data/processed/RQ1/EmailResponder-MCP/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


class TrajectoryParser:
    """Parse execution_path.md files and extract complete execution trajectories."""

    def __init__(self, md_file_path: str, extract_types: List[str] = None):
        """
        Args:
            md_file_path: Path to execution_path.md file
            extract_types: List of node types to extract, default ['Tool']
                          Options: '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)

            # Filter out crew_execution intermediate SPAN (unify hierarchy across models)
            if (
                node_info
                and node_info["type"] == "SPAN"
                and "crew_execution" in node_info.get("action", "")
            ):
                continue  # Skip this intermediate SPAN

            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 marker, RETRY marker and ERROR info removed.

        Returns:
            {'type': str, 'action': str} or None
        """
        # SPAN node: [SPAN] span_name [stats]
        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 [stats]
        # 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 [stats]
        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 identical to 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: Predicted trajectory must contain all required actions (supports wildcards).
        Order doesn't matter, extra actions allowed.

        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 item
                    matched = True
                    break

            if not matched:
                return 0  # Reference action not found

        return 1

    def precision(self, predicted: List[str]) -> float:
        """
        Precision: How many predicted actions are correct according to 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 predictions, no false positives

        if not self.reference:
            return 0.0  # Empty reference but has predictions, all are 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 a 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 item
                    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 reference actions are covered by predicted (supports wildcards).

        Recall = TP / (TP + FN)
        TP: Required calls covered by prediction
        FN: Missing required calls

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

        if not predicted:
            return 0.0  # No predictions, 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 a 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 item
                    break

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

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

    def _filter_get_gmail_thread(self, predicted: List[str]) -> List[str]:
        """
        Filter get_gmail_thread tool and its preceding LLM call.

        If get_gmail_thread appears in the actual trajectory, filter it out
        along with the preceding LLM call.

        Args:
            predicted: Original predicted trajectory

        Returns:
            Filtered trajectory
        """
        if not predicted:
            return predicted

        indices_to_remove: Set[int] = set()

        for i, action in enumerate(predicted):
            # Detect Tool: get_gmail_thread
            if action == "Tool: get_gmail_thread":
                indices_to_remove.add(i)
                # Also filter out the preceding LLM (if exists)
                if i > 0 and predicted[i - 1].startswith("LLM: "):
                    indices_to_remove.add(i - 1)

        if not indices_to_remove:
            return predicted

        return [
            action
            for idx, action in enumerate(predicted)
            if idx not in indices_to_remove
        ]

    def single_tool_use(self, predicted: List[str], tool_name: str) -> int:
        """
        Single-tool use: Check if a specific tool appears in the 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 matches the 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 (for single-tool use metric)

        Returns:
            Dictionary of all evaluation results
        """
        # Unified preprocessing: filter out get_gmail_thread and its preceding LLM
        # All metrics are calculated based on the filtered trajectory
        filtered_predicted = self._filter_get_gmail_thread(predicted)

        results = {
            "exact_match": self.exact_match(filtered_predicted),
            "in_order_match": self.in_order_match(filtered_predicted),
            "any_order_match": self.any_order_match(filtered_predicted),
            "precision": self.precision(filtered_predicted),
            "recall": self.recall(filtered_predicted),
        }

        # Single-tool use metric - calculate overall usage rate (average of all tools)
        if target_tools:
            tool_usage_count = sum(
                self.single_tool_use(filtered_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: Path to YAML config file containing reference trajectory definition
        """
        self.config_file = config_file
        self.config = self._load_config()
        self.reference_trajectory = self.config.get("reference_trajectory", [])
        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 Tool, can be configured to ['Tool', 'AGENT', 'Task Created'] etc.
        self.extract_types = self.config.get("extract_types", ["Tool"])

    def _load_config(self) -> Dict:
        """Load YAML config file."""
        if not os.path.exists(self.config_file):
            print(f"[WARN] Config file does not exist: {self.config_file}")
            return {}

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

    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 a given 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"[WARN] Model directory does not exist: {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 a single model's performance across all samples.

        Args:
            model_name: Model name
            base_dir: RESULTS directory path

        Returns:
            Dictionary of average metrics
        """
        trajectories = self.collect_execution_paths(model_name, base_dir)

        if not trajectories:
            print(f"[WARN] No trajectory data found for model {model_name}")
            return {}

        evaluator = TrajectoryEvaluator(self.reference_trajectory)

        # Accumulate metrics across all samples
        all_metrics = defaultdict(list)

        for session_id, predicted in trajectories:
            metrics = evaluator.evaluate_all(predicted, self.target_tools)

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

        # 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"\nEvaluating model: {model_name}")
            metrics = self.evaluate_model(model_name, str(base_dir))

            if metrics:
                metrics["model"] = model_name
                results.append(metrics)
                print(f"  [OK] Completed, samples: {metrics['num_samples']}")
            else:
                print(f"  [SKIP] No data")

        if not results:
            print("\n[ERROR] 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="Path to reference trajectory config file (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"\nConfig file: {config_path}")

    # Create evaluator
    evaluator = DatasetEvaluator(config_path)

    print(f"Project name: {evaluator.project_name}")
    print(f"Reference trajectory: {evaluator.reference_trajectory}")
    print(f"Target tools: {evaluator.target_tools}")
    print(f"Models to evaluate: {evaluator.models}")

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

    if df.empty:
        print("\n[ERROR] 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[OK] CSV file saved: {csv_file}")

    print("\n" + "=" * 80)
    print("[OK] Evaluation completed")
    print("=" * 80)


if __name__ == "__main__":
    main()