# if True:
# gi.delete_cache()
# gi = GrammarInitializer(grammar_cache)
# max_steps_smiles = gi.init_grammar(1000)
max_steps = 30
model, gen_fitter, disc_fitter = train_policy_gradient(
molecules,
grammar,
EPOCHS=100,
BATCH_SIZE=batch_size,
reward_fun_on=reward_fun,
max_steps=max_steps,
lr_on=0.3e-5,
lr_discrim=5e-4,
p_thresh=0.3,
randomize_reward=True,
drop_rate=drop_rate,
decoder_type='attn_graph', # 'attention',
plot_prefix='hg ',
dashboard='true_reward_lenient_rand30_v2', # 'policy gradient',
save_file='adv_orig_reward_lrand30.h5',
smiles_save_file=None, # 'pg_smiles_hg1.h5',
on_policy_loss_type='advantage', #''best',
off_policy_loss_type='mean')
# preload_file='policy_gradient_run.h5')
while True:
next(gen_fitter)
for _ in range(1):
next(disc_fitter)
# gi = GrammarInitializer(grammar_cache)
root_name = 'canned_' + ver + '_' + str(obj_num) + 'do 0.5 lr4e-5'
max_steps = 45
model, gen_fitter, disc_fitter = train_policy_gradient(
molecules,
grammar,
EPOCHS=100,
BATCH_SIZE=batch_size,
reward_fun_on=reward_fun,
max_steps=max_steps,
lr_on=4e-5,
lr_discrim=0.0,
discrim_wt=0.0,
p_thresh=-10,
drop_rate=drop_rate,
reward_sm=0.0,
decoder_type='attn_graph_node', #'rnn_graph',# 'attention',
plot_prefix='',
dashboard=root_name, # 'policy gradient',
save_file_root_name=root_name,
preload_file_root_name=
root_name, #'guacamol_ar_emb_node_rpev2_0lr2e-5',#'guacamol_ar_nodev2_0lr2e-5',#root_name,
smiles_save_file=root_name.replace(' ', '_') + '_smiles_2.zip',
on_policy_loss_type='advantage_record',
node_temperature_schedule=lambda x: 100,
eps=0.0,
priors='conditional')
# preload_file='policy_gradient_run.h5')
while True:
next(gen_fitter)
# gi.delete_cache()
# gi = GrammarInitializer(grammar_cache)
# max_steps_smiles = gi.init_grammar(100)
max_steps = 50
model, gen_fitter, disc_fitter = train_policy_gradient(
molecules,
grammar,
EPOCHS=100,
BATCH_SIZE=batch_size,
reward_fun_on=reward_fun,
max_steps=max_steps,
lr_on=3e-5,
lr_discrim=5e-4,
discrim_wt=0.0,
p_thresh=-10,
drop_rate=drop_rate,
reward_sm=0.0,
decoder_type='attn_graph', # 'attention',
plot_prefix='',
dashboard='guacamol_' + ver + '_' + str(obj_num) +
'a', # 'policy gradient',
save_file_root_name='guacamol_' + ver + '_' + str(obj_num) + '.h5',
smiles_save_file=None, # 'pg_smiles_hg1.h5',
on_policy_loss_type='advantage', #''best',
off_policy_loss_type='mean')
# preload_file='policy_gradient_run.h5')
while True:
next(gen_fitter)
# for _ in range(1):
for obj_num in range(20):
try:
root_name = 'guac_' + ver + '_' + str(obj_num) + 'do_0.5_lr4e-5_mark'
max_steps = 45
model, gen_fitter, disc_fitter = train_policy_gradient(
molecules,
grammar,
EPOCHS=100,
BATCH_SIZE=batch_size,
reward_fun_on=reward_fun,
max_steps=max_steps,
lr_on=4e-5,
lr_discrim=5e-4,
discrim_wt=0.0,
p_thresh=-10,
drop_rate=drop_rate,
reward_sm=0.0,
decoder_type='attn_graph_node', #'rnn_graph',# 'attention',
plot_prefix='',
dashboard=root_name, # 'policy gradient',
