def test_random_agent_loop(self):
df = load_default_atf_data()
n_seed = 200 # Starting sample size
agent = RandomAgent(n_query=10)
analyzer = StabilityAnalyzer(hull_distance=0.05, parallel=False)
experiment = ATFSampler(dataframe=df)
candidate_data = df
new_loop = Campaign(candidate_data,
agent,
experiment,
analyzer,
create_seed=n_seed)
new_loop.initialize()
self.assertFalse(new_loop.create_seed)
for _ in range(6):
new_loop.run()
self.assertTrue(True)
# Testing the continuation
new_loop = Campaign(candidate_data,
agent,
experiment,
analyzer,
create_seed=n_seed)
self.assertTrue(new_loop.initialized)
self.assertEqual(new_loop.iteration, 6)
self.assertEqual(new_loop.loop_state, None)
new_loop.run()
self.assertTrue(True)
self.assertEqual(new_loop.iteration, 7)
# Copyright Toyota Research Institute 2019
from camd.loop import Loop
from sklearn.neural_network import MLPRegressor
from camd.agent.agents import QBCStabilityAgent
from camd.analysis import AnalyzeStability
from camd.experiment.base import ATFSampler
from camd.utils.data import load_default_atf_data
##########################################################
# Load dataset and filter by n_species of 2 or less
##########################################################
df = load_default_atf_data()
##########################################################
# Binary stable material discovery QBC based agent recipe
##########################################################
n_seed = 5000 # Starting sample size - a seed of this size will be randomly chosen.
n_query = 200 # This many new candidates are "calculated with DFT" (i.e. requested from Oracle -- DFT)
agent = QBCStabilityAgent
agent_params = {
'ml_algorithm': MLPRegressor,
'ml_algorithm_params': {
'hidden_layer_sizes': (84, 50)
},
'n_query': n_query,
'n_members': 10, # Committee size in QBC
'hull_distance':
0.05, # Distance to hull to consider a finding as discovery (eV/atom)
'frac': 0.5 # Fraction of data to choose to form a committee member
}