def test_hypergraph_mask_gen(self):
molecules = True
grammar_cache = 'tmp.pickle'
grammar = 'hypergraph:' + grammar_cache
# create a grammar cache inferred from our sample molecules
g = HypergraphGrammar(cache_file=grammar_cache)
if os.path.isfile(g.cache_file):
os.remove(g.cache_file)
g.strings_to_actions(get_zinc_smiles(5))
mask_gen1 = get_codec(molecules, grammar, 30).mask_gen
mask_gen2 = get_codec(molecules, grammar, 30).mask_gen
mask_gen1.priors = False
mask_gen2.priors = True
policy1 = SoftmaxRandomSamplePolicy(
bias=mask_gen1.grammar.get_log_frequencies())
policy2 = SoftmaxRandomSamplePolicy()
lp = []
for mg in [mask_gen1, mask_gen2]:
mg.reset()
mg.apply_action([None])
logit_priors = mg.action_prior_logits() # that includes any priors
lp.append(
torch.from_numpy(logit_priors).to(device=device,
dtype=torch.float32))
dummy_model_output = torch.ones_like(lp[0])
eff_logits = []
for this_lp, policy in zip(lp, [policy1, policy2]):
eff_logits.append(policy.effective_logits(dummy_model_output))
assert torch.max((eff_logits[0] - eff_logits[1]).abs()) < 1e-6
def test_classic_mask_gen_equations(self):
molecules = False
grammar = 'classic'
codec = get_codec(molecules, grammar, max_seq_length)
actions = run_random_gen(codec.mask_gen)
all_eqs = codec.actions_to_strings(actions)
# the only way of testing correctness we have here is whether the equations parse correctly
parsed_eqs = codec.strings_to_actions(all_eqs)
def test_classic_mask_gen_molecules(self):
molecules = True
grammar = 'classic'
codec = get_codec(molecules, grammar, max_seq_length)
actions = run_random_gen(codec.mask_gen)
new_smiles = codec.actions_to_strings(actions)
# the SMILES produced by that grammar are NOT guaranteed to be valid,
# so can only check that the decoding completes without errors and is grammatically valid
parsed_smiles = codec.strings_to_actions(new_smiles)
def get_node_decoder(grammar,
max_seq_length=15,
drop_rate=0.0,
decoder_type='attn',
rule_policy=None,
reward_fun=lambda x: -1 * np.ones(len(x)),
batch_size=None,
priors='conditional',
bins=10):
codec = get_codec(True, grammar, max_seq_length)
assert 'hypergraph' in grammar, "Only the hypergraph grammar can be used with attn_graph decoder type"
if 'attn' in decoder_type:
model_type = 'transformer'
elif 'rnn' in decoder_type:
model_type = 'rnn'
if 'distr' in decoder_type:
if 'softmax' in decoder_type:
output_type = 'distributions_softmax'
else:
output_type = 'distributions_thompson'
else:
output_type = 'values'
model = get_graph_model(codec,
drop_rate,
model_type,
output_type,
num_bins=bins)
# encoder = GraphEncoder(grammar=codec.grammar,
# d_model=512,
# drop_rate=drop_rate,
# model_type=model_type)
#
#
# model = MultipleOutputHead(model=encoder,
# output_spec={'node': 1, # to be used to select next node to expand
# 'action': codec.feature_len()}, # to select the action for chosen node
# drop_rate=drop_rate)
# don't support using this model in VAE-style models yet
mask_gen = HypergraphMaskGenerator(max_len=max_seq_length,
grammar=codec.grammar)
mask_gen.priors = priors
if rule_policy is None:
rule_policy = SoftmaxRandomSamplePolicy()
stepper = GraphDecoderWithNodeSelection(model, rule_policy=rule_policy)
env = GraphEnvironment(mask_gen,
reward_fun=reward_fun,
batch_size=batch_size)
decoder = DecoderWithEnvironmentNew(stepper, env)
return decoder, stepper
def get_node_decoder(grammar,
max_seq_length=15,
drop_rate=0.0,
decoder_type='attn',
rule_policy=None,
reward_fun=lambda x: -1 * np.ones(len(x)),
batch_size=None,
priors='conditional',
bins=10):
codec = get_codec(True, grammar, max_seq_length)
assert 'hypergraph' in grammar, "Only the hypergraph grammar can be used with attn_graph decoder type"
if 'attn' in decoder_type:
model_type = 'transformer'
elif 'rnn' in decoder_type:
model_type = 'rnn'
elif 'conditional' in decoder_type:
if 'sparse' in decoder_type:
model_type = 'conditional_sparse'
else:
model_type = 'conditional'
if 'distr' in decoder_type:
if 'softmax' in decoder_type:
output_type = 'distributions_softmax'
else:
output_type = 'distributions_thompson'
else:
output_type = 'values'
model = get_graph_model(codec,
drop_rate,
model_type,
output_type,
num_bins=bins)
if model_type == 'conditional_sparse':
priors = 'term_dist_only'
mask_gen = HypergraphMaskGenerator(max_len=max_seq_length,
grammar=codec.grammar,
priors=priors)
mask_gen.priors = priors
if rule_policy is None:
rule_policy = SoftmaxRandomSamplePolicySparse(
) if 'sparse' in decoder_type else SoftmaxRandomSamplePolicy()
stepper_type = GraphDecoderWithNodeSelectionSparse if 'sparse' in decoder_type else GraphDecoderWithNodeSelection
stepper = stepper_type(model, rule_policy=rule_policy)
env = GraphEnvironment(mask_gen,
reward_fun=reward_fun,
batch_size=batch_size)
decoder = DecoderWithEnvironmentNew(stepper, env)
return decoder, stepper
def test_hypergraph_mask_gen(self):
molecules = True
grammar_cache = 'tmp.pickle'
grammar = 'hypergraph:' + grammar_cache
# create a grammar cache inferred from our sample molecules
g = HypergraphGrammar(cache_file=grammar_cache)
if os.path.isfile(g.cache_file):
os.remove(g.cache_file)
g.strings_to_actions(smiles)
codec = get_codec(molecules, grammar, max_seq_length)
self.generate_and_validate(codec)
def test_graph_encoder_with_head(self):
codec = get_codec(molecules=True,
grammar='hypergraph:' + tmp_file,
max_seq_length=max_seq_length)
encoder = GraphEncoder(grammar=gi.grammar,
d_model=512,
drop_rate=0.0)
mol_graphs = [HyperGraph.from_mol(mol) for mol in get_zinc_molecules(5)]
model = MultipleOutputHead(model=encoder,
output_spec={'node': 1, # to be used to select next node to expand
'action': codec.feature_len()}, # to select the action for chosen node
drop_rate=0.1).to(device)
out = model(mol_graphs)
def get_vae(molecules=True,
grammar=True,
weights_file=None,
epsilon_std=1,
decoder_type='step',
**kwargs):
model_args = get_model_args(molecules=molecules, grammar=grammar)
for key, value in kwargs.items():
if key in model_args:
model_args[key] = value
sample_z = model_args.pop('sample_z')
encoder_args = [
'feature_len', 'max_seq_length', 'cnn_encoder_params', 'drop_rate',
'encoder_type', 'rnn_encoder_hidden_n'
]
encoder = get_encoder(**{
key: value
for key, value in model_args.items() if key in encoder_args
})
decoder_args = [
'z_size', 'decoder_hidden_n', 'feature_len', 'max_seq_length',
'drop_rate', 'batch_size'
]
decoder, _ = get_decoder(molecules,
grammar,
decoder_type=decoder_type,
**{
key: value
for key, value in model_args.items()
if key in decoder_args
})
model = generative_playground.models.heads.vae.VariationalAutoEncoderHead(
encoder=encoder,
decoder=decoder,
sample_z=sample_z,
epsilon_std=epsilon_std,
z_size=model_args['z_size'])
if weights_file is not None:
model.load(weights_file)
settings = get_settings(molecules=molecules, grammar=grammar)
codec = get_codec(molecules,
grammar,
max_seq_length=settings['max_seq_length'])
