Update project and configurations
This commit is contained in:
Zou-Seay
235
intelligent_cabin/archive/tests/test_bert.py
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235
intelligent_cabin/archive/tests/test_bert.py
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from __future__ import annotations
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import argparse
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import json
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import sys
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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from app.core.config import settings
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from app.core.bootstrap import build_intent_registry
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from app.services.classifier import BertIntentClassifier
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from app.services.router import build_matcher_pipeline
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DEFAULT_MODEL_DIR = PROJECT_ROOT / "models" / "local_bert_intent"
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NON_BUSINESS_LABELS = {"__social__", "__out_of_scope__"}
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def resolve_model_path(model_path: str) -> Path:
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configured = (model_path or settings.classifier_model_path).strip()
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if configured:
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return Path(configured)
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return DEFAULT_MODEL_DIR
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def resolve_label_map_path(label_map_path: str, model_path: Path) -> Path:
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configured = (label_map_path or settings.classifier_label_map_path).strip()
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if configured:
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return Path(configured)
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return model_path / "label_map.json"
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def build_classifier(
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*,
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model_path: Path,
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label_map_path: Path,
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threshold: float,
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top_k: int,
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) -> BertIntentClassifier:
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return BertIntentClassifier(
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model_path=str(model_path),
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threshold=threshold,
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label_map_path=str(label_map_path),
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fallback=None,
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top_k=top_k,
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)
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def predict_once(
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text: str,
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*,
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model_path: Path,
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label_map_path: Path,
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threshold: float,
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top_k: int,
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warmup: bool,
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) -> dict[str, object]:
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registry = build_intent_registry()
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classifier = build_classifier(
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model_path=model_path,
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label_map_path=label_map_path,
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threshold=threshold,
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top_k=top_k,
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)
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warmup_ok = None
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if warmup:
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warmup_ok = classifier.warmup(settings.classifier_warmup_text)
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if not warmup_ok:
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error_message = getattr(classifier, "_warmup_error_message", None) or "BERT warmup failed"
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raise RuntimeError(error_message)
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intents = registry.list()
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result = classifier.predict(text, intents)
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matcher = build_matcher_pipeline(
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registry,
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["classifier"],
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classifier=classifier,
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route_to_cloud_threshold=settings.local_route_to_cloud_threshold,
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clarify_margin_threshold=settings.local_clarify_margin_threshold,
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classifier_execute_score_threshold=settings.local_classifier_execute_score_threshold,
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classifier_execute_margin_threshold=settings.local_classifier_execute_margin_threshold,
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)
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route_result = matcher.match(text)
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fusion_stage = next((stage for stage in reversed(route_result.debug.stages) if stage.stage == "fusion"), None)
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classifier_stage = next((stage for stage in reversed(route_result.debug.stages) if stage.stage == "classifier"), None)
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return {
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"text": text,
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"config": {
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"model_path": str(model_path),
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"label_map_path": str(label_map_path),
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"threshold": threshold,
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"top_k": top_k,
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"warmup_requested": warmup,
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"warmup_ok": warmup_ok,
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"warmup_elapsed_ms": getattr(classifier, "_warmup_elapsed_ms", None),
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"warmup_error_message": getattr(classifier, "_warmup_error_message", None),
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},
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"classifier_result": {
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"predicted_intent": result.intent.intent_id if result.intent is not None else None,
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"score": round(result.score, 4),
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"model_name": result.model_name,
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"backend_name": result.backend_name,
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"used_fallback": result.used_fallback,
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"fallback_reason": result.fallback_reason,
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"error_message": result.error_message,
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"raw_label": result.raw_label,
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"raw_candidates": result.raw_candidates or [],
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"known_candidates": [
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{"intent_id": intent.intent_id, "score": round(score, 4)}
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for intent, score in (result.candidates or [])
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],
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},
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"route_result": {
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"decision": route_result.debug.decision,
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"decision_reason": route_result.debug.decision_reason,
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"matched_stage": route_result.debug.matched_stage,
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"selected_intent": route_result.debug.selected_intent,
