### use_combo: weather to use Bayesian optimisation or not in combination with Monte Carlo tree search.
# COMBO package is used to engineer the palyout instead of random selection.
### combo_play_out: total number of candidates to be examind by COMBO.
### combo_init_random: the initial random selection for Bayesian optimisation. Default is 1
### combo_step: the interval for Bayesian optimisation to perfrom hyperparameter optimization. Default is 1
### combo_lvl: the level of the tree at which start to apply Bayesian optimisation. Default is 1 (apply at all levels)
myTree = mdts.Tree(no_positions=16,
atom_types=[0, 1],
atom_const=[8, 8],
get_reward=get_reward,
positions_order=range(16),
max_flag=True,
expand_children=2,
play_out=5,
play_out_selection="best",
space=None,
candidate_pool_size=100,
ucb="mean",
use_combo=True,
combo_play_out=20,
combo_init_random=5,
combo_step=5,
combo_lvl=5)
### Start the search for certain number of candidates and returns an object of type Result contains the result of the search
res = myTree.search(display=True, no_candidates=500)
### Optimal reward
print res.optimal_fx
myTree = mdts.Tree(
no_positions=
14, # number of positions in each structure. For example, 16 atoms.
atom_types=[
0, 1
], # atom types. For example, types of atoms: 0 for Si and 1 for Ge.
atom_const=[
7, 7
], # number of each atom type in the structure. For example, 8 atoms Si and 8 atoms Ge.
get_reward=get_reward, # the experiment simulation function.
positions_order=list(
range(14)
), # define the order to assign atoms to the positions in the structure.
max_flag=
False, # if True the algorithm searches for maximum reward, else for minimum.
expand_children=
2, # number of children to expand at each node, 1 means expand one child at a time.
play_out=5, # number of play outs et each node.
play_out_selection=
"best", # when performing multiple playouts, best or mean is returned.
space=None, # numpy ndarray representing the candidates space.
candidate_pool_size=100,
ucb="mean", # taking either average or best ucb score for MC tree search.
use_combo=
False, # weather to use Bayesian optimisation or not in combination with MC tree search.
combo_play_out=20, # total number of candidates to be examind by COMBO.
combo_init_random=
5, # the initial random selection for Bayesian optimisation.
combo_step=
5, # the interval for Bayesian optimisation to perfrom hyperparameter optimization.
combo_lvl=5
) # the level of the tree at which start to apply Bayesian optimisation.
### combo_lvl: the level of the tree at which start to apply Bayesian optimisation. Default is 1 (apply at all levels)
### combo_init_random: the initial random selection for Bayesian optimisation. Default is 1
### combo_step: the interval for Bayesian optimisation to perfrom hyperparameter optimization. Default is 1
### combo_play_out: total number of candidates to be examind by COMBO.
### use_XP: Whether to use neural network expansion policy in combination with MCTS or not (you may use both combo and XP together too)
### XP_batch_size: the size of the batch to be used to train the neural network
myTree = mdts.Tree(no_positions=16,
position_values=[0, 1],
position_values_const=[8, 8],
positions_order=list(range(16)),
max_flag=True,
get_reward=get_reward,
constraints=None,
expand_children=1,
play_out=1,
play_out_selection="best",
use_combo=False,
candidate_pool_size=100,
combo_lvl=1,
combo_init_random=5,
combo_step=5,
combo_play_out=20,
use_XP=False,
XP_batch_size=50)
### Start the search for certain number of candidates and returns an object of type Result contains the result of the search
res = myTree.search(display=True, no_candidates=500)
# ### Optimal reward
print(res.optimal_fx)
t_thickness += bil_thick[j][0]
thickness.append(bil_thick[j][1])
t_thickness += bil_thick[j][1]
return thickness, t_thickness
myTree = mdts.Tree(no_positions=20,
atom_types=list(range(1521)),
atom_const=None,
get_reward=simul_ref,
positions_order="direct",
max_flag=True,
expand_children=2,
play_out=1,
play_out_selection="best",
space=None,
candidate_pool_size=100,
ucb="mean",
use_combo=False,
combo_play_out=20,
combo_init_random=5,
combo_step=5,
combo_lvl=5)
res = myTree.search_PG(display=True,
no_candidates=100) # apply MCTS search with PG
#res=myTree.search(display=True,no_candidates=20000)# apply MCTS search without PG
### Optimal reward