# codec.set_model(model) # todo do we ever use this?
return model, codec
def generic_decoder_test(self, decoder_type, grammar):
codec = get_codec(molecules=True,
grammar=grammar,
max_seq_length=max_seq_length)
decoder, pre_decoder = get_decoder(decoder_type=decoder_type,
max_seq_length=max_seq_length,
grammar=grammar,
feature_len=codec.feature_len(),
z_size=z_size,
batch_size=batch_size)
out = decoder()
# it returns all sorts of things: out_actions_all, out_logits_all, out_rewards_all, out_terminals_all, (info[0], to_pytorch(info[1]))
all_sum = torch.sum(out['logits'])
all_sum.backward()
return all_sum
def get_thompson_globals(
num_bins=50, # TODO: replace with a Value Distribution object
reward_fun_=None,
grammar_cache='hyper_grammar_guac_10k_with_clique_collapse.pickle', # 'hyper_grammar.pickle'
max_seq_length=60,
decay=0.95,
updates_to_refresh=10):
grammar_name = 'hypergraph:' + grammar_cache
codec = get_codec(True, grammar_name, max_seq_length)
reward_proc = RewardProcessor(num_bins)
rule_choice_repo_factory = lambda x: RuleChoiceRepository(
reward_proc=reward_proc, mask=x, decay=decay)
exp_repo_ = ExperienceRepository(
grammar=codec.grammar,
reward_preprocessor=reward_proc,
decay=decay,
conditional_keys=[
key for key in codec.grammar.conditional_frequencies.keys()
],
rule_choice_repo_factory=rule_choice_repo_factory)
# TODO: weave this into the nodes to do node-level action averages as regularization
local_exp_repo_factory = lambda graph: ExperienceRepository(
grammar=codec.grammar,
reward_preprocessor=reward_proc,
decay=decay,
conditional_keys=[i for i in range(len(graph))],
rule_choice_repo_factory=rule_choice_repo_factory)
globals = GlobalParametersThompson(
codec.grammar,
max_seq_length,
exp_repo_,
decay=decay,
updates_to_refresh=updates_to_refresh,
reward_fun=reward_fun_,
reward_proc=reward_proc,
rule_choice_repo_factory=rule_choice_repo_factory,
state_store=None)
return globals
def __init__(
self,
batch_size=20,
reward_fun_=None,
grammar_cache='hyper_grammar_guac_10k_with_clique_collapse.pickle', # 'hyper_grammar.pickle'
max_depth=60,
lr=0.05,
grad_clip=5,
entropy_weight=3,
decay=None,
num_bins=None,
updates_to_refresh=None,
plotter=None,
degenerate=False # use a null model if true
):
grammar_name = 'hypergraph:' + grammar_cache
codec = get_codec(True, grammar_name, max_depth)
super().__init__(codec.grammar,
max_depth,
reward_fun_, {},
plotter=plotter)
if not degenerate:
# create optimizer factory
optimizer_factory = optimizer_factory_gen(lr, grad_clip)
# create model
model = CondtionalProbabilityModel(codec.grammar).to(device)
# create loss object
loss_type = 'advantage_record'
loss_fun = PolicyGradientLoss(loss_type,
entropy_wgt=entropy_weight)
self.model = model
self.process_reward = MCTSRewardProcessor(loss_fun, model,
optimizer_factory,
batch_size)
else:
self.model = PassthroughModel()
self.process_reward = lambda reward, log_ps, actions, params: None
self.decay = decay
self.reward_proc = RewardProcessor(num_bins)
self.updates_to_refresh = updates_to_refresh
def get_model_args(molecules,
grammar,
drop_rate=0.5,
sample_z=False,
encoder_type='rnn'):
settings = get_settings(molecules, grammar)
codec = get_codec(molecules, grammar, settings['max_seq_length'])
model_args = {
'z_size': settings['z_size'],
'decoder_hidden_n': settings['decoder_hidden_n'],
'feature_len': codec.feature_len(),
'max_seq_length': settings['max_seq_length'],
'cnn_encoder_params': settings['cnn_encoder_params'],
'drop_rate': drop_rate,
'sample_z': sample_z,
'encoder_type': encoder_type,
'rnn_encoder_hidden_n': settings['rnn_encoder_hidden_n']
}
return model_args
def __init__(self,
molecules=True,
grammar=True,
reward_fun=None,
batch_size=1,
max_steps=None,
save_dataset=None):
settings = get_settings(molecules, grammar)
self.codec = get_codec(molecules, grammar, settings['max_seq_length'])
self.action_dim = self.codec.feature_len()
self.state_dim = self.action_dim
if max_steps is None:
self._max_episode_steps = settings['max_seq_length']
else:
self._max_episode_steps = max_steps
self.reward_fun = reward_fun
self.batch_size = batch_size
self.save_dataset = save_dataset
self.smiles = None
self.seq_len = None
self.valid = None
self.actions = None
self.done_rewards = None
self.reset()
def train_policy_gradient(molecules=True,
grammar=True,
smiles_source='ZINC',
EPOCHS=None,
BATCH_SIZE=None,
reward_fun_on=None,
reward_fun_off=None,
max_steps=277,
lr_on=2e-4,
lr_discrim=1e-4,
lr_schedule=None,
discrim_wt=2,
p_thresh=0.5,
drop_rate=0.0,
plot_ignore_initial=0,
randomize_reward=False,
save_file_root_name=None,
reward_sm=0.0,
preload_file_root_name=None,
anchor_file=None,
anchor_weight=0.0,
decoder_type='action',
plot_prefix='',
dashboard='policy gradient',
smiles_save_file=None,
on_policy_loss_type='best',
priors=True,
node_temperature_schedule=lambda x: 1.0,
rule_temperature_schedule=lambda x: 1.0,
eps=0.0,
half_float=False,
extra_repetition_penalty=0.0,
entropy_wgt=1.0):
root_location = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
root_location = root_location + '/../../'
def full_path(x):
return os.path.realpath(root_location + 'pretrained/' + x)
zinc_data = get_smiles_from_database(source=smiles_source)
zinc_set = set(zinc_data)
lookbacks = [BATCH_SIZE, 10 * BATCH_SIZE, 100 * BATCH_SIZE]
history_data = [deque(['O'], maxlen=lb) for lb in lookbacks]
if save_file_root_name is not None:
gen_save_file = save_file_root_name + '_gen.h5'
disc_save_file = save_file_root_name + '_disc.h5'
if preload_file_root_name is not None:
gen_preload_file = preload_file_root_name + '_gen.h5'
disc_preload_file = preload_file_root_name + '_disc.h5'
settings = get_settings(molecules=molecules, grammar=grammar)
codec = get_codec(molecules, grammar, settings['max_seq_length'])
discrim_model = GraphDiscriminator(codec.grammar, drop_rate=drop_rate)
if False and preload_file_root_name is not None:
try:
preload_path = full_path(disc_preload_file)
discrim_model.load_state_dict(torch.load(preload_path),
strict=False)
print('Discriminator weights loaded successfully!')
