File size: 8,698 Bytes
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import glob
import json
import os
import traceback
import urllib.error
import urllib.request
from datetime import datetime
from typing import Any, Dict, List
import dspy
from tqdm import tqdm
DEFAULT_API_BASE = "http://172.16.34.21:8040/v1"
DEFAULT_MODEL_PATH = (
"/home/mshahidul/readctrl/code/text_classifier/"
"dspy_model/vllm-Meta-Llama-3.1-8B-Instruct_teacher-gpt5_v1/model.json"
)
DEFAULT_INPUT_PATH = "/home/mshahidul/readctrl/code/RL_model/inference_data"
DEFAULT_INPUT_FILE = (
"/home/mshahidul/readctrl/code/RL_model/inference_data/"
"vllm_inference_qwen-qwen3-4b-instruct-2507_20260213_173334.jsonl"
)
DEFAULT_OUTPUT_DIR = "/home/mshahidul/readctrl/code/rl_inference/test_result"
VALID_LABELS = {
"low_health_literacy",
"intermediate_health_literacy",
"proficient_health_literacy",
}
class HealthLiteracySignature(dspy.Signature):
generated_text = dspy.InputField(
desc="A version of the source text rewritten for a specific audience."
)
literacy_label = dspy.OutputField(
desc=(
"Classification: low_health_literacy (simple words, no jargon), "
"intermediate_health_literacy (moderate technicality), or "
"proficient_health_literacy (highly technical/original level)."
)
)
class HealthLiteracyClassifier(dspy.Module):
def __init__(self):
super().__init__()
self.classifier = dspy.ChainOfThought(HealthLiteracySignature)
def forward(self, generated_text):
return self.classifier(generated_text=generated_text)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Evaluate saved DSPy classifier on saved vLLM inference outputs."
)
parser.add_argument("--model-path", default=DEFAULT_MODEL_PATH)
parser.add_argument(
"--input-path",
default=DEFAULT_INPUT_FILE,
help=(
"Path to vLLM output JSONL (e.g. vllm_inference_*.jsonl). "
"Set to empty string to auto-select latest file in --search-dir."
),
)
parser.add_argument(
"--search-dir",
default=DEFAULT_INPUT_PATH,
help="Directory to auto-search for vllm_inference_*.jsonl",
)
parser.add_argument(
"--api-base",
default=os.environ.get("VLLM_API_BASE", DEFAULT_API_BASE),
)
parser.add_argument("--output-dir", default=DEFAULT_OUTPUT_DIR)
parser.add_argument(
"--max-samples",
type=int,
default=-1,
help="Use -1 for all rows.",
)
parser.add_argument(
"--provide-traceback",
action="store_true",
help="Print full traceback if runtime error happens.",
)
return parser.parse_args()
def check_api_base(api_base: str) -> None:
models_url = api_base.rstrip("/") + "/models"
req = urllib.request.Request(models_url, method="GET")
try:
with urllib.request.urlopen(req, timeout=5) as resp:
if resp.status >= 400:
raise RuntimeError(
f"Endpoint reachable but unhealthy: {models_url} (status={resp.status})"
)
except urllib.error.URLError as exc:
raise ConnectionError(
"Cannot reach OpenAI-compatible endpoint. "
f"api_base={api_base}. "
"Start your vLLM server or pass correct --api-base."
) from exc
def resolve_input_path(input_path: str, search_dir: str) -> str:
if input_path and os.path.exists(input_path):
return input_path
if input_path:
raise FileNotFoundError(f"Input file not found: {input_path}")
candidates = sorted(
glob.glob(os.path.join(search_dir, "vllm_inference_*.jsonl")),
key=os.path.getmtime,
)
if not candidates:
raise FileNotFoundError(
"No vLLM output file found. Expected pattern: "
f"{search_dir}/vllm_inference_*.jsonl"
)
return candidates[-1]
def load_compiled_classifier(path: str):
if hasattr(dspy, "load"):
try:
return dspy.load(path)
except Exception:
pass
classifier = HealthLiteracyClassifier()
try:
classifier.load(path)
except Exception as exc:
raise RuntimeError(f"Failed to load compiled model from {path}") from exc
return classifier
def normalize_pred_label(pred_obj: Any) -> str:
if not pred_obj or not hasattr(pred_obj, "literacy_label"):
return ""
return str(pred_obj.literacy_label).strip().lower()
def load_eval_items(path: str) -> List[Dict[str, Any]]:
items: List[Dict[str, Any]] = []
with open(path, "r", encoding="utf-8") as f:
for line_no, line in enumerate(f, start=1):
if not line.strip():
continue
row = json.loads(line)
gold_label = str(row.get("gold_label", "")).strip()
generated_text = str(row.get("generated_text", "")).strip()
if not generated_text:
generated_text = str(row.get("prediction", "")).strip()
err_msg = str(row.get("error", "")).strip()
if gold_label not in VALID_LABELS:
continue
if err_msg:
continue
if not generated_text:
continue
items.append(
{
"line_no": line_no,
"row_index": row.get("row_index"),
"doc_id": row.get("doc_id"),
"gold_label": gold_label,
"generated_text": generated_text,
}
)
return items
def main() -> None:
args = parse_args()
args.input_path = resolve_input_path(args.input_path, args.search_dir)
if not os.path.exists(args.model_path):
raise FileNotFoundError(f"Model file not found: {args.model_path}")
try:
check_api_base(args.api_base)
lm = dspy.LM(
model="openai/dspy",
api_base=args.api_base,
api_key="EMPTY",
temperature=0.0,
)
dspy.configure(lm=lm)
classifier = load_compiled_classifier(args.model_path)
print(f"[INFO] Using input file: {args.input_path}")
parsed_items = load_eval_items(args.input_path)
if args.max_samples > 0:
parsed_items = parsed_items[: args.max_samples]
if not parsed_items:
raise RuntimeError("No valid rows found in input file for classifier evaluation.")
correct = 0
results: List[Dict[str, Any]] = []
for item in tqdm(parsed_items, desc="Classifying"):
pred = classifier(generated_text=item["generated_text"])
pred_label = normalize_pred_label(pred)
is_correct = item["gold_label"] in pred_label
correct += int(is_correct)
results.append(
{
"line_no": item["line_no"],
"row_index": item["row_index"],
"doc_id": item.get("doc_id"),
"gold_label": item["gold_label"],
"pred_label": pred_label,
"is_correct": is_correct,
}
)
total = len(results)
accuracy = correct / total if total else 0.0
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
os.makedirs(args.output_dir, exist_ok=True)
summary_path = os.path.join(args.output_dir, f"classifier_eval_vllm_{ts}.json")
details_path = os.path.join(args.output_dir, f"classifier_eval_vllm_{ts}.jsonl")
with open(summary_path, "w", encoding="utf-8") as f:
json.dump(
{
"model_path": args.model_path,
"input_path": args.input_path,
"api_base": args.api_base,
"total_samples": total,
"correct_samples": correct,
"accuracy_score": accuracy,
"details_path": details_path,
},
f,
indent=2,
)
with open(details_path, "w", encoding="utf-8") as f:
for r in results:
f.write(json.dumps(r, ensure_ascii=False) + "\n")
print(json.dumps({"total_samples": total, "accuracy_score": accuracy}, indent=2))
print(f"[DONE] Summary saved: {summary_path}")
print(f"[DONE] Details saved: {details_path}")
except Exception as exc:
print(f"[error] {type(exc).__name__}: {exc}")
if args.provide_traceback:
traceback.print_exc()
raise
if __name__ == "__main__":
main()
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