def function_optimize_optuna(trial : Trial):
train_it= 600
stack_layers = trial.suggest_int('stack_layers', 1, 3) #2
max_pool_layers = trial.suggest_categorical('max_pool_layers', [True, False])
if max_pool_layers:
max_pool_layers = stack_layers
else:
max_pool_layers = 0
st_filter = int(trial.suggest_discrete_uniform('st_filter', 30, 50, 10)) #40
inc_filter = int(trial.suggest_discrete_uniform('inc_filter', 30, 50, 10)) #50
extra_porc = trial.suggest_int('extra_porc', 1, 4) # 2
input_factor = trial.suggest_categorical('input_factor', [0.5,0.25]) # 0.5
lr = trial.suggest_categorical('learning_rate', [0.001,0.0001])
#loss_string_options = ['cross_entropy','mse']
#loss_string = trial.suggest_categorical('loss_string', loss_string_options)
loss_string = 'cross_entropy'
replace_max_pool_with_stride = trial.suggest_categorical('replace_max_pool_with_stride', [True,False])
exp_params = {
'tf_config' : tf.ConfigProto(allow_soft_placement = True),
'max_pool_layers' : max_pool_layers,
'stack_layers' : stack_layers,
'input_factor' : input_factor,
'extra_porc' : extra_porc,
'lr' : lr,
'st_filter' : st_filter,
'inc_filter' : inc_filter,
'loss_string' : loss_string,
'replace_max_pool_with_stride' : replace_max_pool_with_stride
}
print("PARAMS : {0}".format(exp_params))
out_dict,out_folder = train_run(train_it, save_model=True, interactive_plot=False, **exp_params)
# save params
metric = float(out_dict['global_F1'])
trial.set_user_attr('out_path',out_folder)
for k in out_dict:
if k != 'global_F1':
trial.set_user_attr(k,float(out_dict[k]))
return metric
def objective(trial: Trial, X: pd.Series, y: pd.Series, workers: int) -> int:
# Get architecture and its parameters
pipeline = define_hyperparameters(trial=trial)
# Evaluate architectures and store settings and metrics
scores = cross_validate(
pipeline,
X,
y,
cv=3,
scoring=["accuracy", "average_precision", "f1"],
n_jobs=workers,
)
trial.set_user_attr(key="model", value=pipeline)
for metric in ("accuracy", "average_precision", "f1"):
trial.set_user_attr(key=metric, value=scores[f"test_{metric}"].mean())
return scores["test_accuracy"].mean()
def __call__(self, trial: trial_module.Trial) -> float:
params = self._get_params(trial) # type: Dict[str, Any]
dataset = copy.copy(self.dataset)
callbacks = self._get_callbacks(trial) # type: List[Callable]
eval_hist = lgb.cv(
params,
dataset,
callbacks=callbacks,
early_stopping_rounds=self.early_stopping_rounds,
feval=self.feval,
fobj=self.fobj,
folds=self.cv,
init_model=self.init_model,
num_boost_round=self.n_estimators,
) # Dict[str, List[float]]
values = eval_hist[
"{}-mean".format(self.eval_name)
] # type: List[float]
best_iteration = len(values) # type: int
trial.set_user_attr("best_iteration", best_iteration)
trial_path = self.model_dir / "trial_{}".format(trial.number)
trial_path.mkdir(exist_ok=True, parents=True)
boosters = callbacks[0].boosters_ # type: ignore
for i, b in enumerate(boosters):
b.best_iteration = best_iteration
b.free_dataset()
booster_path = trial_path / "fold_{}.pkl".format(i)
with booster_path.open("wb") as f:
pickle.dump(b, f)
return values[-1]
def _store_scores(self, trial: Trial, scores: Dict[str, OneDimArrayLikeType]) -> None:
for name, array in scores.items():
if name in ["test_score", "train_score"]:
for i, score in enumerate(array):
trial.set_user_attr("split{}_{}".format(i, name), score)
trial.set_user_attr("mean_{}".format(name), np.nanmean(array))
trial.set_user_attr("std_{}".format(name), np.nanstd(array))
def objective(trial: Trial) -> float:
"""Compute objective value
Parameter
---------
trial : `Trial`
Current trial
Returns
-------
loss : `float`
Loss
"""
# use pyannote.metrics metric when available
try:
metric = self.pipeline.get_metric()
except NotImplementedError as e:
metric = None
losses = []
processing_time = []
evaluation_time = []
# instantiate pipeline with value suggested in current trial
pipeline = self.pipeline.instantiate(
self.pipeline.parameters(trial=trial))
if show_progress != False:
progress_bar = tqdm(total=len(inputs), **show_progress)
progress_bar.update(0)
# accumulate loss for each input
for i, input in enumerate(inputs):
# process input with pipeline
# (and keep track of processing time)
before_processing = time.time()
output = pipeline(input)
after_processing = time.time()
processing_time.append(after_processing - before_processing)
# evaluate output (and keep track of evaluation time)
before_evaluation = time.time()
# when metric is not available, use loss method instead
if metric is None:
loss = pipeline.loss(input, output)
losses.append(loss)
# when metric is available,`input` is expected to be provided
# by a `pyannote.database` protocol
else:
from pyannote.database import get_annotated
_ = metric(input["annotation"],
output,
uem=get_annotated(input))
after_evaluation = time.time()
evaluation_time.append(after_evaluation - before_evaluation)
if show_progress != False:
progress_bar.update(1)
if self.pruner is None:
continue
trial.report(
np.mean(losses) if metric is None else abs(metric), i)
if trial.should_prune(i):
raise optuna.structs.TrialPruned()
if show_progress != False:
progress_bar.close()
trial.set_user_attr("processing_time", sum(processing_time))
trial.set_user_attr("evaluation_time", sum(evaluation_time))
return np.mean(losses) if metric is None else abs(metric)