except Exception as e:
print('failed to load discriminator weights ' + str(e))
if EPOCHS is not None:
settings['EPOCHS'] = EPOCHS
if BATCH_SIZE is not None:
settings['BATCH_SIZE'] = BATCH_SIZE
alt_reward_calc = AdjustedRewardCalculator(reward_fun_on,
zinc_set,
lookbacks,
extra_repetition_penalty,
discrim_wt,
discrim_model=None)
reward_fun = lambda x: adj_reward(discrim_wt,
discrim_model,
reward_fun_on,
zinc_set,
history_data,
extra_repetition_penalty,
x,
alt_calc=alt_reward_calc)
task = SequenceGenerationTask(molecules=molecules,
grammar=grammar,
reward_fun=reward_fun,
batch_size=BATCH_SIZE,
max_steps=max_steps,
save_dataset=None)
node_policy = SoftmaxRandomSamplePolicy(temperature=torch.tensor(1.0),
eps=eps)
rule_policy = SoftmaxRandomSamplePolicy(temperature=torch.tensor(2.0),
eps=eps)
model = get_decoder(molecules,
grammar,
z_size=settings['z_size'],
decoder_hidden_n=200,
feature_len=codec.feature_len(),
max_seq_length=max_steps,
drop_rate=drop_rate,
batch_size=BATCH_SIZE,
decoder_type=decoder_type,
reward_fun=reward_fun,
task=task,
node_policy=node_policy,
rule_policy=rule_policy,
priors=priors)[0]
if preload_file_root_name is not None:
try:
preload_path = full_path(gen_preload_file)
model.load_state_dict(torch.load(preload_path, map_location='cpu'),
strict=False)
print('Generator weights loaded successfully!')
except Exception as e:
print('failed to load generator weights ' + str(e))
anchor_model = None
# construct the loader to feed the discriminator
def make_callback(data):
def hc(inputs, model, outputs, loss_fn, loss):
graphs = outputs['graphs']
smiles = [g.to_smiles() for g in graphs]
for s in smiles: # only store unique instances of molecules so discriminator can't guess on frequency
if s not in data:
data.append(s)
return hc
class TemperatureCallback:
def __init__(self, policy, temperature_function):
self.policy = policy
self.counter = 0
self.temp_fun = temperature_function
def __call__(self, inputs, model, outputs, loss_fn, loss):
self.counter += 1
target_temp = self.temp_fun(self.counter)
self.policy.set_temperature(target_temp)
# need to have something there to begin with, else the DataLoader constructor barfs
def get_rl_fitter(model,
loss_obj,
train_gen,
save_path,
fit_plot_prefix='',
model_process_fun=None,
lr=None,
lr_schedule=lr_schedule,
extra_callbacks=[],
loss_display_cap=float('inf'),
anchor_model=None,
anchor_weight=0):
nice_params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.Adam(nice_params, lr=lr, eps=1e-4)
if lr_schedule is None:
lr_schedule = lambda x: 1.0
scheduler = lr_scheduler.LambdaLR(optimizer, lr_schedule)
if dashboard is not None:
metric_monitor = MetricPlotter(
plot_prefix=fit_plot_prefix,
loss_display_cap=loss_display_cap,
dashboard_name=dashboard,
plot_ignore_initial=plot_ignore_initial,
process_model_fun=model_process_fun,
smooth_weight=reward_sm,
save_location=os.path.dirname(save_path))
else:
metric_monitor = None
checkpointer = Checkpointer(valid_batches_to_checkpoint=10,
save_path=save_path,
save_always=True)
fitter = fit_rl(train_gen=train_gen,
model=model,
optimizer=optimizer,
scheduler=scheduler,
epochs=EPOCHS,
loss_fn=loss_obj,
grad_clip=5,
half_float=half_float,
anchor_model=anchor_model,
anchor_weight=anchor_weight,
callbacks=[metric_monitor, checkpointer] +
extra_callbacks)
return fitter
class GeneratorToIterable:
def __init__(self, gen):
self.gen = gen
# we assume the generator is finite
self.len = 0
for _ in gen():
self.len += 1
def __len__(self):
return self.len
def __iter__(self):
return self.gen()
def my_gen():
for _ in range(1000):
yield to_gpu(torch.zeros(BATCH_SIZE, settings['z_size']))
# the on-policy fitter
gen_extra_callbacks = [make_callback(d) for d in history_data]
if smiles_save_file is not None:
smiles_save_path = os.path.realpath(root_location + 'pretrained/' +
smiles_save_file)
gen_extra_callbacks.append(MoleculeSaver(smiles_save_path, gzip=True))
print('Saved SMILES to {}'.format(smiles_save_file))
if node_temperature_schedule is not None:
gen_extra_callbacks.append(
TemperatureCallback(node_policy, node_temperature_schedule))
if rule_temperature_schedule is not None:
gen_extra_callbacks.append(
TemperatureCallback(rule_policy, rule_temperature_schedule))
fitter1 = get_rl_fitter(
model,
PolicyGradientLoss(
on_policy_loss_type,
entropy_wgt=entropy_wgt), # last_reward_wgt=reward_sm),
GeneratorToIterable(my_gen),
full_path(gen_save_file),
plot_prefix + 'on-policy',
model_process_fun=model_process_fun,
lr=lr_on,
extra_callbacks=gen_extra_callbacks,
anchor_model=anchor_model,
anchor_weight=anchor_weight)
#
# # get existing molecule data to add training
pre_dataset = EvenlyBlendedDataset(
2 * [history_data[0]] + history_data[1:],
labels=False) # a blend of 3 time horizons
dataset = EvenlyBlendedDataset([pre_dataset, zinc_data], labels=True)
