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from tqdm import tqdm
import numpy as np
from prompting_utils import *
from utils import *
from sql_comparison import *
import pickle
def add_fk_tabcols(tables, schema_pk_fk):
to_add = defaultdict(lambda: set())
for tab in tables:
if tab in schema_pk_fk:
tab_to_tab = schema_pk_fk[tab]
for ftab in tables:
if ftab in tab_to_tab:
for pkfk in tab_to_tab[ftab]:
# print(pkfk)
to_add[pkfk[0][0]].add(pkfk[0][1])
to_add[pkfk[1][0]].add(pkfk[1][1])
return {k:v for k,v in to_add.items()}
def score_prediction(gt: DataBase, pred: DataBase, all_relevant:bool=False, beta:float=1):
pred_total = pred.get_column_count(include_derived_columns=all_relevant)
if pred_total == 0:
return 0,0,0
correct_count = gt.compare_column_overlap(other_db=pred, include_derived_columns=all_relevant)
gt_total = gt.get_column_count(include_derived_columns=all_relevant)
precision = correct_count / pred_total
recall = correct_count / gt_total
if precision+recall == 0:
f1 = 0
else:
if beta == 1:
f1 = 2 * (precision * recall) / (precision + recall)
else:
f1 = (1+beta**2)*(precision*recall)/(((beta**2)*precision)+recall)
return precision, recall, f1
def micro_counts(gt: DataBase, pred: DataBase, all_relevant:bool=False):
pred_total = pred.get_column_count(include_derived_columns=all_relevant)
if pred_total == 0:
return 0,0,0
correct_count = gt.compare_column_overlap(other_db=pred, include_derived_columns=all_relevant)
gt_total = gt.get_column_count(include_derived_columns=all_relevant)
truepos = correct_count
falsepos = pred_total - correct_count
falseneg = gt_total - correct_count
return truepos, falsepos, falseneg
def get_executable_SQL_list(question_SQLs, gt_db: DataBase, pred_db: DataBase, base_schema: DataBase):
schema_dict = base_schema.get_schema_for_sqlglot()
all_real_cols = set()
for t,table in base_schema.tables.items():
all_real_cols |= table.columns
good = 0
total = 0
bad_content = []
req_tabcols = defaultdict(lambda: set())
executable_q_gt = []
for question, sql in question_SQLs.items():
clean_q1 = cleanup_query(sql, schema_dict)
tab_cols = []
for column_expression in clean_q1.find_all(exp.Column):
if str(column_expression.this).replace('"', '') not in all_real_cols:
# print(column_expression, column_expression.name, type(column_expression))
continue
try:
if str(column_expression.args['table'].this).replace('"', '').startswith("_"):
continue
# a common pattern where sqlglot seems to fail to fully parse out some table aliases
elif str(column_expression.args['table'].this).replace('"', '') in {"t", "t1"}:
continue
tab_cols.append((str(column_expression.args['table'].this).replace('"', ''),
str(column_expression.this).replace('"', '')))
except KeyError:
pass
except AttributeError:
pass
tab_cols = set(tab_cols)
pred_can_execute = True
for (tab,col) in tab_cols:
if not (tab in pred_db.tables and col in pred_db.tables[tab].columns):
pred_can_execute = False
req_tabcols[tab].add(col)
if not (tab in gt_db.tables and col in gt_db.tables[tab].columns):
bad_content.append((sql, (tab, col)))
req_tabcols[tab].add(col)
if pred_can_execute:
executable_q_gt.append((question, sql))
return executable_q_gt
def compare_derived_col_sims(gt_db: DataBase, pred_db: DataBase, base_schema: DataBase, cutoff_threshold: float = 0.0):
sims = []
diffcounts = []
schema = base_schema.get_schema_for_sqlglot()
sim_score_matrix = []
diff_score_matrix = []
gt_order_dict = {}
pred_order_dict = {}
for gt_ind, gt_derived in enumerate(gt_db.derived_columns):
gt_sql = gt_derived.provenance_sql
gt_order_dict[gt_ind] = gt_sql
this_sims = []
this_diffs = []
for pred_ind, pred_derived in enumerate(pred_db.derived_columns):
pred_sql = pred_derived.provenance_sql
