File size: 2,342 Bytes
778d47d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | import json
import itertools
from torch.utils.data import Dataset
class SchemaItemClassifierDataset(Dataset):
def __init__(self, dataset_dir):
super(SchemaItemClassifierDataset, self).__init__()
self.texts: list[str] = []
self.all_column_names: list[list[list[str]]] = []
self.all_column_labels: list[list[list[int]]] = []
self.all_table_names: list[list[str]] = []
self.all_table_labels: list[list[int]] = []
dataset = json.load(open(dataset_dir))
assert type(dataset) == list
for data in dataset:
table_names_in_one_db = []
column_names_in_one_db = []
for table in data["schema"]["schema_items"]:
# table_names_in_one_db.append(table["table_name"])
# column_names_in_one_db.append(table["column_names"])
table_names_in_one_db.append(table["table_name"] + " ( " + table["table_comment"] + " ) " \
if table["table_comment"] != "" else table["table_name"])
column_names_in_one_db.append([column_name + " ( " + column_comment + " ) " \
if column_comment != "" else column_name \
for column_name, column_comment in zip(table["column_names"], table["column_comments"])])
self.texts.append(data["text"])
self.all_table_names.append(table_names_in_one_db)
self.all_column_names.append(column_names_in_one_db)
self.all_table_labels.append(data["table_labels"])
self.all_column_labels.append(list(itertools.chain(*data["column_labels"])))
def __len__(self):
return len(self.texts)
def __getitem__(self, index):
text = self.texts[index]
table_names_in_one_db = self.all_table_names[index]
table_labels_in_one_db = self.all_table_labels[index]
column_infos_in_one_db = self.all_column_names[index]
column_labels_in_one_db = self.all_column_labels[index]
return {
"text": text,
"table_names_in_one_db": table_names_in_one_db,
"table_labels_in_one_db": table_labels_in_one_db,
"column_infos_in_one_db": column_infos_in_one_db,
"column_labels_in_one_db": column_labels_in_one_db
}
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