discrim_loader = DataLoader(dataset, shuffle=True, batch_size=50)
class MyLoss(nn.Module):
def __init__(self):
super().__init__()
self.celoss = nn.CrossEntropyLoss()
def forward(self, x):
# tmp = discriminator_reward_mult(x['smiles'])
# tmp2 = F.softmax(x['p_zinc'], dim=1)[:,1].detach().cpu().numpy()
# import numpy as np
# assert np.max(np.abs(tmp-tmp2)) < 1e-6
return self.celoss(x['p_zinc'].to(device),
x['dataset_index'].to(device))
fitter2 = get_rl_fitter(discrim_model,
MyLoss(),
IterableTransform(
discrim_loader, lambda x: {
'smiles': x['X'],
'dataset_index': x['dataset_index']
}),
full_path(disc_save_file),
plot_prefix + ' discriminator',
lr=lr_discrim,
model_process_fun=None)
def on_policy_gen(fitter, model):
while True:
# model.policy = SoftmaxRandomSamplePolicy()#bias=codec.grammar.get_log_frequencies())
yield next(fitter)
return model, fitter1, fitter2 #,on_policy_gen(fitter1, model)
def __init__(self,
grammar,
smiles_source='ZINC',
BATCH_SIZE=None,
reward_fun=None,
max_steps=277,
num_batches=100,
lr=2e-4,
entropy_wgt=1.0,
lr_schedule=None,
root_name=None,
preload_file_root_name=None,
save_location=None,
plot_metrics=True,
metric_smooth=0.0,
decoder_type='graph_conditional',
on_policy_loss_type='advantage_record',
priors='conditional',
rule_temperature_schedule=None,
eps=0.0,
half_float=False,
extra_repetition_penalty=0.0):
self.num_batches = num_batches
self.save_location = save_location
self.molecule_saver = MoleculeSaver(None, gzip=True)
self.metric_monitor = None # to be populated by self.set_root_name(...)
zinc_data = get_smiles_from_database(source=smiles_source)
zinc_set = set(zinc_data)
lookbacks = [BATCH_SIZE, 10 * BATCH_SIZE, 100 * BATCH_SIZE]
history_data = [deque(['O'], maxlen=lb) for lb in lookbacks]
if root_name is not None:
pass
# gen_save_file = root_name + '_gen.h5'
if preload_file_root_name is not None:
gen_preload_file = preload_file_root_name + '_gen.h5'
settings = get_settings(molecules=True, grammar=grammar)
codec = get_codec(True, grammar, settings['max_seq_length'])
if BATCH_SIZE is not None:
settings['BATCH_SIZE'] = BATCH_SIZE
self.alt_reward_calc = AdjustedRewardCalculator(
reward_fun,
zinc_set,
lookbacks,
extra_repetition_penalty,
0,
discrim_model=None)
self.reward_fun = lambda x: adj_reward(0,
None,
reward_fun,
zinc_set,
history_data,
extra_repetition_penalty,
x,
alt_calc=self.alt_reward_calc)
task = SequenceGenerationTask(molecules=True,
grammar=grammar,
reward_fun=self.alt_reward_calc,
batch_size=BATCH_SIZE,
max_steps=max_steps,
save_dataset=None)
if 'sparse' in decoder_type:
rule_policy = SoftmaxRandomSamplePolicySparse()
else:
rule_policy = SoftmaxRandomSamplePolicy(
temperature=torch.tensor(1.0), eps=eps)
# TODO: strip this down to the normal call
self.model = get_decoder(True,
grammar,
z_size=settings['z_size'],
decoder_hidden_n=200,
feature_len=codec.feature_len(),
max_seq_length=max_steps,
batch_size=BATCH_SIZE,
decoder_type=decoder_type,
reward_fun=self.alt_reward_calc,
task=task,
rule_policy=rule_policy,
priors=priors)[0]
if preload_file_root_name is not None:
try:
preload_path = os.path.realpath(save_location +
gen_preload_file)
self.model.load_state_dict(torch.load(preload_path,
map_location='cpu'),
strict=False)
print('Generator weights loaded successfully!')
except Exception as e:
print('failed to load generator weights ' + str(e))
# construct the loader to feed the discriminator
def make_callback(data):
def hc(inputs, model, outputs, loss_fn, loss):
graphs = outputs['graphs']
smiles = [g.to_smiles() for g in graphs]
for s in smiles: # only store unique instances of molecules so discriminator can't guess on frequency
if s not in data:
data.append(s)
return hc
if plot_metrics:
# TODO: save_file for rewards data goes here?
self.metric_monitor_factory = lambda name: MetricPlotter(
plot_prefix='',
loss_display_cap=float('inf'),
dashboard_name=name,
save_location=save_location,
process_model_fun=model_process_fun,
smooth_weight=metric_smooth)
else:
self.metric_monitor_factory = lambda x: None
# the on-policy fitter
gen_extra_callbacks = [make_callback(d) for d in history_data]
gen_extra_callbacks.append(self.molecule_saver)
if rule_temperature_schedule is not None:
gen_extra_callbacks.append(
TemperatureCallback(rule_policy, rule_temperature_schedule))
nice_params = filter(lambda p: p.requires_grad,
self.model.parameters())
self.optimizer = optim.Adam(nice_params, lr=lr, eps=1e-4)
if lr_schedule is None:
lr_schedule = lambda x: 1.0
self.scheduler = lr_scheduler.LambdaLR(self.optimizer, lr_schedule)
self.loss = PolicyGradientLoss(on_policy_loss_type,
entropy_wgt=entropy_wgt)
self.fitter_factory = lambda: make_fitter(BATCH_SIZE, settings[
'z_size'], [self.metric_monitor] + gen_extra_callbacks, self)
self.fitter = self.fitter_factory()
self.set_root_name(root_name)
print('Runner initialized!')