pred_order_dict[pred_ind] = pred_sql
sim = sql_diff_sim(gt_sql, pred_sql, schema=schema)
diff_count, total_count = get_nonalias_diff(gt_sql, pred_sql, schema=schema)
if sim is None:
this_sims.append(-1)
this_diffs.append(999)
else:
this_diffs.append(diff_count)
this_sims.append(sim)
sim_score_matrix.append(this_sims)
diff_score_matrix.append(this_diffs)
gt_sim_maps = solve_optimal_mapping(sim_score_matrix)
for from_ind, to_ind in gt_sim_maps.items():
sims.append(sim_score_matrix[from_ind][to_ind])
diffcounts.append(diff_score_matrix[from_ind][to_ind])
for gt_ind in gt_order_dict.keys():
if gt_ind not in gt_sim_maps:
sims.append(0)
diffcounts.append(999)
return sims, diffcounts
def calc_results(all_predictions, gt_data, schema_data,
evaluate_derived_columns_soft_match:bool=False):
schema_table_pk_fk = {}
for db, tabcols in raw_schema_data.items():
schema_table_pk_fk[db] = defaultdict(lambda: defaultdict(lambda: list()))
tables = []
for tab, cd in tabcols['tables'].items():
tables.append(Table(name=tab.lower(), columns=set(col['name'] for col in cd['columns']),
column_types={col['name']: col['type'].upper() for col in cd['columns']}))
for col in cd['columns']:
if col['foreign_key']:
assert len(col['foreign_key']) == 2
schema_table_pk_fk[db][tab.lower()][col['foreign_key'][0]].append(
((tab.lower(), col['name']),
(col['foreign_key'][0], col['foreign_key'][1])))
schema_data[db] = DataBase(name=db, tables={t.name: t for t in tables})
assert not any("." in c for c in {col['name']: col['type'].upper() for col in cd['columns']})
assert not any("__" in c for c in {col['name']: col['type'].upper() for col in cd['columns']})
sizes = []
gt_sizes = []
precisions = []
recalls = []
f1s = []
micro_tp = 0
micro_fp = 0
micro_fn = 0
all_relevant_sizes = []
gt_all_relevant_sizes = []
all_relevant_precisions = []
all_relevant_recalls = []
all_relevant_f1s = []
macro_derived_sims = []
micro_derived_sims = []
macro_derived_diffs = []
micro_derived_diffs = []
derived_soft_prec = []
derived_soft_rec = []
derived_soft_f1 = []
is_hard = []
print("starting eval...")
with open((BENCH_DIR / "v7_hard_dp_bench_with_topics.json").resolve(), 'r') as f:
hard_subset = json.load(f)
hard_subset = set(hard_subset.keys())
for database, pred_list in tqdm(all_predictions.items()):
if not pred_list:
continue
base_schema = schema_data[database]
for dpr_pred in pred_list:
if database in hard_subset:
is_hard.append(1)
else:
is_hard.append(0)
gt_db = gt_to_db(dbname=database, raw_gt_data_product=gt_data[database])
gt_db = filter_real_table_columns(real_db=base_schema, filter_db=gt_db, debug=True)
pred_tabcols = dpr_pred['PREDICTION']
if pred_tabcols is not None:
for tab,cols in add_fk_tabcols(list(pred_tabcols.keys()), schema_table_pk_fk[database]).items():
pred_tabcols[tab] += list(cols)
pred_tabcols[tab] = list(set(pred_tabcols[tab]))
pred_db = viewgen_pred_to_db(pred_content=dpr_pred['PREDICTION'], dbname=database)
pred_db = filter_real_table_columns(real_db=base_schema, filter_db=pred_db)
all_relevant_result = score_prediction(gt=gt_db, pred=pred_db, all_relevant=True)
non_derivation_result = score_prediction(gt=gt_db, pred=pred_db, all_relevant=False)
micro_tpt, micro_fpt, micro_fnt = micro_counts(gt_db, pred_db, all_relevant=True)
micro_tp += micro_tpt
micro_fp += micro_fpt
micro_fn += micro_fnt
dpr_pred['all_relevant_res'] = all_relevant_result
dpr_pred['nonderived_res'] = non_derivation_result
precisions.append(non_derivation_result[0])
recalls.append(non_derivation_result[1])
f1s.append(non_derivation_result[2])
all_relevant_precisions.append(all_relevant_result[0])
all_relevant_recalls.append(all_relevant_result[1])
all_relevant_f1s.append(all_relevant_result[2])