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"confidence_grade": route_result.debug.confidence_grade,
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"unknown_detected": route_result.debug.unknown_detected,
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"classifier_score": round(classifier_stage.score, 4) if classifier_stage is not None else None,
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"fusion_score": round(fusion_stage.score, 4) if fusion_stage is not None else None,
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},
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}
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def summarize_business_view(result: dict[str, object]) -> dict[str, object]:
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classifier_result = dict(result.get("classifier_result") or {})
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route_result = dict(result.get("route_result") or {})
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predicted_intent = classifier_result.get("predicted_intent")
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raw_label = classifier_result.get("raw_label")
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effective_label = raw_label if raw_label in NON_BUSINESS_LABELS else predicted_intent
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if effective_label in NON_BUSINESS_LABELS:
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classifier_result["predicted_intent"] = None
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classifier_result["non_business_label"] = effective_label
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classifier_result["business_interpretation"] = "non_business_label_detected"
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route_result["selected_intent"] = None
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route_result["decision"] = "reject"
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route_result["decision_reason"] = "classifier detected a non-business label"
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route_result["unknown_detected"] = True
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else:
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classifier_result["non_business_label"] = None
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classifier_result["business_interpretation"] = "known_business_intent_or_uncertain"
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return {
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"text": result.get("text"),
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"config": result.get("config"),
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"classifier_result": classifier_result,
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"route_result": route_result,
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}
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def interactive_loop(
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*,
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model_path: Path,
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label_map_path: Path,
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threshold: float,
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top_k: int,
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warmup: bool,
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mode: str,
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) -> None:
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print("当前 BERT 测试已启动，输入一句话直接查看预测结果，输入 exit 退出。")
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print(f"model_path={model_path}")
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print(f"label_map_path={label_map_path}")
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print(f"threshold={threshold} top_k={top_k} warmup={warmup} mode={mode}")
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while True:
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try:
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text = input("\n请输入问题> ").strip()
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except EOFError:
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print()
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break
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if not text:
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continue
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if text.lower() in {"exit", "quit", "q"}:
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break
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result = predict_once(
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text,
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model_path=model_path,
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label_map_path=label_map_path,
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threshold=threshold,
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top_k=top_k,
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warmup=warmup,
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)
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if mode == "business":
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result = summarize_business_view(result)
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print(json.dumps(result, ensure_ascii=False, indent=2))
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def main() -> None:
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parser = argparse.ArgumentParser(description="当前项目 BERT 意图识别测试脚本")
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parser.add_argument("--text", type=str, default="", help="单次测试文本")
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parser.add_argument("--threshold", type=float, default=settings.classifier_bert_threshold, help="BERT 置信度阈值")
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parser.add_argument("--top-k", type=int, default=settings.classifier_top_k, help="返回候选数量")
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parser.add_argument("--model-path", type=str, default="", help="模型目录，默认取 .env 或 models/local_bert_intent")
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parser.add_argument("--label-map-path", type=str, default="", help="标签映射文件，默认取 .env 或 model_path/label_map.json")
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parser.add_argument("--warmup", action="store_true", help="先执行一次 warmup 再预测")
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parser.add_argument(
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"--mode",
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choices=("classifier", "business"),
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default="classifier",
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help="classifier 显示原始分类结果；business 会把非业务标签折叠成未命中业务意图",
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)
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args = parser.parse_args()
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model_path = resolve_model_path(args.model_path)
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label_map_path = resolve_label_map_path(args.label_map_path, model_path)
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if args.text.strip():
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result = predict_once(
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args.text.strip(),
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model_path=model_path,
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label_map_path=label_map_path,
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threshold=args.threshold,
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top_k=args.top_k,
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warmup=args.warmup,
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)
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if args.mode == "business":
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result = summarize_business_view(result)
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print(json.dumps(result, ensure_ascii=False, indent=2))
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return
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interactive_loop(
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model_path=model_path,
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label_map_path=label_map_path,
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threshold=args.threshold,
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top_k=args.top_k,
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warmup=args.warmup,
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mode=args.mode,
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)
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if __name__ == "__main__":
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main()
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