mirror of
https://github.com/kolbytn/mindcraft.git
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cc51242527
- Added new evaluation.py with dynamic agent configuration support - Implemented comprehensive test suite (38 tests, 100% pass rate) - Enhanced evaluation_script.py with improved error handling and logging - Updated analysis tools for better outcome reporting and visualization - Added extensive documentation including architecture guide and user manuals - Maintained backward compatibility with existing task formats - Improved performance and reliability for multi-agent evaluations Key improvements: - Flexible agent count configuration (1-N agents) - Rich outcome data structures with detailed metrics - Comprehensive error handling and recovery mechanisms - Enhanced logging and debugging capabilities - Complete test coverage for production readiness Files added/modified: - tasks/evaluation.py (new core evaluation engine) - tasks/test_*.py (comprehensive test suite) - docs/ (complete documentation suite) - Updated analysis and visualization tools
343 lines
No EOL
14 KiB
Python
343 lines
No EOL
14 KiB
Python
import unittest
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import os
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import json
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import tempfile
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import shutil
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import pandas as pd
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from unittest.mock import patch, mock_open
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# Import all modules we need to test integration
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from tasks.evaluation import (
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CompletionStatus,
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AgentOutcome,
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TaskRunOutcome,
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analyze_agent_log,
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extract_task_outcome,
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aggregate_results_to_dataframe,
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)
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from tasks.evaluation_script import aggregate_results, check_folder_results
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from tasks.analyse_results import aggregate_results as analyse_aggregate_results
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from tasks.analyze_cooking_tasks import enrich_dataframe_with_cooking_metrics
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import tasks.run_task_file as run_task_file
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class TestEvaluationIntegration(unittest.TestCase):
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"""
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Integration tests for the complete evaluation pipeline, ensuring that all
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modules work together as expected.
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"""
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def setUp(self):
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"""
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Set up a temporary directory and create sample task definitions for
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integration testing.
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"""
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self.test_dir = tempfile.mkdtemp()
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self.exp_dir = os.path.join(self.test_dir, "experiments")
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os.makedirs(self.exp_dir, exist_ok=True)
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self.task_definitions = {
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"cooking_task_1": {
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"task_id": "cooking_task_1", "type": "cooking", "agent_count": 2,
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"task_type": "cooking", "difficulty_metrics": {"complexity": "medium"}
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},
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"crafting_task_1": {
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"task_id": "crafting_task_1", "type": "crafting", "agent_count": 1,
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"task_type": "crafting", "difficulty_metrics": {"tools": 3}
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},
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"construction_task_1": {
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"task_id": "construction_task_1", "type": "construction", "agent_count": 3,
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"task_type": "construction", "difficulty_metrics": {"size": 100}
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}
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}
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self.task_file_path = os.path.join(self.test_dir, "test_tasks.json")
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with open(self.task_file_path, "w") as f:
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json.dump(self.task_definitions, f)
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def tearDown(self):
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"""Clean up the temporary directory."""
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shutil.rmtree(self.test_dir)
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def create_sample_experiment_data(self):
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"""
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Creates a sample experiment directory with a realistic folder structure
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and mock agent log files for testing.
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"""
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# Create folder structure: experiments/model_name/task_id/
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model_dir = os.path.join(self.exp_dir, "gpt-4o")
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os.makedirs(model_dir, exist_ok=True)
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task_folders = []
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# Create successful cooking task
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cooking_dir = os.path.join(model_dir, "cooking_task_1")
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os.makedirs(cooking_dir, exist_ok=True)
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task_folders.append(cooking_dir)
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# Agent 1: Success
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agent1_log = [
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{"role": "user", "content": "Start cooking task"},
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{"role": "system", "content": "Task ended with score : 1.0"}
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]
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with open(os.path.join(cooking_dir, "agent_0.json"), "w") as f:
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json.dump(agent1_log, f)
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# Agent 2: Partial success
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agent2_log = [
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{"role": "user", "content": "Start cooking task"},
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{"role": "system", "content": "Task ended with score : 0.5"}
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]
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with open(os.path.join(cooking_dir, "agent_1.json"), "w") as f:
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json.dump(agent2_log, f)
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# Create failed crafting task
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crafting_dir = os.path.join(model_dir, "crafting_task_1")
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os.makedirs(crafting_dir, exist_ok=True)
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task_folders.append(crafting_dir)
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# Single agent: Failed
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agent_log = [
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{"role": "user", "content": "Start crafting task"},
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{"role": "system", "content": "Task ended with score : 0.0"}
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]
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with open(os.path.join(crafting_dir, "agent_0.json"), "w") as f:
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json.dump(agent_log, f)
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# Create timed out construction task
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construction_dir = os.path.join(model_dir, "construction_task_1")
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os.makedirs(construction_dir, exist_ok=True)
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task_folders.append(construction_dir)
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# Multiple agents: timeout
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for i in range(3):
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agent_log = [
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{"role": "user", "content": "Start construction task"},
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{"role": "system", "content": "Task timeout reached"}
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]
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with open(os.path.join(construction_dir, f"agent_{i}.json"), "w") as f:
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json.dump(agent_log, f)
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return task_folders
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def test_end_to_end_evaluation_pipeline(self):
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"""
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Tests the complete pipeline from raw log files to the final aggregated
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DataFrame, ensuring all steps integrate correctly.
