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89 lines
No EOL
3.7 KiB
Python
89 lines
No EOL
3.7 KiB
Python
import os
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import json
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from collections import defaultdict
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from prettytable import PrettyTable
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import re
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def extract_success_scores(root_dir):
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task_scores = {} # Stores task-wise scores
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material_groups = defaultdict(list)
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room_groups = defaultdict(list)
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# Regex pattern to extract material and room numbers
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pattern = re.compile(r"materials_(\d+)_rooms_(\d+)")
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# Iterate through each task folder
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for task_folder in os.listdir(root_dir):
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task_path = os.path.join(root_dir, task_folder)
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if os.path.isdir(task_path):
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logs_found = False # Flag to track if logs exist
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# Check for JSON files
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for file_name in os.listdir(task_path):
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if file_name.endswith(".json"):
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logs_found = True # JSON file exists
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file_path = os.path.join(task_path, file_name)
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# Read JSON file
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try:
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with open(file_path, 'r') as file:
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data = json.load(file)
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# Extract success score from the last system message
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for turn in reversed(data.get("turns", [])):
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if turn["role"] == "system" and "Task ended with score" in turn["content"]:
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score = float(turn["content"].split(":")[-1].strip())
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task_scores[task_folder] = score # Store per-task score
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break # Stop searching if found
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# Stop checking other files in the folder if score is found
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if task_folder in task_scores:
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break
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except Exception as e:
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print(f"Error reading {file_path}: {e}")
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# If no logs were found, print a message
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if not logs_found:
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print(f"No log files found in {task_folder}")
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# Group scores by material and room
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for task, score in task_scores.items():
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match = pattern.search(task)
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if match:
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material = int(match.group(1)) # Extract material number
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room = int(match.group(2)) # Extract room number
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material_groups[material].append(score)
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room_groups[room].append(score)
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else:
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print(f"Warning: Task folder '{task}' does not match expected format.")
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# Calculate average scores
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def calculate_average(group):
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return {key: sum(values) / len(values) for key, values in group.items()}
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avg_material_scores = calculate_average(material_groups)
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avg_room_scores = calculate_average(room_groups)
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# Display results using PrettyTable
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def display_table(title, data):
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table = PrettyTable(["Category", "Average Score"])
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for key, value in sorted(data.items()):
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table.add_row([key, round(value, 2)])
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print(f"\n{title}")
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print(table)
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def display_task_scores():
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table = PrettyTable(["Task", "Success Score"])
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for task, score in sorted(task_scores.items()):
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table.add_row([task, round(score, 2)])
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print("\nTask-wise Success Scores")
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print(table)
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# Print all tables
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display_task_scores()
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display_table("Average Success Score by Material (Grouped by Number)", avg_material_scores)
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display_table("Average Success Score by Room (Grouped by Number)", avg_room_scores)
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# Example usage (replace 'root_directory' with actual path)
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root_directory = "experiments/exp_03-22_19-29"
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extract_success_scores(root_directory) |