| """ |
| MINDI 1.5 Vision-Coder — Evaluation System |
| |
| Evaluates model quality on code generation benchmarks, |
| UI quality metrics, and end-to-end task completion. |
| """ |
|
|
| from __future__ import annotations |
|
|
| from dataclasses import dataclass, field |
| from pathlib import Path |
| from typing import Any, Optional |
|
|
|
|
| @dataclass |
| class EvalMetrics: |
| """Aggregated evaluation metrics.""" |
| pass_at_1: float = 0.0 |
| pass_at_5: float = 0.0 |
| ui_quality_score: float = 0.0 |
| syntax_error_rate: float = 0.0 |
| type_error_rate: float = 0.0 |
| avg_iterations: float = 0.0 |
| total_examples: int = 0 |
| details: list[dict[str, Any]] = field(default_factory=list) |
|
|
|
|
| class Evaluator: |
| """Evaluates MINDI 1.5 model across multiple quality dimensions.""" |
|
|
| def __init__( |
| self, |
| eval_data_dir: Optional[Path] = None, |
| results_dir: Optional[Path] = None, |
| ) -> None: |
| self.eval_data_dir = eval_data_dir or Path("./data/processed/eval") |
| self.results_dir = results_dir or Path("./logs/evaluation") |
| self.results_dir.mkdir(parents=True, exist_ok=True) |
|
|
| async def run_evaluation( |
| self, |
| pipeline: Any, |
| num_samples: int = 100, |
| ) -> EvalMetrics: |
| """Run full evaluation suite against the inference pipeline.""" |
| |
| return EvalMetrics(total_examples=num_samples) |
|
|
| def save_results(self, metrics: EvalMetrics, run_name: str = "eval") -> Path: |
| """Save evaluation results to disk.""" |
| import json |
|
|
| output_path = self.results_dir / f"{run_name}_results.json" |
| with open(output_path, "w", encoding="utf-8") as f: |
| json.dump( |
| { |
| "pass_at_1": metrics.pass_at_1, |
| "pass_at_5": metrics.pass_at_5, |
| "ui_quality_score": metrics.ui_quality_score, |
| "syntax_error_rate": metrics.syntax_error_rate, |
| "type_error_rate": metrics.type_error_rate, |
| "avg_iterations": metrics.avg_iterations, |
| "total_examples": metrics.total_examples, |
| }, |
| f, |
| indent=2, |
| ) |
| return output_path |
|
|