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9b2cded | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 | """Cross-encoder architecture for SQL error classification."""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional, Tuple
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from src.multi_tower_model import QueryContext, contexts_from_dataframe
from src.sql_features import extract_sql_features
DEFAULT_CROSS_ENCODER = "cross-encoder/ms-marco-MiniLM-L6-v2"
DEFAULT_FINETUNED_CE = "cross-encoder/ms-marco-MiniLM-L6-v2"
PAIR_NAMES = (
"intent_vs_student",
"reference_vs_student",
"intent_vs_reference",
)
@dataclass(frozen=True)
class CrossEncoderPair:
name: str
text_a: str
text_b: str
def _intent_text(ctx: QueryContext) -> str:
return f"QUESTION: {ctx.question} SCHEMA: {ctx.schema}"
def _reference_text(ctx: QueryContext) -> str:
return f"REFERENCE: {ctx.correct_query}"
def _student_text(ctx: QueryContext) -> str:
parts = [f"STUDENT: {ctx.student_query}"]
if ctx.error_message:
parts.append(f"ERROR: {ctx.error_message}")
return " ".join(parts)
def _context_text(ctx: QueryContext) -> str:
"""Full task context for fine-tuned cross-encoder."""
return (
f"QUESTION: {ctx.question} "
f"SCHEMA: {ctx.schema} "
f"REFERENCE: {ctx.correct_query}"
)
def build_pairs(ctx: QueryContext) -> List[CrossEncoderPair]:
intent, reference, student = (
_intent_text(ctx),
_reference_text(ctx),
_student_text(ctx),
)
return [
CrossEncoderPair("intent_vs_student", intent, student),
CrossEncoderPair("reference_vs_student", reference, student),
CrossEncoderPair("intent_vs_reference", intent, reference),
]
class CrossEncoderClassifier:
"""
Hybrid cross-encoder: frozen pairwise relevance + linear head.
Unlike bi-encoders (multi-tower), the cross-encoder attends jointly over
each (context, student) pair — better for logical and filtering errors.
Three pairs are scored:
1. intent vs student — does the query address the question?
2. reference vs student — how far is the student from the answer?
3. intent vs reference — task-answer alignment baseline
Pair scores + SQL rule features → LogisticRegression → 15 classes.
"""
def __init__(
self,
cross_encoder_name: str = DEFAULT_CROSS_ENCODER,
batch_size: int = 32,
max_length: int = 512,
):
self.cross_encoder_name = cross_encoder_name
self.batch_size = batch_size
self.max_length = max_length
self.cross_encoder = None
self.scaler = StandardScaler()
self.clf = LogisticRegression(
max_iter=1000,
solver="lbfgs",
class_weight="balanced",
random_state=42,
)
self.classes_: Optional[np.ndarray] = None
def _load_cross_encoder(self):
if self.cross_encoder is None:
from sentence_transformers import CrossEncoder
self.cross_encoder = CrossEncoder(
self.cross_encoder_name,
max_length=self.max_length,
)
def _pair_batches(self, contexts: List[QueryContext]) -> List[List[Tuple[str, str]]]:
"""One batch list per pair type across all contexts."""
pair_lists: List[List[Tuple[str, str]]] = [[], [], []]
for ctx in contexts:
pairs = build_pairs(ctx)
for i, pair in enumerate(pairs):
pair_lists[i].append((pair.text_a, pair.text_b))
return pair_lists
def _score_pairs(
self,
contexts: List[QueryContext],
show_progress: bool = False,
) -> np.ndarray:
self._load_cross_encoder()
pair_batches = self._pair_batches(contexts)
scores = []
for batch in pair_batches:
raw = self.cross_encoder.predict(
batch,
batch_size=self.batch_size,
show_progress_bar=show_progress,
)
scores.append(np.asarray(raw, dtype=np.float64).reshape(-1, 1))
return np.hstack(scores) # (n, 3)
def _build_features(
self,
contexts: List[QueryContext],
show_progress: bool = False,
) -> np.ndarray:
pair_scores = self._score_pairs(contexts, show_progress=show_progress)
s_is, s_rs, s_ir = pair_scores[:, 0], pair_scores[:, 1], pair_scores[:, 2]
derived = np.column_stack(
[
s_rs - s_is, # reference closer than intent?