def get_decoder(
molecules=True,
grammar=True,
z_size=200,
decoder_hidden_n=200,
feature_len=12, # TODO: remove this
max_seq_length=15,
drop_rate=0.0,
decoder_type='step',
task=None,
batch_size=None):
codec = get_codec(molecules, grammar, max_seq_length)
if decoder_type == 'old':
stepper = ResettingRNNDecoder(z_size=z_size,
hidden_n=decoder_hidden_n,
feature_len=codec.feature_len(),
max_seq_length=max_seq_length,
steps=max_seq_length,
drop_rate=drop_rate)
stepper = OneStepDecoderContinuous(stepper)
elif decoder_type == 'attn_graph':
assert 'hypergraph' in grammar, "Only the hypergraph grammar can be used with attn_graph decoder type"
encoder = GraphEncoder(grammar=codec.grammar,
d_model=512,
drop_rate=drop_rate)
model = MultipleOutputHead(
model=encoder,
output_spec={
'node': 1, # to be used to select next node to expand
'action': codec.feature_len()
}, # to select the action for chosen node
drop_rate=drop_rate)
# don't support using this model in VAE-style scenarios yet
model.init_encoder_output = lambda x: None
mask_gen = HypergraphMaskGenerator(max_len=max_seq_length,
grammar=codec.grammar)
stepper = GraphDecoder(model=model, mask_gen=mask_gen)
else:
if decoder_type == 'step':
stepper = SimpleRNNDecoder(z_size=z_size,
hidden_n=decoder_hidden_n,
feature_len=codec.feature_len(),
max_seq_length=max_seq_length,
drop_rate=drop_rate,
use_last_action=False)
elif decoder_type == 'action':
stepper = SimpleRNNDecoder(
z_size=z_size, # + feature_len,
hidden_n=decoder_hidden_n,
feature_len=codec.feature_len(),
max_seq_length=max_seq_length,
drop_rate=drop_rate,
use_last_action=True)
elif decoder_type == 'action_resnet':
stepper = ResNetRNNDecoder(
z_size=z_size, # + feature_len,
hidden_n=decoder_hidden_n,
feature_len=codec.feature_len(),
max_seq_length=max_seq_length,
drop_rate=drop_rate,
use_last_action=True)
elif decoder_type == 'attention':
stepper = SelfAttentionDecoderStep(num_actions=codec.feature_len(),
max_seq_len=max_seq_length,
drop_rate=drop_rate,
enc_output_size=z_size)
elif decoder_type == 'random':
stepper = RandomDecoder(feature_len=codec.feature_len(),
max_seq_length=max_seq_length)
else:
raise NotImplementedError('Unknown decoder type: ' +
str(decoder_type))
if grammar is not False:
# add a masking layer
mask_gen = get_codec(molecules, grammar, max_seq_length).mask_gen
stepper = MaskingHead(stepper, mask_gen)
policy = SoftmaxRandomSamplePolicy(
) #bias=codec.grammar.get_log_frequencies())
decoder = to_gpu(
SimpleDiscreteDecoderWithEnv(
stepper, policy, task=task,
batch_size=batch_size)) # , bypass_actions=True))
return decoder, stepper
def train_policy_gradient_ppo(molecules=True,
grammar=True,
smiles_source='ZINC',
EPOCHS=None,
BATCH_SIZE=None,
reward_fun_on=None,
reward_fun_off=None,
max_steps=277,
lr_on=2e-4,
lr_discrim=1e-4,
discrim_wt=2,
p_thresh=0.5,
drop_rate=0.0,
plot_ignore_initial=0,
randomize_reward=False,
save_file_root_name=None,
reward_sm=0.0,
preload_file_root_name=None,
anchor_file=None,
anchor_weight=0.0,
decoder_type='action',
plot_prefix='',
dashboard='policy gradient',
smiles_save_file=None,
on_policy_loss_type='best',
priors=True,
node_temperature_schedule=lambda x: 1.0,
rule_temperature_schedule=lambda x: 1.0,
eps=0.0,
half_float=False,
extra_repetition_penalty=0.0):
root_location = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
root_location = root_location + '/../../'
def full_path(x):
return os.path.realpath(root_location + 'pretrained/' + x)
if save_file_root_name is not None:
gen_save_file = save_file_root_name + '_gen.h5'
disc_save_file = save_file_root_name + '_disc.h5'
if preload_file_root_name is not None:
gen_preload_file = preload_file_root_name + '_gen.h5'
disc_preload_file = preload_file_root_name + '_disc.h5'
settings = get_settings(molecules=molecules, grammar=grammar)
codec = get_codec(molecules, grammar, settings['max_seq_length'])
discrim_model = GraphDiscriminator(codec.grammar, drop_rate=drop_rate)
if False and preload_file_root_name is not None:
try:
preload_path = full_path(disc_preload_file)
discrim_model.load_state_dict(torch.load(preload_path),
strict=False)
print('Discriminator weights loaded successfully!')
except Exception as e:
print('failed to load discriminator weights ' + str(e))
zinc_data = get_smiles_from_database(source=smiles_source)
zinc_set = set(zinc_data)
lookbacks = [BATCH_SIZE, 10 * BATCH_SIZE, 100 * BATCH_SIZE]
history_data = [deque(['O'], maxlen=lb) for lb in lookbacks]
def originality_mult(smiles_list):
out = []
for s in smiles_list:
if s in zinc_set:
out.append(0.5)
elif s in history_data[0]:
out.append(0.5)
elif s in history_data[1]:
out.append(0.70)
elif s in history_data[2]:
out.append(0.85)
else:
out.append(1.0)
return np.array(out)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def discriminator_reward_mult(smiles_list):
orig_state = discrim_model.training
discrim_model.eval()
discrim_out_logits = discrim_model(smiles_list)['p_zinc']
discrim_probs = F.softmax(discrim_out_logits, dim=1)
prob_zinc = discrim_probs[:, 1].detach().cpu().numpy()
if orig_state:
discrim_model.train()
return prob_zinc
def apply_originality_penalty(x, orig_mult):
assert x <= 1, "Reward must be no greater than 0"
if x > 0.5: # want to punish nearly-perfect scores less and less
out = math.pow(x, 1 / orig_mult)
else: # continuous join at 0.5
penalty = math.pow(0.5, 1 / orig_mult) - 0.5
out = x + penalty
out -= extra_repetition_penalty * (1 - 1 / orig_mult)
return out
def adj_reward(x):
if discrim_wt > 1e-5:
p = discriminator_reward_mult(x)
else:
p = 0
rwd = np.array(reward_fun_on(x))
orig_mult = originality_mult(x)
# we assume the reward is <=1, first term will dominate for reward <0, second for 0 < reward < 1
# reward = np.minimum(rwd/orig_mult, np.power(np.abs(rwd),1/orig_mult))
reward = np.array([
apply_originality_penalty(x, om) for x, om in zip(rwd, orig_mult)
])
out = reward + discrim_wt * p * orig_mult
return out
def adj_reward_old(x):
p = discriminator_reward_mult(x)
w = sigmoid(-(p - p_thresh) / 0.01)
if randomize_reward:
rand = np.random.uniform(size=p.shape)
w *= rand
reward = np.maximum(reward_fun_on(x), p_thresh)
weighted_reward = w * p + (1 - w) * reward
out = weighted_reward * originality_mult(x) #
return out
if EPOCHS is not None:
settings['EPOCHS'] = EPOCHS
if BATCH_SIZE is not None:
settings['BATCH_SIZE'] = BATCH_SIZE
task = SequenceGenerationTask(molecules=molecules,
grammar=grammar,
reward_fun=adj_reward,
batch_size=BATCH_SIZE,
max_steps=max_steps,
save_dataset=None)
node_policy = SoftmaxRandomSamplePolicy(temperature=torch.tensor(1.0),
eps=eps)
rule_policy = SoftmaxRandomSamplePolicy(temperature=torch.tensor(1.0),
eps=eps)
model = get_decoder(molecules,
grammar,
z_size=settings['z_size'],
decoder_hidden_n=200,
feature_len=codec.feature_len(),
max_seq_length=max_steps,
drop_rate=drop_rate,
batch_size=BATCH_SIZE,
decoder_type=decoder_type,
reward_fun=adj_reward,
task=task,
node_policy=node_policy,
rule_policy=rule_policy,
priors=priors)[0]
if preload_file_root_name is not None:
try:
preload_path = full_path(gen_preload_file)
model.load_state_dict(torch.load(preload_path, map_location='cpu'),
strict=False)
print('Generator weights loaded successfully!')
