From a9823491e67d186abc5cdb4f926e6057710748ff Mon Sep 17 00:00:00 2001 From: Ren Ma <54947602+Ren-Ma@users.noreply.github.com> Date: Thu, 30 May 2024 16:35:15 +0800 Subject: [PATCH] Support Logic Reasoning Benchmark (#1973) --- .../metadata.json | 10 + .../output.jsonl | 6 + .../logic_reasoning/.cache_program/facts.kfb | 12 + .../logic_reasoning/.cache_program/rules.krb | 52 +++ evaluation/logic_reasoning/README.md | 43 ++ evaluation/logic_reasoning/__init__.py | 0 evaluation/logic_reasoning/instruction.txt | 20 + evaluation/logic_reasoning/logic_inference.py | 220 +++++++++ evaluation/logic_reasoning/run_infer.py | 436 ++++++++++++++++++ .../logic_reasoning/scripts/run_infer.sh | 37 ++ 10 files changed, 836 insertions(+) create mode 100644 evaluation/evaluation_outputs/outputs/logic_reasoning/CodeActAgent/ProntoQA/gpt-4o-2024-05-13_maxiter_10_N_v1.5/metadata.json create mode 100644 evaluation/evaluation_outputs/outputs/logic_reasoning/CodeActAgent/ProntoQA/gpt-4o-2024-05-13_maxiter_10_N_v1.5/output.jsonl create mode 100644 evaluation/logic_reasoning/.cache_program/facts.kfb create mode 100644 evaluation/logic_reasoning/.cache_program/rules.krb create mode 100644 evaluation/logic_reasoning/README.md create mode 100644 evaluation/logic_reasoning/__init__.py create mode 100644 evaluation/logic_reasoning/instruction.txt create mode 100644 evaluation/logic_reasoning/logic_inference.py create mode 100644 evaluation/logic_reasoning/run_infer.py create mode 100755 evaluation/logic_reasoning/scripts/run_infer.sh diff --git a/evaluation/evaluation_outputs/outputs/logic_reasoning/CodeActAgent/ProntoQA/gpt-4o-2024-05-13_maxiter_10_N_v1.5/metadata.json b/evaluation/evaluation_outputs/outputs/logic_reasoning/CodeActAgent/ProntoQA/gpt-4o-2024-05-13_maxiter_10_N_v1.5/metadata.json new file mode 100644 index 0000000000..e1d31d34a4 --- /dev/null +++ b/evaluation/evaluation_outputs/outputs/logic_reasoning/CodeActAgent/ProntoQA/gpt-4o-2024-05-13_maxiter_10_N_v1.5/metadata.json @@ -0,0 +1,10 @@ +{ + "Dataset": "ProntoQA", + "Data split": "validation", + "Number of Samples": 6, + "Agent class": "CodeActAgent", + "Model name": "gpt-4o-2024-05-13", + "Start_time": "2024-05-29 17:51:09", + "End_time": "2024-05-29 17:52:24", + "Final Accuracy": "0.83" +} \ No newline at end of file diff --git a/evaluation/evaluation_outputs/outputs/logic_reasoning/CodeActAgent/ProntoQA/gpt-4o-2024-05-13_maxiter_10_N_v1.5/output.jsonl b/evaluation/evaluation_outputs/outputs/logic_reasoning/CodeActAgent/ProntoQA/gpt-4o-2024-05-13_maxiter_10_N_v1.5/output.jsonl new file mode 100644 index 0000000000..5a6eff4e18 --- /dev/null +++ b/evaluation/evaluation_outputs/outputs/logic_reasoning/CodeActAgent/ProntoQA/gpt-4o-2024-05-13_maxiter_10_N_v1.5/output.jsonl @@ -0,0 +1,6 @@ +{"id": "ProntoQA_1", "instance": {"id": "ProntoQA_1", "answer": "B", "context": "Jompuses are not shy. Jompuses are yumpuses. Each yumpus is aggressive. Each yumpus is a dumpus. Dumpuses are not wooden. Dumpuses are wumpuses. Wumpuses are red. Every wumpus is an impus. Each impus is opaque. Impuses are tumpuses. Numpuses are sour. Tumpuses are not sour. Tumpuses are vumpuses. Vumpuses are earthy. Every vumpus is a zumpus. Zumpuses are small. Zumpuses are rompuses. Max is a yumpus.", "options": ["A) True", "B) False"], "question": "Is the following statement true or false? Max is sour.", "raw_logic_programs": ["Predicates:\nJompus($x, bool) ::: Does x belong to Jompus?\nShy($x, bool) ::: Is x shy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nAggressive($x, bool) ::: Is x aggressive?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nWooden($x, bool) ::: Is x wooden?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nRed($x, bool) ::: Is x red?\nImpus($x, bool) ::: Does x belong to Impus?\nOpaque($x, bool) ::: Is x opaque?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nSour($x, bool) ::: Is x sour?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nEarthy($x, bool) ::: Is x earthy?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\n\nFacts:\nYumpus(Max, True)\n\nRules:\nJompus($x, True) >>> Shy($x, False)\nJompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Aggressive($x, True)\nYumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Wooden($x, False)\nDumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Red($x, True)\nWumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Opaque($x, True)\nImpus($x, True) >>> Tumpus($x, True)\nNumpus($x, True) >>> Sour($x, True)\nTumpus($x, True) >>> Sour($x, False)\nTumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Earthy($x, True)\nVumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Small($x, True)\nZumpus($x, True) >>> Rompus($x, True)\n\nQuery:\nSour(Max, False)"]}, "instruction": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/42483/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nJompus($x, bool) ::: Does x belong to Jompus?\nShy($x, bool) ::: Is x shy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nAggressive($x, bool) ::: Is x aggressive?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nWooden($x, bool) ::: Is x wooden?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nRed($x, bool) ::: Is x red?\nImpus($x, bool) ::: Does x belong to Impus?\nOpaque($x, bool) ::: Is x opaque?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nSour($x, bool) ::: Is x sour?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nEarthy($x, bool) ::: Is x earthy?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\n\nFacts:\nYumpus(Max, True)\n\nRules:\nJompus($x, True) >>> Shy($x, False)\nJompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Aggressive($x, True)\nYumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Wooden($x, False)\nDumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Red($x, True)\nWumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Opaque($x, True)\nImpus($x, True) >>> Tumpus($x, True)\nNumpus($x, True) >>> Sour($x, True)\nTumpus($x, True) >>> Sour($x, False)\nTumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Earthy($x, True)\nVumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Small($x, True)\nZumpus($x, True) >>> Rompus($x, True)\n\nQuery:\nSour(Max, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "metadata": {"dataset": "ProntoQA", "data_split": "validation", "agent_class": "CodeActAgent", "model_name": "gpt-4o-2024-05-13", "max_iterations": 10, "start_time": "2024-05-29 10:41:37"}, "history": [[{"id": 0, "timestamp": "2024-05-29T10:42:13.877632", "source": "user", "message": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/42483/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nJompus($x, bool) ::: Does x belong to Jompus?\nShy($x, bool) ::: Is x shy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nAggressive($x, bool) ::: Is x aggressive?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nWooden($x, bool) ::: Is x wooden?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nRed($x, bool) ::: Is x red?\nImpus($x, bool) ::: Does x belong to Impus?\nOpaque($x, bool) ::: Is x opaque?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nSour($x, bool) ::: Is x sour?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nEarthy($x, bool) ::: Is x earthy?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\n\nFacts:\nYumpus(Max, True)\n\nRules:\nJompus($x, True) >>> Shy($x, False)\nJompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Aggressive($x, True)\nYumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Wooden($x, False)\nDumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Red($x, True)\nWumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Opaque($x, True)\nImpus($x, True) >>> Tumpus($x, True)\nNumpus($x, True) >>> Sour($x, True)\nTumpus($x, True) >>> Sour($x, False)\nTumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Earthy($x, True)\nVumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Small($x, True)\nZumpus($x, True) >>> Rompus($x, True)\n\nQuery:\nSour(Max, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "action": "message", "args": {"content": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/42483/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nJompus($x, bool) ::: Does x belong to Jompus?\nShy($x, bool) ::: Is x shy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nAggressive($x, bool) ::: Is x aggressive?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nWooden($x, bool) ::: Is x wooden?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nRed($x, bool) ::: Is x red?\nImpus($x, bool) ::: Does x belong to Impus?\nOpaque($x, bool) ::: Is x opaque?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nSour($x, bool) ::: Is x sour?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nEarthy($x, bool) ::: Is x earthy?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\n\nFacts:\nYumpus(Max, True)\n\nRules:\nJompus($x, True) >>> Shy($x, False)\nJompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Aggressive($x, True)\nYumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Wooden($x, False)\nDumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Red($x, True)\nWumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Opaque($x, True)\nImpus($x, True) >>> Tumpus($x, True)\nNumpus($x, True) >>> Sour($x, True)\nTumpus($x, True) >>> Sour($x, False)\nTumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Earthy($x, True)\nVumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Small($x, True)\nZumpus($x, True) >>> Rompus($x, True)\n\nQuery:\nSour(Max, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 3, "timestamp": "2024-05-29T10:42:15.986571", "source": "agent", "message": "Running Python code interactively: %pip install scitools-pyke", "action": "run_ipython", "args": {"code": "%pip install scitools-pyke", "thought": "Let's start by installing the required package `scitools-pyke` and then proceed with the logic reasoning task.\n\nFirst, I will install the package:", "kernel_init_code": "from agentskills import *"}}, {"id": 4, "timestamp": "2024-05-29T10:42:20.571809", "source": "agent", "message": "Code executed in IPython cell.", "cause": 3, "observation": "run_ipython", "content": "[Package installed successfully]\n[Kernel restarted successfully to load the package]", "extras": {"code": "%pip install scitools-pyke"}}], [{"id": 5, "timestamp": "2024-05-29T10:42:30.538420", "source": "agent", "message": "Running Python code interactively: import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/42483')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nworkspace_mount_path = \"/Users/ma/Desktop/projects/opendevin/_eval_workspace/42483\"\n\nengine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n\nlogic_programs = \"\"\"\nPredicates:\nJompus($x, bool) ::: Does x belong to Jompus?\nShy($x, bool) ::: Is x shy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nAggressive($x, bool) ::: Is x aggressive?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nWooden($x, bool) ::: Is x wooden?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nRed($x, bool) ::: Is x red?\nImpus($x, bool) ::: Does x belong to Impus?\nOpaque($x, bool) ::: Is x opaque?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nSour($x, bool) ::: Is x sour?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nEarthy($x, bool) ::: Is x earthy?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\n\nFacts:\nYumpus(Max, True)\n\nRules:\nJompus($x, True) >>> Shy($x, False)\nJompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Aggressive($x, True)\nYumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Wooden($x, False)\nDumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Red($x, True)\nWumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Opaque($x, True)\nImpus($x, True) >>> Tumpus($x, True)\nNumpus($x, True) >>> Sour($x, True)\nTumpus($x, True) >>> Sour($x, False)\nTumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Earthy($x, True)\nVumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Small($x, True)\nZumpus($x, True) >>> Rompus($x, True)\n\nQuery:\nSour(Max, False)\n\"\"\"\n\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "action": "run_ipython", "args": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/42483')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nworkspace_mount_path = \"/Users/ma/Desktop/projects/opendevin/_eval_workspace/42483\"\n\nengine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n\nlogic_programs = \"\"\"\nPredicates:\nJompus($x, bool) ::: Does x belong to Jompus?