s_is - s_ir, # student-intent gap vs baseline
s_rs - s_ir, # student-reference gap vs baseline
s_is * s_rs, # interaction
np.abs(s_rs - s_is), # intent-reference disagreement
]
)
sql_feats = np.array(
[extract_sql_features(c.student_query, c.correct_query) for c in contexts],
dtype=np.float64,
)
return np.hstack([pair_scores, derived, sql_feats])
def _prepare_features(self, contexts: List[QueryContext]) -> np.ndarray:
X = self.scaler.transform(self._build_features(contexts))
return np.nan_to_num(X, nan=0.0, posinf=1e3, neginf=-1e3)
def fit(self, contexts: List[QueryContext], y: np.ndarray) -> "CrossEncoderClassifier":
X = self._build_features(contexts, show_progress=True)
X = self.scaler.fit_transform(X)
X = np.nan_to_num(X, nan=0.0, posinf=1e3, neginf=-1e3)
self.clf.fit(X, y)
self.classes_ = self.clf.classes_
return self
def predict(self, contexts: List[QueryContext]) -> np.ndarray:
return self.clf.predict(self._prepare_features(contexts))
def predict_proba(self, contexts: List[QueryContext]) -> np.ndarray:
return self.clf.predict_proba(self._prepare_features(contexts))
def explain_pair_scores(self, ctx: QueryContext) -> dict:
scores = self._score_pairs([ctx])[0]
return {
PAIR_NAMES[0]: float(scores[0]),
PAIR_NAMES[1]: float(scores[1]),
PAIR_NAMES[2]: float(scores[2]),
}
class FineTunedCrossEncoderClassifier:
"""
End-to-end fine-tuned cross-encoder (highest accuracy).
Single cross-attention pass over [task_context | student_query] with
num_labels=15. Slower to train; best on smaller high-quality datasets.
"""
def __init__(
self,
cross_encoder_name: str = DEFAULT_FINETUNED_CE,
batch_size: int = 16,
max_length: int = 512,
num_labels: int = 15,
):
self.cross_encoder_name = cross_encoder_name
self.batch_size = batch_size
self.max_length = max_length
self.num_labels = num_labels
self.model = None
self.classes_: Optional[np.ndarray] = None
def _load_model(self, num_labels: Optional[int] = None):
if self.model is None:
from sentence_transformers import CrossEncoder
self.model = CrossEncoder(
self.cross_encoder_name,
num_labels=num_labels or self.num_labels,
max_length=self.max_length,
)
def _to_examples(self, contexts: List[QueryContext], labels: Optional[np.ndarray] = None):
from sentence_transformers import InputExample
examples = []
for i, ctx in enumerate(contexts):
label = float(labels[i]) if labels is not None else 0.0
examples.append(
InputExample(
texts=[_context_text(ctx), _student_text(ctx)],
label=label,
)
)
return examples
def fit(
self,
contexts: List[QueryContext],
y: np.ndarray,
epochs: int = 1,
warmup_steps: int = 100,
output_path: Optional[Path] = None,
) -> "FineTunedCrossEncoderClassifier":
from torch.utils.data import DataLoader
self._load_model(num_labels=len(np.unique(y)))
train_examples = self._to_examples(contexts, y)
loader = DataLoader(
train_examples,
shuffle=True,
batch_size=self.batch_size,
)
self.model.fit(
train_dataloader=loader,
epochs=epochs,
warmup_steps=min(warmup_steps, max(10, len(train_examples) // 10)),
show_progress_bar=True,
output_path=str(output_path) if output_path else None,
)
self.classes_ = np.sort(np.unique(y))
return self
def predict(self, contexts: List[QueryContext]) -> np.ndarray:
self._load_model()
pairs = [[_context_text(c), _student_text(c)] for c in contexts]
logits = self.model.predict(
pairs,
batch_size=self.batch_size,
show_progress_bar=False,
convert_to_numpy=True,
)
logits = np.asarray(logits)
if logits.ndim == 1:
return logits.astype(int)
return logits.argmax(axis=1)
def predict_proba(self, contexts: List[QueryContext]) -> np.ndarray:
self._load_model()
pairs = [[_context_text(c), _student_text(c)] for c in contexts]
logits = self.model.predict(
pairs,
batch_size=self.batch_size,
show_progress_bar=False,
convert_to_numpy=True,
)
logits = np.asarray(logits, dtype=np.float64)
if logits.ndim == 1:
# binary fallback
probs = np.zeros((len(contexts), len(self.classes_)))
for i, pred in enumerate(logits.astype(int)):
idx = np.where(self.classes_ == pred)[0][0]
probs[i, idx] = 1.0
return probs
# softmax
exp = np.exp(logits - logits.max(axis=1, keepdims=True))
return exp / exp.sum(axis=1, keepdims=True)
def save(self, path: Path) -> Path:
path.mkdir(parents=True, exist_ok=True)
self._load_model()
self.model.save(str(path))
return path
@classmethod
def load(cls, path: Path) -> "FineTunedCrossEncoderClassifier":
from sentence_transformers import CrossEncoder
instance = cls()
instance.model = CrossEncoder(str(path))
instance.classes_ = np.arange(instance.model.num_labels)
return instance
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