File size: 6,281 Bytes
1db7196 | 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 | import argparse
import json
import os
import traceback
import urllib.error
import urllib.request
import dspy
from dspy.evaluate import Evaluate
DEFAULT_API_BASE = "http://172.16.34.22:8040/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_TEST_PATH = "/home/mshahidul/readctrl/code/text_classifier/data/verified_combined_0-80_clean200.json"
DEFAULT_OUTPUT_PATH = (
"/home/mshahidul/readctrl/code/text_classifier/accuracy/"
"vllm-llama-3.1-8b-awq-int4_teacher-gpt5_v1_clean200_eval.json"
)
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():
parser = argparse.ArgumentParser(
description="Load a saved DSPy model and evaluate on test set."
)
parser.add_argument("--model-path", default=DEFAULT_MODEL_PATH)
parser.add_argument("--test-path", default=DEFAULT_TEST_PATH)
parser.add_argument(
"--api-base",
default=os.environ.get("VLLM_API_BASE", DEFAULT_API_BASE),
)
parser.add_argument("--num-threads", type=int, default=1)
parser.add_argument("--output-path", default=DEFAULT_OUTPUT_PATH)
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):
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 load_testset(path):
examples = []
if path.endswith(".jsonl"):
with open(path, "r") as f:
for line in f:
if not line.strip():
continue
record = json.loads(line)
example = dspy.Example(
generated_text=record["generated_text"],
literacy_label=record["literacy_label"],
).with_inputs("generated_text")
examples.append(example)
else:
with open(path, "r") as f:
records = json.load(f)
for record in records:
text = record.get("generated_text", record.get("diff_label_texts"))
label = record.get("literacy_label", record.get("label"))
if not text or not label:
continue
example = dspy.Example(
generated_text=text,
literacy_label=label,
).with_inputs("generated_text")
examples.append(example)
return examples
def health_literacy_metric(gold, pred, trace=None):
if not pred or not hasattr(pred, "literacy_label"):
return False
gold_label = str(gold.literacy_label).strip().lower()
pred_label = str(pred.literacy_label).strip().lower()
return gold_label in pred_label
def load_compiled_classifier(path):
if hasattr(dspy, "load"):
try:
return dspy.load(path)
except Exception as exc:
print(
f"[warning] dspy.load failed ({type(exc).__name__}); "
"trying module.load(...)"
)
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 main():
args = parse_args()
if not os.path.exists(args.model_path):
raise FileNotFoundError(f"Model file not found: {args.model_path}")
if not os.path.exists(args.test_path):
raise FileNotFoundError(f"Test file not found: {args.test_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)
testset = load_testset(args.test_path)
compiled_classifier = load_compiled_classifier(args.model_path)
evaluator = Evaluate(
devset=testset,
metric=health_literacy_metric,
num_threads=args.num_threads,
display_progress=True,
)
evaluation_result = evaluator(compiled_classifier)
accuracy_score = (
float(evaluation_result.score)
if hasattr(evaluation_result, "score")
else float(evaluation_result)
)
output_data = {
"model_path": args.model_path,
"test_path": args.test_path,
"accuracy_score": accuracy_score,
"num_results": len(getattr(evaluation_result, "results", []) or []),
}
os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
with open(args.output_path, "w") as f:
json.dump(output_data, f, indent=2)
print(evaluation_result)
print(json.dumps(output_data, indent=2))
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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