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restructure + add files

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#!/usr/bin/env python3
"""
Trajectory evaluation script - LandingPageGenerator-A2A 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 an execution_path.md file and extract the execution trajectory."""

    def __init__(self, md_file_path: str, extract_types: List[str] = None):
        """
        Args:
            md_file_path: Path to execution_path.md
            extract_types: Node types to extract. Defaults to ['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 the file and return the extracted 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 the "Execution Path Tree" section (inside a 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 drawing characters and keep only the node text
            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 details (keep node type/name, drop the [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 info from a line.
        Note: the input line should already have had the error marker (❌),
        retry markers, and ERROR details removed.
        Returns:
            {'type': str, 'action': str} or None
        """
        # SPAN node: [SPAN] span_name [stats]
        # Use a relaxed match to handle possible leftover 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 [stats]
        # For Crew_xxx.kickoff, normalize to wildcard Crew***.kickoff
        chain_match = re.match(r"\[Chain\]\s+([^\[]+?)(?:\s+\[.*?\])*\s*$", line)
        if chain_match:
            chain_name = chain_match.group(1).strip()
            # Wildcard normalization: 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*$", line)
        if agent_match:
            agent_name = agent_match.group(1).strip()
            # Remove the ._execute_core suffix
            agent_name = re.sub(r"\._execute_core$", "", agent_name)
            return {"type": "AGENT", "action": f"Agent: {agent_name}"}

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

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

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

        return None


class TrajectoryEvaluator:
    """Trajectory evaluator implementing 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 with wildcard support.
        Args:
            predicted_action: Executed action
            reference_action: Reference action (may include 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: the predicted trajectory must be identical to the reference (wildcards supported).
        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: the reference trajectory must be a subsequence of the predicted trajectory
        (wildcards supported). Extra actions are allowed, but core steps must appear in order.
        Returns:
            1 if in-order match, 0 otherwise
        """
        if not self.reference:
            return 1  # An empty reference trajectory 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 whether 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: the predicted trajectory must contain all required actions (wildcards supported).
        Order does not matter; extra actions are allowed.
        Returns:
            1 if any-order match, 0 otherwise
        """
        if not self.reference:
            return 1

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

        # For each reference action, try to find a match in predicted actions
        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 action
                    matched = True
                    break

            if not matched:
                return 0  # A reference action was not matched

        return 1

    def precision(self, predicted: List[str]) -> float:
        """
        Precision: how many predicted actions are considered correct by the reference (wildcards supported).
        Precision = TP / (TP + FP)
        TP: Correct actions in prediction
        FP: Incorrect / extra actions 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 with predictions => all are incorrect

        # Create a 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 remaining reference actions
            for i, ref_action in enumerate(ref_remaining):
                if self._match_action(pred_action, ref_action):
                    tp += 1
                    ref_remaining.pop(i)  # Remove matched reference action
                    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 much of the reference is covered by the prediction (wildcards supported).
        Recall = TP / (TP + FN)
        TP: Required actions covered by prediction
        FN: Required actions missed
        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 prediction => recall is 0

        # Create a 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 remaining predicted actions
            for i, pred_action in enumerate(pred_remaining):
                if self._match_action(pred_action, ref_action):
                    tp += 1
                    pred_remaining.pop(i)  # Remove matched predicted action
                    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 whether a specific tool appears in the trajectory (wildcards supported).
        Args:
            predicted: Predicted trajectory
            tool_name: Target tool name
        Returns:
            1 if tool is used, 0 otherwise
        """
        # Check whether any action 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: Tools to check usage for (for the single-tool use metric)
        Returns:
            A dict of 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: overall usage rate (average across all target 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: Path to the 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: defaults to Tool only; can be configured to include others.
        self.extract_types = self.config.get("extract_types", ["Tool"])

    def _load_config(self) -> Dict:
        """Load the 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 collect_execution_paths(
        self, model_name: str, base_dir: str
    ) -> List[Tuple[str, List[str]]]:
        """
        Collect and parse all execution_path.md files for the given model.
        Args:
            model_name: Model name
            base_dir: RESULTS directory path
        Returns:
            A list of (session_id, trajectory)
        """
        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 over 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 on all samples.
        Args:
            model_name: Model name
            base_dir: RESULTS directory path
        Returns:
            A dict of average metrics
        """
        trajectories = self.collect_execution_paths(model_name, base_dir)

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

        evaluator = TrajectoryEvaluator(self.reference_trajectory)

        # Accumulate metrics across 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)

        # Compute 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)

        # Compute unique path ratio and path entropy
        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

        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 a summary table.
        Args:
            base_dir: RESULTS directory path (defaults to two levels above this script)
        Returns:
            A DataFrame with evaluation results for all models
        """
        if base_dir is None:
            # Default path: two levels above this script
            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"  Done. Samples: {metrics['num_samples']}")
            else:
                print("  Skipped (no data)")

        if not results:
            print("\nNo model data")
            return pd.DataFrame()

        # Create DataFrame
        df = pd.DataFrame(results)

        # Column order: model 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():
    """Entry point."""
    import argparse

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

    args = parser.parse_args()

    # If config is not absolute, resolve relative to this script
    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")
    print("=" * 80)
    print(f"\nConfig file: {config_path}")

    # Create evaluator
    evaluator = DatasetEvaluator(config_path)

    print(f"Project: {evaluator.project_name}")
    print(f"Reference trajectory: {evaluator.reference_trajectory}")
    print(f"Target tools: {evaluator.target_tools}")
    print(f"Models: {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("Summary")
    print("=" * 80)

    # Display formatting
    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 saved: {csv_file}")

    print("\n" + "=" * 80)
    print("Done")
    print("=" * 80)


if __name__ == "__main__":
    main()

	#!/usr/bin/env python3
	"""
	Trajectory evaluation script - LandingPageGenerator-A2A 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 an execution_path.md file and extract the execution trajectory."""

	def __init__(self, md_file_path: str, extract_types: List[str] = None):
	"""
	Args:
	md_file_path: Path to execution_path.md
	extract_types: Node types to extract. Defaults to ['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 the file and return the extracted 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 the "Execution Path Tree" section (inside a 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 drawing characters and keep only the node text
	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 details (keep node type/name, drop the [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 info from a line.