\nShy($x, bool) ::: Is x shy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nAggressive($x, bool) ::: Is x aggressive?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nWooden($x, bool) ::: Is x wooden?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nRed($x, bool) ::: Is x red?\nImpus($x, bool) ::: Does x belong to Impus?\nOpaque($x, bool) ::: Is x opaque?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nSour($x, bool) ::: Is x sour?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nEarthy($x, bool) ::: Is x earthy?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\n\nFacts:\nYumpus(Max, True)\n\nRules:\nJompus($x, True) >>> Shy($x, False)\nJompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Aggressive($x, True)\nYumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Wooden($x, False)\nDumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Red($x, True)\nWumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Opaque($x, True)\nImpus($x, True) >>> Tumpus($x, True)\nNumpus($x, True) >>> Sour($x, True)\nTumpus($x, True) >>> Sour($x, False)\nTumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Earthy($x, True)\nVumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Small($x, True)\nZumpus($x, True) >>> Rompus($x, True)\n\nQuery:\nSour(Max, False)\n\"\"\"\n\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "thought": "Great! Now, let's proceed with the logic reasoning task. I will import the necessary module, instantiate the `LogicInferenceEngine` class, and use the `safe_execute_program` method to prove the logic programs.", "kernel_init_code": "from agentskills import *"}}, {"id": 6, "timestamp": "2024-05-29T10:42:31.030667", "source": "agent", "message": "Code executed in IPython cell.", "cause": 5, "observation": "run_ipython", "content": "'A'", "extras": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/42483')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nworkspace_mount_path = \"/Users/ma/Desktop/projects/opendevin/_eval_workspace/42483\"\n\nengine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n\nlogic_programs = \"\"\"\nPredicates:\nJompus($x, bool) ::: Does x belong to Jompus?\nShy($x, bool) ::: Is x shy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nAggressive($x, bool) ::: Is x aggressive?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nWooden($x, bool) ::: Is x wooden?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nRed($x, bool) ::: Is x red?\nImpus($x, bool) ::: Does x belong to Impus?\nOpaque($x, bool) ::: Is x opaque?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nSour($x, bool) ::: Is x sour?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nEarthy($x, bool) ::: Is x earthy?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\n\nFacts:\nYumpus(Max, True)\n\nRules:\nJompus($x, True) >>> Shy($x, False)\nJompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Aggressive($x, True)\nYumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Wooden($x, False)\nDumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Red($x, True)\nWumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Opaque($x, True)\nImpus($x, True) >>> Tumpus($x, True)\nNumpus($x, True) >>> Sour($x, True)\nTumpus($x, True) >>> Sour($x, False)\nTumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Earthy($x, True)\nVumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Small($x, True)\nZumpus($x, True) >>> Rompus($x, True)\n\nQuery:\nSour(Max, False)\n\"\"\"\n\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer"}}], [{"id": 7, "timestamp": "2024-05-29T10:42:32.847071", "source": "agent", "message": "The answer to the query `Sour(Max, False)` is 'A', which indicates that the query is true based on the given facts and rules.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "action": "message", "args": {"content": "The answer to the query `Sour(Max, False)` is 'A', which indicates that the query is true based on the given facts and rules.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "wait_for_response": true}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 9, "timestamp": "2024-05-29T10:42:32.847594", "source": "user", "message": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "action": "message", "args": {"content": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 13, "timestamp": "2024-05-29T10:42:35.136123", "source": "agent", "message": "All done! What's next on the agenda?", "action": "finish", "args": {"outputs": {}, "thought": ""}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}]], "final_message": "'A'", "messages": ["", "", "", "'A'"], "error": null, "test_result": {"result": false}} +{"id": "ProntoQA_2", "instance": {"id": "ProntoQA_2", "answer": "A", "context": "Every tumpus is not angry. Tumpuses are rompuses. Every numpus is not bright. Rompuses are not luminous. Rompuses are yumpuses. Yumpuses are transparent. Yumpuses are zumpuses. Each zumpus is not bitter. Zumpuses are impuses. Impuses are red. Each impus is a dumpus. Every dumpus is happy. Each dumpus is a vumpus. Vumpuses are bright. Every vumpus is a jompus. Jompuses are large. Each jompus is a wumpus. Stella is a yumpus.", "options": ["A) True", "B) False"], "question": "Is the following statement true or false? Stella is bright.", "raw_logic_programs": ["Predicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nAngry($x, bool) ::: Is x angry?\nRompus($x, bool) ::: Does x belong to Rompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBright($x, bool) ::: Is x bright?\nLuminous($x, bool) ::: Is x luminous?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nTransparent($x, bool) ::: Is x transparent?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nBitter($x, bool) ::: Is x bitter?\nImpus($x, bool) ::: Does x belong to Impus?\nRed($x, bool) ::: Is x red?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nHappy($x, bool) ::: Is x happy?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nJompus($x, bool) ::: Does x belong to Jompus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\n\nFacts:\nYumpus(Stella, True)\n\nRules:\nTumpus($x, True) >>> Angry($x, False)\nTumpus($x, True) >>> Rompus($x, True)\nNumpus($x, True) >>> Bright($x, False)\nRompus($x, True) >>> Luminous($x, False)\nRompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Transparent($x, True)\nYumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Bitter($x, False)\nZumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Red($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Happy($x, True)\nDumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Bright($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Large($x, True)\nJompus($x, True) >>> Wumpus($x, True)\n\nQuery:\nBright(Stella, True)"]}, "instruction": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/35688/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nAngry($x, bool) ::: Is x angry?\nRompus($x, bool) ::: Does x belong to Rompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBright($x, bool) ::: Is x bright?\nLuminous($x, bool) ::: Is x luminous?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nTransparent($x, bool) ::: Is x transparent?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nBitter($x, bool) ::: Is x bitter?\nImpus($x, bool) ::: Does x belong to Impus?\nRed($x, bool) ::: Is x red?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nHappy($x, bool) ::: Is x happy?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nJompus($x, bool) ::: Does x belong to Jompus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\n\nFacts:\nYumpus(Stella, True)\n\nRules:\nTumpus($x, True) >>> Angry($x, False)\nTumpus($x, True) >>> Rompus($x, True)\nNumpus($x, True) >>> Bright($x, False)\nRompus($x, True) >>> Luminous($x, False)\nRompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Transparent($x, True)\nYumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Bitter($x, False)\nZumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Red($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Happy($x, True)\nDumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Bright($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Large($x, True)\nJompus($x, True) >>> Wumpus($x, True)\n\nQuery:\nBright(Stella, True)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "metadata": {"dataset": "ProntoQA", "data_split": "validation", "agent_class": "CodeActAgent", "model_name": "gpt-4o-2024-05-13", "max_iterations": 10, "start_time": "2024-05-29 15:05:58"}, "history": [[{"id": 0, "timestamp": "2024-05-29T15:06:38.661585", "source": "user", "message": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/35688/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nAngry($x, bool) ::: Is x angry?\nRompus($x, bool) ::: Does x belong to Rompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBright($x, bool) ::: Is x bright?\nLuminous($x, bool) ::: Is x luminous?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nTransparent($x, bool) ::: Is x transparent?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nBitter($x, bool) ::: Is x bitter?\nImpus($x, bool) ::: Does x belong to Impus?\nRed($x, bool) ::: Is x red?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nHappy($x, bool) ::: Is x happy?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nJompus($x, bool) ::: Does x belong to Jompus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\n\nFacts:\nYumpus(Stella, True)\n\nRules:\nTumpus($x, True) >>> Angry($x, False)\nTumpus($x, True) >>> Rompus($x, True)\nNumpus($x, True) >>> Bright($x, False)\nRompus($x, True) >>> Luminous($x, False)\nRompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Transparent($x, True)\nYumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Bitter($x, False)\nZumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Red($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Happy($x, True)\nDumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Bright($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Large($x, True)\nJompus($x, True) >>> Wumpus($x, True)\n\nQuery:\nBright(Stella, True)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "action": "message", "args": {"content": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/35688/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nAngry($x, bool) ::: Is x angry?\nRompus($x, bool) ::: Does x belong to Rompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBright($x, bool) ::: Is x bright?\nLuminous($x, bool) ::: Is x luminous?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nTransparent($x, bool) ::: Is x transparent?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nBitter($x, bool) ::: Is x bitter?\nImpus($x, bool) ::: Does x belong to Impus?\nRed($x, bool) ::: Is x red?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nHappy($x, bool) ::: Is x happy?