import json import os import re import random import sqlparse from tqdm import tqdm from nltk.tokenize import word_tokenize from nltk import ngrams from sql_metadata import Parser from utils.db_utils import get_db_schema from utils.bird_csv_utils import load_db_descriptions import subprocess random.seed(42) def extract_large_numbers(text): number_information = [] patterns = { 'thousand': 10**3, 'million': 10**6, 'billion': 10**9, 'trillion': 10**12 } for word, multiplier in patterns.items(): matches = re.findall(r'(\d+\.?\d*)\s*{}'.format(word), text, flags=re.IGNORECASE) for match in matches: number = float(match) * multiplier number_information.append(match + " " + word + " = " + str(int(number))) for phrase, number in {'thousands of': 10**3, 'millions of': 10**6, 'billions of': 10**9, 'trillions of': 10**12}.items(): if phrase in text: number_information.append(phrase + " = " + str(int(number))) large_number_evidence = "" for info in number_information: large_number_evidence += info + "; " return large_number_evidence.strip() def remove_table_alias(s): try: tables_aliases = Parser(s).tables_aliases except Exception as e: return s new_tables_aliases = {} for i in range(1,11): if "t{}".format(i) in tables_aliases.keys(): new_tables_aliases["t{}".format(i)] = tables_aliases["t{}".format(i)] tables_aliases = new_tables_aliases for k, v in tables_aliases.items(): # remove AS clauses s = s.replace("AS " + k + " ", "") # replace table alias with thier original names s = s.replace(k, v) return s def remove_similar_comments(names, comments): ''' Remove table (or column) comments that have a high degree of similarity with their names Arguments: names: a list of table (or column) names comments: a list of table (or column) comments Returns: new_comments: a list of new table (or column) comments ''' new_comments = [] for name, comment in zip(names, comments): if name.replace("_", "").replace(" ", "") == comment.replace("_", "").replace(" ", ""): new_comments.append("") else: new_comments.append(comment) return new_comments def str_replace_ignore_case(evidence, schema_item_name): evidence = re.sub(re.escape(schema_item_name), schema_item_name, evidence, 0, re.IGNORECASE) return evidence def obtain_n_grams(sequence, max_n): ''' returns all grams of sequence less than or equal to `max_n` ''' tokens = word_tokenize(sequence) all_grams = [] for n in range(1, max_n + 1): all_grams.extend([" ".join(gram) for gram in ngrams(tokens, n)]) return all_grams def preprocess_evidence(evidence, schema_items): if evidence.strip() == "": return "" evidence = evidence.strip() # if evidence does not end with ";", add a ";" char if not evidence.endswith(";"): evidence += ";" # lowercase schema items appeared in the evidence for table in schema_items: if table["table_name"] in evidence.lower(): evidence = str_replace_ignore_case(evidence, table["table_name"]) for column_name in table["column_names"]: if column_name in evidence.lower(): evidence = str_replace_ignore_case(evidence, column_name) evidence = evidence.replace("< =", "<=").replace("> =", ">=") return evidence import os from multiprocessing import Pool from itertools import repeat from tqdm import tqdm import sqlparse # from moz_sql_parser import Parser # Assuming you're using moz_sql_parser def process_data(data, db_path, db_comments, db_content_index_api, source, use_evidence, mode, all_db_descriptions=None): sample = {} db_id = data["db_id"] sample["source"] = source sample["db_id"] = db_id sample["db_path"] = os.path.join(db_path, db_id, db_id + ".sqlite") if "spider-syn" in source: sample["question"] = data["SpiderSynQuestion"] sample["evidence"] = "" elif "bird" in source: sample["question"] = data["question"] elif "bank" in source: sample["question"] = data["question"] sample["evidence"] = extract_large_numbers(data["question"]) else: sample["question"] = data["question"] sample["evidence"] = "" if "\n" in