Final version

examples/aflow/experiments/optimize_drop.py

@@ -5,7 +5,6 @@
 
 
 from metagpt.configs.models_config import ModelsConfig
-from metagpt.ext.aflow.data.download_data import download
 from metagpt.ext.aflow.scripts.optimizer import DatasetType, Optimizer, QuestionType
 
 # DatasetType, QuestionType, and OptimizerType definitions
@@ -24,7 +23,7 @@ optimized_path: str = "metagpt/ext/aflow/scripts/optimized"  # Optimized Result
 initial_round: int = 1  # Corrected the case from Initial_round to initial_round
 max_rounds: int = 20  # The max iteration of AFLOW.
 check_convergence: bool = True  # Whether Early Stop
-validation_rounds: int = 5 # The validation rounds of AFLOW.
+validation_rounds: int = 5  # The validation rounds of AFLOW.
 
 # Config llm model, you can modify `config/config2.yaml` to use more llms.
 mini_llm_config = ModelsConfig.default().get("gpt-4o-mini")
@@ -33,7 +32,7 @@ claude_llm_config = ModelsConfig.default().get("claude-3-5-sonnet-20240620")
 # Config operators.
 operators = [
     "Custom",  # It's basic unit of a fixed node. optimizer can modify its prompt to get vairous nodes.
-    "AnswerGenerate",              # It's for qa
+    "AnswerGenerate",  # It's for qa
     # "CustomCodeGenerate",         # It's for code
     "ScEnsemble",  # It's for code, math and qa
     # "Test",                       # It's for code

examples/aflow/experiments/optimize_gsm8k.py

@@ -5,7 +5,6 @@
 
 
 from metagpt.configs.models_config import ModelsConfig
-from metagpt.ext.aflow.data.download_data import download
 from metagpt.ext.aflow.scripts.optimizer import DatasetType, Optimizer, QuestionType
 
 # DatasetType, QuestionType, and OptimizerType definitions
@@ -24,7 +23,7 @@ optimized_path: str = "metagpt/ext/aflow/scripts/optimized"  # Optimized Result
 initial_round: int = 1  # Corrected the case from Initial_round to initial_round
 max_rounds: int = 20  # The max iteration of AFLOW.
 check_convergence: bool = True  # Whether Early Stop
-validation_rounds: int = 5 # The validation rounds of AFLOW.
+validation_rounds: int = 5  # The validation rounds of AFLOW.
 
 # Config llm model, you can modify `config/config2.yaml` to use more llms.
 mini_llm_config = ModelsConfig.default().get("gpt-4o-mini")

examples/aflow/experiments/optimize_hotpotqa.py

@@ -5,7 +5,6 @@
 
 
 from metagpt.configs.models_config import ModelsConfig
-from metagpt.ext.aflow.data.download_data import download
 from metagpt.ext.aflow.scripts.optimizer import DatasetType, Optimizer, QuestionType
 
 # DatasetType, QuestionType, and OptimizerType definitions
@@ -24,7 +23,7 @@ optimized_path: str = "metagpt/ext/aflow/scripts/optimized"  # Optimized Result
 initial_round: int = 1  # Corrected the case from Initial_round to initial_round
 max_rounds: int = 20  # The max iteration of AFLOW.
 check_convergence: bool = True  # Whether Early Stop
-validation_rounds: int = 5 # The validation rounds of AFLOW.
+validation_rounds: int = 5  # The validation rounds of AFLOW.
 
 # Config llm model, you can modify `config/config2.yaml` to use more llms.
 mini_llm_config = ModelsConfig.default().get("gpt-4o-mini")
@@ -33,7 +32,7 @@ claude_llm_config = ModelsConfig.default().get("claude-3-5-sonnet-20240620")
 # Config operators.
 operators = [
     "Custom",  # It's basic unit of a fixed node. optimizer can modify its prompt to get vairous nodes.
-    "AnswerGenerate",              # It's for qa
+    "AnswerGenerate",  # It's for qa
     # "CustomCodeGenerate",         # It's for code
     "ScEnsemble",  # It's for code, math and qa
     # "Test",                       # It's for code

examples/aflow/experiments/optimize_humaneval.py

@@ -5,7 +5,6 @@
 
 
 from metagpt.configs.models_config import ModelsConfig
-from metagpt.ext.aflow.data.download_data import download
 from metagpt.ext.aflow.scripts.optimizer import DatasetType, Optimizer, QuestionType
 
 # DatasetType, QuestionType, and OptimizerType definitions
@@ -24,7 +23,7 @@ optimized_path: str = "metagpt/ext/aflow/scripts/optimized"  # Optimized Result
 initial_round: int = 1  # Corrected the case from Initial_round to initial_round
 max_rounds: int = 20  # The max iteration of AFLOW.
 check_convergence: bool = True  # Whether Early Stop
-validation_rounds: int = 5 # The validation rounds of AFLOW.
+validation_rounds: int = 5  # The validation rounds of AFLOW.
 
