<jupyter_start><jupyter_text>Testing performance non-regression on Grid'5000 clusters<jupyter_code>csv_url_prefix = 'https://gitlab.in2p3.fr/tom.cornebize/g5k_data/raw/master/'
changelog_url = 'https://gitlab.in2p3.fr/tom.cornebize/g5k_data/raw/master/exp_changelog.csv'
cluster = 'yeti'
factor = 'avg_gflops'
%load_ext autoreload
%autoreload 2
import requests
import pandas
import io
all_files = {
    'avg_gflops': 'stats.csv',
    'mean_temperature': 'stats_monitoring.csv',
    'mean_frequency': 'stats_monitoring.csv',
}
csv_url = csv_url_prefix + all_files[factor]
import non_regression_tests as nrt

df = nrt.format(nrt.get(csv_url))
changelog = nrt.format_changelog(nrt.get(changelog_url))
df = nrt.filter(df, cluster=cluster)
df.head()
nrt.plot_latest_distribution(df, factor)
marked=nrt.mark_weird(df, select_func=lambda x: nrt.select_after_changelog(x, changelog, nmin=8), naive=False, confidence=0.99, col=factor)
nb_weird = len(marked[marked.weird == True])
nb_total = len(marked[marked.weird != 'NA'])
print(f'{nb_weird/nb_total*100:.2f}% of measures are abnormal ({nb_weird}/{nb_total})')
nrt.plot_evolution_cluster(marked, factor, changelog)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13900589721988205, 'fraction_numerical': 0.016006739679865205, 'mean_word_length': 7.425531914893617, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 2, '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': '46590', 'license': 'MIT', 'n_tokens_mistral': 490, 'n_tokens_neox': 463, 'n_words': 71, 'path': 'G5K_non-regression-test.ipynb', 'repo_name': 'Ezibenroc/calibration_analysis', 'size': '1187'}