except Exception as e:
print('failed to load generator weights ' + str(e))
anchor_model = None
from generative_playground.molecules.rdkit_utils.rdkit_utils import NormalizedScorer
import numpy as np
scorer = NormalizedScorer()
if reward_fun_off is None:
reward_fun_off = reward_fun_on
# construct the loader to feed the discriminator
def make_callback(data):
def hc(inputs, model, outputs, loss_fn, loss):
graphs = outputs['graphs']
smiles = [g.to_smiles() for g in graphs]
for s in smiles: # only store unique instances of molecules so discriminator can't guess on frequency
if s not in data:
data.append(s)
return hc
class TemperatureCallback:
def __init__(self, policy, temperature_function):
self.policy = policy
self.counter = 0
self.temp_fun = temperature_function
def __call__(self, inputs, model, outputs, loss_fn, loss):
self.counter += 1
target_temp = self.temp_fun(self.counter)
self.policy.set_temperature(target_temp)
# need to have something there to begin with, else the DataLoader constructor barfs
def get_rl_fitter(model,
loss_obj,
train_gen,
save_path,
fit_plot_prefix='',
model_process_fun=None,
lr=None,
extra_callbacks=[],
loss_display_cap=float('inf'),
anchor_model=None,
anchor_weight=0):
nice_params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.Adam(nice_params, lr=lr, eps=1e-4)
scheduler = lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.99)
if dashboard is not None:
metric_monitor = MetricPlotter(
plot_prefix=fit_plot_prefix,
loss_display_cap=loss_display_cap,
dashboard_name=dashboard,
plot_ignore_initial=plot_ignore_initial,
process_model_fun=model_process_fun,
smooth_weight=reward_sm)
else:
metric_monitor = None
checkpointer = Checkpointer(valid_batches_to_checkpoint=1,
save_path=save_path,
save_always=True)
fitter = fit_rl(train_gen=train_gen,
model=model,
optimizer=optimizer,
scheduler=scheduler,
epochs=EPOCHS,
loss_fn=loss_obj,
grad_clip=5,
half_float=half_float,
anchor_model=anchor_model,
anchor_weight=anchor_weight,
callbacks=[metric_monitor, checkpointer] +
extra_callbacks)
return fitter
def my_gen():
for _ in range(1000):
yield to_gpu(torch.zeros(BATCH_SIZE, settings['z_size']))
# the on-policy fitter
gen_extra_callbacks = [make_callback(d) for d in history_data]
if smiles_save_file is not None:
smiles_save_path = os.path.realpath(root_location + 'pretrained/' +
smiles_save_file)
gen_extra_callbacks.append(MoleculeSaver(smiles_save_path, gzip=True))
print('Saved SMILES to {}'.format(smiles_save_file))
if node_temperature_schedule is not None:
gen_extra_callbacks.append(
TemperatureCallback(node_policy, node_temperature_schedule))
if rule_temperature_schedule is not None:
gen_extra_callbacks.append(
TemperatureCallback(rule_policy, rule_temperature_schedule))
fitter1 = get_rl_fitter(
model,
PolicyGradientLoss(on_policy_loss_type), # last_reward_wgt=reward_sm),
GeneratorToIterable(my_gen),
full_path(gen_save_file),
plot_prefix + 'on-policy',
model_process_fun=model_process_fun,
lr=lr_on,
extra_callbacks=gen_extra_callbacks,
anchor_model=anchor_model,
anchor_weight=anchor_weight)
#
# # get existing molecule data to add training
pre_dataset = EvenlyBlendedDataset(
2 * [history_data[0]] + history_data[1:],
labels=False) # a blend of 3 time horizons
dataset = EvenlyBlendedDataset([pre_dataset, zinc_data], labels=True)
discrim_loader = DataLoader(dataset, shuffle=True, batch_size=50)
class MyLoss(nn.Module):
def __init__(self):
super().__init__()
self.celoss = nn.CrossEntropyLoss()
def forward(self, x):
# tmp = discriminator_reward_mult(x['smiles'])
# tmp2 = F.softmax(x['p_zinc'], dim=1)[:,1].detach().cpu().numpy()
# import numpy as np
# assert np.max(np.abs(tmp-tmp2)) < 1e-6
return self.celoss(x['p_zinc'].to(device),
x['dataset_index'].to(device))
fitter2 = get_rl_fitter(discrim_model,
MyLoss(),
IterableTransform(
discrim_loader, lambda x: {
'smiles': x['X'],
'dataset_index': x['dataset_index']
}),
full_path(disc_save_file),
plot_prefix + ' discriminator',
lr=lr_discrim,
model_process_fun=None)
def on_policy_gen(fitter, model):
while True:
# model.policy = SoftmaxRandomSamplePolicy()#bias=codec.grammar.get_log_frequencies())
yield next(fitter)
return model, fitter1, fitter2 #,on_policy_gen(fitter1, model)
def test_custom_grammar_mask_gen(self):
molecules = True
grammar = 'new'
codec = get_codec(molecules, grammar, max_seq_length)
self.generate_and_validate(codec)
def check_codec(self, input, molecules, grammar):
settings = get_settings(molecules, grammar)
codec = get_codec(molecules, grammar, settings['max_seq_length'])
actions = codec.strings_to_actions([input])
re_input = codec.actions_to_strings(actions)
self.assertEqual(input, re_input[0])
def get_decoder(
molecules=True,
grammar=True,
z_size=200,
decoder_hidden_n=200,
feature_len=12, # TODO: remove this
max_seq_length=15,
drop_rate=0.0,
decoder_type='step',
task=None,
node_policy=None,
rule_policy=None,
reward_fun=lambda x: -1 * np.ones(len(x)),
batch_size=None,
priors=True):
codec = get_codec(molecules, grammar, max_seq_length)
if decoder_type == 'old':
stepper = ResettingRNNDecoder(z_size=z_size,
hidden_n=decoder_hidden_n,
feature_len=codec.feature_len(),
max_seq_length=max_seq_length,
steps=max_seq_length,
drop_rate=drop_rate)
stepper = OneStepDecoderContinuous(stepper)
elif 'graph' in decoder_type and decoder_type not in [
'attn_graph', 'rnn_graph'
]:
return get_node_decoder(grammar, max_seq_length, drop_rate,
decoder_type, rule_policy, reward_fun,
batch_size, priors)
elif decoder_type in ['attn_graph', 'rnn_graph']: # deprecated
assert 'hypergraph' in grammar, "Only the hypergraph grammar can be used with attn_graph decoder type"
