def __enter__(self):
assert (self.__log_dir is not None)
iter_index_int = self.__get_iter_index()
iter_index = str(iter_index_int)
for d_type in self.__iter_supported_data_types():
train_log_filepath = join(
self.__log_dir,
Common.create_log_train_filename(iter_index=iter_index_int,
data_type=d_type))
eval_log_filepath = join(
self.__log_dir,
Common.create_log_eval_filename(iter_index=iter_index_int,
data_type=d_type))
eval_verbose_log_filepath = join(
self.__log_dir,
self.__log_eval_iter_verbose_filename.format(iter=iter_index,
dtype=d_type))
create_dir_if_not_exists(train_log_filepath)
create_dir_if_not_exists(eval_log_filepath)
create_dir_if_not_exists(eval_verbose_log_filepath)
self.__train_iter_log_files[d_type] = open(train_log_filepath,
u"w",
buffering=0)
self.__eval_iter_log_files[d_type] = open(eval_log_filepath,
u"w",
buffering=0)
self.__eval_iter_verbose_log_files[d_type] = open(
eval_verbose_log_filepath, u"w", buffering=0)
def __iter_files_per_iteration(self, result_type, folding_type):
assert(isinstance(folding_type, FoldingType))
iters = self.__cv_count if folding_type == FoldingType.CrossValidation else 1
if result_type == ResultType.F1 or \
result_type == ResultType.DSDiffF1Improvement or \
result_type == ResultType.DSDiffAttImprovement:
yield join(Common.log_dir, Common.log_test_eval_exp_filename)
elif result_type == ResultType.TrainingEpochTime or \
result_type == ResultType.TrainingTotalTime or \
result_type == ResultType.TrainingAccuracy or \
result_type == ResultType.EpochsCount:
for it_index in range(iters):
yield join(Common.log_dir, Common.create_log_train_filename(data_type=DataType.Train,
iter_index=it_index))
elif result_type == ResultType.F1LastTrain:
for it_index in range(iters):
yield join(Common.log_dir, Common.create_log_eval_filename(data_type=DataType.Train,
iter_index=it_index))
elif result_type == ResultType.F1LastTest or \
result_type == ResultType.F1NeutLastTest or \
result_type == ResultType.PrecNeutLastTest or \
result_type == ResultType.RecallNeutLastTest:
for it_index in range(iters):
yield join(Common.log_dir, Common.create_log_eval_filename(data_type=DataType.Test,
iter_index=it_index))
elif result_type == ResultType.LearningRate:
for it_index in range(iters):
yield join(Common.log_dir, Common.model_config_name)
elif result_type == ResultType.TrainingPosSamplesCount or \
result_type == ResultType.TrainingNegSamplesCount or \
result_type == ResultType.TrainingNeuSamplesCount:
# returning back from the model dir to the experiment dir.
yield u".."
else:
raise NotImplementedError("Not supported type: {}".format(result_type))