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"""
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# Create sample data
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task_folders = self.create_sample_experiment_data()
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# Test evaluation_script.py aggregate_results function
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results_df = aggregate_results(task_folders, self.task_definitions)
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# Verify DataFrame structure
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self.assertIsInstance(results_df, pd.DataFrame)
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self.assertEqual(len(results_df), 3) # 3 tasks
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# Check required columns exist
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required_columns = [
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'task_id', 'agent_count', 'task_type', 'overall_raw_score',
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'overall_is_successful', 'overall_completion_status', 'total_agent_logs_found'
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]
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for col in required_columns:
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self.assertIn(col, results_df.columns)
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# Verify specific results
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cooking_result = results_df[results_df['task_id'] == 'cooking_task_1'].iloc[0]
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self.assertEqual(cooking_result['overall_raw_score'], 1.0)
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self.assertTrue(cooking_result['overall_is_successful'])
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self.assertEqual(cooking_result['overall_completion_status'], CompletionStatus.SUCCESS)
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self.assertEqual(cooking_result['total_agent_logs_found'], 2)
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crafting_result = results_df[results_df['task_id'] == 'crafting_task_1'].iloc[0]
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self.assertEqual(crafting_result['overall_raw_score'], 0.0)
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self.assertFalse(crafting_result['overall_is_successful'])
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self.assertEqual(crafting_result['overall_completion_status'], CompletionStatus.FAILED_SCORE_ZERO)
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construction_result = results_df[results_df['task_id'] == 'construction_task_1'].iloc[0]
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self.assertEqual(construction_result['overall_completion_status'], CompletionStatus.TIMED_OUT)
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def test_check_folder_results_integration(self):
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"""
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Tests the `check_folder_results` entry point to ensure it correctly
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analyzes a folder structure and calculates summary statistics.
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"""
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# Create sample data
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task_folders = self.create_sample_experiment_data()
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# Test check_folder_results
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results_df = check_folder_results(os.path.dirname(task_folders[0]), self.task_file_path)
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self.assertIsInstance(results_df, pd.DataFrame)
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self.assertEqual(len(results_df), 3)
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# Check success rate calculation
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success_rate = results_df['overall_is_successful'].mean()
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self.assertAlmostEqual(success_rate, 1/3) # Only cooking task succeeded
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def test_analyse_results_integration(self):
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"""
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Tests integration with the `analyse_results.py` script, ensuring it
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can process the output of the main evaluation pipeline.
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"""
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task_folders = self.create_sample_experiment_data()
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# Test the analyse_results aggregate function
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results_df = analyse_aggregate_results(task_folders, self.task_definitions)
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self.assertIsInstance(results_df, pd.DataFrame)
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self.assertEqual(len(results_df), 3)
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# Verify model_name is set (should be extracted from folder structure)
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self.assertTrue(all(results_df['model_name'] == 'gpt-4o'))
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def test_cooking_analysis_integration(self):
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"""
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Tests the integration of the cooking-specific analysis script, ensuring
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it can enrich the main results DataFrame without errors.
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"""
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task_folders = self.create_sample_experiment_data()
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results_df = aggregate_results(task_folders, self.task_definitions)
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# Test cooking-specific enrichment
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enriched_df = enrich_dataframe_with_cooking_metrics(results_df)
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# Should have additional cooking columns
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self.assertIn('target_items', enriched_df.columns)
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self.assertIn('num_blocked_agents', enriched_df.columns)
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def test_error_handling_integration(self):
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"""
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Tests that errors, such as malformed logs or missing task definitions,
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are handled gracefully across the entire pipeline.
