data/processed/RQ1/GameBuilder-MCP/evaluate_trajectory.py

#!/usr/bin/env python3
"""
Trajectory evaluation script.

This script 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 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, default ['Tool'].
                Optional: '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 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 the 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 while keeping the node content
            clean_line = re.sub(r"^[│├└─\s]+", "", line).strip()
            if not clean_line:
                continue

            # Remove the 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/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)

            # Filter out the intermediate crew_execution SPAN layer (normalize 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 single line.

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

        Returns:
            {'type': str, 'action': str} or None
        """
        # SPAN node: [SPAN] span_name [stats]
        # Use a more permissive match to handle potential leftover 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 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 [time]
        tool_match = re.match(
            r"\[Tool\]\s+([^\[\]]+?)(?:\s+\[[\d.]+(?:ms|s)\])?(?:\s*@@@)?\s*$", line
        )
        if tool_match:
            tool_name = tool_match.group(1).strip()
            # Remove the ._use suffix
            tool_name = re.sub(r"\._use$", "", tool_name)
            return {"type": "Tool", "action": f"Tool: {tool_name}"}

        # LLM node: [LLM] model_name (tokens) [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 implementing 6 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: Predicted 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: predicted trajectory must equal the reference (with 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 must be a subsequence of the predicted trajectory
        (with wildcards). 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  # 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 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: predicted must contain all required actions (with wildcards).
        Order is ignored; extra actions are allowed.

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

        # Work on a copy for matching
        pred_remaining = predicted.copy()

        # For each reference action, 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 the matched one
                    matched = True
                    break

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

        return 1

    def precision(self, predicted: List[str]) -> float:
        """
        Precision: fraction of predicted actions considered correct by the reference
        (with wildcards).

        Precision = TP / (TP + FP)
        TP: Correctly predicted actions
        FP: Incorrectly predicted actions

        Returns:
            precision value (0.0 - 1.0)
        """
        if not predicted:
            return 1.0  # No predictions implies no false positives

        if not self.reference:
            return 0.0  # Empty reference but non-empty prediction => all wrong

        # Work on a copy for matching
        ref_remaining = self.reference.copy()

        tp = 0  # True Positives
        for pred_action in predicted:
            # Try to match in the 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
                    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: fraction of reference actions covered by the prediction (with wildcards).

        Recall = TP / (TP + FN)
        TP: Correctly predicted actions
        FN: Missed actions

        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

        # Work on a copy 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
                    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
        (with wildcards).

        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 single-tool use)

        Returns:
            A dict of metrics.
        """
        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 (mean across 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 defining the reference trajectory
        """
        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")
        # Extract types: default is only Tool; can be configured as ['Tool', 'AGENT', ...]
        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:
            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 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 extract 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 across all samples.

        Args:
            model_name: Model name
            base_dir: RESULTS directory path

        Returns:
            A dict of averaged 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)

        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 produce a summary table.

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

        Returns:
            DataFrame with evaluation results
        """
        if base_dir is None:
            # Default: 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()

        # Build DataFrame
        df = pd.DataFrame(results)

        # Reorder columns: 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 entry point."""
    import argparse

    parser = argparse.ArgumentParser(
        description="Evaluate trajectory metrics for the configured project"
    )
    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",
    )
    parser.add_argument(
        "--format",
        type=str,
        choices=["csv"],
        default="csv",
        help="Output format (csv only)",
    )

    args = parser.parse_args()

    # If config is relative, resolve it 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("Evaluation Summary")
    print("=" * 80)

    # 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("Evaluation complete")
    print("=" * 80)


if __name__ == "__main__":
    main()