sizes.append(pred_db.get_column_count(include_derived_columns=False) / base_schema.get_column_count(
include_derived_columns=False))
gt_sizes.append(gt_db.get_column_count(include_derived_columns=False) / base_schema.get_column_count(
include_derived_columns=False))
all_relevant_sizes.append(
pred_db.get_column_count(include_derived_columns=True) / base_schema.get_column_count(
include_derived_columns=True))
gt_all_relevant_sizes.append(
gt_db.get_column_count(include_derived_columns=True) / base_schema.get_column_count(
include_derived_columns=True))
if evaluate_derived_columns_soft_match:
if len(gt_db.derived_columns) == 0:
continue
sim, diff = compare_derived_col_sims(gt_db, pred_db, base_schema)
micro_derived_sims += sim
micro_derived_diffs += diff
macro_derived_sims.append(np.mean(sim))
macro_derived_diffs.append(np.mean(diff))
if len(pred_db.derived_columns) == 0:
derived_soft_prec.append(0)
derived_soft_rec.append(0)
derived_soft_f1.append(0)
else:
derived_soft_prec.append(sprec:=(sum(sim) / len(pred_db.derived_columns)))
derived_soft_rec.append(srec:=(sum(sim) / len(gt_db.derived_columns)))
if sprec == 0 or srec == 0:
derived_soft_f1.append(0)
else:
derived_soft_f1.append(2*(sprec*srec)/(sprec+srec))
print("XXXXXXXX")
print("XXXXXXXX")
print("Results")
print("Non-derived columns only:")
print(f"GT DP size: avg: {np.mean(gt_sizes):.3f}")
print(f"Predicted DP size avg: {np.mean(sizes):.3f}")
print("- P/R/F1-")
print(f"Precision: {np.mean(precisions):.3f}")
print(f"Recall: {np.mean(recalls):.3f}")
print(f"F1: {np.mean(f1s):.3f}")
print("-")
print("Including derived columnss:")
print(f"GT DP size: avg: {np.mean(gt_all_relevant_sizes):.3f}")
print(f"Predicted DP size avg: {np.mean(all_relevant_sizes):.3f}")
print("- P/R/F1-")
print(f"Precision: {np.mean(all_relevant_precisions):.3f}")
print(f"Recall: {np.mean(all_relevant_recalls):.3f}")
print(f"F1: {np.mean(all_relevant_f1s):.3f}")
print("-")
# print(f"{np.mean(precisions):.3f} & {np.mean(recalls):.3f} & {np.mean(f1s):.3f} & {np.mean(all_relevant_precisions):.3f} & {np.mean(all_relevant_recalls):.3f} & {np.mean(all_relevant_f1s):.3f}")
if evaluate_derived_columns_soft_match:
print(f"Derived column stats")
print(f"Average similarity: {np.mean(macro_derived_sims):.3f}, Median diff count between ASTs: {np.median(macro_derived_diffs):.3f}")
print(f"Soft Precision: {np.mean(derived_soft_prec):.3f} Soft Recall: {np.mean(derived_soft_rec):.3f} Soft F1: {np.mean(derived_soft_f1):.3f}")
print("XXXXXXXX")
print(f"HARD TABLES ({np.sum(is_hard)/3} DBs)")
print("XXXXXXXX")
hard_gt_sizes = [v for ind, v in enumerate(gt_sizes) if is_hard[ind] == 1]
print(f"GT DP size: avg: {np.mean(hard_gt_sizes):.3f}")
print(f"Predicted DP size avg: {np.mean([v for ind, v in enumerate(sizes) if is_hard[ind] == 1]):.3f}")
print("- P/R/F1-")
print(f"Precision: {np.mean([v for ind, v in enumerate(precisions) if is_hard[ind] == 1]):.3f}")
print(f"Recall: {np.mean([v for ind, v in enumerate(recalls) if is_hard[ind] == 1]):.3f}")
print(f"F1: {np.mean([v for ind, v in enumerate(f1s) if is_hard[ind] == 1]):.3f}")
print("-")
print("Including derived columnss:")
print(f"GT DP size: avg: {np.mean([v for ind, v in enumerate(gt_all_relevant_sizes) if is_hard[ind] == 1]):.3f}")
print(f"Predicted DP size avg: {np.mean([v for ind, v in enumerate(all_relevant_sizes) if is_hard[ind] == 1]):.3f}")
print("- P/R/F1-")
print(f"Precision: {np.mean([v for ind, v in enumerate(all_relevant_precisions) if is_hard[ind] == 1]):.3f}")
print(f"Recall: {np.mean([v for ind, v in enumerate(all_relevant_recalls) if is_hard[ind] == 1]):.3f}")
print(f"F1: {np.mean([v for ind, v in enumerate(all_relevant_f1s) if is_hard[ind] == 1]):.3f}")
