data/processed/RQ1/RecruitmentAssistant-A2A/evaluate_trajectory.py

#!/usr/bin/env python3
"""
Trajectory Evaluation Script - EmailResponder Project

Evaluates 6 trajectory metrics:
1. Exact match
2. In-order match
3. Any-order match
4. Precision
5. Recall
6. Single-tool use
"""

import os
import re
import yaml
from pathlib import Path
from typing import List, Dict, Tuple, Set
from collections import defaultdict
import pandas as pd
import math
from itertools import permutations, product


class TrajectoryParser:
    """Parse execution_path.md file and extract complete execution trajectory"""

    def __init__(self, md_file_path: str, extract_types: List[str] = None):
        """
        Args:
            md_file_path: execution_path.md file path
            extract_types: List of node types to extract, default ['Tool']
                          Available types: 'SPAN', 'Chain', 'Tool', 'AGENT', 'LLM', 'Task Created', 'Crew Created'
        """
        self.md_file_path = md_file_path
        self.extract_types = extract_types or ["Tool"]
        self.trajectory = []

    def parse(self) -> List[str]:
        """Parse file and return execution trajectory sequence"""
        if not os.path.exists(self.md_file_path):
            return []

        with open(self.md_file_path, "r", encoding="utf-8") as f:
            content = f.read()

        # Extract Execution Path Tree section (in code block)
        tree_match = re.search(
            r"## Execution Path Tree.*?```\n(.*?)```", content, re.DOTALL
        )
        if not tree_match:
            return []

        tree_content = tree_match.group(1)
        trajectory = []

        for line in tree_content.split("\n"):
            # Remove tree structure characters, keep node content
            clean_line = re.sub(r"^[│├└─\s]+", "", line).strip()
            if not clean_line:
                continue

            # Remove error marker ❌
            clean_line = re.sub(r"^❌\s+", "", clean_line)

            # Remove RETRY markers (retry N) and [RETRYN]
            clean_line = re.sub(r"\s*\(retry\s+\d+\)", "", clean_line)
            clean_line = re.sub(r"\s*\[RETRY\d+\]", "", clean_line)

            # Remove ERROR info (keep node type and name, remove [ERROR:...] part)
            clean_line = re.sub(r"\s*\[ERROR:[^\]]*\]", "", clean_line)

            # Extract node info
            node_info = self._extract_node_info(clean_line)

            if node_info and node_info["type"] in self.extract_types:
                trajectory.append(node_info["action"])

        self.trajectory = trajectory
        return trajectory

    def _extract_node_info(self, line: str) -> dict:
        """Extract node information from a line

        Note: Input line should already have error markers ❌, RETRY markers, and ERROR info removed

        Returns:
            {'type': str, 'action': str} or None
        """
        # SPAN node: [SPAN] span_name [statistics]
        # More lenient matching, handling possible residual special characters
        span_match = re.match(r"\[SPAN\]\s+([^\[]+?)(?:\s+\[.*?\])*\s*$", line)
        if span_match:
            span_name = span_match.group(1).strip()
            return {"type": "SPAN", "action": f"SPAN: {span_name}"}

        # Chain node: [Chain] chain_name [statistics]
        # For Crew_xxx.kickoff format, use wildcard Crew***.kickoff
        chain_match = re.match(r"\[Chain\]\s+([^\[]+?)(?:\s+\[.*?\])*\s*$", line)
        if chain_match:
            chain_name = chain_match.group(1).strip()
            # Wildcard handling: Crew_UUID.kickoff -> Crew***.kickoff
            chain_name = re.sub(
                r"Crew_[a-f0-9\-]+\.kickoff", "Crew***.kickoff", chain_name
            )
            return {"type": "Chain", "action": f"Chain: {chain_name}"}

        # AGENT node: [AGENT] agent_name._execute_core [statistics]
        agent_match = re.match(r"\[AGENT\]\s+([^\[]+?)(?:\s+\[.*?\])*\s*$", line)
        if agent_match:
            agent_name = agent_match.group(1).strip()
            # Remove ._execute_core suffix
            agent_name = re.sub(r"\._execute_core$", "", agent_name)
            return {"type": "AGENT", "action": f"Agent: {agent_name}"}

