| 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"] + " ( " + 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 |
| } |
|
|