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"""
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# Create a folder with invalid JSON
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error_dir = os.path.join(self.exp_dir, "error_test")
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os.makedirs(error_dir, exist_ok=True)
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# Invalid JSON file
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with open(os.path.join(error_dir, "invalid.json"), "w") as f:
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f.write("invalid json content")
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# Missing task definition
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missing_task_dir = os.path.join(self.exp_dir, "missing_task")
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os.makedirs(missing_task_dir, exist_ok=True)
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valid_log = [{"role": "system", "content": "Task ended with score : 1.0"}]
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with open(os.path.join(missing_task_dir, "agent.json"), "w") as f:
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json.dump(valid_log, f)
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# Test that pipeline handles errors gracefully
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task_folders = [error_dir, missing_task_dir]
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results_df = aggregate_results(task_folders, self.task_definitions)
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# Should return empty DataFrame for folders with no valid task definitions
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self.assertTrue(results_df.empty or len(results_df) == 0)
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def test_empty_folder_handling(self):
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"""
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Tests that the pipeline can handle empty experiment folders without
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crashing and assigns the correct 'NO_SCORE_LOGGED' status.
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"""
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empty_dir = os.path.join(self.exp_dir, "cooking_task_1")
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os.makedirs(empty_dir, exist_ok=True)
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# No JSON files in this directory
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results_df = aggregate_results([empty_dir], self.task_definitions)
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# Should handle empty folders gracefully
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if not results_df.empty:
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result = results_df.iloc[0]
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self.assertEqual(result['total_agent_logs_found'], 0)
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self.assertEqual(result['overall_completion_status'], CompletionStatus.NO_SCORE_LOGGED)
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def test_backward_compatibility(self):
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"""
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Tests that the integrated system maintains backward compatibility by
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producing results consistent with legacy success criteria.
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"""
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task_folders = self.create_sample_experiment_data()
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results_df = aggregate_results(task_folders, self.task_definitions)
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# Test backward compatibility expectations
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# Success should be determined by score of 1.0
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successful_tasks = results_df[results_df['overall_raw_score'] == 1.0]
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self.assertTrue(all(successful_tasks['overall_is_successful']))
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# Failed tasks should have is_successful = False
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failed_tasks = results_df[results_df['overall_raw_score'] == 0.0]
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self.assertTrue(all(~failed_tasks['overall_is_successful']))
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def test_run_task_file_integration(self):
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"""
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Verifies that the interfaces exposed by `run_task_file.py` are
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compatible with the rest of the evaluation ecosystem.
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"""
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# Test that we can parse the function structure
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self.assertTrue(hasattr(run_task_file, 'run_task'))
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self.assertTrue(hasattr(run_task_file, 'main'))
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# Test command construction (without actually running)
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task_path = self.task_file_path
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task_id = "cooking_task_1"
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profiles = ["profile1.json", "profile2.json"]
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# Verify the command would be constructed correctly
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expected_cmd_parts = ["node", "main.js", "--task_path", task_path, "--task_id", task_id]
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# This verifies the integration interface exists
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def test_performance_with_large_dataset(self):
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"""
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Tests the performance of the integrated pipeline with a larger dataset
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to ensure it remains efficient and scalable.
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"""
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# Create multiple task folders to test performance
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model_dir = os.path.join(self.exp_dir, "claude-3-5-sonnet")
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os.makedirs(model_dir, exist_ok=True)
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task_folders = []
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large_task_defs = {}
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# Create 20 tasks to test performance
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for i in range(20):
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task_id = f"perf_test_task_{i}"
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task_dir = os.path.join(model_dir, task_id)
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os.makedirs(task_dir, exist_ok=True)
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task_folders.append(task_dir)
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# Add to task definitions
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large_task_defs[task_id] = {
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"task_id": task_id,
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"type": "cooking",
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"agent_count": 2,
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"task_type": "cooking"
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}
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# Create agent logs
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for agent_idx in range(2):
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agent_log = [
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{"role": "user", "content": f"Start task {i}"},
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{"role": "system", "content": f"Task ended with score : {1.0 if i % 2 == 0 else 0.0}"}
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]
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with open(os.path.join(task_dir, f"agent_{agent_idx}.json"), "w") as f:
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json.dump(agent_log, f)
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# Test that pipeline handles larger datasets efficiently
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import time
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start_time = time.time()
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results_df = aggregate_results(task_folders, large_task_defs)
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end_time = time.time()
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# Should complete within reasonable time (< 5 seconds for 20 tasks)
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self.assertLess(end_time - start_time, 5.0)
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self.assertEqual(len(results_df), 20)
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# Verify success rate calculation
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expected_success_rate = 0.5 # Every other task succeeds
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actual_success_rate = results_df['overall_is_successful'].mean()
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self.assertAlmostEqual(actual_success_rate, expected_success_rate, places=2)
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if __name__ == '__main__':
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unittest.main() |