        # Tool node: [Tool] tool_name._use [time]
        tool_match = re.match(
            r"\[Tool\]\s+([^\[\]]+?)(?:\s+\[[\d.]+(?:ms|s)\])?(?:\s*@@@)?\s*$", line
        )
        if tool_match:
            tool_name = tool_match.group(1).strip()
            # Remove ._use suffix
            tool_name = re.sub(r"\._use$", "", tool_name)
            return {"type": "Tool", "action": f"Tool: {tool_name}"}

        # LLM node: [LLM] model_name (tokens) [time]
        llm_match = re.match(r"\[LLM\]\s+([^\(\[]+)", line)
        if llm_match:
            model_name = llm_match.group(1).strip()
            return {"type": "LLM", "action": f"LLM: {model_name}"}

        # Task Created node: [Task Created] [time]
        task_match = re.match(r"\[Task Created\]", line)
        if task_match:
            return {"type": "Task Created", "action": "Task Created"}

        # Crew Created node: [Crew Created] [time]
        crew_match = re.match(r"\[Crew Created\]", line)
        if crew_match:
            return {"type": "Crew Created", "action": "Crew Created"}

        return None


class TrajectoryEvaluator:
    """Trajectory Evaluator - Implements 6 evaluation metrics"""

    def __init__(self, reference_trajectory: List[str]):
        """
        Args:
            reference_trajectory: Reference trajectory (ground truth)
        """
        self.reference = reference_trajectory

    def _match_action(self, predicted_action: str, reference_action: str) -> bool:
        """
        Match two actions, supports wildcards

        Args:
            predicted_action: Actually executed action
            reference_action: Reference action (may contain wildcards)

        Returns:
            True if match, False otherwise
        """
        # Exact match
        if predicted_action == reference_action:
            return True

        # Wildcard match: LLM: * matches any LLM: <model_name>
        if reference_action == "LLM: *" and predicted_action.startswith("LLM: "):
            return True

        return False

    def exact_match(self, predicted: List[str]) -> int:
        """
        Exact match: Predicted trajectory must be exactly the same as reference (supports wildcards)

        Returns:
            1 if exact match, 0 otherwise
        """
        if len(predicted) != len(self.reference):
            return 0

        for i in range(len(predicted)):
            if not self._match_action(predicted[i], self.reference[i]):
                return 0

        return 1

    def in_order_match(self, predicted: List[str]) -> int:
        """
        In-order match: Reference trajectory must be a subsequence of predicted (supports wildcards)
        Allows extra actions, but core steps must appear in order

        Returns:
            1 if in-order match, 0 otherwise
        """
        if not self.reference:
            return 1  # Empty reference always matches

        ref_idx = 0
        for pred_action in predicted:
            if ref_idx < len(self.reference) and self._match_action(
                pred_action, self.reference[ref_idx]
            ):
                ref_idx += 1

        # Check if all reference steps were found in order
        return 1 if ref_idx == len(self.reference) else 0

    def any_order_match(self, predicted: List[str]) -> int:
        """
        Any-order match: As long as predicted contains all necessary actions (supports wildcards)
        Order doesn't matter, allows extra actions

        Returns:
            1 if any-order match, 0 otherwise
        """
        if not self.reference:
            return 1

        # Create copy of predicted actions for matching
        pred_remaining = predicted.copy()

        # For each reference action, try to find a match in predicted
        for ref_action in self.reference:
            matched = False
            for i, pred_action in enumerate(pred_remaining):
                if self._match_action(pred_action, ref_action):
                    pred_remaining.pop(i)  # Remove matched
                    matched = True
                    break

            if not matched:
                return 0  # Reference action not found

        return 1

    def precision(self, predicted: List[str]) -> float:
        """
        Precision: How many in predicted trajectory are considered correct by reference (supports wildcards)

        Precision = TP / (TP + FP)
        TP: Correct tool calls in prediction
        FP: Incorrect/extra tool calls in prediction

        Returns:
            precision value (0.0 - 1.0)
        """
        if not predicted:
            return 1.0  # No prediction, no false prediction

        if not self.reference:
            return 0.0  # Reference is empty but has prediction, all wrong

        # Create copy of reference actions for matching
        ref_remaining = self.reference.copy()

        tp = 0  # True Positives
        for pred_action in predicted:
            # Try to find match in reference
            for i, ref_action in enumerate(ref_remaining):
                if self._match_action(pred_action, ref_action):
                    tp += 1
                    ref_remaining.pop(i)  # Remove matched
                    break

        fp = len(predicted) - tp  # False Positives

        return tp / (tp + fp) if (tp + fp) > 0 else 0.0

    def recall(self, predicted: List[str]) -> float:
        """
        Recall: How many in reference trajectory are covered by predicted (supports wildcards)