if 'attn' in decoder_type:
encoder = GraphEncoder(grammar=codec.grammar,
d_model=512,
drop_rate=drop_rate,
model_type='transformer')
elif 'rnn' in decoder_type:
encoder = GraphEncoder(grammar=codec.grammar,
d_model=512,
drop_rate=drop_rate,
model_type='rnn')
model = MultipleOutputHead(
model=encoder,
output_spec={
'node': 1, # to be used to select next node to expand
'action': codec.feature_len()
}, # to select the action for chosen node
drop_rate=drop_rate)
# don't support using this model in VAE-style models yet
model.init_encoder_output = lambda x: None
mask_gen = HypergraphMaskGenerator(max_len=max_seq_length,
grammar=codec.grammar)
mask_gen.priors = priors
# bias=codec.grammar.get_log_frequencies())
if node_policy is None:
node_policy = SoftmaxRandomSamplePolicy()
if rule_policy is None:
rule_policy = SoftmaxRandomSamplePolicy()
if 'node' in decoder_type:
stepper = GraphDecoderWithNodeSelection(model,
node_policy=node_policy,
rule_policy=rule_policy)
env = GraphEnvironment(mask_gen,
reward_fun=reward_fun,
batch_size=batch_size)
decoder = DecoderWithEnvironmentNew(stepper, env)
else:
stepper = GraphDecoder(model=model, mask_gen=mask_gen)
decoder = to_gpu(
SimpleDiscreteDecoderWithEnv(stepper,
rule_policy,
task=task,
batch_size=batch_size))
return decoder, stepper
else:
if decoder_type == 'step':
stepper = SimpleRNNDecoder(z_size=z_size,
hidden_n=decoder_hidden_n,
feature_len=codec.feature_len(),
max_seq_length=max_seq_length,
drop_rate=drop_rate,
use_last_action=False)
elif decoder_type == 'action':
stepper = SimpleRNNDecoder(
z_size=z_size, # + feature_len,
hidden_n=decoder_hidden_n,
feature_len=codec.feature_len(),
max_seq_length=max_seq_length,
drop_rate=drop_rate,
use_last_action=True)
elif decoder_type == 'action_resnet':
stepper = ResNetRNNDecoder(
z_size=z_size, # + feature_len,
hidden_n=decoder_hidden_n,
feature_len=codec.feature_len(),
max_seq_length=max_seq_length,
drop_rate=drop_rate,
use_last_action=True)
elif decoder_type == 'attention':
stepper = SelfAttentionDecoderStep(num_actions=codec.feature_len(),
max_seq_len=max_seq_length,
drop_rate=drop_rate,
enc_output_size=z_size)
elif decoder_type == 'random':
stepper = RandomDecoder(feature_len=codec.feature_len(),
max_seq_length=max_seq_length)
else:
raise NotImplementedError('Unknown decoder type: ' +
str(decoder_type))
if grammar is not False and '_graph' not in decoder_type:
# add a masking layer
mask_gen = get_codec(molecules, grammar, max_seq_length).mask_gen
stepper = MaskingHead(stepper, mask_gen)
policy = SoftmaxRandomSamplePolicy(
) # bias=codec.grammar.get_log_frequencies())
decoder = to_gpu(
SimpleDiscreteDecoderWithEnv(
stepper, policy, task=task,
batch_size=batch_size)) # , bypass_actions=True))
return decoder, stepper
def train_policy_gradient(molecules=True,
grammar=True,
EPOCHS=None,
BATCH_SIZE=None,
reward_fun_on=None,
reward_fun_off=None,
max_steps=277,
lr_on=2e-4,
lr_discrim=1e-4,
p_thresh=0.5,
drop_rate=0.0,
plot_ignore_initial=0,
randomize_reward=False,
save_file=None,
reward_sm=0.0,
preload_file=None,
anchor_file=None,
anchor_weight=0.0,
decoder_type='action',
plot_prefix='',
dashboard='policy gradient',
smiles_save_file=None,
on_policy_loss_type='best',
off_policy_loss_type='mean',
sanity_checks=True):
root_location = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
root_location = root_location + '/../../'
gen_save_path = root_location + 'pretrained/gen_' + save_file
disc_save_path = root_location + 'pretrained/disc_' + save_file
if smiles_save_file is not None:
smiles_save_path = root_location + 'pretrained/' + smiles_save_file
save_dataset = IncrementingHDF5Dataset(smiles_save_path)
else:
save_dataset = None
settings = get_settings(molecules=molecules, grammar=grammar)
codec = get_codec(molecules, grammar, settings['max_seq_length'])
discrim_model = GraphDiscriminator(codec.grammar, drop_rate=drop_rate)
zinc_data = get_zinc_smiles()
zinc_set = set(zinc_data)
lookbacks = [BATCH_SIZE, 10 * BATCH_SIZE, 100 * BATCH_SIZE]
history_data = [deque(['O'], maxlen=lb) for lb in lookbacks]
def originality_mult(smiles_list):
out = []
for s in smiles_list:
if s in zinc_set:
out.append(0.5)
elif s in history_data[0]:
out.append(0.5)
elif s in history_data[1]:
out.append(0.70)
elif s in history_data[2]:
out.append(0.85)
else:
out.append(1.0)
return np.array(out)
def sigmoid(x):
tmp = -x #(
return 1 / (1 + np.exp(-x))
def discriminator_reward_mult(smiles_list):
orig_state = discrim_model.training
discrim_model.eval()
discrim_out_logits = discrim_model(smiles_list)['p_zinc']
discrim_probs = F.softmax(discrim_out_logits, dim=1)
prob_zinc = discrim_probs[:, 1].detach().cpu().numpy()
if orig_state:
discrim_model.train()
return prob_zinc
def adj_reward(x):
p = discriminator_reward_mult(x)
reward = np.maximum(reward_fun_on(x), 0)
out = reward * originality_mult(x) + 2 * p
return out
def adj_reward_old(x):
p = discriminator_reward_mult(x)
w = sigmoid(-(p - p_thresh) / 0.01)
if randomize_reward:
rand = np.random.uniform(size=p.shape)
w *= rand
reward = np.maximum(reward_fun_on(x), p_thresh)
weighted_reward = w * p + (1 - w) * reward
out = weighted_reward * originality_mult(x) #
return out
if EPOCHS is not None:
settings['EPOCHS'] = EPOCHS
if BATCH_SIZE is not None:
settings['BATCH_SIZE'] = BATCH_SIZE
task = SequenceGenerationTask(molecules=molecules,
grammar=grammar,
reward_fun=adj_reward,
batch_size=BATCH_SIZE,
max_steps=max_steps,
save_dataset=save_dataset)
model = get_decoder(molecules,
grammar,
z_size=settings['z_size'],
decoder_hidden_n=200,
feature_len=codec.feature_len(),
max_seq_length=max_steps,
drop_rate=drop_rate,
decoder_type=decoder_type,
task=task)[0]
