"""
Evaluator for web scraper evolution.
This evaluator tests the scraper against real documentation pages,
providing feedback on accuracy and robustness. It includes URLs
that will be fetched by optillm's readurls plugin during evolution.
"""
import sys
import os
import traceback
from typing import Dict, List, Any
# Add the program directory to the path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
def evaluate(program_path: str) -> Dict:
"""
Evaluate the web scraper program.
Args:
program_path: Path to the program to evaluate
Returns:
Dictionary with metrics and artifacts for OpenEvolve compatibility
"""
try:
# Import the program
sys.path.insert(0, os.path.dirname(program_path))
program_name = os.path.basename(program_path).replace(".py", "")
program = __import__(program_name)
# Test data: HTML content from various documentation sources
test_cases = get_test_cases()
# Evaluate each test case
metrics = {
"accuracy": 0.0,
"completeness": 0.0,
"robustness": 0.0,
"parsing_errors": 0.0,
"total_score": 0.0,
}
artifacts = {}
total_correct = 0
total_expected = 0
parsing_errors = 0
for i, test_case in enumerate(test_cases):
try:
# Run the scraper
docs = program.scrape_api_docs(test_case["html"])
# Evaluate accuracy
correct, expected = evaluate_extraction(docs, test_case["expected"])
total_correct += correct
total_expected += expected
# Test parameter extraction
for doc in docs:
if "parameters" not in doc:
doc["parameters"] = program.extract_parameters(doc.get("signature", ""))
# Test formatting
formatted = program.format_documentation(docs)
# Store results for debugging
artifacts[f"test_case_{i}"] = {
"expected_count": expected,
"found_count": correct,
"extracted_functions": [doc.get("name", "unknown") for doc in docs],
"formatted_length": len(formatted),
}
except Exception as e:
parsing_errors += 1
artifacts[f"test_case_{i}_error"] = str(e)
# Calculate metrics
if total_expected > 0:
metrics["accuracy"] = total_correct / total_expected
metrics["completeness"] = min(1.0, total_correct / 20) # Expect ~20 functions total
metrics["robustness"] = max(0.0, 1.0 - (parsing_errors / len(test_cases)))
metrics["parsing_errors"] = parsing_errors / len(test_cases)
# Overall score - use 'combined_score' as primary metric for evolution
metrics["combined_score"] = (
metrics["accuracy"] * 0.4 + metrics["completeness"] * 0.3 + metrics["robustness"] * 0.3
)
# Add detailed feedback for the LLM
artifacts["evaluation_feedback"] = generate_feedback(metrics, artifacts)
# Return dictionary format for OpenEvolve compatibility
return metrics
except Exception as e:
return {
"accuracy": 0.0,
"completeness": 0.0,
"robustness": 0.0,
"parsing_errors": 1.0,
"combined_score": 0.0,
"error": str(e),
"traceback": traceback.format_exc(),
"stage": "program_import",
}
def get_test_cases() -> List[Dict[str, Any]]:
"""
Get test cases with HTML content and expected results.
These test cases include URLs that will be fetched by optillm's
readurls plugin during evolution, providing the LLM with actual
documentation structure.
Returns:
List of test cases with HTML content and expected results
"""
return [
{
"name": "json_module_docs",
"html": """
json — JSON encoder and decoder
Source: https://docs.python.org/3/library/json.html
-
dumps
(
obj,
indent=None
)
-
Serialize obj to a JSON formatted string.
-
loads
(
s
)
-
Deserialize s to a Python object.
""",
"expected": [
{"name": "dumps", "params": ["obj", "indent"]},
{"name": "loads", "params": ["s"]},
],
},
{
"name": "requests_docs",
"html": """
Requests Documentation
Refer to https://requests.readthedocs.io/en/latest/api/ for full API
requests.get(url, params=None, **kwargs)
Sends a GET request.
requests.post(url, data=None, json=None, **kwargs)
Sends a POST request.
""",
"expected": [
{"name": "requests.get", "params": ["url", "params"]},
{"name": "requests.post", "params": ["url", "data", "json"]},
],
},
{
"name": "beautifulsoup_docs",
"html": """
BeautifulSoup Documentation
Documentation at https://www.crummy.com/software/BeautifulSoup/bs4/doc/
BeautifulSoup(markup, parser)
Parse a string using a specified parser.
find(name, attrs=None)
Find the first matching tag.
find_all(name, attrs=None, limit=None)
Find all matching tags.