        Recall = TP / (TP + FN)
        TP: Necessary calls covered by prediction
        FN: Missed necessary calls

        Returns:
            recall value (0.0 - 1.0)
        """
        if not self.reference:
            return 1.0  # Reference is empty, nothing to recall

        if not predicted:
            return 0.0  # No prediction, recall is 0

        # Create copy of predicted actions for matching
        pred_remaining = predicted.copy()

        tp = 0  # True Positives
        for ref_action in self.reference:
            # Try to find match in predicted
            for i, pred_action in enumerate(pred_remaining):
                if self._match_action(pred_action, ref_action):
                    tp += 1
                    pred_remaining.pop(i)  # Remove matched
                    break

        fn = len(self.reference) - tp  # False Negatives

        return tp / (tp + fn) if (tp + fn) > 0 else 0.0

    def single_tool_use(self, predicted: List[str], tool_name: str) -> int:
        """
        Single-tool use: Check if specific tool appears in trajectory (supports wildcards)

        Args:
            predicted: Predicted trajectory
            tool_name: Target tool name

        Returns:
            1 if tool is used, 0 otherwise
        """
        # Check if any action in predicted trajectory matches target tool
        for pred_action in predicted:
            if self._match_action(pred_action, tool_name):
                return 1
        return 0

    def evaluate_all(
        self, predicted: List[str], target_tools: List[str] = None
    ) -> Dict[str, float]:
        """
        Evaluate all metrics

        Args:
            predicted: Predicted trajectory
            target_tools: List of tools to check usage (for single-tool use)

        Returns:
            Dictionary of all metric evaluation results
        """
        results = {
            "exact_match": self.exact_match(predicted),
            "in_order_match": self.in_order_match(predicted),
            "any_order_match": self.any_order_match(predicted),
            "precision": self.precision(predicted),
            "recall": self.recall(predicted),
        }

        # Single-tool use metric - calculate overall usage rate (average of all tools)
        if target_tools:
            tool_usage_count = sum(
                self.single_tool_use(predicted, tool) for tool in target_tools
            )
            results["single_tool_use"] = (
                tool_usage_count / len(target_tools) if target_tools else 0.0
            )

        return results


class DatasetEvaluator:
    """Dataset-level evaluator"""

    def __init__(self, config_file: str):
        """
        Args:
            config_file: YAML config file path containing reference trajectory definition
        """
        self.config_file = config_file
        self.config = self._load_config()
        self.reference_trajectory = self.config.get("reference_trajectory", [])
        # Dynamic reference trajectory: select based on unified_web_search count
        self.reference_trajectory_3x = self.config.get("reference_trajectory_3x", None)
        self.reference_trajectory_4x = self.config.get("reference_trajectory_4x", None)
        self.use_dynamic_reference = (
            self.reference_trajectory_3x is not None
            and self.reference_trajectory_4x is not None
        )
        self.target_tools = self.config.get("target_tools", [])
        self.models = self.config.get("models", [])
        self.project_name = self.config.get("project_name", "EmailResponder")
        # Trajectory extraction types: default only extract Tool, can also configure as ['Tool', 'AGENT', 'Task Created'] etc.
        self.extract_types = self.config.get("extract_types", ["Tool"])

        # Permutable tool groups configuration (for dynamic tool permutation optimization)
        self.permutable_tool_groups = self.config.get("permutable_tool_groups", {})

    def _load_config(self) -> Dict:
        """Load YAML config file"""
        if not os.path.exists(self.config_file):
            print(f"⚠️  Config file not found: {self.config_file}")
            return {}

        with open(self.config_file, "r", encoding="utf-8") as f:
            return yaml.safe_load(f)

    def _count_unified_web_search_in_analyze_job(self, exec_path_file: str) -> int:
        """
        Count unified_web_search occurrences under analyze_job SPAN

        Args:
            exec_path_file: execution_path.md file path

        Returns:
            unified_web_search call count
        """
        if not os.path.exists(exec_path_file):
            return 0

        with open(exec_path_file, "r", encoding="utf-8") as f:
            content = f.read()

