<jupyter_start><jupyter_text>Experiment emulators<jupyter_code>import numpy as np 

from olympus import Emulator
from olympus import list_trained_emulators
# checking trained emulators
print(list_trained_emulators())<jupyter_output>['photobleaching_bnn', 'n9_bnn', 'n9_nn', 'color_mixing', 'alkox_bnn', 'alkox_nn']<jupyter_text>We choose to focus on `alkox_bnn`<jupyter_code># initialize emulator
emulator = Emulator(model = 'bnn', dataset = 'alkox')
# print emulator information
print(emulator)
# check on parameter space 
print(emulator.get_param_space()) # <-- ATTENTION: The param ranges look incorrect!
# check on goal
print(emulator.get_goal())
# run single prediction
params = np.zeros(4)
values = emulator.run(params)
print(values)
# run multiple predictions
params = np.zeros(4)
values = emulator.run(params, num_samples = 5)
print(values)<jupyter_output>[39.23987862 39.73520907 39.32980841 38.94721425 39.45238658]
{'fraction_non_alphanumeric': 0.11135135135135135, 'fraction_numerical': 0.05945945945945946, 'mean_word_length': 7.654205607476635, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 7, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '262008', 'license': 'MIT', 'n_tokens_mistral': 370, 'n_tokens_neox': 327, 'n_words': 75, 'path': 'examples/experiment_emulators.ipynb', 'repo_name': 'priyansh-1902/olympus', 'size': '925'}