<jupyter_start><jupyter_text>Finally, we render our visualisation and run our search:<jupyter_code>import bokeh.plotting as bp
from bokeh.io import output_notebook, push_notebook
from time import time
output_notebook()
from monitoring.model_selection import random_search_cv
random_search_cv(clf, X_train, y_train, param_dist, log2file=True, log2tbx=True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07012987012987013, 'fraction_numerical': 0.005194805194805195, 'mean_word_length': 10.028571428571428, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '639725', 'license': 'MIT', 'n_tokens_mistral': 135, 'n_tokens_neox': 131, 'n_words': 32, 'path': 'notebooks/2 - RF classifier example.ipynb', 'repo_name': 'thomasgreg/monitoring-experiments', 'size': '385'}