        # Extract Execution Path Tree
        tree_match = re.search(
            r"## Execution Path Tree.*?```\n(.*?)```", content, re.DOTALL
        )
        if not tree_match:
            return 0

        tree_content = tree_match.group(1)
        lines = tree_content.split("\n")

        # Find analyze_job SPAN range
        in_analyze_job = False
        analyze_job_level = -1
        count = 0

        for line in lines:
            # Calculate current line level (by leading tree characters)
            level = len(re.match(r"^([│├└─\s]*)", line).group(1))

            # Clean line content
            clean_line = re.sub(r"^[│├└─\s]+", "", line).strip()
            clean_line = re.sub(r"^❌\s+", "", clean_line)

            # Check if entering analyze_job SPAN
            if "[SPAN] analyze_job" in clean_line:
                in_analyze_job = True
                analyze_job_level = level
                continue

            # If in analyze_job SPAN
            if in_analyze_job:
                # If encountering same or higher level SPAN, left analyze_job
                if "[SPAN]" in clean_line and level <= analyze_job_level:
                    break

                # Count unified_web_search
                if "[Tool] unified_web_search" in clean_line:
                    count += 1

        return count

    def _generate_permuted_trajectories(
        self, base_trajectory: List[str]
    ) -> List[List[str]]:
        """
        Generate all possible tool permutation trajectories based on permutable_tool_groups config

        Args:
            base_trajectory: Base reference trajectory

        Returns:
            List of all possible permutation trajectories (including original)
        """
        if not self.permutable_tool_groups:
            # No permutable tool groups configured, return original trajectory
            return [base_trajectory]

        # Collect all permutable tool groups and their positions in trajectory
        tool_groups_positions = []

        for group_name, tools in self.permutable_tool_groups.items():
            # Find positions of this tool group in trajectory
            positions = []
            tool_indices = {}

            for i, action in enumerate(base_trajectory):
                for tool in tools:
                    if action == tool:
                        positions.append(i)
                        tool_indices[i] = tool
                        break

            # Only permute when all tools are found
            if len(positions) == len(tools):
                # Record positions and tools for this group
                tools_at_positions = [tool_indices[pos] for pos in positions]
                tool_groups_positions.append((positions, tools_at_positions))

        if not tool_groups_positions:
            # No complete permutable tool groups found
            return [base_trajectory]

        # Generate all possible permutation combinations
        all_trajectories = []

        # Generate all permutations for each tool group
        group_permutations = []
        for positions, tools in tool_groups_positions:
            # Generate all permutations for this tool group
            perms = list(permutations(tools))
            group_permutations.append([(positions, perm) for perm in perms])

        # Cartesian product: combine all tool group permutations
        all_group_combinations = list(product(*group_permutations))

        # Generate a new trajectory for each combination
        for combination in all_group_combinations:
            new_trajectory = base_trajectory.copy()

            # Apply all tool permutations in this combination
            for positions, perm in combination:
                for pos, tool in zip(positions, perm):
                    new_trajectory[pos] = tool

            all_trajectories.append(new_trajectory)

        return all_trajectories

    def _find_best_reference_trajectory(
        self, predicted: List[str], candidate_references: List[List[str]]
    ) -> Tuple[List[str], Dict[str, float]]:
        """
        Select the best reference trajectory from multiple candidates

        Args:
            predicted: Predicted trajectory
            candidate_references: Candidate reference trajectory list

        Returns:
            (Best reference trajectory, corresponding match score dict)
        """
        best_reference = candidate_references[0]
        best_score = -1
        best_metrics = {}

        for ref_trajectory in candidate_references:
            evaluator = TrajectoryEvaluator(ref_trajectory)

            # Calculate three key matching metrics
            exact = evaluator.exact_match(predicted)
            in_order = evaluator.in_order_match(predicted)
            any_order = evaluator.any_order_match(predicted)

            # Composite score: exact_match has highest weight, followed by in_order_match
            # Use weighted sum: exact*3 + in_order*2 + any_order*1
            score = exact * 3 + in_order * 2 + any_order * 1

            if score > best_score:
                best_score = score
                best_reference = ref_trajectory
                best_metrics = {
                    "exact_match": exact,
                    "in_order_match": in_order,
                    "any_order_match": any_order,
                }

        return best_reference, best_metrics

    def collect_execution_paths(
        self, model_name: str, base_dir: str
    ) -> List[Tuple[str, List[str]]]:
        """
        Collect and parse all execution_path.md files for specified model