save_file_root_name=root_name,
preload_file_root_name=root_name,
smiles_save_file=None, # 'pg_smiles_hg1.h5',
on_policy_loss_type='advantage_record',
half_float=False,
node_temperature_schedule=lambda x: 100,
eps=2.0)
except Exception as ex:
print('{}: {}({})'.format(obj_num, type(ex), str(ex)))
# preload_file='policy_gradient_run.h5')
reward_fun = lambda x: np.tanh(0.1*scorer(x)) + atom_penalty(x) # lambda x: reward_aromatic_rings(x)#
# later will run this ahead of time
# gi = GrammarInitializer(grammar_cache)
max_steps = 60
root_name = 'classic_logP'
model, gen_fitter, disc_fitter = train_policy_gradient(molecules,
grammar,
EPOCHS=100,
BATCH_SIZE=batch_size,
reward_fun_on=reward_fun,
max_steps=max_steps,
lr_on=3e-5,
lr_discrim=5e-4,
discrim_wt=0.3,
p_thresh=-10,
drop_rate=drop_rate,
reward_sm=0.5,
decoder_type='attn_graph_node', # 'attention',
plot_prefix='',
dashboard=root_name, # 'policy gradient',
save_file_root_name=root_name,
smiles_save_file=root_name + '_smiles',
on_policy_loss_type='advantage_record',
half_float=False,
priors='conditional')
# preload_file='policy_gradient_run.h5')
while True:
next(gen_fitter)
for _ in range(1):
next(disc_fitter)
# if True:
# gi.delete_cache()
# gi = GrammarInitializer(grammar_cache)
# max_steps_smiles = gi.init_grammar(100)
max_steps = 50
model, gen_fitter, disc_fitter = train_policy_gradient(
molecules,
grammar,
EPOCHS=100,
BATCH_SIZE=batch_size,
reward_fun_on=reward_fun,
max_steps=max_steps,
lr_on=3e-5,
lr_discrim=5e-4,
discrim_wt=0.25,
p_thresh=-10,
drop_rate=drop_rate,
reward_sm=0.5,
decoder_type='attn_graph', # 'attention',
plot_prefix='hg ',
dashboard='reward_linear_faster_half_2', # 'policy gradient',
save_file_root_name='true_reward_linear_faster_half_2.h5',
smiles_save_file=None, # 'pg_smiles_hg1.h5',
on_policy_loss_type='advantage', #''best',
off_policy_loss_type='mean')
# preload_file='policy_gradient_run.h5')
while True:
next(gen_fitter)
for _ in range(1):
next(disc_fitter)
root_name = 'xtest91' + ver + '_' + str(obj_num) + '_lr4e-5'
max_steps = 60
model, gen_fitter, disc_fitter = train_policy_gradient(
molecules,
grammar,
EPOCHS=100,
BATCH_SIZE=batch_size,
reward_fun_on=reward_fun,
max_steps=max_steps,
lr_on=0.05, # crazy, to be used with the cosine schedule!
lr_discrim=0.0,
# lr_schedule=shifted_cosine_schedule,
discrim_wt=0.0,
p_thresh=-10,
drop_rate=drop_rate,
reward_sm=0.0,
decoder_type='graph_conditional', #'rnn_graph',# 'attention',
plot_prefix='',
dashboard=root_name, # 'policy gradient',
save_file_root_name=root_name,
preload_file_root_name=
None, #root_name, #'guacamol_ar_emb_node_rpev2_0lr2e-5',#'guacamol_ar_nodev2_0lr2e-5',#root_name,
smiles_save_file=root_name.replace(' ', '_') + '_smiles.zip',
on_policy_loss_type='advantage_record',
rule_temperature_schedule=toothy_exp_schedule,
eps=0.0,
priors='conditional',
entropy_wgt=1.0)
# preload_file='policy_gradient_run.h5')
while True:
