def __run():
# Setup default evaluator.
evaluator = ThreeClassEvaluator(DataType.Test)
experiment_data = RuSentRelTrainingData(
labels_scaler=labels_scaler,
stemmer=stemmer,
evaluator=evaluator,
opinion_formatter=RuSentRelOpinionCollectionFormatter(),
callback=CallbackEvalF1NPU(DataType.Test))
extra_name_suffix = Common.create_exp_name_suffix(
use_balancing=balanced_input,
terms_per_context=terms_per_context,
dist_in_terms_between_att_ends=dist_in_terms_between_attitude_ends)
# Composing experiment.
experiment = create_experiment(exp_type=exp_type,
experiment_data=experiment_data,
folding_type=folding_type,
rusentrel_version=rusentrel_version,
experiment_io_type=CustomNetworkExperimentIO,
ruattitudes_version=ra_version,
load_ruattitude_docs=False,
extra_name_suffix=extra_name_suffix)
full_model_name = Common.create_full_model_name(folding_type=folding_type,
model_name=model_name,
input_type=model_input_type)
model_io = NeuralNetworkModelIO(
full_model_name=full_model_name,
target_dir=experiment.ExperimentIO.get_target_dir(),
# From this depends on whether we have a specific dir or not.
source_dir=None if model_name_tag is None else u"",
model_name_tag=ModelNameTagArg.NO_TAG if model_name_tag is None else model_name_tag)
# Setup model io.
experiment_data.set_model_io(model_io)
# Check dir existence in advance.
model_dir = model_io.get_model_dir()
if not exists(model_dir):
print u"Skipping [path not exists]: {}".format(model_dir)
return
engine = ExperimentF1pnuEvaluator(experiment=experiment,
data_type=DataType.Test,
max_epochs_count=max_epochs_count,
forced=force_eval)
# Starting evaluation process.
engine.run()
callback.set_eval_on_epochs(test_epochs_range)
# Setup evaluation mode.
eval_mode = EvaluationModes.Extraction if labels_count == 3 else EvaluationModes.Classification
# Creating experiment
evaluator = TwoClassEvaluator(eval_mode)
experiment_data = RuSentRelTrainingData(
labels_scaler=Common.create_labels_scaler(labels_count),
stemmer=stemmer,
opinion_formatter=Common.create_opinion_collection_formatter(),
evaluator=evaluator,
callback=callback)
extra_name_suffix = Common.create_exp_name_suffix(
use_balancing=balanced_input,
terms_per_context=terms_per_context,
dist_in_terms_between_att_ends=dist_in_terms_between_attitude_ends)
experiment = create_experiment(
exp_type=exp_type,
experiment_data=experiment_data,
folding_type=folding_type,
rusentrel_version=rusentrel_version,
ruattitudes_version=ra_version,
experiment_io_type=CustomNetworkExperimentIO,
extra_name_suffix=extra_name_suffix,
load_ruattitude_docs=False)
full_model_name = Common.create_full_model_name(
folding_type=folding_type,
model_name=model_name,