        Args:
            model_name: Model name
            base_dir: RESULTS directory path

        Returns:
            [(session_id, trajectory), ...] list
        """
        model_dir = Path(base_dir) / model_name / self.project_name / "test_results"

        if not model_dir.exists():
            print(f"⚠️  Model directory not found: {model_dir}")
            return []

        results = []

        # Iterate through all session subdirectories
        for session_dir in sorted(model_dir.iterdir()):
            if not session_dir.is_dir():
                continue

            exec_path_file = session_dir / "execution_path.md"
            if not exec_path_file.exists():
                continue

            # Parse trajectory (using configured extraction types)
            parser = TrajectoryParser(
                str(exec_path_file), extract_types=self.extract_types
            )
            trajectory = parser.parse()

            results.append((session_dir.name, trajectory))

        return results

    def evaluate_model(self, model_name: str, base_dir: str) -> Dict[str, float]:
        """
        Evaluate single model's performance on all samples

        Args:
            model_name: Model name
            base_dir: RESULTS directory path

        Returns:
            Average metrics dictionary
        """
        model_dir = Path(base_dir) / model_name / self.project_name / "test_results"

        if not model_dir.exists():
            print(f"⚠️  Model {model_name} has no trajectory data found")
            return {}

        # Accumulate metrics for all samples
        all_metrics = defaultdict(list)
        trajectories = []

        # Iterate through all sessions
        for session_dir in sorted(model_dir.iterdir()):
            if not session_dir.is_dir():
                continue

            exec_path_file = session_dir / "execution_path.md"
            if not exec_path_file.exists():
                continue

            # Parse trajectory
            parser = TrajectoryParser(
                str(exec_path_file), extract_types=self.extract_types
            )
            predicted = parser.parse()

            # Dynamically select reference trajectory
            if self.use_dynamic_reference:
                # Count unified_web_search occurrences in analyze_job
                search_count = self._count_unified_web_search_in_analyze_job(
                    str(exec_path_file)
                )

                # Select reference trajectory based on count
                if search_count <= 3:
                    reference = self.reference_trajectory_3x
                else:
                    reference = self.reference_trajectory_4x
            else:
                # Use default reference trajectory
                reference = self.reference_trajectory

            # Dynamic tool permutation optimization
            if self.permutable_tool_groups:
                # Generate all possible tool permutation trajectories
                candidate_references = self._generate_permuted_trajectories(reference)

                # Select the best from all candidate reference trajectories
                if len(candidate_references) > 1:
                    reference, _ = self._find_best_reference_trajectory(
                        predicted, candidate_references
                    )
                # else: keep reference unchanged

            # Create evaluator and evaluate
            evaluator = TrajectoryEvaluator(reference)
            metrics = evaluator.evaluate_all(predicted, self.target_tools)

            for key, value in metrics.items():
                all_metrics[key].append(value)

            trajectories.append((session_dir.name, predicted))

        # Calculate averages
        avg_metrics = {}
        for key, values in all_metrics.items():
            avg_metrics[key] = sum(values) / len(values) if values else 0.0

        num_samples = len(trajectories)

        if num_samples > 0:
            path_counter = defaultdict(int)
            for session_id, predicted in trajectories:
                path_key = tuple(predicted)
                path_counter[path_key] += 1

            unique_paths = len(path_counter)
            unique_path_ratio = unique_paths / num_samples if num_samples > 0 else 0.0

            probs = [count / num_samples for count in path_counter.values()]
            H = -sum(p * math.log(p) for p in probs if p > 0)
            if len(probs) > 1:
                path_entropy = H / math.log(len(probs))
            else:
                path_entropy = 0.0
        else:
            unique_path_ratio = 0.0
            path_entropy = 0.0

        avg_metrics["unique_path_ratio"] = unique_path_ratio
        avg_metrics["path_entropy"] = path_entropy

        # Add sample count
        avg_metrics["num_samples"] = num_samples

        return avg_metrics

    def evaluate_all_models(self, base_dir: str = None) -> pd.DataFrame:
        """
        Evaluate all models and generate summary table

        Args:
            base_dir: RESULTS directory path, defaults to two levels up from script directory