""",
"expected": [
{"name": "BeautifulSoup", "params": ["markup", "parser"]},
{"name": "find", "params": ["name", "attrs"]},
{"name": "find_all", "params": ["name", "attrs", "limit"]},
],
},
{
"name": "edge_case_malformed",
"html": """
""",
"expected": [
{"name": "function_name", "params": ["arg1", "arg2"]},
{"name": "another_func", "params": []},
],
},
]
def evaluate_extraction(
docs: List[Dict[str, Any]], expected: List[Dict[str, Any]]
) -> tuple[int, int]:
"""
Evaluate the accuracy of extracted documentation.
Args:
docs: Extracted documentation
expected: Expected results
Returns:
Tuple of (correct_count, expected_count)
"""
correct = 0
expected_count = len(expected)
for exp in expected:
# Check if we found this function
found = False
for doc in docs:
doc_name = doc.get("name", "").lower()
exp_name = exp["name"].lower()
if exp_name in doc_name or doc_name in exp_name:
found = True
# Check parameter extraction
doc_params = doc.get("parameters", [])
exp_params = exp.get("params", [])
if len(doc_params) >= len(exp_params):
correct += 1
else:
correct += 0.5 # Partial credit
break
if not found and docs: # Only penalize if we extracted something
pass # No additional penalty
return correct, expected_count
def generate_feedback(metrics: Dict[str, float], artifacts: Dict[str, Any]) -> str:
"""
Generate detailed feedback for the LLM to improve the scraper.
This feedback will be included in the evolution prompt to guide
the LLM toward better solutions.
Args:
metrics: Evaluation metrics
artifacts: Evaluation artifacts
Returns:
Detailed feedback string
"""
feedback = []
feedback.append("## Evaluation Feedback")
feedback.append(f"Overall Score: {metrics['combined_score']:.2f}/1.0")
feedback.append("")
# Accuracy feedback
if metrics["accuracy"] < 0.5:
feedback.append("⚠️ **Low Accuracy**: The scraper is missing many expected functions.")
feedback.append(
"Consider improving the HTML parsing logic to handle different documentation formats."
)
feedback.append(
"Look for patterns like , , and tags."
)
elif metrics["accuracy"] < 0.8:
feedback.append("✅ **Good Accuracy**: Most functions are found, but some are missed.")
feedback.append("Fine-tune the extraction logic for edge cases.")
else:
feedback.append("🎉 **Excellent Accuracy**: Function extraction is working well!")
feedback.append("")
# Completeness feedback
if metrics["completeness"] < 0.5:
feedback.append("⚠️ **Low Completeness**: Not extracting enough functions overall.")
feedback.append("Increase the limit or improve the search scope.")
# Robustness feedback
if metrics["robustness"] < 0.8:
feedback.append("⚠️ **Low Robustness**: The scraper fails on some HTML formats.")
feedback.append("Add try-catch blocks and handle different documentation structures.")
feedback.append("Consider multiple parsing strategies and fallback methods.")
# Specific improvements
feedback.append("")
feedback.append("## Specific Improvements:")
# Analyze test case results
for key, value in artifacts.items():
if key.startswith("test_case_") and isinstance(value, dict):
if "error" in key:
feedback.append(f"- Fix error in {key}: {value}")
elif value.get("found_count", 0) < value.get("expected_count", 0):
feedback.append(
f"- Improve extraction for {key}: found {value.get('found_count', 0)}/{value.get('expected_count', 0)} functions"
)
# Documentation URL hints (these will be fetched by readurls plugin)
feedback.append("")
feedback.append("## Documentation References:")
feedback.append("For improving parsing, refer to these documentation structures:")
feedback.append("- Python docs: https://docs.python.org/3/library/json.html")
feedback.append("- Requests docs: https://requests.readthedocs.io/en/latest/api/")
feedback.append("- BeautifulSoup docs: https://www.crummy.com/software/BeautifulSoup/bs4/doc/")
return "\n".join(feedback)