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nJompus($x, bool) ::: Does x belong to Jompus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\n\nFacts:\nYumpus(Stella, True)\n\nRules:\nTumpus($x, True) >>> Angry($x, False)\nTumpus($x, True) >>> Rompus($x, True)\nNumpus($x, True) >>> Bright($x, False)\nRompus($x, True) >>> Luminous($x, False)\nRompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Transparent($x, True)\nYumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Bitter($x, False)\nZumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Red($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Happy($x, True)\nDumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Bright($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Large($x, True)\nJompus($x, True) >>> Wumpus($x, True)\n\nQuery:\nBright(Stella, True)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 3, "timestamp": "2024-05-29T15:06:40.628273", "source": "agent", "message": "Running Python code interactively: %pip install scitools-pyke", "action": "run_ipython", "args": {"code": "%pip install scitools-pyke", "thought": "Let's start by installing the required package `scitools-pyke`.", "kernel_init_code": "from agentskills import *"}}, {"id": 4, "timestamp": "2024-05-29T15:06:45.277211", "source": "agent", "message": "Code executed in IPython cell.", "cause": 3, "observation": "run_ipython", "content": "[Package installed successfully]\n[Kernel restarted successfully to load the package]", "extras": {"code": "%pip install scitools-pyke"}}], [{"id": 5, "timestamp": "2024-05-29T15:07:06.638816", "source": "agent", "message": "Running Python code interactively: import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nlogic_programs = \"\"\"\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nAngry($x, bool) ::: Is x angry?\nRompus($x, bool) ::: Does x belong to Rompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBright($x, bool) ::: Is x bright?\nLuminous($x, bool) ::: Is x luminous?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nTransparent($x, bool) ::: Is x transparent?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nBitter($x, bool) ::: Is x bitter?\nImpus($x, bool) ::: Does x belong to Impus?\nRed($x, bool) ::: Is x red?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nHappy($x, bool) ::: Is x happy?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nJompus($x, bool) ::: Does x belong to Jompus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\n\nFacts:\nYumpus(Stella, True)\n\nRules:\nTumpus($x, True) >>> Angry($x, False)\nTumpus($x, True) >>> Rompus($x, True)\nNumpus($x, True) >>> Bright($x, False)\nRompus($x, True) >>> Luminous($x, False)\nRompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Transparent($x, True)\nYumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Bitter($x, False)\nZumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Red($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Happy($x, True)\nDumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Bright($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Large($x, True)\nJompus($x, True) >>> Wumpus($x, True)\n\nQuery:\nBright(Stella, True)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, '/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "action": "run_ipython", "args": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nlogic_programs = \"\"\"\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nAngry($x, bool) ::: Is x angry?\nRompus($x, bool) ::: Does x belong to Rompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBright($x, bool) ::: Is x bright?\nLuminous($x, bool) ::: Is x luminous?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nTransparent($x, bool) ::: Is x transparent?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nBitter($x, bool) ::: Is x bitter?\nImpus($x, bool) ::: Does x belong to Impus?\nRed($x, bool) ::: Is x red?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nHappy($x, bool) ::: Is x happy?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nJompus($x, bool) ::: Does x belong to Jompus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\n\nFacts:\nYumpus(Stella, True)\n\nRules:\nTumpus($x, True) >>> Angry($x, False)\nTumpus($x, True) >>> Rompus($x, True)\nNumpus($x, True) >>> Bright($x, False)\nRompus($x, True) >>> Luminous($x, False)\nRompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Transparent($x, True)\nYumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Bitter($x, False)\nZumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Red($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Happy($x, True)\nDumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Bright($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Large($x, True)\nJompus($x, True) >>> Wumpus($x, True)\n\nQuery:\nBright(Stella, True)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, '/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "thought": "Now, let's proceed with the logic reasoning task. I will import the necessary module, instantiate the `LogicInferenceEngine` class, and use the `safe_execute_program` method to prove the logic programs.", "kernel_init_code": "from agentskills import *"}}, {"id": 6, "timestamp": "2024-05-29T15:07:07.122155", "source": "agent", "message": "Code executed in IPython cell.", "cause": 5, "observation": "run_ipython", "content": "'A'", "extras": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nlogic_programs = \"\"\"\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nAngry($x, bool) ::: Is x angry?\nRompus($x, bool) ::: Does x belong to Rompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBright($x, bool) ::: Is x bright?\nLuminous($x, bool) ::: Is x luminous?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nTransparent($x, bool) ::: Is x transparent?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nBitter($x, bool) ::: Is x bitter?\nImpus($x, bool) ::: Does x belong to Impus?\nRed($x, bool) ::: Is x red?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nHappy($x, bool) ::: Is x happy?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nJompus($x, bool) ::: Does x belong to Jompus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\n\nFacts:\nYumpus(Stella, True)\n\nRules:\nTumpus($x, True) >>> Angry($x, False)\nTumpus($x, True) >>> Rompus($x, True)\nNumpus($x, True) >>> Bright($x, False)\nRompus($x, True) >>> Luminous($x, False)\nRompus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Transparent($x, True)\nYumpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Bitter($x, False)\nZumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Red($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Happy($x, True)\nDumpus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Bright($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Large($x, True)\nJompus($x, True) >>> Wumpus($x, True)\n\nQuery:\nBright(Stella, True)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, '/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer"}}], [{"id": 7, "timestamp": "2024-05-29T15:07:09.295867", "source": "agent", "message": "The answer to the query `Bright(Stella, True)` is `A`, which indicates that the query is true based on the given facts and rules.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "action": "message", "args": {"content": "The answer to the query `Bright(Stella, True)` is `A`, which indicates that the query is true based on the given facts and rules.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "wait_for_response": true}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 9, "timestamp": "2024-05-29T15:07:09.296958", "source": "user", "message": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "action": "message", "args": {"content": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 13, "timestamp": "2024-05-29T15:07:11.075946", "source": "agent", "message": "All done! What's next on the agenda?", "action": "finish", "args": {"outputs": {}, "thought": ""}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}]], "final_message": "'A'", "messages": ["", "", "", "'A'"], "error": null, "test_result": {"result": true}} +{"id": "ProntoQA_3", "instance": {"id": "ProntoQA_3", "answer": "A", "context": "Vumpuses are floral. Vumpuses are tumpuses. Tumpuses are brown. Each tumpus is a wumpus. Wumpuses are small. Each wumpus is a rompus. Each zumpus is metallic. Every rompus is happy. Rompuses are impuses. Each impus is amenable. Each impus is a dumpus. Every dumpus is not metallic. Dumpuses are numpuses. Each numpus is bitter. Each numpus is a jompus. Every jompus is cold. Each jompus is a yumpus. Wren is a tumpus.", "options": ["A) True", "B) False"], "question": "Is the following statement true or false? Wren is not metallic.", "raw_logic_programs": ["Predicates:\nVumpus($x, bool) ::: Does x belong to Vumpus?\nFloral($x, bool) ::: Is x floral?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nBrown($x, bool) ::: Is x brown?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nMetallic($x, bool) ::: Is x metallic?\nHappy($x, bool) ::: Is x happy?\nImpus($x, bool) ::: Does x belong to Impus?\nAmenable($x, bool) ::: Is x amenable?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBitter($x, bool) ::: Is x bitter?\nJompus($x, bool) ::: Does x belong to Jompus?\nCold($x, bool) ::: Is x cold?\nYumpus($x, bool) ::: Does x belong to Yumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nVumpus($x, True) >>> Floral($x, True)\nVumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Brown($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Small($x, True)\nWumpus($x, True) >>> Rompus($x, True)\nZumpus($x, True) >>> Metallic($x, True)\nRompus($x, True) >>> Happy($x, True)\nRompus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Amenable($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Metallic($x, False)\nDumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Bitter($x, True)\nNumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Cold($x, True)\nJompus($x, True) >>> Yumpus($x, True)\n\nQuery:\nMetallic(Wren, False)"]}, "instruction": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/35688/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nVumpus($x, bool) ::: Does x belong to Vumpus?\nFloral($x, bool) ::: Is x floral?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nBrown($x, bool) ::: Is x brown?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nMetallic($x, bool) ::: Is x metallic?\nHappy($x, bool) ::: Is x happy?\nImpus($x, bool) ::: Does x belong to Impus?\nAmenable($x, bool) ::: Is x amenable?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBitter($x, bool) ::: Is x bitter?\nJompus($x, bool) ::: Does x belong to Jompus?\nCold($x, bool) ::: Is x cold?\nYumpus($x, bool) ::: Does x belong to Yumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nVumpus($x, True) >>> Floral($x, True)\nVumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Brown($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Small($x, True)\nWumpus($x, True) >>> Rompus($x, True)\nZumpus($x, True) >>> Metallic($x, True)\nRompus($x, True) >>> Happy($x, True)\nRompus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Amenable($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Metallic($x, False)\nDumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Bitter($x, True)\nNumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Cold($x, True)\nJompus($x, True) >>> Yumpus($x, True)\n\nQuery:\nMetallic(Wren, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "metadata": {"dataset": "ProntoQA", "data_split": "validation", "agent_class": "CodeActAgent", "model_name": "gpt-4o-2024-05-13", "max_iterations": 10, "start_time": "2024-05-29 15:05:58"}, "history": [[{"id": 17, "timestamp": "2024-05-29T15:07:45.612184", "source": "user", "message": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/35688/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nVumpus($x, bool) ::: Does x belong to Vumpus?\nFloral($x, bool) ::: Is x floral?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nBrown($x, bool) ::: Is x brown?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nMetallic($x, bool) ::: Is x metallic?\nHappy($x, bool) ::: Is x happy?\nImpus($x, bool) ::: Does x belong to Impus?\nAmenable($x, bool) ::: Is x amenable?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBitter($x, bool) ::: Is x bitter?