data/processed/RQ1/MarkdownValidator/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 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 information (keep node type and name, remove [ERROR:...] part)
            clean_line = re.sub(r"\s*\[ERROR:[^\]]*\]", "", clean_line)

            # Extract node information
            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 markers ❌, RETRY markers, and ERROR info removed

        Returns:
            {'type': str, 'action': str} or None
        """
        # SPAN node: [SPAN] span_name [statistics]
        # More lenient matching to handle possible remaining 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: Predicted trajectory just needs to contain 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 of the 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 predictions, no wrong predictions

        if not self.reference:
            return 0.0  # Reference is empty but has predictions, 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 of the 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 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 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", [])
        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 ['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"Warning: 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 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"Warning: 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
        """
        trajectories = self.collect_execution_paths(model_name, base_dir)

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

        evaluator = TrajectoryEvaluator(self.reference_trajectory)

        # Accumulate metrics for 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 location

        Returns:
            DataFrame containing evaluation results for all models
        """
        if base_dir is None:
            # Default path: two levels up from script location
            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"  Completed, samples: {metrics['num_samples']}")
            else:
                print(f"  Skipped (no data)")

        if not results:
            print("\nNo model data found")
            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",
        ]

        # Only keep 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 location)",
    )
    parser.add_argument(
        "--output",
        type=str,
        default="evaluation_results.csv",
        help="Output CSV file path",
    )
    parser.add_argument(
        "--format",
        type=str,
        choices=["csv", "both"],
        default="csv",
        help="Output format: csv or both",
    )

    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("\nEvaluation 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"\nCSV file saved: {csv_file}")

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


if __name__ == "__main__":
    main()