<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>['wf3_NeuralNet', 'hplc_BayesNeuralNet', 'snar_BayesNeuralNet', 'n9_NeuralNet', 'hplc_NeuralNet', 'suzuki_BayesNeuralNet', 'bob_NeuralNet', 'pce10_BayesNeuralNet', 'fullerenes_BayesNeuralNet', 'alkox_NeuralNet', 'benzylation_NeuralNet', 'fullerenes_NeuralNet', 'alkox_BayesNeuralNet', 'snar_NeuralNet', 'n9_BayesNeuralNet', 'suzuki_NeuralNet', 'benzylation_BayesNeuralNet', 'pce10_NeuralNet', 'bob_BayesNeuralNet', 'wf3_BayesNeuralNet']<jupyter_text>Here we will use `alkox_BayesNeuralNet`<jupyter_code># initialize emulator
emulator = Emulator(dataset='alkox', model='BayesNeuralNet')
# print emulator information
print(emulator)
# 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) + 0.0, 
          np.zeros(4) + 0.5, 
          np.zeros(4) + 1.0]
values = emulator.run(params, num_samples = 5)
print(values)<jupyter_output>[[47.04858576]
 [35.74277879]
 [20.47608956]]
{'fraction_non_alphanumeric': 0.1241883116883117, 'fraction_numerical': 0.03977272727272727, 'mean_word_length': 8.412213740458014, 'pattern_counts': {'":': 0, '<': 7, '<?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': '220226', 'license': 'MIT', 'n_tokens_mistral': 511, 'n_tokens_neox': 476, 'n_words': 78, 'path': 'docs/source/examples/notebooks/use_emulators.ipynb', 'repo_name': 'priyansh-1902/olympus', 'size': '1232'}