\nJompus($x, bool) ::: Does x belong to Jompus?\nCold($x, bool) ::: Is x cold?\nYumpus($x, bool) ::: Does x belong to Yumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nVumpus($x, True) >>> Floral($x, True)\nVumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Brown($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Small($x, True)\nWumpus($x, True) >>> Rompus($x, True)\nZumpus($x, True) >>> Metallic($x, True)\nRompus($x, True) >>> Happy($x, True)\nRompus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Amenable($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Metallic($x, False)\nDumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Bitter($x, True)\nNumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Cold($x, True)\nJompus($x, True) >>> Yumpus($x, True)\n\nQuery:\nMetallic(Wren, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "action": "message", "args": {"content": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/35688/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nVumpus($x, bool) ::: Does x belong to Vumpus?\nFloral($x, bool) ::: Is x floral?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nBrown($x, bool) ::: Is x brown?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nMetallic($x, bool) ::: Is x metallic?\nHappy($x, bool) ::: Is x happy?\nImpus($x, bool) ::: Does x belong to Impus?\nAmenable($x, bool) ::: Is x amenable?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBitter($x, bool) ::: Is x bitter?\nJompus($x, bool) ::: Does x belong to Jompus?\nCold($x, bool) ::: Is x cold?\nYumpus($x, bool) ::: Does x belong to Yumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nVumpus($x, True) >>> Floral($x, True)\nVumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Brown($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Small($x, True)\nWumpus($x, True) >>> Rompus($x, True)\nZumpus($x, True) >>> Metallic($x, True)\nRompus($x, True) >>> Happy($x, True)\nRompus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Amenable($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Metallic($x, False)\nDumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Bitter($x, True)\nNumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Cold($x, True)\nJompus($x, True) >>> Yumpus($x, True)\n\nQuery:\nMetallic(Wren, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 20, "timestamp": "2024-05-29T15:07:47.955137", "source": "agent", "message": "Running Python code interactively: %pip install scitools-pyke", "action": "run_ipython", "args": {"code": "%pip install scitools-pyke", "thought": "Let's start by installing the required package `scitools-pyke`.", "kernel_init_code": "from agentskills import *"}}, {"id": 21, "timestamp": "2024-05-29T15:07:52.300112", "source": "agent", "message": "Code executed in IPython cell.", "cause": 20, "observation": "run_ipython", "content": "[Package installed successfully]\n[Kernel restarted successfully to load the package]", "extras": {"code": "%pip install scitools-pyke"}}], [{"id": 22, "timestamp": "2024-05-29T15:08:08.081969", "source": "agent", "message": "Running Python code interactively: import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nlogic_programs = \"\"\"\nPredicates:\nVumpus($x, bool) ::: Does x belong to Vumpus?\nFloral($x, bool) ::: Is x floral?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nBrown($x, bool) ::: Is x brown?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nMetallic($x, bool) ::: Is x metallic?\nHappy($x, bool) ::: Is x happy?\nImpus($x, bool) ::: Does x belong to Impus?\nAmenable($x, bool) ::: Is x amenable?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBitter($x, bool) ::: Is x bitter?\nJompus($x, bool) ::: Does x belong to Jompus?\nCold($x, bool) ::: Is x cold?\nYumpus($x, bool) ::: Does x belong to Yumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nVumpus($x, True) >>> Floral($x, True)\nVumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Brown($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Small($x, True)\nWumpus($x, True) >>> Rompus($x, True)\nZumpus($x, True) >>> Metallic($x, True)\nRompus($x, True) >>> Happy($x, True)\nRompus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Amenable($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Metallic($x, False)\nDumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Bitter($x, True)\nNumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Cold($x, True)\nJompus($x, True) >>> Yumpus($x, True)\n\nQuery:\nMetallic(Wren, False)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, '/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "action": "run_ipython", "args": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nlogic_programs = \"\"\"\nPredicates:\nVumpus($x, bool) ::: Does x belong to Vumpus?\nFloral($x, bool) ::: Is x floral?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nBrown($x, bool) ::: Is x brown?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nMetallic($x, bool) ::: Is x metallic?\nHappy($x, bool) ::: Is x happy?\nImpus($x, bool) ::: Does x belong to Impus?\nAmenable($x, bool) ::: Is x amenable?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBitter($x, bool) ::: Is x bitter?\nJompus($x, bool) ::: Does x belong to Jompus?\nCold($x, bool) ::: Is x cold?\nYumpus($x, bool) ::: Does x belong to Yumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nVumpus($x, True) >>> Floral($x, True)\nVumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Brown($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Small($x, True)\nWumpus($x, True) >>> Rompus($x, True)\nZumpus($x, True) >>> Metallic($x, True)\nRompus($x, True) >>> Happy($x, True)\nRompus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Amenable($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Metallic($x, False)\nDumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Bitter($x, True)\nNumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Cold($x, True)\nJompus($x, True) >>> Yumpus($x, True)\n\nQuery:\nMetallic(Wren, False)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, '/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "thought": "Now, let's proceed with the logic reasoning task by using the code in `/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688/logic_inference.py`.", "kernel_init_code": "from agentskills import *"}}, {"id": 23, "timestamp": "2024-05-29T15:08:08.562428", "source": "agent", "message": "Code executed in IPython cell.", "cause": 22, "observation": "run_ipython", "content": "'A'", "extras": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nlogic_programs = \"\"\"\nPredicates:\nVumpus($x, bool) ::: Does x belong to Vumpus?\nFloral($x, bool) ::: Is x floral?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nBrown($x, bool) ::: Is x brown?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nSmall($x, bool) ::: Is x small?\nRompus($x, bool) ::: Does x belong to Rompus?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nMetallic($x, bool) ::: Is x metallic?\nHappy($x, bool) ::: Is x happy?\nImpus($x, bool) ::: Does x belong to Impus?\nAmenable($x, bool) ::: Is x amenable?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nBitter($x, bool) ::: Is x bitter?\nJompus($x, bool) ::: Does x belong to Jompus?\nCold($x, bool) ::: Is x cold?\nYumpus($x, bool) ::: Does x belong to Yumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nVumpus($x, True) >>> Floral($x, True)\nVumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Brown($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Small($x, True)\nWumpus($x, True) >>> Rompus($x, True)\nZumpus($x, True) >>> Metallic($x, True)\nRompus($x, True) >>> Happy($x, True)\nRompus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Amenable($x, True)\nImpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Metallic($x, False)\nDumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Bitter($x, True)\nNumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Cold($x, True)\nJompus($x, True) >>> Yumpus($x, True)\n\nQuery:\nMetallic(Wren, False)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, '/Users/ma/Desktop/projects/opendevin/_eval_workspace/35688')\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer"}}], [{"id": 24, "timestamp": "2024-05-29T15:08:10.035241", "source": "agent", "message": "The answer to the query `Metallic(Wren, False)` is `A`.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "action": "message", "args": {"content": "The answer to the query `Metallic(Wren, False)` is `A`.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "wait_for_response": true}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 26, "timestamp": "2024-05-29T15:08:10.035957", "source": "user", "message": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "action": "message", "args": {"content": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 30, "timestamp": "2024-05-29T15:08:11.826573", "source": "agent", "message": "All done! What's next on the agenda?", "action": "finish", "args": {"outputs": {}, "thought": ""}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}]], "final_message": "'A'", "messages": ["", "", "", "'A'"], "error": null, "test_result": {"result": true}} +{"id": "ProntoQA_4", "instance": {"id": "ProntoQA_4", "answer": "B", "context": "Rompuses are spicy. Every rompus is an impus. Yumpuses are not small. Impuses are orange. Impuses are zumpuses. Zumpuses are not hot. Zumpuses are numpuses. Numpuses are metallic. Numpuses are wumpuses. Every wumpus is not kind. Each wumpus is a dumpus. Each dumpus is not bright. Every dumpus is a jompus. Jompuses are small. Jompuses are vumpuses. Each vumpus is not shy. Every vumpus is a tumpus. Alex is a zumpus.", "options": ["A) True", "B) False"], "question": "Is the following statement true or false? Alex is not small.", "raw_logic_programs": ["Predicates:\nSpicy($x, bool) ::: Is x spicy?\nImpus($x, bool) ::: Does x belong to Impus?\nSmall($x, bool) ::: Is x small?\nHot($x, bool) ::: Is x hot?\nMetallic($x, bool) ::: Is x metallic?\nKind($x, bool) ::: Is x kind?\nBright($x, bool) ::: Is x bright?\n\nFacts:\nZumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Spicy($x, True)\nRompus($x, True) >>> Impus($x, True)\nYumpus($x, True) >>> Small($x, False)\nImpus($x, True) >>> Orange($x, True)\nImpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Hot($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Metallic($x, True)\nNumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Kind($x, False)\nWumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Bright($x, False)\nDumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Small($x, True)\nJompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Shy($x, False)\nVumpus($x, True) >>> Tumpus($x, True)\n\nQuery:\nSmall(Alex, False)"]}, "instruction": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/80034/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nSpicy($x, bool) ::: Is x spicy?\nImpus($x, bool) ::: Does x belong to Impus?\nSmall($x, bool) ::: Is x small?\nHot($x, bool) ::: Is x hot?\nMetallic($x, bool) ::: Is x metallic?\nKind($x, bool) ::: Is x kind?\nBright($x, bool) ::: Is x bright?\n\nFacts:\nZumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Spicy($x, True)\nRompus($x, True) >>> Impus($x, True)\nYumpus($x, True) >>> Small($x, False)\nImpus($x, True) >>> Orange($x, True)\nImpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Hot($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Metallic($x, True)\nNumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Kind($x, False)\nWumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Bright($x, False)\nDumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Small($x, True)\nJompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Shy($x, False)\nVumpus($x, True) >>> Tumpus($x, True)\n\nQuery:\nSmall(Alex, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "history": [[{"id": 0, "timestamp": "2024-05-29T17:16:37.408157", "source": "user", "message": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/80034/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nSpicy($x, bool) ::: Is x spicy?