def __init__(self, args):
self.GLOBAL_BEST_DEVEL_PRED_RESULTS = []
self.args = args
self._LogFileHandler = open(args.logfileaddress, "wt")
self.lp("Program started ...")
self.__validate_args__()
self.PARAMS = collections.OrderedDict()
self.PARAMS[
"train_filename"] = args.data_folder + '/' + args.ann_set + '/' + args.ann_type + '/' + args.ann_type + '-train-annotations.txt'
self.PARAMS[
"devel_filename"] = args.data_folder + '/' + args.ann_set + '/' + args.ann_type + '/' + args.ann_type + '-devel-annotations.txt'
self.PARAMS[
"test_filename"] = args.data_folder + '/' + args.ann_set + '/' + args.ann_type + '/' + args.ann_type + '-test-annotations.txt'
self.PARAMS["X_lower_row_len"] = 1 # Lower text length threshold
self.PARAMS["X_upper_row_len"] = 400 # Upper text length threshold
self.PARAMS["X_used_row_len"] = -1
self.PARAMS[
"default_embeddings_dim"] = 300 # default size of the used word embeddings when no pre-created embeddings model is given
MSG = ["" * 80, "PARAMETERS:", "-" * 20]
for key in self.PARAMS.keys():
MSG.append(GF.NVLR(key, 20) + " : " + str(self.PARAMS[key]))
MSG.append("*" * 80)
self.lp(MSG)
def __validate_args__(self):
self.lp("Validating args ...")
D = self.args.__dict__
MSG = ["" * 80, "Command-Line args:", "-" * 20]
for key in sorted(D.keys()):
MSG.append(GF.NVLR(key, 20) + " : " + str(D[key]))
MSG.append("*" * 80)
self.lp(MSG)
def __LoadData__(self):
self.lp("Fetching information about the data set ...")
# ----------------------------------
train_data_obj = X_y_dataHandler(ann_set=self.args.ann_set,
include_o_labels=0)
train_data_obj.load_data_set(self.PARAMS["train_filename"])
# ----------------------------------
devel_data_obj = X_y_dataHandler(ann_set=self.args.ann_set,
include_o_labels=0)
devel_data_obj.load_data_set(self.PARAMS["devel_filename"])
# ----------------------------------
test_data_obj = X_y_dataHandler(ann_set=self.args.ann_set,
include_o_labels=0)
test_data_obj.load_data_set(self.PARAMS["test_filename"])
# ----------------------------------
X_word_max_value = max([
train_data_obj.get_X_max_word_value(),
devel_data_obj.get_X_max_word_value(),
test_data_obj.get_X_max_word_value()
])
X_lemma_max_value = max([
train_data_obj.get_X_max_lemma_value(),
devel_data_obj.get_X_max_lemma_value(),
test_data_obj.get_X_max_lemma_value()
])
X_pos_max_value = max([
train_data_obj.get_X_max_pos_value(),
devel_data_obj.get_X_max_pos_value(),
test_data_obj.get_X_max_pos_value()
])
y_max_value = max([
train_data_obj.get_y_max_value(),
devel_data_obj.get_y_max_value(),
test_data_obj.get_y_max_value()
])
# ----------------------------------
X_data_max_row_len = max([
train_data_obj.get_X_max_len(),
devel_data_obj.get_X_max_len(),
test_data_obj.get_X_max_len()
])
if X_data_max_row_len <= self.PARAMS["X_lower_row_len"]:
X_used_row_len = self.PARAMS["X_lower_row_len"]
elif X_data_max_row_len >= self.PARAMS["X_upper_row_len"]:
X_used_row_len = self.PARAMS["X_upper_row_len"]
else:
X_used_row_len = X_data_max_row_len
# ----------------------------------
train_data_obj.make_numpy_arrays(X_used_row_len,
y_max_value,
padding_side=self.args.padding_side)
# ----------------------------------
devel_data_obj.make_numpy_arrays(X_used_row_len,
y_max_value,
padding_side=self.args.padding_side)
# ----------------------------------
test_data_obj.make_numpy_arrays(X_used_row_len,
y_max_value,
padding_side=self.args.padding_side)
# ----------------------------------
# Need to check again due to potential removal of the O label column
y_max_value = max([
train_data_obj.get_y_max_value(),
devel_data_obj.get_y_max_value(),
test_data_obj.get_y_max_value()
])
train_data_size = train_data_obj.get_size()
devel_data_size = devel_data_obj.get_size()
test_data_size = test_data_obj.get_size()
MSG = ["*" * 80, "Information about data:", "-" * 30]
MSG.append(
GF.NVLR('word max value', 40) + ": " + str(X_word_max_value))
MSG.append(
GF.NVLR('lemma max value', 40) + ": " + str(X_lemma_max_value))
MSG.append(GF.NVLR('pos max value', 40) + ": " + str(X_pos_max_value))
MSG.append(GF.NVLR('used row length', 40) + ": " + str(X_used_row_len))
MSG.append(
GF.NVLR('max row length', 40) + ": " + str(X_data_max_row_len))
MSG.append(GF.NVLR('max value', 40) + ": " + str(y_max_value))
MSG.append(
GF.NVLR('Train data', 40) + ": " + self.PARAMS["train_filename"])
MSG.append(GF.NVLR('Train size', 40) + ": " + str(train_data_size))
MSG.append("")
MSG.append(
GF.NVLR('Devel data', 40) + ": " + self.PARAMS["devel_filename"])
MSG.append(GF.NVLR('Devel size', 40) + ": " + str(devel_data_size))
MSG.append("")
MSG.append(
GF.NVLR('Test data', 40) + ": " + self.PARAMS["test_filename"])
MSG.append(GF.NVLR('Test size', 40) + ": " + str(test_data_size))
MSG.append("")
self.lp(MSG)
self.train_data_obj = train_data_obj
self.devel_data_obj = devel_data_obj
self.test_data_obj = test_data_obj
self.PARAMS["X_word_max_value"] = X_word_max_value
self.PARAMS["X_lemma_max_value"] = X_lemma_max_value
self.PARAMS["X_pos_max_value"] = X_pos_max_value
self.PARAMS["X_used_row_len"] = X_used_row_len
self.PARAMS["y_max_value"] = y_max_value
