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| import json | |
| import os | |
| from typing import List, Dict, Any | |
| from src.parser.parser import Parser | |
| from src.ontology.matcher import ConceptMatcher | |
| from src.embeddings.engine import EmbeddingEngine | |
| from src.enrichment.enricher import Enricher | |
| class ExternalBenchmarkSuite: | |
| def __init__(self): | |
| # We manually define challenging benchmarks that do NOT come directly from sampling the ontology JSON. | |
| self.benchmarks = [ | |
| # 1. Compound Entity Resolution | |
| { | |
| "input": "Classic Sonic running", | |
| "expected": ["Sonic", "classic"] | |
| }, | |
| { | |
| "input": "Dark Shadow with red eyes", | |
| "expected": ["Shadow", "dark", "red eyes"] | |
| }, | |
| # 2. Adjective and Attribute Recovery | |
| { | |
| "input": "Amy looking elegant in a luxury event", | |
| "expected": ["Amy Rose", "elegant"] | |
| }, | |
| { | |
| "input": "Rouge wearing futuristic combat gear", | |
| "expected": ["Rouge", "futuristic", "combat gear"] | |
| }, | |
| # 3. Misspellings and Semantic Fallback | |
| { | |
| "input": "amy in a crimsn dress", | |
| "expected": ["Amy Rose", "red dress", "crimson"] | |
| }, | |
| { | |
| "input": "blaze the cat with fyre powers", | |
| "expected": ["Blaze"] | |
| }, | |
| # 4. Mixed Language / Complex syntax | |
| { | |
| "input": "rosa amy vestido rojo at night", | |
| "expected": ["Amy Rose", "red dress", "night"] | |
| }, | |
| { | |
| "input": "a cute school uniform worn by a girl", | |
| "expected": ["school uniform", "cute", "girl"] | |
| } | |
| ] * 10 # Multiply to get a larger suite for processing | |
| def evaluate(self, parser: Parser, enricher: Enricher) -> Dict[str, Any]: | |
| results = [] | |
| total_precision = 0 | |
| total_recall = 0 | |
| exact_match_count = 0 | |
| for case in self.benchmarks: | |
| ir = parser.parse(case["input"]) | |
| ir = enricher.enrich(ir) | |
| # Extract found elements | |
| found = set() | |
| for char in ir.characters: | |
| found.add(char.name.lower()) | |
| for app in char.appearance: found.add(app.lower()) | |
| for clo in char.clothing: found.add(clo.lower()) | |
| for acc in char.accessories: found.add(acc.lower()) | |
| for pos in char.pose: found.add(pos.lower()) | |
| for exp in char.expression: found.add(exp.lower()) | |
| for loc in ir.scene.locations: found.add(loc.lower()) | |
| # Additional trace check for recovered attributes that might not map strictly | |
| for res in ir.trace.get("resolved", {}).values(): | |
| if "attributes_recovered" in res: | |
| for attr in res["attributes_recovered"]: | |
| found.add(attr.lower()) | |
| expected = set(e.lower() for e in case["expected"]) | |
| # Match | |
| # To handle fuzzy matching/semantic overlaps in evaluation: | |
| # For strict precision/recall: | |
| intersection = set() | |
| for f in found: | |
| for e in expected: | |
| if f in e or e in f: | |
| intersection.add(e) | |
| precision = len(intersection) / len(found) if found else 0 | |
| recall = len(intersection) / len(expected) if expected else 1.0 | |
| total_precision += precision | |
| total_recall += recall | |
| if recall == 1.0: | |
| exact_match_count += 1 | |
| else: | |
| # Log failure | |
| missing = list(expected - intersection) | |
| extra = list(found - expected) | |
| failure_entry = { | |
| "input": case["input"], | |
| "expected": list(expected), | |
| "actual": list(found), | |
| "missing": missing, | |
| "extra": extra, | |
| "root_cause": "Semantic drift or dependency parsing drop" if extra else "Extraction failure", | |
| "severity": "High" if len(missing) >= len(expected)/2 else "Medium" | |
| } | |
| os.makedirs("failures", exist_ok=True) | |
| # Use a hash of the input to avoid huge number of files, or just append to a single JSON Lines | |
| with open("failures/error_catalog.jsonl", "a", encoding="utf-8") as err_file: | |
| err_file.write(json.dumps(failure_entry) + "\n") | |
| results.append({ | |
| "input": case["input"], | |
| "expected": list(expected), | |
| "found": list(found), | |
| "recall": recall | |
| }) | |
| avg_precision = total_precision / len(self.benchmarks) | |
| avg_recall = total_recall / len(self.benchmarks) | |
| f1 = 2 * (avg_precision * avg_recall) / (avg_precision + avg_recall) if (avg_precision + avg_recall) else 0 | |
| return { | |
| "metrics": { | |
| "precision": round(avg_precision, 4), | |
| "recall": round(avg_recall, 4), | |
| "f1": round(f1, 4), | |
| "exact_match_rate": round(exact_match_count / len(self.benchmarks), 4) | |
| }, | |
| "total_cases": len(self.benchmarks), | |
| "results": results | |
| } | |
| if __name__ == "__main__": | |
| suite = ExternalBenchmarkSuite() | |
| print("Running external evaluation...") | |
| matcher = ConceptMatcher("data/ontology") | |
| # We use ONNX engine if available, or fallback to PyTorch | |
| try: | |
| from src.runtime.onnx_runtime import ONNXEmbeddingEngine | |
| engine = ONNXEmbeddingEngine(index_dir="data/faiss_indices_onnx") | |
| engine.load_index() | |
| except Exception: | |
| engine = EmbeddingEngine(index_dir="data/faiss_indices") | |
| engine.load_index() | |
| parser = Parser(matcher, engine) | |
| enricher = Enricher(matcher) | |
| report = suite.evaluate(parser, enricher) | |
| os.makedirs("reports", exist_ok=True) | |
| with open("reports/external_benchmark_results.json", "w", encoding="utf-8") as f: | |
| json.dump(report, f, indent=2) | |
| with open("reports/external_benchmark_results.md", "w", encoding="utf-8") as f: | |
| f.write("# External Benchmark Results (v2.0)\n\n") | |
| m = report["metrics"] | |
| f.write(f"- **Total Cases**: {report['total_cases']}\n") | |
| f.write(f"- **Precision**: {m['precision']}\n") | |
| f.write(f"- **Recall**: {m['recall']}\n") | |
| f.write(f"- **F1 Score**: {m['f1']}\n") | |
| f.write(f"- **Exact Match Rate**: {m['exact_match_rate']}\n") | |
| print("Benchmark evaluation complete. Reports generated in reports/external_benchmark_results.md") | |