\nImpus($x, bool) ::: Does x belong to Impus?\nSmall($x, bool) ::: Is x small?\nHot($x, bool) ::: Is x hot?\nMetallic($x, bool) ::: Is x metallic?\nKind($x, bool) ::: Is x kind?\nBright($x, bool) ::: Is x bright?\n\nFacts:\nZumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Spicy($x, True)\nRompus($x, True) >>> Impus($x, True)\nYumpus($x, True) >>> Small($x, False)\nImpus($x, True) >>> Orange($x, True)\nImpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Hot($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Metallic($x, True)\nNumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Kind($x, False)\nWumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Bright($x, False)\nDumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Small($x, True)\nJompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Shy($x, False)\nVumpus($x, True) >>> Tumpus($x, True)\n\nQuery:\nSmall(Alex, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "action": "message", "args": {"content": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/80034/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nSpicy($x, bool) ::: Is x spicy?\nImpus($x, bool) ::: Does x belong to Impus?\nSmall($x, bool) ::: Is x small?\nHot($x, bool) ::: Is x hot?\nMetallic($x, bool) ::: Is x metallic?\nKind($x, bool) ::: Is x kind?\nBright($x, bool) ::: Is x bright?\n\nFacts:\nZumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Spicy($x, True)\nRompus($x, True) >>> Impus($x, True)\nYumpus($x, True) >>> Small($x, False)\nImpus($x, True) >>> Orange($x, True)\nImpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Hot($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Metallic($x, True)\nNumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Kind($x, False)\nWumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Bright($x, False)\nDumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Small($x, True)\nJompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Shy($x, False)\nVumpus($x, True) >>> Tumpus($x, True)\n\nQuery:\nSmall(Alex, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 3, "timestamp": "2024-05-29T17:16:39.011346", "source": "agent", "message": "Running Python code interactively: %pip install scitools-pyke", "action": "run_ipython", "args": {"code": "%pip install scitools-pyke", "thought": "Let's start by installing the required package `scitools-pyke`.", "kernel_init_code": "from agentskills import *"}}, {"id": 4, "timestamp": "2024-05-29T17:16:44.064480", "source": "agent", "message": "Code executed in IPython cell.", "cause": 3, "observation": "run_ipython", "content": "[Package installed successfully]\n[Kernel restarted successfully to load the package]", "extras": {"code": "%pip install scitools-pyke"}}], [{"id": 5, "timestamp": "2024-05-29T17:16:57.423471", "source": "agent", "message": "Running Python code interactively: import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nlogic_programs = \"\"\"\nPredicates:\nSpicy($x, bool) ::: Is x spicy?\nImpus($x, bool) ::: Does x belong to Impus?\nSmall($x, bool) ::: Is x small?\nHot($x, bool) ::: Is x hot?\nMetallic($x, bool) ::: Is x metallic?\nKind($x, bool) ::: Is x kind?\nBright($x, bool) ::: Is x bright?\n\nFacts:\nZumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Spicy($x, True)\nRompus($x, True) >>> Impus($x, True)\nYumpus($x, True) >>> Small($x, False)\nImpus($x, True) >>> Orange($x, True)\nImpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Hot($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Metallic($x, True)\nNumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Kind($x, False)\nWumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Bright($x, False)\nDumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Small($x, True)\nJompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Shy($x, False)\nVumpus($x, True) >>> Tumpus($x, True)\n\nQuery:\nSmall(Alex, False)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, '/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034')\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "action": "run_ipython", "args": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nlogic_programs = \"\"\"\nPredicates:\nSpicy($x, bool) ::: Is x spicy?\nImpus($x, bool) ::: Does x belong to Impus?\nSmall($x, bool) ::: Is x small?\nHot($x, bool) ::: Is x hot?\nMetallic($x, bool) ::: Is x metallic?\nKind($x, bool) ::: Is x kind?\nBright($x, bool) ::: Is x bright?\n\nFacts:\nZumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Spicy($x, True)\nRompus($x, True) >>> Impus($x, True)\nYumpus($x, True) >>> Small($x, False)\nImpus($x, True) >>> Orange($x, True)\nImpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Hot($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Metallic($x, True)\nNumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Kind($x, False)\nWumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Bright($x, False)\nDumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Small($x, True)\nJompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Shy($x, False)\nVumpus($x, True) >>> Tumpus($x, True)\n\nQuery:\nSmall(Alex, False)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, '/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034')\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "thought": "Now, let's proceed by importing the necessary module and executing the logic program using the provided code.", "kernel_init_code": "from agentskills import *"}}, {"id": 6, "timestamp": "2024-05-29T17:16:57.957336", "source": "agent", "message": "Code executed in IPython cell.", "cause": 5, "observation": "run_ipython", "content": "'B'", "extras": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nlogic_programs = \"\"\"\nPredicates:\nSpicy($x, bool) ::: Is x spicy?\nImpus($x, bool) ::: Does x belong to Impus?\nSmall($x, bool) ::: Is x small?\nHot($x, bool) ::: Is x hot?\nMetallic($x, bool) ::: Is x metallic?\nKind($x, bool) ::: Is x kind?\nBright($x, bool) ::: Is x bright?\n\nFacts:\nZumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Spicy($x, True)\nRompus($x, True) >>> Impus($x, True)\nYumpus($x, True) >>> Small($x, False)\nImpus($x, True) >>> Orange($x, True)\nImpus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Hot($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Metallic($x, True)\nNumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Kind($x, False)\nWumpus($x, True) >>> Dumpus($x, True)\nDumpus($x, True) >>> Bright($x, False)\nDumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Small($x, True)\nJompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Shy($x, False)\nVumpus($x, True) >>> Tumpus($x, True)\n\nQuery:\nSmall(Alex, False)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, '/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034')\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer"}}], [{"id": 7, "timestamp": "2024-05-29T17:17:00.461597", "source": "agent", "message": "The answer to the query `Small(Alex, False)` is 'B', which indicates that the query is false based on the given facts and rules.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "action": "message", "args": {"content": "The answer to the query `Small(Alex, False)` is 'B', which indicates that the query is false based on the given facts and rules.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "wait_for_response": true}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 9, "timestamp": "2024-05-29T17:17:00.461890", "source": "user", "message": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "action": "message", "args": {"content": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 13, "timestamp": "2024-05-29T17:17:02.982155", "source": "agent", "message": "All done! What's next on the agenda?", "action": "finish", "args": {"outputs": {}, "thought": ""}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}]], "final_message": "B", "messages": ["", "", "", "'B'"], "error": null, "test_result": {"result": true}} +{"id": "ProntoQA_5", "instance": {"id": "ProntoQA_5", "answer": "A", "context": "Rompuses are mean. Rompuses are zumpuses. Each zumpus is not happy. Each zumpus is a numpus. Each numpus is not temperate. Each numpus is a tumpus. Tumpuses are not large. Tumpuses are yumpuses. Every yumpus is earthy. Each yumpus is a jompus. Jompuses are blue. Every jompus is a wumpus. Wumpuses are not dull. Wumpuses are impuses. Each vumpus is dull. Impuses are sour. Impuses are dumpuses. Alex is a numpus.", "options": ["A) True", "B) False"], "question": "Is the following statement true or false? Alex is not dull.", "raw_logic_programs": ["Predicates:\nRompus($x, bool) ::: Does x belong to Rompus?\nMean($x, bool) ::: Is x mean?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nHappy($x, bool) ::: Is x happy?\nNumpus($x, bool) ::: Does x belong to Numpus?\nTemperate($x, bool) ::: Is x temperate?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nEarthy($x, bool) ::: Is x earthy?\nJompus($x, bool) ::: Does x belong to Jompus?\nBlue($x, bool) ::: Is x blue?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nDull($x, bool) ::: Is x dull?\nImpus($x, bool) ::: Does x belong to Impus?\nSour($x, bool) ::: Is x sour?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nVumpus($x, bool) ::: Does x belong to Vumpus?\n\nFacts:\nNumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Mean($x, True)\nRompus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Happy($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Temperate($x, False)\nNumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Large($x, False)\nTumpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Earthy($x, True)\nYumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Blue($x, True)\nJompus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Impus($x, True)\nVumpus($x, True) >>> Dull($x, True)\nImpus($x, True) >>> Sour($x, True)\nImpus($x, True) >>> Dumpus($x, True)\n\nQuery:\nDull(Alex, False)"]}, "instruction": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/80034/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nRompus($x, bool) ::: Does x belong to Rompus?\nMean($x, bool) ::: Is x mean?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nHappy($x, bool) ::: Is x happy?\nNumpus($x, bool) ::: Does x belong to Numpus?\nTemperate($x, bool) ::: Is x temperate?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nEarthy($x, bool) ::: Is x earthy?\nJompus($x, bool) ::: Does x belong to Jompus?\nBlue($x, bool) ::: Is x blue?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nDull($x, bool) ::: Is x dull?\nImpus($x, bool) ::: Does x belong to Impus?\nSour($x, bool) ::: Is x sour?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nVumpus($x, bool) ::: Does x belong to Vumpus?\n\nFacts:\nNumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Mean($x, True)\nRompus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Happy($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Temperate($x, False)\nNumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Large($x, False)\nTumpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Earthy($x, True)\nYumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Blue($x, True)\nJompus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Impus($x, True)\nVumpus($x, True) >>> Dull($x, True)\nImpus($x, True) >>> Sour($x, True)\nImpus($x, True) >>> Dumpus($x, True)\n\nQuery:\nDull(Alex, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "history": [[{"id": 17, "timestamp": "2024-05-29T17:17:54.764382", "source": "user", "message": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/80034/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nRompus($x, bool) ::: Does x belong to Rompus?