sample["question"]: sample["question"] = sample["question"].replace("\n", " ") db_descriptions = all_db_descriptions.get(db_id, {}) if all_db_descriptions else {} sample["schema"] = get_db_schema( db_content_index_api, source, sample["question"], sample["db_path"], db_comments, db_id, db_descriptions=db_descriptions, ) if "bird" in source: evidence = preprocess_evidence(data["evidence"], sample["schema"]["schema_items"]) sample["evidence"] = evidence if "\n" in sample["evidence"]: sample["evidence"] = sample["evidence"].replace("\n", " ") sample["text"] = sample["evidence"] + " " + sample["question"] \ if use_evidence and sample["evidence"] != "" else sample["question"] if mode in ["train", "dev"]: sql = data["SQL"] if source in ["bird-dev", "bird-train"] else data["query"] sample['sql'] = sql # sample["sql"] = remove_table_alias(sqlparse.format(sql, keyword_case="upper", identifier_case="lower")) elif mode == "test": sample["sql"] = "" sample["table_labels"], sample["column_labels"] = [], [] try: sql_tokens = [token.value for token in Parser(sample["sql"].lower()).tokens] except Exception as e: sql_tokens = sample["sql"].lower().split() for table_info in sample["schema"]["schema_items"]: if mode in ["train", "dev"]: table_name = table_info["table_name"] sample["table_labels"].append(1 if table_name in sql_tokens else 0) sample["column_labels"].append([ 1 if column_name in sql_tokens or f"{table_name}.{column_name}" in sql_tokens else 0 for column_name in table_info["column_names"] ]) elif mode == "test": sample["table_labels"].append(0) sample["column_labels"].append([0 for _ in range(len(table_info["column_names"]))]) # Coarse-grained matching between the input text and all contents in the database return sample def process_data_wrapper(args): return process_data(*args) def _load_bird_descriptions(db_path, source): """Return {db_id: descriptions} when source is BIRD, else empty dict.""" if "bird" not in source: return {} from pathlib import Path all_desc = {} root = Path(db_path) for db_dir in sorted(root.iterdir()): if db_dir.is_dir(): all_desc[db_dir.name] = load_db_descriptions(str(db_dir)) return all_desc def spider_style_dataset( dataset_path, db_path, db_content_index_api, source, table_json_path, use_evidence, mode, output_file ): ''' Load spider-style dataset Arguments: dataset_path: directory to load the dataset from db_path: directory of databases (used for extracting schema, including tables, columns, column contents, and foreign keys) db_content_index_path: directory of database content sparse index source: source of examples table_json_path: directory to load additional database information (used for extracting comments for tables and columns) use_evidence: whether to use the additional evidence in the input sequence Returns: returned_dataset: prepared dataset ''' dataset = json.load(open(dataset_path)) additional_db_info = json.load(open(table_json_path)) # load old results from output_file if it exists if os.path.exists(output_file): with open(output_file, 'r', encoding='utf-8') as f: processed_dataset = [json.loads(line) for line in f] processed_dataset_dict = {f"{sample['db_id']} {sample['question']}": sample for sample in processed_dataset} else: processed_dataset_dict = dict() # filter out processed samples dataset = [data for data in dataset if f"{data['db_id']} {data.get('question', '')}" not in processed_dataset_dict] db_comments = dict() # record comments for tables and columns for db_info in additional_db_info: comment_dict = dict() column_names = [column_name.lower() for _, column_name in db_info["column_names_original"]] table_idx_of_each_column = [t_idx for t_idx, _ in db_info["column_names_original"]] column_comments = [column_comment.lower() for _, column_comment in db_info["column_names"]] assert len(column_names) == len(column_comments) column_comments = remove_similar_comments(column_names, column_comments) table_names = [table_name.lower() for table_name in db_info["table_names_original"]] table_comments = [table_comment.lower() for table_comment in db_info["table_names"]] assert len(table_names) == len(table_comments) table_comments = remove_similar_comments(table_names, table_comments) # enumerate each table and its columns for table_idx, (table_name, table_comment) in enumerate(zip(table_names, table_comments)): comment_dict[table_name] = { "table_comment": table_comment, "column_comments": dict() } for t_idx, column_name, column_comment in zip(table_idx_of_each_column, column_names, column_comments): # record columns in current table if t_idx == table_idx: comment_dict[table_name]["column_comments"][column_name] = column_comment db_comments[db_info["db_id"]] = comment_dict all_db_descriptions = _load_bird_descriptions(db_path, source) args_iter = zip( dataset, repeat(db_path), repeat(db_comments), repeat(db_content_index_api), repeat(source), repeat(use_evidence), repeat(mode), repeat(all_db_descriptions), ) pool = Pool(processes=16) f_out = open(output_file, 'a+', encoding='utf-8') try: for sample in tqdm( pool.imap_unordered(process_data_wrapper, args_iter), total=len(dataset), desc="Processing dataset" ): # Write the JSON serialized sample to the file f_out.write(json.dumps(sample, ensure_ascii=False) + '\n') except Exception as e: print(e) f_out.close() pool.close() import sys sys.exit() f_out.close() pool.close() # rearrange the dataset, load jsonl file, rearrange the dataset to correct order with the same order as the original dataset, key= {db_id + question} processed_dataset = [] with open(output_file, 'r', encoding='utf-8') as f_in: for line in f_in: sample = json.loads(line) processed_dataset.append(sample) dataset = json.load(open(dataset_path)) rearranged_dataset = [] for data in dataset: db_id = data["db_id"] if "spider-syn" in source: question = data["SpiderSynQuestion"] else: question = data["question"] key = db_id + " " + question.replace("\n", " ") for sample in processed_dataset: if sample["db_id"] + " " + sample["question"].replace("\n", " ") == key: rearranged_dataset.append(sample) break # save the rearranged dataset to json file, replace jsonl in output_file to json with open(output_file.replace(".jsonl", ".json"), 'w+', encoding='utf-8') as f_out: json.dump(rearranged_dataset, f_out, indent=2, ensure_ascii=False) return rearranged_dataset if __name__ == "__main__": print("BIRD-dev (with evidence)") # BIRD dev set (1534 examples) bird_with_evidence_dev = spider_style_dataset( dataset_path = "./data/bird-062024/dev/dev.json", db_path = "./data/bird-062024/dev/dev_databases", db_content_index_api = "http://localhost:8005", source = "bird-dev", table_json_path = "./data/bird-062024/dev/dev_tables.json", use_evidence = True, mode = "dev", output_file="data/full_value_matching_sft_bird_062024_with_evidence_dev_text2sql.jsonl" ) # print("BIRD (with evidence) train") # # BIRD training set with evidence (9428 examples) # bird_with_evidence_train = spider_style_dataset( # dataset_path = "./data/bird-062024/train/train.json", # db_path = "./data/bird-062024/train/train_databases", # db_content_index_api = "http://localhost:8005", # source = "bird-train", # table_json_path = "./data/bird-062024/train/train_tables.json", # use_evidence = True, # mode = "train", # output_file="data/full_value_matching_sft_bird_062024_with_evidence_train_text2sql.jsonl" # ) # print("BIRD-dev (with evidence)") # bird_with_evidence_dev = spider_style_dataset( # dataset_path = "data/sft_data_collections/bird/dev/dev.json", # db_path = "data/sft_data_collections/bird/dev/dev_databases", # db_content_index_path = "data/sft_data_collections/bird/dev/db_contents_index", # source = "bird-dev", # table_json_path = "data/sft_data_collections/bird/dev/dev_tables.json", # use_evidence = True, # mode = "dev", # output_file="./data/sft_bird_with_evidence_dev_text2sql.jsonl" # ) # print("BIRD (with evidence) train") # # BIRD training set with evidence (9428 examples) # bird_with_evidence_train = spider_style_dataset( # dataset_path = "data/sft_data_collections/bird/train/train.json", # db_path = "data/sft_data_collections/bird/train/train_databases", # db_content_index_path = "data/sft_data_collections/bird/train/db_contents_index", # source = "bird-train", # table_json_path = "data/sft_data_collections/bird/train/train_tables.json", # use_evidence = True, # mode = "train", # output_file="./data/sft_bird_with_evidence_train_text2sql.jsonl" # ) # print("preparing training sets.....") # print("spider-train") # spider_train = [] # # Spider training set-1 (7000 + 1658 examples) # for spider_train_set in ["train_spider.json", "train_others.json"]: # spider_train.extend( # spider_style_dataset( # dataset_path = os.path.join("./data/sft_data_collections/spider/", spider_train_set), # db_path = "./data/sft_data_collections/spider/database", # db_content_index_path = "./data/sft_data_collections/spider/db_contents_index", # source = "spider-train", # table_json_path = "./data/sft_data_collections/spider/tables.json", # use_evidence = False, # mode = "train", # output_file=f"./data/sft_spider_train_text2sql_{spider_train_set}.jsonl" # ) # ) # with open("./data/sft_spider_train_text2sql.json", "w") as f: # f.write(json.dumps(spider_train, indent = 2, ensure_ascii = False)) # print("preparing training sets.....") # print("spider-train") # spider_train = [] # # Spider training set-1 (7000 + 1658 examples) # for spider_train_set in ["train_spider.json", "train_others.json"]: # spider_train.extend( # spider_style_dataset( # dataset_path = os.path.join("./data/sft_data_collections/spider/", spider_train_set), # db_path = "./data/sft_data_collections/spider/database", # db_content_index_api = "http://localhost:8005", # source = "spider-train", # table_json_path = "./data/sft_data_collections/spider/tables_update.json", # use_evidence = False, # mode = "train", # output_file=f"./data/sft_spider_train_with_meaning_text2sql_{spider_train_set}.jsonl" # ) # ) # with open("./data/sft_spider_train_with_meaning_text2sql.json", "w") as f: # f.write(json.dumps(spider_train, indent = 2, ensure_ascii = False)) # print("preparing training sets.....") # print("spider-train-augmented") # spider_train = [] # spider_dev = spider_style_dataset( # dataset_path = "./data/sft_data_collections/spider/train_augmented.json", # db_path = "./data/sft_data_collections/spider/database", # db_content_index_api = "http://localhost:8005", # source = "spider-train", # table_json_path = "./data/sft_data_collections/spider/tables.json", # use_evidence = False, # mode = "train", # output_file='./data/sft_spider_train_augmented_text2sql.jsonl' # ) # print("BIRD (without evidence) train") # # BIRD training set (9428 examples) # bird_train = spider_style_dataset( # dataset_path = "./data/bird-062024/train/train.json", # db_path = "./data/bird-062024/train/train_databases", # db_content_index_path = "./data/bird-062024/train/db_contents_index", # source = "bird-train", # table_json_path = "./data/bird-062024/train/train_tables.json", # use_evidence = False, # mode = "train" # ) # with open("./data/sft_bird_train_text2sql.json", "w") as f: # f.write(json.dumps(bird_train, indent = 2, ensure_ascii = False)) # with open("./data/sft_bird_with_evidence_train_text2sql.json", "w") as f: # f.write(json.dumps(bird_with_evidence_train, indent = 2, ensure_ascii = False)) # print("---------------------------------------------------------------------------") # print("preparing dev sets.....") # print("spider-dev") # # Spider development set (1034 examples) # spider_dev = spider_style_dataset( # dataset_path = "./data/sft_data_collections/spider/dev.json", # db_path = "./data/sft_data_collections/spider/database", # db_content_index_api = "http://localhost:8005", # source = "spider-dev", # table_json_path = "./data/sft_data_collections/spider/tables.json", # use_evidence = False, # mode = "dev", # output_file='./data/1_value_sft_spider_dev_text2sql.jsonl' # ) # print("---------------------------------------------------------------------------") # print("preparing dev sets.....") # print("spider-dev") # # Spider development set (1034 examples) # spider_dev = spider_style_dataset( # dataset_path = "./data/sft_data_collections/spider/dev.json", # db_path = "./data/sft_data_collections/spider/database", # db_content_index_api = "http://localhost:8005", # source = "spider-dev", # table_json_path = "./data/sft_data_collections/spider/tables_update.json", # use_evidence = False, # mode = "dev", # output_file='./data/sft_spider_dev_with_meaning_text2sql.jsonl' # ) # print("spider-dk") # # Spider-DK development set (535 examples) # spider_dk = spider_style_dataset( # dataset_path = "./data/sft_data_collections/Spider-DK/Spider-DK.json", # db_path = "./data/sft_data_collections/spider/database", # db_content_index_api = f"http://localhost:8005", # source = "spider-dk", # table_json_path = "./data/sft_data_collections/Spider-DK/tables.json", # use_evidence = False, # mode = "dev", # output_file='./data/1_value_sft_spider_dk_text2sql.jsonl' # ) # print("spider-syn") # # Spider-Syn development set (1034 examples) # spider_syn = spider_style_dataset( # dataset_path = "./data/sft_data_collections/Spider-Syn/Spider-Syn/dev.json", # db_path = "./data/sft_data_collections/spider/database", # db_content_index_api = f"http://localhost:8005", # source = "spider-syn-dev", # table_json_path = "./data/sft_data_collections/spider/tables.json", # use_evidence = False, # mode = "dev", # output_file='./data/1_value_sft_spider_syn_text2sql.jsonl' # ) # print("spider-realistic") # # Spider-Realistic development set (507 examples) # spider_realistic = spider_style_dataset( # dataset_path = "./data/sft_data_collections/spider-realistic/spider-realistic.json", # db_path = "./data/sft_data_collections/spider/database", # db_content_index_api = f"http://localhost:8005", # source = "spider-realistic", # table_json_path = "./data/sft_data_collections/spider/tables.json", # use_evidence = False, # mode = "dev", # output_file='./data/1_value_sft_spider_realistic_text2sql.jsonl' # ) # import signal # print("DR.spider") # dr_spider = [] # # Dr.Spider has 17 perturbation test sets # test_set_names = os.listdir("./data/sft_data_collections/diagnostic-robustness-text-to-sql/data") # test_set_names.remove("Spider-dev") # port = 8005 # for test_set_name in test_set_names: # if test_set_name.startswith("DB_"): # database_file_path = "database_post_perturbation" # table_file_name = "tables_post_perturbation.json" # else: # database_file_path = "databases" # table_file_name = "tables.json" # source = "dr.spider-{}".format(test_set_name) # # run db content retrieval for each test set # process = subprocess.Popen(f"python db_content_retrieval/lsh_api.py --port {port} --db_content_index {source}", shell=True) # pid = process.pid # # os.system(f"python db_content_retrieval/lsh_api.py --db_content_index {source}") # import time # time.sleep(10) # dr_spider.extend( # spider_style_dataset( # dataset_path = os.path.join("./data/sft_data_collections/diagnostic-robustness-text-to-sql/data/", test_set_name, "questions_post_perturbation.json"), # db_path = os.path.join("./data/sft_data_collections/diagnostic-robustness-text-to-sql/data/", test_set_name, database_file_path), # db_content_index_api = f"http://localhost:{port}", # source = source, # table_json_path = os.path.join("./data/sft_data_collections/diagnostic-robustness-text-to-sql/data/", test_set_name, table_file_name), # use_evidence = False, # mode = "dev", # output_file=f'./data/sft_dr_spider_text2sql_{test_set_name}.jsonl' # ) # ) # # kill db content retrieval server # # os.kill(pid, signal.SIGTERM) # usually kills processes # # os.kill(pid, signal.SIGKILL) # should always kill a process # os.system(f"kill -9 `ps aux | grep lsh_api.py | awk '{{print $2}}'`") # time.sleep(2) # with open("./data/sft_dr_spider_text2sql.json", "w") as f: # f.write(json.dumps(dr_spider, indent = 2, ensure_ascii = False)) # print("BIRD-dev (without evidence)") # # BIRD dev set (1534 examples) # bird_dev = spider_style_dataset( # dataset_path = "./data/bird-062024/dev/dev.json", # db_path = "./data/bird-062024/dev/dev_databases", # db_content_index_path = "./data/bird-062024/dev/db_contents_index", # source = "bird-dev", # table_json_path = "./data/bird-062024/dev/dev_tables.json", # use_evidence = False, # mode = "dev" # ) # with open("./data/sft_bird_dev_text2sql.json", "w") as f: # f.write(json.dumps(bird_dev, indent = 2, ensure_ascii = False)) # print("Bank_Financials dev set") # # Bank_Financials dev set (92 examples) # bank_dev = spider_style_dataset( # dataset_path = "./data/sft_data_collections/domain_datasets/Bank_Financials_dev.json", # db_path = "./data/sft_data_collections/domain_datasets/databases", # db_content_index_api = "http://localhost:8005", # source = "bank_financials-dev", # table_json_path = "./data/sft_data_collections/domain_datasets/tables.json", # use_evidence = True, # mode = "dev", # output_file="./data/sft_bank_financials_dev_text2sql.jsonl" # ) # print("Aminer_Simplified dev set") # # Aminer_Simplified dev set (xxx examples) # aminer_dev = spider_style_dataset( # dataset_path = "./data/sft_data_collections/domain_datasets/Aminer_Simplified_dev.json", # db_path = "./data/sft_data_collections/domain_datasets/databases", # db_content_index_api = "http://localhost:8005", # source = "aminer_simplified-dev", # table_json_path = "./data/sft_data_collections/domain_datasets/tables.json", # use_evidence = True, # mode = "dev", # output_file="./data/sft_aminer_simplified_dev_text2sql.jsonl" # ) # print("Bank_Financials train") # # Bank_Financials train set # bank_train = spider_style_dataset( # dataset_path = "./data/sft_data_collections/domain_datasets/Bank_Financials_train.json", # db_path = "./data/sft_data_collections/domain_datasets/databases", # db_content_index_api = "http://localhost:8005", # source = "bank_financials-train", # table_json_path = "./data/sft_data_collections/domain_datasets/tables.json", # use_evidence = True, # mode = "train", # output_file="./data/sft_bank_financials_train_text2sql.jsonl" # ) # print("Aminer_Simplified train") # # Aminer_Simplified train set # aminer_train = spider_style_dataset( # dataset_path = "./data/sft_data_collections/domain_datasets/Aminer_Simplified_train.json", # db_path = "./data/sft_data_collections/domain_datasets/databases", # db_content_index_api = "http://localhost:8005", # source = "aminer_simplified-train", # table_json_path = "./data/sft_data_collections/domain_datasets/tables.json", # use_evidence = True, # mode = "train", # output_file="./data/sft_aminer_simplified_train_text2sql.jsonl" # ) # print("Spider + BIRD + Bank_Financials + Aminer_Simplified train set (ALL MERGED)") # # merge all available training data # with open("./data/sft_all_merged_train_text2sql.json", "w") as f: # f.write(json.dumps(spider_train + bird_with_evidence_train + bank_train + aminer_train, indent = 2, ensure_ascii = False)) pass # Other un-official SFT files for testing # print("preparing training sets.....") # print("spider-train") # os.remove('./data/sft_spider_train_domain_geo.jsonl') # spider_style_dataset( # dataset_path = './data/sft_data_collections/spider_domain_geo.json', # db_path = "./data/sft_data_collections/spider/database", # db_content_index_api = "http://localhost:8005", # source = "spider-train", # table_json_path = "./data/sft_data_collections/spider/tables.json", # use_evidence = False, # mode = "train", # output_file=f"./data/sft_spider_train_domain_geo.jsonl" # )