 # Config llm model, you can modify `config/config2.yaml` to use more llms.
 mini_llm_config = ModelsConfig.default().get("gpt-4o-mini")
@@ -34,9 +33,9 @@ claude_llm_config = ModelsConfig.default().get("claude-3-5-sonnet-20240620")
 operators = [
     "Custom",  # It's basic unit of a fixed node. optimizer can modify its prompt to get vairous nodes.
     # "AnswerGenerate",              # It's for qa
-    "CustomCodeGenerate",         # It's for code
+    "CustomCodeGenerate",  # It's for code
     "ScEnsemble",  # It's for code, math and qa
-    "Test",                       # It's for code
+    "Test",  # It's for code
     # "Programmer",  # It's for math
 ]
 

examples/aflow/experiments/optimize_math.py

@@ -5,7 +5,6 @@
 
 
 from metagpt.configs.models_config import ModelsConfig
-from metagpt.ext.aflow.data.download_data import download
 from metagpt.ext.aflow.scripts.optimizer import DatasetType, Optimizer, QuestionType
 
 # DatasetType, QuestionType, and OptimizerType definitions
@@ -24,7 +23,7 @@ optimized_path: str = "metagpt/ext/aflow/scripts/optimized"  # Optimized Result
 initial_round: int = 1  # Corrected the case from Initial_round to initial_round
 max_rounds: int = 20  # The max iteration of AFLOW.
 check_convergence: bool = True  # Whether Early Stop
-validation_rounds: int = 5 # The validation rounds of AFLOW.
+validation_rounds: int = 5  # The validation rounds of AFLOW.
 
 # Config llm model, you can modify `config/config2.yaml` to use more llms.
 mini_llm_config = ModelsConfig.default().get("gpt-4o-mini")

examples/aflow/experiments/optimize_mbpp.py

@@ -5,7 +5,6 @@
 
 
 from metagpt.configs.models_config import ModelsConfig
-from metagpt.ext.aflow.data.download_data import download
 from metagpt.ext.aflow.scripts.optimizer import DatasetType, Optimizer, QuestionType
 
 # DatasetType, QuestionType, and OptimizerType definitions
@@ -24,7 +23,7 @@ optimized_path: str = "metagpt/ext/aflow/scripts/optimized"  # Optimized Result
 initial_round: int = 1  # Corrected the case from Initial_round to initial_round
 max_rounds: int = 20  # The max iteration of AFLOW.
 check_convergence: bool = True  # Whether Early Stop
-validation_rounds: int = 5 # The validation rounds of AFLOW.
+validation_rounds: int = 5  # The validation rounds of AFLOW.
 
 # Config llm model, you can modify `config/config2.yaml` to use more llms.
 mini_llm_config = ModelsConfig.default().get("gpt-4o-mini")
@@ -34,9 +33,9 @@ claude_llm_config = ModelsConfig.default().get("claude-3-5-sonnet-20240620")
 operators = [
     "Custom",  # It's basic unit of a fixed node. optimizer can modify its prompt to get vairous nodes.
     # "AnswerGenerate",              # It's for qa
-    "CustomCodeGenerate",         # It's for code
+    "CustomCodeGenerate",  # It's for code
     "ScEnsemble",  # It's for code, math and qa
-    "Test",                       # It's for code
+    "Test",  # It's for code
     # "Programmer",  # It's for math
 ]
 

examples/aflow/optimize.py

@@ -24,7 +24,7 @@ optimized_path: str = "metagpt/ext/aflow/scripts/optimized"  # Optimized Result
 initial_round: int = 1  # Corrected the case from Initial_round to initial_round
 max_rounds: int = 20  # The max iteration of AFLOW.
 check_convergence: bool = True  # Whether Early Stop
-validation_rounds: int = 5 # The validation rounds of AFLOW.
+validation_rounds: int = 5  # The validation rounds of AFLOW.
 
 # Config llm model, you can modify `config/config2.yaml` to use more llms.
 mini_llm_config = ModelsConfig.default().get("gpt-4o-mini")

metagpt/ext/aflow/benchmark/mbpp.py

@@ -4,8 +4,8 @@ from typing import Any, Callable, Dict, List, Optional, Tuple
 
 from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_fixed
 
-from metagpt.ext.aflow.scripts.utils import sanitize
 from metagpt.ext.aflow.benchmark.benchmark import BaseBenchmark
+from metagpt.ext.aflow.scripts.utils import sanitize
 from metagpt.logs import logger
 
 

metagpt/ext/aflow/data/download_data.py

@@ -55,11 +55,7 @@ datasets_to_download: Dict[str, Dict[str, str]] = {
         "filename": "aflow_data.tar.gz",
         "extract_path": "metagpt/ext/aflow/data",
     },
-    "results": {
-        "url": "", 
-        "filename": "result.tar.gz", 
-        "extract_path": "metagpt/ext/aflow/data/results"
-    },  
+    "results": {"url": "", "filename": "result.tar.gz", "extract_path": "metagpt/ext/aflow/data/results"},
     "initial_rounds": {
         "url": "https://drive.google.com/uc?export=download&id=1UBoW4WBWjX2gs4I_jq3ALdXeLdwDJMdP",
         "filename": "initial_rounds.tar.gz",
@@ -72,14 +68,17 @@ def is_directory_empty(path: str) -> bool:
     """Check if the directory is empty"""
     return len(os.listdir(path)) == 0
 
+
 def download(datasets):
     """Main function to process all selected datasets"""
     for dataset_name in datasets:
         dataset = datasets_to_download[dataset_name]
         extract_path = dataset["extract_path"]
-        
+
         if os.path.exists(extract_path) and not is_directory_empty(extract_path):
-            logger.info(f"Target folder {extract_path} for {dataset_name} is not empty, skipping download and extraction.")
+            logger.info(
+                f"Target folder {extract_path} for {dataset_name} is not empty, skipping download and extraction."
+            )
             continue
-        
+
         process_dataset(dataset["url"], dataset["filename"], extract_path)