\nMean($x, bool) ::: Is x mean?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nHappy($x, bool) ::: Is x happy?\nNumpus($x, bool) ::: Does x belong to Numpus?\nTemperate($x, bool) ::: Is x temperate?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nEarthy($x, bool) ::: Is x earthy?\nJompus($x, bool) ::: Does x belong to Jompus?\nBlue($x, bool) ::: Is x blue?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nDull($x, bool) ::: Is x dull?\nImpus($x, bool) ::: Does x belong to Impus?\nSour($x, bool) ::: Is x sour?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nVumpus($x, bool) ::: Does x belong to Vumpus?\n\nFacts:\nNumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Mean($x, True)\nRompus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Happy($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Temperate($x, False)\nNumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Large($x, False)\nTumpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Earthy($x, True)\nYumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Blue($x, True)\nJompus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Impus($x, True)\nVumpus($x, True) >>> Dull($x, True)\nImpus($x, True) >>> Sour($x, True)\nImpus($x, True) >>> Dumpus($x, True)\n\nQuery:\nDull(Alex, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "action": "message", "args": {"content": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. You first need to install a python package through ```pip install scitools-pyke```. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/80034/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nRompus($x, bool) ::: Does x belong to Rompus?\nMean($x, bool) ::: Is x mean?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nHappy($x, bool) ::: Is x happy?\nNumpus($x, bool) ::: Does x belong to Numpus?\nTemperate($x, bool) ::: Is x temperate?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nEarthy($x, bool) ::: Is x earthy?\nJompus($x, bool) ::: Does x belong to Jompus?\nBlue($x, bool) ::: Is x blue?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nDull($x, bool) ::: Is x dull?\nImpus($x, bool) ::: Does x belong to Impus?\nSour($x, bool) ::: Is x sour?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nVumpus($x, bool) ::: Does x belong to Vumpus?\n\nFacts:\nNumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Mean($x, True)\nRompus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Happy($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Temperate($x, False)\nNumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Large($x, False)\nTumpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Earthy($x, True)\nYumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Blue($x, True)\nJompus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Impus($x, True)\nVumpus($x, True) >>> Dull($x, True)\nImpus($x, True) >>> Sour($x, True)\nImpus($x, True) >>> Dumpus($x, True)\n\nQuery:\nDull(Alex, False)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 20, "timestamp": "2024-05-29T17:17:56.628057", "source": "agent", "message": "Running Python code interactively: %pip install scitools-pyke", "action": "run_ipython", "args": {"code": "%pip install scitools-pyke", "thought": "Let's start by installing the required package `scitools-pyke`.", "kernel_init_code": "from agentskills import *"}}, {"id": 21, "timestamp": "2024-05-29T17:18:01.477265", "source": "agent", "message": "Code executed in IPython cell.", "cause": 20, "observation": "run_ipython", "content": "[Package installed successfully]\n[Kernel restarted successfully to load the package]", "extras": {"code": "%pip install scitools-pyke"}}], [{"id": 22, "timestamp": "2024-05-29T17:18:17.639407", "source": "agent", "message": "Running Python code interactively: import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nworkspace_mount_path = \"/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034\"\n\nengine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n\nlogic_programs = \"\"\"\nPredicates:\nRompus($x, bool) ::: Does x belong to Rompus?\nMean($x, bool) ::: Is x mean?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nHappy($x, bool) ::: Is x happy?\nNumpus($x, bool) ::: Does x belong to Numpus?\nTemperate($x, bool) ::: Is x temperate?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nEarthy($x, bool) ::: Is x earthy?\nJompus($x, bool) ::: Does x belong to Jompus?\nBlue($x, bool) ::: Is x blue?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nDull($x, bool) ::: Is x dull?\nImpus($x, bool) ::: Does x belong to Impus?\nSour($x, bool) ::: Is x sour?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nVumpus($x, bool) ::: Does x belong to Vumpus?\n\nFacts:\nNumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Mean($x, True)\nRompus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Happy($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Temperate($x, False)\nNumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Large($x, False)\nTumpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Earthy($x, True)\nYumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Blue($x, True)\nJompus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Impus($x, True)\nVumpus($x, True) >>> Dull($x, True)\nImpus($x, True) >>> Sour($x, True)\nImpus($x, True) >>> Dumpus($x, True)\n\nQuery:\nDull(Alex, False)\n\"\"\"\n\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "action": "run_ipython", "args": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nworkspace_mount_path = \"/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034\"\n\nengine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n\nlogic_programs = \"\"\"\nPredicates:\nRompus($x, bool) ::: Does x belong to Rompus?\nMean($x, bool) ::: Is x mean?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nHappy($x, bool) ::: Is x happy?\nNumpus($x, bool) ::: Does x belong to Numpus?\nTemperate($x, bool) ::: Is x temperate?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nEarthy($x, bool) ::: Is x earthy?\nJompus($x, bool) ::: Does x belong to Jompus?\nBlue($x, bool) ::: Is x blue?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nDull($x, bool) ::: Is x dull?\nImpus($x, bool) ::: Does x belong to Impus?\nSour($x, bool) ::: Is x sour?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nVumpus($x, bool) ::: Does x belong to Vumpus?\n\nFacts:\nNumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Mean($x, True)\nRompus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Happy($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Temperate($x, False)\nNumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Large($x, False)\nTumpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Earthy($x, True)\nYumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Blue($x, True)\nJompus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Impus($x, True)\nVumpus($x, True) >>> Dull($x, True)\nImpus($x, True) >>> Sour($x, True)\nImpus($x, True) >>> Dumpus($x, True)\n\nQuery:\nDull(Alex, False)\n\"\"\"\n\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "thought": "Next, let's import the necessary module and instantiate the `LogicInferenceEngine` class. Then, we will use the `safe_execute_program` method to prove the logic programs.", "kernel_init_code": "from agentskills import *"}}, {"id": 23, "timestamp": "2024-05-29T17:18:18.119648", "source": "agent", "message": "Code executed in IPython cell.", "cause": 22, "observation": "run_ipython", "content": "'A'", "extras": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nworkspace_mount_path = \"/Users/ma/Desktop/projects/opendevin/_eval_workspace/80034\"\n\nengine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n\nlogic_programs = \"\"\"\nPredicates:\nRompus($x, bool) ::: Does x belong to Rompus?\nMean($x, bool) ::: Is x mean?\nZumpus($x, bool) ::: Does x belong to Zumpus?\nHappy($x, bool) ::: Is x happy?\nNumpus($x, bool) ::: Does x belong to Numpus?\nTemperate($x, bool) ::: Is x temperate?\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nEarthy($x, bool) ::: Is x earthy?\nJompus($x, bool) ::: Does x belong to Jompus?\nBlue($x, bool) ::: Is x blue?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nDull($x, bool) ::: Is x dull?\nImpus($x, bool) ::: Does x belong to Impus?\nSour($x, bool) ::: Is x sour?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nVumpus($x, bool) ::: Does x belong to Vumpus?\n\nFacts:\nNumpus(Alex, True)\n\nRules:\nRompus($x, True) >>> Mean($x, True)\nRompus($x, True) >>> Zumpus($x, True)\nZumpus($x, True) >>> Happy($x, False)\nZumpus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Temperate($x, False)\nNumpus($x, True) >>> Tumpus($x, True)\nTumpus($x, True) >>> Large($x, False)\nTumpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Earthy($x, True)\nYumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Blue($x, True)\nJompus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Impus($x, True)\nVumpus($x, True) >>> Dull($x, True)\nImpus($x, True) >>> Sour($x, True)\nImpus($x, True) >>> Dumpus($x, True)\n\nQuery:\nDull(Alex, False)\n\"\"\"\n\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer"}}], [{"id": 24, "timestamp": "2024-05-29T17:18:19.708516", "source": "agent", "message": "The answer to the query `Dull(Alex, False)` is `A`.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "action": "message", "args": {"content": "The answer to the query `Dull(Alex, False)` is `A`.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "wait_for_response": true}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 26, "timestamp": "2024-05-29T17:18:19.708790", "source": "user", "message": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "action": "message", "args": {"content": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 30, "timestamp": "2024-05-29T17:18:23.882041", "source": "agent", "message": "All done! What's next on the agenda?", "action": "finish", "args": {"outputs": {}, "thought": ""}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}]], "final_message": "A", "messages": ["", "", "", "'A'"], "error": null, "test_result": {"result": true}} +{"id": "ProntoQA_6", "instance": {"id": "ProntoQA_6", "answer": "A", "context": "Every tumpus is large. Tumpuses are wumpuses. Each wumpus is not opaque. Every dumpus is not dull. Every wumpus is a rompus. Every rompus is brown. Each rompus is a vumpus. Each vumpus is temperate. Vumpuses are jompuses. Jompuses are dull. Jompuses are numpuses. Every numpus is liquid. Each numpus is an impus. Impuses are spicy. Every impus is a yumpus. Every yumpus is not nervous. Every yumpus is a zumpus. Wren is a tumpus.", "options": ["A) True", "B) False"], "question": "Is the following statement true or false? Wren is dull.", "raw_logic_programs": ["Predicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nOpaque($x, bool) ::: Is x opaque?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nDull($x, bool) ::: Is x dull?\nRompus($x, bool) ::: Does x belong to Rompus?\nBrown($x, bool) ::: Is x brown?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nTemperate($x, bool) ::: Is x temperate?\nJompus($x, bool) ::: Does x belong to Jompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nLiquid($x, bool) ::: Is x liquid?\nImpus($x, bool) ::: Does x belong to Impus?\nSpicy($x, bool) ::: Is x spicy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nNervous($x, bool) ::: Is x nervous?\nZumpus($x, bool) ::: Does x belong to Zumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nTumpus($x, True) >>> Large($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Opaque($x, False)\nDumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Rompus($x, True)\nRompus($x, True) >>> Brown($x, True)\nRompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Temperate($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Dull($x, True)\nJompus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Liquid($x, True)\nNumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Spicy($x, True)\nImpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Nervous($x, False)\nYumpus($x, True) >>> Zumpus($x, True)\n\nQuery:\nDull(Wren, True)"]}, "instruction": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/76300/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nOpaque($x, bool) ::: Is x opaque?