        Returns:
            DataFrame containing all model evaluation results
        """
        if base_dir is None:
            # Default path: two levels up from script directory
            base_dir = Path(__file__).parent.parent.parent

        results = []

        for model_name in self.models:
            print(f"\n📊 Evaluating model: {model_name}")
            metrics = self.evaluate_model(model_name, str(base_dir))

            if metrics:
                metrics["model"] = model_name
                results.append(metrics)
                print(f"  ✅ Completed, sample count: {metrics['num_samples']}")
            else:
                print(f"  ❌ Skipped (no data)")

        if not results:
            print("\n❌ No model data available")
            return pd.DataFrame()

        # Create DataFrame
        df = pd.DataFrame(results)

        # Adjust column order: model name first
        cols = [
            "model",
            "num_samples",
            "exact_match",
            "in_order_match",
            "any_order_match",
            "precision",
            "recall",
            "single_tool_use",
            "unique_path_ratio",
            "path_entropy",
        ]

        # Keep only existing columns
        cols = [col for col in cols if col in df.columns]

        df = df[cols]

        return df


def main():
    """Main function"""
    import argparse

    parser = argparse.ArgumentParser(
        description="Evaluate trajectory metrics for EmailResponder project"
    )
    parser.add_argument(
        "--config",
        type=str,
        default="reference_trajectory.yaml",
        help="Reference trajectory config file path (YAML format)",
    )
    parser.add_argument(
        "--base-dir",
        type=str,
        default=None,
        help="RESULTS directory path (defaults to two levels up from script directory)",
    )
    parser.add_argument(
        "--output",
        type=str,
        default="evaluation_results.csv",
        help="Output CSV file path",
    )

    args = parser.parse_args()

    # If config not specified, use config file in script directory
    config_path = args.config
    if not os.path.isabs(config_path):
        config_path = os.path.join(os.path.dirname(__file__), config_path)

    print("=" * 80)
    print("Trajectory Evaluation Tool - EmailResponder")
    print("=" * 80)
    print(f"\n📁 Config file: {config_path}")

    # Create evaluator
    evaluator = DatasetEvaluator(config_path)

    print(f"📋 Project name: {evaluator.project_name}")

    # Display reference trajectory info
    if evaluator.use_dynamic_reference:
        print(f"🎯 Reference trajectory: Dynamic selection")
        print(
            f"   - 3x version (unified_web_search<=3): {len(evaluator.reference_trajectory_3x)} steps"
        )
        print(
            f"   - 4x version (unified_web_search>=4): {len(evaluator.reference_trajectory_4x)} steps"
        )
    else:
        print(f"🎯 Reference trajectory: {evaluator.reference_trajectory}")

    print(f"🔧 Target tools: {evaluator.target_tools}")
    print(f"🤖 Evaluation models: {evaluator.models}")

    # Display tool permutation info
    if evaluator.permutable_tool_groups:
        print(f"\n🔀 Dynamic tool permutation optimization: Enabled")
        total_permutations = 1
        for group_name, tools in evaluator.permutable_tool_groups.items():
            num_perms = math.factorial(len(tools))
            total_permutations *= num_perms
            print(f"   - {group_name}: {len(tools)} tools, {num_perms} permutations")
        print(f"   - Total permutation combinations: {total_permutations}")
        print(
            f"   - Evaluation strategy: After dynamic 3x/4x selection, choose permutation with highest tool order match"
        )
    else:
        print(f"\n🔀 Dynamic tool permutation optimization: Disabled")

    # Evaluate all models
    df = evaluator.evaluate_all_models(args.base_dir)

    if df.empty:
        print("\n❌ Evaluation failed: No data")
        return

    print("\n" + "=" * 80)
    print("📊 Evaluation Results Summary")
    print("=" * 80)

    # Format display
    pd.set_option("display.max_columns", None)
    pd.set_option("display.width", None)
    pd.set_option("display.float_format", lambda x: f"{x:.4f}")

    print("\n" + df.to_string(index=False))

    # Save results
    output_dir = os.path.dirname(args.output) or "."
    os.makedirs(output_dir, exist_ok=True)

    csv_file = args.output
    df.to_csv(csv_file, index=False)
    print(f"\n✅ CSV file saved: {csv_file}")

    print("\n" + "=" * 80)
    print("✅ Evaluation completed")
    print("=" * 80)


if __name__ == "__main__":
    main()