print("-")
# print(f"{np.mean([v for ind,v in enumerate(precisions) if is_hard[ind]==1]):.3f} & {np.mean([v for ind,v in enumerate(recalls) if is_hard[ind]==1]):.3f} & {np.mean([v for ind,v in enumerate(f1s) if is_hard[ind]==1]):.3f} & {np.mean([v for ind,v in enumerate(all_relevant_precisions) if is_hard[ind]==1]):.3f} & {np.mean([v for ind,v in enumerate(all_relevant_recalls) if is_hard[ind]==1]):.3f} & {np.mean([v for ind,v in enumerate(all_relevant_f1s) if is_hard[ind]==1]):.3f}")
if evaluate_derived_columns_soft_match:
print(f"Derived column stats")
print(
f"Average similarity: {np.mean([v for ind, v in enumerate(macro_derived_sims) if is_hard[ind] == 1]):.3f}, Median diff count between ASTs: {np.median([v for ind, v in enumerate(macro_derived_diffs) if is_hard[ind] == 1]):.3f}")
print(
f"Soft Precision: {np.mean([v for ind, v in enumerate(derived_soft_prec) if is_hard[ind] == 1]):.3f} Soft Recall: {np.mean([v for ind, v in enumerate(derived_soft_rec) if is_hard[ind] == 1]):.3f} Soft F1: {np.mean([v for ind, v in enumerate(derived_soft_f1) if is_hard[ind] == 1]):.3f}")
return {
'pred_ratio': np.mean(sizes),
'precision': np.mean(precisions),
'recall': np.mean(recalls),
'f1': np.mean(f1s),
'include_derived_pred_ratio': np.mean(all_relevant_sizes),
'include_derived_precision': np.mean(all_relevant_precisions),
'include_derived_recall': np.mean(all_relevant_recalls),
'include_derived_f1': np.mean(all_relevant_f1s),
}
sql_exec_cache = {}
def eval_text2sql(data):
with open((BENCH_DIR / "v7_hard_dp_bench_with_topics.json").resolve(), 'r') as f:
hard_subset = json.load(f)
hard_subset = set(hard_subset.keys())
from benchmarks.dpbench_evaluation.sql_comparison import execute_sql
correct_per_db = defaultdict(lambda: 0)
total_per_db = defaultdict(lambda: 0)
hard_correct_per_db = defaultdict(lambda: 0)
hard_total_per_db = defaultdict(lambda: 0)
for d in tqdm(data):
gt = d['GT']
pred = d['pred']
db = d['database']
if gt in sql_exec_cache:
gt_res = sql_exec_cache[gt]
else:
gt_res = execute_sql(gt, db)
sql_exec_cache[gt] = gt_res
pred_res = execute_sql(pred, db)
if gt_res == pred_res:
correct_per_db[db] += 1
total_per_db[db] += 1
if db in hard_subset:
if gt_res == pred_res:
hard_correct_per_db[db] += 1
hard_total_per_db[db] += 1
all_corr = sum(list(correct_per_db.values()))
total_count = sum(list(total_per_db.values()))
hard_all_corr = sum(list(hard_correct_per_db.values()))
hard_total_count = sum(list(hard_total_per_db.values()))
print(f"{all_corr} out of {total_count} correct ({all_corr / total_count:.3f})")
print(f"HARD: {hard_total_count} potentially runnable, {hard_all_corr} correct ({100*(hard_all_corr / hard_total_count):.3f}%) (out of 900 total questions: {100*(hard_all_corr / 900):.3f}%)")
if __name__ == "__main__":
with open((BENCH_DIR / 'v7_dp_bench_with_topics.json').resolve(), 'r') as f:
dpr_data = json.load(f)
with open((SCHEMA_DIR / 'bird_train_tables.json').resolve(), 'r') as f:
raw_schema_data = json.load(f)
with open((SCHEMA_DIR / 'bird_dev_tables.json').resolve(), 'r') as f:
raw_schema_data |= json.load(f)
dp_result_file = 'v6_dpbench_baseline_gptoss_120b.pkl'
with open((DATA_DIR / 'results' / dp_result_file).resolve(), 'rb') as f:
experiment_res = pickle.load(f)
print(f"Results for {dp_result_file}")
calc_results(experiment_res, dpr_data, schema_data=raw_schema_data, evaluate_derived_columns_soft_match=False)
text2sql_result_file = 'llama4_redo_gptoss_120v_text2sql.json'
with open((DATA_DIR / 'results' / text2sql_result_file).resolve(), 'r') as f:
text2sql_experiment_res = json.load(f)
print(f"Results for {text2sql_result_file}")
eval_text2sql(text2sql_experiment_res)
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