number of distinct points in our dataset.
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# To count them, we compute the number of "distinct" feature vectors and we
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# bound to the minimum of K_MAX and this number.
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nodup = pd.read_csv(path, index_col=0).drop_duplicates()
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max_distinct = len(nodup)
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for n in range(2, min(K_MAX, max_distinct)):
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X_h, Y_h = cluster_hierarchical(path, n, save_to_disk=False)
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df.loc[n, 'k_means'] = silhouette_score(X_h, Y_h)
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X_k, Y_k = cluster_kmeans(path, n, save_to_disk=False)
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df.loc[n, 'hierarchical'] = silhouette_score(X_k, Y_k)
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k_kmeans = df[['k_means']].idxmax()[0]
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k_hierarchical = df[['hierarchical']].idxmax()[0]
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print("K_means optimal value: " + str(k_kmeans))
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print("Hierarchical optimal value: " + str(k_hierarchical))
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df.to_csv(OUT_DIR + '/' + clazz_name + '_silhouette.csv')
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if autorun:
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cluster_hierarchical(path, k_hierarchical)
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cluster_kmeans(path, k_kmeans)
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def compute_silhouette(path: str, clazz_name: str, suffix: str):
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df_y = pd.read_csv(OUT_DIR + '/' + clazz_name + '_' + suffix + '.csv')
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Y = df_y.iloc[:, 1].values
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df = pd.read_csv(path)
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X = df.drop(df.columns[0], axis=1).to_numpy()
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print("Silhouette for " + suffix + ": " + str(silhouette_score(X, Y)))
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def main():
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parser = argparse.ArgumentParser(description='Compute silhouette metric.')
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parser.add_argument('--validate', action='store_true',
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help='compute optimal k for each algorithm')
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parser.add_argument('--autorun', action='store_true',
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help='if validating, computes CSV for optimal clustering automatically')
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args = parser.parse_args()
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if args.validate:
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clean_output()
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filelist = glob.glob(IN_DIR + '/*.csv')
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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('.')]
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print(clazz_name)
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if args.validate:
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validate(f, clazz_name, args.autorun)
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compute_silhouette(f, clazz_name, 'kmeans')
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compute_silhouette(f, clazz_name, 'hierarchical')
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print()
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if __name__ == '__main__':
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main()
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