# TODO: really ugly, refactor! In fact this model doesn't need a MaskingHead at all!
model.stepper.model.mask_gen.priors = True #'conditional' # use empirical priors for the mask gen
# if preload_file is not None:
# try:
# preload_path = root_location + 'pretrained/' + preload_file
# model.load_state_dict(torch.load(preload_path))
# except:
# pass
anchor_model = None
from generative_playground.molecules.rdkit_utils.rdkit_utils import NormalizedScorer
import rdkit.Chem.rdMolDescriptors as desc
import numpy as np
scorer = NormalizedScorer()
def model_process_fun(model_out, visdom, n):
# TODO: rephrase this to return a dict, instead of calling visdom directly
from rdkit import Chem
from rdkit.Chem.Draw import MolToFile
# actions, logits, rewards, terminals, info = model_out
smiles, valid = model_out['info']
total_rewards = model_out['rewards'].sum(1)
best_ind = torch.argmax(total_rewards).data.item()
this_smile = smiles[best_ind]
mol = Chem.MolFromSmiles(this_smile)
pic_save_path = root_location + 'images/' + 'tmp.svg'
if mol is not None:
try:
MolToFile(mol, pic_save_path, imageType='svg')
with open(pic_save_path, 'r') as myfile:
data = myfile.read()
data = data.replace('svg:', '')
visdom.append('best molecule of batch', 'svg', svgstr=data)
except Exception as e:
print(e)
scores, norm_scores = scorer.get_scores([this_smile])
visdom.append(
'score component',
'line',
X=np.array([n]),
Y=np.array(
[[x for x in norm_scores[0]] + [norm_scores[0].sum()] +
[scores[0].sum()] + [desc.CalcNumAromaticRings(mol)] +
[total_rewards[best_ind].item()]]),
opts={
'legend': [
'logP', 'SA', 'cycle', 'norm_reward', 'reward',
'Aromatic rings', 'eff_reward'
]
})
visdom.append('fraction valid',
'line',
X=np.array([n]),
Y=np.array([valid.mean().data.item()]))
if reward_fun_off is None:
reward_fun_off = reward_fun_on
# construct the loader to feed the discriminator
def make_callback(data):
def hc(inputs, model, outputs, loss_fn, loss):
graphs = outputs['graphs']
smiles = [g.to_smiles() for g in graphs]
for s in smiles: # only store unique instances of molecules so discriminator can't guess on frequency
if s not in data:
data.append(s)
return hc
# need to have something there to begin with, else the DataLoader constructor barfs
def get_rl_fitter(model,
loss_obj,
train_gen,
save_path,
fit_plot_prefix='',
model_process_fun=None,
lr=None,
extra_callbacks=[],
loss_display_cap=float('inf'),
anchor_model=None,
anchor_weight=0):
nice_params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.Adam(nice_params, lr=lr)
scheduler = lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.99)
if dashboard is not None:
metric_monitor = MetricPlotter(
plot_prefix=fit_plot_prefix,
loss_display_cap=loss_display_cap,
dashboard_name=dashboard,
plot_ignore_initial=plot_ignore_initial,
process_model_fun=model_process_fun,
smooth_weight=0.9)
else:
metric_monitor = None
checkpointer = Checkpointer(valid_batches_to_checkpoint=1,
save_path=save_path,
save_always=True)
fitter = fit_rl(train_gen=train_gen,
model=model,
optimizer=optimizer,
scheduler=scheduler,
epochs=EPOCHS,
loss_fn=loss_obj,
grad_clip=5,
anchor_model=anchor_model,
anchor_weight=anchor_weight,
callbacks=[metric_monitor, checkpointer] +
extra_callbacks)
return fitter
class GeneratorToIterable:
def __init__(self, gen):
self.gen = gen
# we assume the generator is finite
self.len = 0
for _ in gen():
self.len += 1
def __len__(self):
return self.len
def __iter__(self):
return self.gen()
def my_gen():
for _ in range(1000):
yield to_gpu(torch.zeros(BATCH_SIZE, settings['z_size']))
# the on-policy fitter
history_callbacks = [make_callback(d) for d in history_data]
fitter1 = get_rl_fitter(model,
PolicyGradientLoss(on_policy_loss_type,
last_reward_wgt=reward_sm),
GeneratorToIterable(my_gen),
gen_save_path,
plot_prefix + 'on-policy',
model_process_fun=model_process_fun,
lr=lr_on,
extra_callbacks=history_callbacks,
anchor_model=anchor_model,
anchor_weight=anchor_weight)
#
# # get existing molecule data to add training
pre_dataset = EvenlyBlendedDataset(
2 * [history_data[0]] + history_data[1:],
labels=False) # a blend of 3 time horizons
dataset = EvenlyBlendedDataset([pre_dataset, zinc_data], labels=True)
discrim_loader = DataLoader(dataset, shuffle=True, batch_size=50)
celoss = nn.CrossEntropyLoss()
def my_loss(x):
# tmp = discriminator_reward_mult(x['smiles'])
# tmp2 = F.softmax(x['p_zinc'], dim=1)[:,1].detach().cpu().numpy()
# import numpy as np
# assert np.max(np.abs(tmp-tmp2)) < 1e-6
return celoss(x['p_zinc'].to(device), x['dataset_index'].to(device))
fitter2 = get_rl_fitter(discrim_model,
my_loss,
IterableTransform(
discrim_loader, lambda x: {
'smiles': x['X'],
'dataset_index': x['dataset_index']
}),
disc_save_path,
plot_prefix + ' discriminator',
lr=lr_discrim,
model_process_fun=None)
def on_policy_gen(fitter, model):
while True:
model.policy = SoftmaxRandomSamplePolicy(
) #bias=codec.grammar.get_log_frequencies())
yield next(fitter)
return model, on_policy_gen(fitter1, model), fitter2