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nDull($x, bool) ::: Is x dull?\nRompus($x, bool) ::: Does x belong to Rompus?\nBrown($x, bool) ::: Is x brown?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nTemperate($x, bool) ::: Is x temperate?\nJompus($x, bool) ::: Does x belong to Jompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nLiquid($x, bool) ::: Is x liquid?\nImpus($x, bool) ::: Does x belong to Impus?\nSpicy($x, bool) ::: Is x spicy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nNervous($x, bool) ::: Is x nervous?\nZumpus($x, bool) ::: Does x belong to Zumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nTumpus($x, True) >>> Large($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Opaque($x, False)\nDumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Rompus($x, True)\nRompus($x, True) >>> Brown($x, True)\nRompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Temperate($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Dull($x, True)\nJompus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Liquid($x, True)\nNumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Spicy($x, True)\nImpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Nervous($x, False)\nYumpus($x, True) >>> Zumpus($x, True)\n\nQuery:\nDull(Wren, True)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "history": [[{"id": 0, "timestamp": "2024-05-29T17:52:04.959139", "source": "user", "message": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/76300/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nOpaque($x, bool) ::: Is x opaque?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nDull($x, bool) ::: Is x dull?\nRompus($x, bool) ::: Does x belong to Rompus?\nBrown($x, bool) ::: Is x brown?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nTemperate($x, bool) ::: Is x temperate?\nJompus($x, bool) ::: Does x belong to Jompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nLiquid($x, bool) ::: Is x liquid?\nImpus($x, bool) ::: Does x belong to Impus?\nSpicy($x, bool) ::: Is x spicy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nNervous($x, bool) ::: Is x nervous?\nZumpus($x, bool) ::: Does x belong to Zumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nTumpus($x, True) >>> Large($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Opaque($x, False)\nDumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Rompus($x, True)\nRompus($x, True) >>> Brown($x, True)\nRompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Temperate($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Dull($x, True)\nJompus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Liquid($x, True)\nNumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Spicy($x, True)\nImpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Nervous($x, False)\nYumpus($x, True) >>> Zumpus($x, True)\n\nQuery:\nDull(Wren, True)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "action": "message", "args": {"content": "You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. \nyou can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. The code should be enclosed using \"\" tag.\nIn this task, you need to use the code in /Users/ma/Desktop/projects/opendevin/_eval_workspace/76300/logic_inference.py to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. \n\nAn example would be look like this:\n \n import sys\n sys.path.append(workspace_mount_path)\n engine = LogicInferenceEngine(dataset_name, workspace_mount_path)\n answer, flag, error_message = engine.safe_execute_program(logic_programs)\n \n\nPlease send the *answer* variable through message.\n\ndataset_name:\nProntoQA\n\nlogic_programs:\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nOpaque($x, bool) ::: Is x opaque?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nDull($x, bool) ::: Is x dull?\nRompus($x, bool) ::: Does x belong to Rompus?\nBrown($x, bool) ::: Is x brown?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nTemperate($x, bool) ::: Is x temperate?\nJompus($x, bool) ::: Does x belong to Jompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nLiquid($x, bool) ::: Is x liquid?\nImpus($x, bool) ::: Does x belong to Impus?\nSpicy($x, bool) ::: Is x spicy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nNervous($x, bool) ::: Is x nervous?\nZumpus($x, bool) ::: Does x belong to Zumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nTumpus($x, True) >>> Large($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Opaque($x, False)\nDumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Rompus($x, True)\nRompus($x, True) >>> Brown($x, True)\nRompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Temperate($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Dull($x, True)\nJompus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Liquid($x, True)\nNumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Spicy($x, True)\nImpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Nervous($x, False)\nYumpus($x, True) >>> Zumpus($x, True)\n\nQuery:\nDull(Wren, True)\n\nWhen you think you have solved the question, please first send your answer to user through message and then exit.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 3, "timestamp": "2024-05-29T17:52:18.436287", "source": "agent", "message": "Running Python code interactively: import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/76300')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nworkspace_mount_path = \"/Users/ma/Desktop/projects/opendevin/_eval_workspace/76300\"\n\nlogic_programs = \"\"\"\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nOpaque($x, bool) ::: Is x opaque?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nDull($x, bool) ::: Is x dull?\nRompus($x, bool) ::: Does x belong to Rompus?\nBrown($x, bool) ::: Is x brown?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nTemperate($x, bool) ::: Is x temperate?\nJompus($x, bool) ::: Does x belong to Jompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nLiquid($x, bool) ::: Is x liquid?\nImpus($x, bool) ::: Does x belong to Impus?\nSpicy($x, bool) ::: Is x spicy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nNervous($x, bool) ::: Is x nervous?\nZumpus($x, bool) ::: Does x belong to Zumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nTumpus($x, True) >>> Large($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Opaque($x, False)\nDumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Rompus($x, True)\nRompus($x, True) >>> Brown($x, True)\nRompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Temperate($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Dull($x, True)\nJompus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Liquid($x, True)\nNumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Spicy($x, True)\nImpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Nervous($x, False)\nYumpus($x, True) >>> Zumpus($x, True)\n\nQuery:\nDull(Wren, True)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, workspace_mount_path)\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "action": "run_ipython", "args": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/76300')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nworkspace_mount_path = \"/Users/ma/Desktop/projects/opendevin/_eval_workspace/76300\"\n\nlogic_programs = \"\"\"\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nOpaque($x, bool) ::: Is x opaque?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nDull($x, bool) ::: Is x dull?\nRompus($x, bool) ::: Does x belong to Rompus?\nBrown($x, bool) ::: Is x brown?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nTemperate($x, bool) ::: Is x temperate?\nJompus($x, bool) ::: Does x belong to Jompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nLiquid($x, bool) ::: Is x liquid?\nImpus($x, bool) ::: Does x belong to Impus?\nSpicy($x, bool) ::: Is x spicy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nNervous($x, bool) ::: Is x nervous?\nZumpus($x, bool) ::: Does x belong to Zumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nTumpus($x, True) >>> Large($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Opaque($x, False)\nDumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Rompus($x, True)\nRompus($x, True) >>> Brown($x, True)\nRompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Temperate($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Dull($x, True)\nJompus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Liquid($x, True)\nNumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Spicy($x, True)\nImpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Nervous($x, False)\nYumpus($x, True) >>> Zumpus($x, True)\n\nQuery:\nDull(Wren, True)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, workspace_mount_path)\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer", "thought": "Let's proceed with the task by executing the logic inference using the provided code and logic programs.\n\nFirst, we need to import the necessary module and instantiate the `LogicInferenceEngine` class. Then, we will use the `safe_execute_program` method to evaluate the logic programs and get the answer for the query.\n\nLet's start by executing the code:", "kernel_init_code": "from agentskills import *"}}, {"id": 4, "timestamp": "2024-05-29T17:52:18.916243", "source": "agent", "message": "Code executed in IPython cell.", "cause": 3, "observation": "run_ipython", "content": "'A'", "extras": {"code": "import sys\nsys.path.append('/Users/ma/Desktop/projects/opendevin/_eval_workspace/76300')\nfrom logic_inference import LogicInferenceEngine\n\ndataset_name = \"ProntoQA\"\nworkspace_mount_path = \"/Users/ma/Desktop/projects/opendevin/_eval_workspace/76300\"\n\nlogic_programs = \"\"\"\nPredicates:\nTumpus($x, bool) ::: Does x belong to Tumpus?\nLarge($x, bool) ::: Is x large?\nWumpus($x, bool) ::: Does x belong to Wumpus?\nOpaque($x, bool) ::: Is x opaque?\nDumpus($x, bool) ::: Does x belong to Dumpus?\nDull($x, bool) ::: Is x dull?\nRompus($x, bool) ::: Does x belong to Rompus?\nBrown($x, bool) ::: Is x brown?\nVumpus($x, bool) ::: Does x belong to Vumpus?\nTemperate($x, bool) ::: Is x temperate?\nJompus($x, bool) ::: Does x belong to Jompus?\nNumpus($x, bool) ::: Does x belong to Numpus?\nLiquid($x, bool) ::: Is x liquid?\nImpus($x, bool) ::: Does x belong to Impus?\nSpicy($x, bool) ::: Is x spicy?\nYumpus($x, bool) ::: Does x belong to Yumpus?\nNervous($x, bool) ::: Is x nervous?\nZumpus($x, bool) ::: Does x belong to Zumpus?\n\nFacts:\nTumpus(Wren, True)\n\nRules:\nTumpus($x, True) >>> Large($x, True)\nTumpus($x, True) >>> Wumpus($x, True)\nWumpus($x, True) >>> Opaque($x, False)\nDumpus($x, True) >>> Dull($x, False)\nWumpus($x, True) >>> Rompus($x, True)\nRompus($x, True) >>> Brown($x, True)\nRompus($x, True) >>> Vumpus($x, True)\nVumpus($x, True) >>> Temperate($x, True)\nVumpus($x, True) >>> Jompus($x, True)\nJompus($x, True) >>> Dull($x, True)\nJompus($x, True) >>> Numpus($x, True)\nNumpus($x, True) >>> Liquid($x, True)\nNumpus($x, True) >>> Impus($x, True)\nImpus($x, True) >>> Spicy($x, True)\nImpus($x, True) >>> Yumpus($x, True)\nYumpus($x, True) >>> Nervous($x, False)\nYumpus($x, True) >>> Zumpus($x, True)\n\nQuery:\nDull(Wren, True)\n\"\"\"\n\nengine = LogicInferenceEngine(dataset_name, workspace_mount_path)\nanswer, flag, error_message = engine.safe_execute_program(logic_programs)\nanswer"}}], [{"id": 5, "timestamp": "2024-05-29T17:52:20.956844", "source": "agent", "message": "The answer to the query `Dull(Wren, True)` is `A`, which indicates that the query is true based on the given facts and rules.