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388137e | 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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | """Error analysis and generalization slicing for Text-to-SQL evaluation."""
import re
from typing import Any, Dict, List
def categorize_error(
pred_sql: str,
gold_sql: str,
exec_success: bool,
is_valid_sql: bool,
) -> str:
"""Categorize the type of failure for a predicted SQL query.
Categories:
- correct
- syntax_error
- missing_group_by
- wrong_aggregation
- wrong_join
- wrong_column
- value_mismatch
- other_semantic_error
"""
if exec_success:
return "correct"
if not is_valid_sql:
return "syntax_error"
pred_upper = pred_sql.upper()
gold_upper = gold_sql.upper()
# Check for GROUP BY mismatch
if ("GROUP BY" in gold_upper) and ("GROUP BY" not in pred_upper):
return "missing_group_by"
# Check for aggregation functions (COUNT, SUM, AVG, MIN, MAX)
agg_funcs = ["COUNT(", "SUM(", "AVG(", "MIN(", "MAX("]
gold_aggs = [f for f in agg_funcs if f in gold_upper]
pred_aggs = [f for f in agg_funcs if f in pred_upper]
if gold_aggs != pred_aggs:
return "wrong_aggregation"
# Check for JOIN mismatch
if ("JOIN" in gold_upper) and ("JOIN" not in pred_upper):
return "wrong_join"
# Check if WHERE clause values differ
where_gold = re.findall(r"'[^']+'|\b\d+\b", gold_sql)
where_pred = re.findall(r"'[^']+'|\b\d+\b", pred_sql)
if where_gold != where_pred:
return "value_mismatch"
# Check column names mismatch heuristics
return "other_semantic_error"
def compare_systems_diff(
eval_a_results: List[Dict[str, Any]],
eval_c_results: List[Dict[str, Any]],
) -> Dict[str, Any]:
"""Identify questions fixed or broken by System C compared to System A."""
fixed = []
broken = []
both_correct = []
both_wrong = []
for idx, (res_a, res_c) in enumerate(zip(eval_a_results, eval_c_results)):
a_match = res_a.get("exec_match", False)
c_match = res_c.get("exec_match", False)
q = res_c.get("question", f"Question #{idx}")
db = res_c.get("db_id", "")
item = {
"index": idx,
"db_id": db,
"question": q,
"gold": res_c.get("gold", ""),
"pred_a": res_a.get("pred", ""),
"pred_c": res_c.get("pred", ""),
}
if not a_match and c_match:
fixed.append(item)
elif a_match and not c_match:
broken.append(item)
elif a_match and c_match:
both_correct.append(item)
else:
both_wrong.append(item)
return {
"fixed_count": len(fixed),
"broken_count": len(broken),
"both_correct_count": len(both_correct),
"both_wrong_count": len(both_wrong),
"fixed_questions": fixed[:20], # sample up to 20
"broken_questions": broken[:20],
}
def compute_generalization_slices(detailed_results: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Compute accuracy sliced by database, SQL features (JOIN, GROUP BY, etc.)."""
by_db: Dict[str, Dict[str, int]] = {}
by_feature: Dict[str, Dict[str, int]] = {
"has_join": {"total": 0, "correct": 0},
"has_group_by": {"total": 0, "correct": 0},
"has_order_by": {"total": 0, "correct": 0},
"has_subquery": {"total": 0, "correct": 0},
}
for res in detailed_results:
db = res.get("db_id", "unknown")
correct = res.get("exec_match", False)
if db not in by_db:
by_db[db] = {"total": 0, "correct": 0}
by_db[db]["total"] += 1
if correct:
by_db[db]["correct"] += 1
gold = res.get("gold", "").upper()
if "JOIN" in gold:
by_feature["has_join"]["total"] += 1
if correct:
by_feature["has_join"]["correct"] += 1
if "GROUP BY" in gold:
by_feature["has_group_by"]["total"] += 1
if correct:
by_feature["has_group_by"]["correct"] += 1
if "ORDER BY" in gold:
by_feature["has_order_by"]["total"] += 1
if correct:
by_feature["has_order_by"]["correct"] += 1
if "SELECT" in gold and gold.count("SELECT") > 1:
by_feature["has_subquery"]["total"] += 1
if correct:
by_feature["has_subquery"]["correct"] += 1
db_accuracy = {}
for db, stats in by_db.items():
tot = stats["total"]
db_accuracy[db] = {
"total": tot,
"correct": stats["correct"],
"accuracy": stats["correct"] / tot if tot > 0 else 0.0,
}
feature_accuracy = {}
for feat, stats in by_feature.items():
tot = stats["total"]
feature_accuracy[feat] = {
"total": tot,
"correct": stats["correct"],
"accuracy": stats["correct"] / tot if tot > 0 else 0.0,
}
return {
"by_database": db_accuracy,
"by_feature": feature_accuracy,
}
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