	Note: the input line should already have had the error marker (❌),
	retry markers, and ERROR details removed.

	Returns:
	{'type': str, 'action': str} or None
	"""
	# SPAN node: [SPAN] span_name [stats]
	# Use a relaxed match to handle possible leftover 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 [stats]
	# For Crew_xxx.kickoff, normalize to wildcard Crew***.kickoff
	chain_match = re.match(r"\[Chain\]\s+([^\[]+?)(?:\s+\[.?\])\s*$", line)
	if chain_match:
	chain_name = chain_match.group(1).strip()
	# Wildcard normalization: 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*$", line)
	if agent_match:
	agent_name = agent_match.group(1).strip()
	# Remove the ._execute_core suffix
	agent_name = re.sub(r"\._execute_core$", "", agent_name)
	return {"type": "AGENT", "action": f"Agent: {agent_name}"}

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

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

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

	return None


	class TrajectoryEvaluator:
	"""Trajectory evaluator implementing 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 with wildcard support.

	Args:
	predicted_action: Executed action
	reference_action: Reference action (may include 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: the predicted trajectory must be identical to the reference (wildcards supported).

	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: the reference trajectory must be a subsequence of the predicted trajectory
	(wildcards supported). Extra actions are allowed, but core steps must appear in order.

	Returns:
	1 if in-order match, 0 otherwise
	"""
	if not self.reference:
	return 1 # An empty reference trajectory 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 whether 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: the predicted trajectory must contain all required actions (wildcards supported).
	Order does not matter; extra actions are allowed.

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

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

	# For each reference action, try to find a match in predicted actions
	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 action
	matched = True
	break

	if not matched:
	return 0 # A reference action was not matched

	return 1

	def precision(self, predicted: List[str]) -> float:
	"""
	Precision: how many predicted actions are considered correct by the reference (wildcards supported).

	Precision = TP / (TP + FP)
	TP: Correct actions in prediction
	FP: Incorrect / extra actions 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 with predictions => all are incorrect

	# Create a 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 remaining reference actions
	for i, ref_action in enumerate(ref_remaining):
	if self._match_action(pred_action, ref_action):
	tp += 1
	ref_remaining.pop(i) # Remove matched reference action
	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 much of the reference is covered by the prediction (wildcards supported).

	Recall = TP / (TP + FN)
	TP: Required actions covered by prediction
	FN: Required actions missed

	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 prediction => recall is 0

	# Create a 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 remaining predicted actions
	for i, pred_action in enumerate(pred_remaining):
	if self._match_action(pred_action, ref_action):
	tp += 1
	pred_remaining.pop(i) # Remove matched predicted action
	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 whether a specific tool appears in the trajectory (wildcards supported).

	Args:
	predicted: Predicted trajectory
	tool_name: Target tool name

	Returns:
	1 if tool is used, 0 otherwise
	"""
	# Check whether any action 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: Tools to check usage for (for the single-tool use metric)

	Returns:
	A dict of 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: overall usage rate (average across all target 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: Path to the 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: defaults to Tool only; can be configured to include others.
	self.extract_types = self.config.get("extract_types", ["Tool"])

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

	Args:
	model_name: Model name
	base_dir: RESULTS directory path

	Returns:
	A list of (session_id, trajectory)
	"""
	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 over 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 on all samples.

	Args:
	model_name: Model name
	base_dir: RESULTS directory path

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

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

	evaluator = TrajectoryEvaluator(self.reference_trajectory)

	# Accumulate metrics across 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)

	# Compute 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)

	# Compute unique path ratio and path entropy
	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

	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 a summary table.

	Args:
	base_dir: RESULTS directory path (defaults to two levels above this script)

	Returns:
	A DataFrame with evaluation results for all models
	"""
	if base_dir is None:
	# Default path: two levels above this script
	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" Done. Samples: {metrics['num_samples']}")
	else:
	print(" Skipped (no data)")

	if not results:
	print("\nNo model data")
	return pd.DataFrame()

	# Create DataFrame
	df = pd.DataFrame(results)

	# Column order: model 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():
	"""Entry point."""
	import argparse

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

	args = parser.parse_args()

	# If config is not absolute, resolve relative to this script
	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")
	print("=" * 80)
	print(f"\nConfig file: {config_path}")

	# Create evaluator
	evaluator = DatasetEvaluator(config_path)

	print(f"Project: {evaluator.project_name}")
	print(f"Reference trajectory: {evaluator.reference_trajectory}")
	print(f"Target tools: {evaluator.target_tools}")
	print(f"Models: {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("Summary")
	print("=" * 80)

	# Display formatting
	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 saved: {csv_file}")

	print("\n" + "=" * 80)
	print("Done")
	print("=" * 80)


	if __name__ == "__main__":
	main()