data/processed/RQ1/GameBuilder/evaluate_trajectory.py

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

Evaluate 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 different 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 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, handle possible residual special characters
        span_match = re.match(r"\[SPAN\]\s+([^\[]+?)(?:\s+\[.*?\])*\s*$", line)
        if span_match:
            span_name = span_match.group(1).strip()
            return {"type": "SPAN", "action": f"SPAN: {span_name}"}

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

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

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

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

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

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

        return None


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

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

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

        Args:
            predicted_action: Actual 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 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 if all reference steps were found in order
        return 1 if ref_idx == len(self.reference) else 0

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

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

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

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

            if not matched:
                return 0  # Reference action not found

        return 1

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

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

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

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

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

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

        fp = len(predicted) - tp  # False Positives

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

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

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

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

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

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

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

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

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

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

        If [Tool] Get Gmail Thread appears in actual trajectory, filter that tool,
        and also filter 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 (note: capitalized + space format)
            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 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 detect usage (for single-tool use)

        Returns:
            Dictionary of all metric evaluation results
        """
        # Unified preprocessing: first filter out Get Gmail Thread tool and its preceding LLM,
        # all metrics are calculated based on 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: YAML config file path, contains 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", "GameBuilder")
        # Trajectory extraction types: default only extract Tool, can also be configured as ['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"⚠️  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"⚠️  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"⚠️  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 average
        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"\n📊 Evaluating model: {model_name}")
            metrics = self.evaluate_model(model_name, str(base_dir))

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

        if not results:
            print("\n❌ No data for any model")
            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 GameBuilder 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"],
        default="csv",
        help="Output format: csv only",
    )

    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 - GameBuilder")
    print("=" * 80)
    print(f"\n📁 Config 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"🤖 Evaluating models: {evaluator.models}")

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

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

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

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

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

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

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

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


if __name__ == "__main__":
    main()