| """ |
| Utilities for loading BIRD dataset column/value descriptions from |
| database_description/*.csv files (one CSV per table). |
| |
| Adapted from CHESS (https://github.com/ShayanTalaei/CHESS) |
| chess/src/database_utils/db_catalog/csv_utils.py. |
| |
| Schema returned: |
| { |
| table_name_lower: { |
| column_name_lower: { |
| "original_column_name": str, |
| "column_name": str, # expanded/human-readable name |
| "column_description": str, |
| "data_format": str, |
| "value_description": str, |
| } |
| } |
| } |
| """ |
|
|
| import logging |
| from pathlib import Path |
| from typing import Dict |
|
|
| import pandas as pd |
|
|
|
|
| def load_db_descriptions( |
| db_directory_path: str, |
| use_value_description: bool = True, |
| ) -> Dict[str, Dict[str, Dict[str, str]]]: |
| """Load table/column descriptions from BIRD database_description/*.csv. |
| |
| Args: |
| db_directory_path: Path to the database directory (containing |
| database_description/ sub-folder). |
| use_value_description: Whether to include value_description field. |
| |
| Returns: |
| Nested dict table → column → field → value. |
| Returns {} when the description folder does not exist. |
| """ |
| encoding_types = ["utf-8-sig", "cp1252", "latin-1"] |
| description_path = Path(db_directory_path) / "database_description" |
|
|
| if not description_path.exists(): |
| return {} |
|
|
| table_description: Dict[str, Dict[str, Dict[str, str]]] = {} |
|
|
| for csv_file in sorted(description_path.glob("*.csv")): |
| table_name = csv_file.stem.lower().strip() |
| table_description[table_name] = {} |
| loaded = False |
|
|
| for encoding in encoding_types: |
| try: |
| df = pd.read_csv(csv_file, index_col=False, encoding=encoding) |
| for _, row in df.iterrows(): |
| col_key = str(row.get("original_column_name", "")).lower().strip() |
| if not col_key: |
| continue |
|
|
| def _clean(val, remove_prefix: str = "") -> str: |
| if not pd.notna(val): |
| return "" |
| s = str(val).replace("\n", " ").strip() |
| if remove_prefix and s.lower().startswith(remove_prefix): |
| s = s[len(remove_prefix):].strip() |
| return s |
|
|
| col_description = _clean( |
| row.get("column_description", ""), |
| remove_prefix="commonsense evidence:", |
| ) |
| value_desc = "" |
| if use_value_description: |
| value_desc = _clean( |
| row.get("value_description", ""), |
| remove_prefix="commonsense evidence:", |
| ) |
| if value_desc.lower().startswith("not useful"): |
| value_desc = value_desc[10:].strip() |
|
|
| table_description[table_name][col_key] = { |
| "original_column_name": str(row.get("original_column_name", col_key)), |
| "column_name": _clean(row.get("column_name", "")), |
| "column_description": col_description, |
| "data_format": _clean(row.get("data_format", "")), |
| "value_description": value_desc, |
| } |
|
|
| logging.debug("Loaded descriptions from %s (%s)", csv_file, encoding) |
| loaded = True |
| break |
| except Exception: |
| continue |
|
|
| if not loaded: |
| logging.warning("Could not read descriptions from %s", csv_file) |
|
|
| return table_description |
|
|
|
|
| def load_all_db_descriptions( |
| bird_dataset_path: str, |
| use_value_description: bool = True, |
| ) -> Dict[str, Dict[str, Dict[str, Dict[str, str]]]]: |
| """Load descriptions for every database under a BIRD split directory. |
| |
| Args: |
| bird_dataset_path: Path to a BIRD split, e.g. |
| /data/bird/train/train_databases |
| Each sub-directory is one db_id. |
| use_value_description: Whether to include value_description. |
| |
| Returns: |
| {db_id: load_db_descriptions(db_dir)} |
| """ |
| root = Path(bird_dataset_path) |
| all_descriptions: Dict[str, Dict] = {} |
|
|
| for db_dir in sorted(root.iterdir()): |
| if db_dir.is_dir(): |
| db_id = db_dir.name |
| all_descriptions[db_id] = load_db_descriptions( |
| str(db_dir), use_value_description=use_value_description |
| ) |
|
|
| return all_descriptions |
|
|
