<jupyter_start><jupyter_text>This notebook measures Python hdbscan performance with the Gaussian 100000 data<jupyter_code>import numpy as np
import pandas as pd
from sklearn.metrics import adjusted_mutual_info_score
from sklearn.metrics import normalized_mutual_info_score
import hdbscan
# same as /Users/gstewart/temp/development/mscs/cpsc69700/RefImpl/HDBSCAN_Star/xtrabig-gaussians-6centers.csv but with headers
df = pd.read_csv('../../../data/xtrabig-gaussians-6centers.csv')

print(df.shape)
%time clusterer = hdbscan.HDBSCAN(min_cluster_size=10, prediction_data=True).fit(df)
# run 1
# time: 30.3s

# run 2
# time: 27.7s

# run 3
# time: 28.9s
hdbscan_label_list = clusterer.labels_.tolist()
print(len(hdbscan_label_list ))<jupyter_output>100000
{'fraction_non_alphanumeric': 0.10252996005326231, 'fraction_numerical': 0.043941411451398134, 'mean_word_length': 6.09433962264151, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '265564', 'license': 'Apache-2.0', 'n_tokens_mistral': 304, 'n_tokens_neox': 278, 'n_words': 57, 'path': 'notebooks/Compare Performance/Gaussians 100000/hdbscan Performance Gaussians100000.ipynb', 'repo_name': 'geoffreydstewart/TribuoHdbscan', 'size': '751'}