def run_initial_scan(num_batches=100,
batch_size=30,
snapshot_dir=None,
entropy_wgt=0.0,
root_name=None,
obj_num=None,
ver='v2',
lr=0.01,
attempt='',
plot=False):
grammar_cache = 'hyper_grammar_guac_10k_with_clique_collapse.pickle' # 'hyper_grammar.pickle'
grammar = 'hypergraph:' + grammar_cache
reward_funs = guacamol_goal_scoring_functions(ver)
reward_fun = reward_funs[obj_num]
first_runner = lambda: PolicyGradientRunner(
grammar,
BATCH_SIZE=batch_size,
reward_fun=reward_fun,
max_steps=60,
num_batches=num_batches,
lr=lr,
entropy_wgt=entropy_wgt,
# lr_schedule=shifted_cosine_schedule,
root_name=root_name,
preload_file_root_name=None,
plot_metrics=plot,
save_location=snapshot_dir,
metric_smooth=0.0,
decoder_type='graph_conditional', # 'rnn_graph',# 'attention',
on_policy_loss_type='advantage_record',
rule_temperature_schedule=None,
# lambda x: toothy_exp_schedule(x, scale=num_batches),
eps=0.0,
priors='conditional',
)
run = 0
while True:
model = first_runner()
orig_name = model.root_name
model.set_root_name(generate_root_name(orig_name, {}))
model.run()
def run_genetic_opt(
top_N=10,
p_mutate=0.2,
mutate_num_best=64,
mutate_use_total_probs=False,
p_crossover=0.2,
num_batches=100,
batch_size=30,
snapshot_dir=None,
entropy_wgt=0.0,
root_name=None,
obj_num=None,
ver='v2',
lr=0.01,
num_runs=100,
num_explore=5,
plot_single_runs=True,
steps_with_no_improvement=10,
reward_aggregation=np.median,
attempt='', # only used for disambiguating plotting
max_steps=90,
past_runs_graph_file=None):
manager = mp.Manager()
queue = manager.Queue()
relationships = nx.DiGraph()
grammar_cache = 'hyper_grammar_guac_10k_with_clique_collapse.pickle' # 'hyper_grammar.pickle'
grammar = 'hypergraph:' + grammar_cache
reward_funs = guacamol_goal_scoring_functions(ver)
reward_fun = reward_funs[obj_num]
split_name = root_name.split('_')
split_name[0] += 'Stats'
dash_name = '_'.join(split_name) + attempt
vis = Dashboard(dash_name, call_every=1)
first_runner_factory = lambda: PolicyGradientRunner(
grammar,
BATCH_SIZE=batch_size,
reward_fun=reward_fun,
max_steps=max_steps,
num_batches=num_batches,
lr=lr,
entropy_wgt=entropy_wgt,
# lr_schedule=shifted_cosine_schedule,
root_name=root_name,
preload_file_root_name=None,
plot_metrics=plot_single_runs,
save_location=snapshot_dir,
metric_smooth=0.0,
decoder_type='graph_conditional_sparse',
# 'graph_conditional', # 'rnn_graph',# 'attention',
on_policy_loss_type='advantage_record',
rule_temperature_schedule=None,
# lambda x: toothy_exp_schedule(x, scale=num_batches),
eps=0.0,
priors='conditional',
)
init_thresh = 50
pca_dim = 10
if past_runs_graph_file:
params, rewards = extract_params_rewards(past_runs_graph_file)
sampler = ParameterSampler(params,
rewards,
init_thresh=init_thresh,
pca_dim=pca_dim)
else:
sampler = None
data_cache = {}
best_so_far = float('-inf')
steps_since_best = 0
initial = True
should_stop = False
run = 0
with mp.Pool(4) as p:
while not should_stop:
data_cache = populate_data_cache(snapshot_dir, data_cache)
if run < num_explore:
model = first_runner_factory()
if sampler:
model.params = sampler.sample()
else:
model = (pick_model_to_run(data_cache,
PolicyGradientRunner,
snapshot_dir,
num_best=top_N)
if data_cache else first_runner_factory())
orig_name = model.root_name
model.set_root_name(generate_root_name(orig_name, data_cache))
if run > num_explore:
relationships.add_edge(orig_name, model.root_name)
if random.random() < p_crossover and len(data_cache) > 1:
second_model = pick_model_for_crossover(
data_cache, model, PolicyGradientRunner, snapshot_dir)
model = classic_crossover(model, second_model)
relationships.add_edge(second_model.root_name,
model.root_name)
if random.random() < p_mutate:
model = mutate(model,
pick_best=mutate_num_best,
total_probs=mutate_use_total_probs)
relationships.node[model.root_name]['mutated'] = True
else:
relationships.node[model.root_name]['mutated'] = False
with open(
snapshot_dir + '/' + model.root_name + '_lineage.pkl',
'wb') as f:
pickle.dump(relationships, f)
model.save()
if initial is True:
for _ in range(4):
print('Starting {}'.format(run))
p.apply_async(run_model,
(queue, model.root_name, run, snapshot_dir))
run += 1
initial = False
else:
print('Starting {}'.format(run))
p.apply_async(run_model,
(queue, model.root_name, run, snapshot_dir))
run += 1
finished_run, finished_root_name = queue.get(block=True)
print('Finished: {}'.format(finished_root_name))
data_cache = populate_data_cache(snapshot_dir, data_cache)
my_rewards = data_cache[finished_root_name]['best_rewards']
metrics = {
'max': my_rewards.max(),
'median': np.median(my_rewards),
'min': my_rewards.min()
}
metric_dict = {
'type': 'line',
'X': np.array([finished_run]),
'Y': np.array([[val for key, val in metrics.items()]]),
'opts': {
'legend': [key for key, val in metrics.items()]
}
}
vis.plot_metric_dict({'worker rewards': metric_dict})
this_agg_reward = reward_aggregation(my_rewards)
if this_agg_reward > best_so_far:
best_so_far = this_agg_reward
steps_since_best = 0
else:
steps_since_best += 1
should_stop = (
steps_since_best >= steps_with_no_improvement
and finished_run > num_explore + steps_with_no_improvement)
p.terminate()
return extract_best(data_cache, 1)