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "action": "message", "args": {"content": "The answer to the query `Dull(Wren, True)` is `A`, which indicates that the query is true based on the given facts and rules.\n\nIf you have any further questions or need additional assistance, feel free to ask!", "wait_for_response": true}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 7, "timestamp": "2024-05-29T17:52:20.957375", "source": "user", "message": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "action": "message", "args": {"content": "Please continue working on the task on whatever approach you think is suitable.\nIf you think you have solved the task, please run the following command: exit .\nIMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n", "wait_for_response": false}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}], [{"id": 11, "timestamp": "2024-05-29T17:52:22.414061", "source": "agent", "message": "All done! What's next on the agenda?", "action": "finish", "args": {"outputs": {}, "thought": ""}}, {"message": "No observation", "observation": "null", "content": "", "extras": {}}]], "final_message": "A", "messages": ["", "", "", "'A'"], "error": null, "test_result": {"result": true}} diff --git a/evaluation/logic_reasoning/.cache_program/facts.kfb b/evaluation/logic_reasoning/.cache_program/facts.kfb new file mode 100644 index 0000000000..92ea1430cc --- /dev/null +++ b/evaluation/logic_reasoning/.cache_program/facts.kfb @@ -0,0 +1,12 @@ +Cold(Bob, True) +Quiet(Bob, True) +Red(Bob, True) +Smart(Bob, True) +Kind(Charlie, True) +Quiet(Charlie, True) +Red(Charlie, True) +Rough(Charlie, True) +Cold(Dave, True) +Kind(Dave, True) +Smart(Dave, True) +Quiet(Fiona, True) diff --git a/evaluation/logic_reasoning/.cache_program/rules.krb b/evaluation/logic_reasoning/.cache_program/rules.krb new file mode 100644 index 0000000000..aed3e3b685 --- /dev/null +++ b/evaluation/logic_reasoning/.cache_program/rules.krb @@ -0,0 +1,52 @@ +fact1 + foreach + facts.Quiet($x, True) + facts.Cold($x, True) + assert + facts.Smart($x, True) + +fact2 + foreach + facts.Red($x, True) + facts.Cold($x, True) + assert + facts.Round($x, True) + +fact3 + foreach + facts.Kind($x, True) + facts.Rough($x, True) + assert + facts.Red($x, True) + +fact4 + foreach + facts.Quiet($x, True) + assert + facts.Rough($x, True) + +fact5 + foreach + facts.Cold($x, True) + facts.Smart($x, True) + assert + facts.Red($x, True) + +fact6 + foreach + facts.Rough($x, True) + assert + facts.Cold($x, True) + +fact7 + foreach + facts.Red($x, True) + assert + facts.Rough($x, True) + +fact8 + foreach + facts.Smart(Dave, True) + facts.Kind(Dave, True) + assert + facts.Quiet(Dave, True) \ No newline at end of file diff --git a/evaluation/logic_reasoning/README.md b/evaluation/logic_reasoning/README.md new file mode 100644 index 0000000000..37bebf4731 --- /dev/null +++ b/evaluation/logic_reasoning/README.md @@ -0,0 +1,43 @@ +# Logic Reasoning Evaluation + +This folder contains evaluation harness for evaluating agents on the logic reasoning benchmark [ProntoQA](https://github.com/asaparov/prontoqa) and [ProofWriter](https://allenai.org/data/proofwriter). + +## Configure OpenDevin and your LLM + +Create a `config.toml` file if it does not exist at the root of the workspace. + +Add the following configurations: + +```toml +[core] +max_iterations = 100 +cache_dir = "/tmp/cache" +ssh_hostname = "localhost" +enable_auto_lint = true + +# TODO: Change these to the model you want to evaluate +[eval_gpt4_1106_preview] +model = "gpt-4-1106-preview" +api_key = "XXX" +temperature = 0.0 + +[eval_some_openai_compatible_model] +model = "openai/MODEL_NAME" +base_url = "https://OPENAI_COMPATIBLE_URL/v1" +api_key = "XXX" +temperature = 0.0 +``` + +## Run Inference on logic_reasoning +The following code will run inference on the first example of the ProntoQA dataset with model gpt-4o. +```bash +./evaluation/logic_reasoning/scripts/run_infer.sh ProntoQA gpt-4o 1 +``` + + +## Examples + +See example output in +`./evaluation_outputs/outputs/logic_reasoning/CodeActAgent/ProntoQA/gpt-4o-2024-05-13_maxiter_10_N_v1.5/output.jsonl` +and final evaluation performance in +`./evaluation_outputs/outputs/logic_reasoning/CodeActAgent/ProntoQA/gpt-4o-2024-05-13_maxiter_10_N_v1.5/metadata.json` \ No newline at end of file diff --git a/evaluation/logic_reasoning/__init__.py b/evaluation/logic_reasoning/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/evaluation/logic_reasoning/instruction.txt b/evaluation/logic_reasoning/instruction.txt new file mode 100644 index 0000000000..447634793e --- /dev/null +++ b/evaluation/logic_reasoning/instruction.txt @@ -0,0 +1,20 @@ +You are a helpful assistant assigned with logic reasoning task. You need to determine the correctness of a query given some facts and fules. +you can interact with an interactive Python (Jupyter Notebook) environment and receive the corresponding output when needed. The code should be enclosed using "" tag. +In this task, you need to use the code in [[logic_inference_path.py]] to help you. Specifically, you first need to instantiate a **LogicInferenceEngine** class and use the **safe_execute_program** method to prove the **logic programs**. You should receive *answer*, *flag*, *error_message* from the output. + +An example would be look like this: + + import sys + sys.path.append(workspace_mount_path) + engine = LogicInferenceEngine(dataset_name, workspace_mount_path) + answer, flag, error_message = engine.safe_execute_program(logic_programs) + + +Please send the *answer* variable through message. + +dataset_name: +[[dataset_name]] + +logic_programs: +[[logic_programs]] + diff --git a/evaluation/logic_reasoning/logic_inference.py b/evaluation/logic_reasoning/logic_inference.py new file mode 100644 index 0000000000..16f85d73dc --- /dev/null +++ b/evaluation/logic_reasoning/logic_inference.py @@ -0,0 +1,220 @@ +import os +import random +import re +import shutil + +from pyke import knowledge_engine + + +class PykeProgram: + def __init__( + self, logic_program: str, dataset_name='ProntoQA', workspace_mount_path='./' + ) -> None: + self.logic_program = logic_program + self.flag = self.parse_logic_program() + self.dataset_name = dataset_name + self.cache_dir = os.path.join(workspace_mount_path, '.cache_program') + + # prepare the files for facts and rules + try: + self.create_fact_file(self.Facts) + self.create_rule_file(self.Rules) + self.flag = True + except Exception: + self.flag = False + + self.answer_map = { + 'ProntoQA': self.answer_map_prontoqa, + 'ProofWriter': self.answer_map_proofwriter, + } + + def parse_logic_program(self): + keywords = ['Query:', 'Rules:', 'Facts:', 'Predicates:'] + program_str = self.logic_program + for keyword in keywords: + try: + program_str, segment_list = self._parse_segment(program_str, keyword) + setattr(self, keyword[:-1], segment_list) + except Exception: + setattr(self, keyword[:-1], None) + + return self.validate_program() + + def _parse_segment(self, program_str, key_phrase): + remain_program_str, segment = program_str.split(key_phrase) + segment_list = segment.strip().split('\n') + for i in range(len(segment_list)): + segment_list[i] = segment_list[i].split(':::')[0].strip() + return remain_program_str, segment_list + + # check if the program is valid; if not, try to fix it + def validate_program(self): + if self.Rules is not None and self.Facts is not None: + if not self.Rules[0] == '' and not self.Facts[0] == '': + return True + # try to fix the program + tmp_rules = [] + tmp_facts = [] + statements = self.Facts if self.Facts is not None else self.Rules + if statements is None: + return False + + for fact in statements: + if fact.find('>>>') >= 0: # this is a rule + tmp_rules.append(fact) + else: + tmp_facts.append(fact) + self.Rules = tmp_rules + self.Facts = tmp_facts + return False + + def create_fact_file(self, facts): + with open(os.path.join(self.cache_dir, 'facts.kfb'), 'w') as f: + for fact in facts: + # check for invalid facts + if not fact.find('$x') >= 0: + f.write(fact + '\n') + + def create_rule_file(self, rules): + pyke_rules = [] + for idx, rule in enumerate(rules): + pyke_rules.append(self.parse_forward_rule(idx + 1, rule)) + + with open(os.path.join(self.cache_dir, 'rules.krb'), 'w') as f: + f.write('\n\n'.join(pyke_rules)) + + # example rule: Furry($x, True) && Quite($x, True) >>> White($x, True) + def parse_forward_rule(self, f_index, rule): + premise, conclusion = rule.split('>>>') + premise = premise.strip() + # split the premise into multiple facts if needed + premise = premise.split('&&') + premise_list = [p.strip() for p in premise] + + conclusion = conclusion.strip() + # split the conclusion into multiple facts if needed + conclusion = conclusion.split('&&') + conclusion_list = [c.strip() for c in conclusion] + + # create the Pyke rule + pyke_rule = f"""fact{f_index}\n\tforeach""" + for p in premise_list: + pyke_rule += f"""\n\t\tfacts.{p}""" + pyke_rule += """\n\tassert""" + for c in conclusion_list: + pyke_rule += f"""\n\t\tfacts.{c}""" + return pyke_rule + + """ + for example: Is Marvin from Mars? + Query: FromMars(Marvin, $label) + """ + + def check_specific_predicate(self, subject_name, predicate_name, engine): + results = [] + with engine.prove_goal( + f'facts.{predicate_name}({subject_name}, $label)' + ) as gen: + for vars, plan in gen: + results.append(vars['label']) + + with engine.prove_goal( + f'rules.{predicate_name}({subject_name}, $label)' + ) as gen: + for vars, plan in gen: + results.append(vars['label']) + + if len(results) == 1: + return results[0] + elif len(results) == 2: + return results[0] and results[1] + elif len(results) == 0: + return None + + """ + Input Example: Metallic(Wren, False) + """ + + def parse_query(self, query): + pattern = r'(\w+)\(([^,]+),\s*([^)]+)\)' + match = re.match(pattern, query) + if match: + function_name = match.group(1) + arg1 = match.group(2) + arg2 = match.group(3) + arg2 = True if arg2 == 'True' else False + return function_name, arg1, arg2 + else: + raise ValueError(f'Invalid query: {query}') + + def execute_program(self): + # delete the compiled_krb dir + complied_krb_dir = './models/compiled_krb' + if os.path.exists(complied_krb_dir): + print('removing compiled_krb') + # os.system(f'rm -rf {complied_krb_dir}/*') + shutil.rmtree(complied_krb_dir) + + # absolute_path = os.path.abspath(complied_krb_dir) + # print(absolute_path) + try: + engine = knowledge_engine.engine(self.cache_dir) + engine.reset() + engine.activate('rules') + engine.get_kb('facts') + + # parse the logic query into pyke query + predicate, subject, value_to_check = self.parse_query(self.Query[0]) + result = self.check_specific_predicate(subject, predicate, engine) + answer = self.answer_map[self.dataset_name](result, value_to_check) + except Exception as err: + return None, err + + return answer, '' + + def answer_mapping(self, answer): + return answer + + def answer_map_prontoqa(self, result, value_to_check): + if result == value_to_check: + return 'A' + else: + return 'B' + + def answer_map_proofwriter(self, result, value_to_check): + if result is None: + return 'C' + elif result == value_to_check: + return 'A' + else: + return 'B' + + +class LogicInferenceEngine: + def __init__(self, dataset_name, workspace_mount_path): + self.dataset_name = dataset_name + self.workspace_mount_path = workspace_mount_path + + def random_backup(self): + if self.dataset_name == 'ProntoQA': + return random.choice(['A', 'B']) + elif self.dataset_name == 'ProofWriter': + return random.choice(['A', 'B', 'C']) + + def safe_execute_program(self, logic_program): + program = PykeProgram( + logic_program, self.dataset_name, self.workspace_mount_path + ) + # cannot parse the program + if not program.flag: + answer = self.random_backup() + return answer, 'parsing error', '' + # execuate the program + answer, error_message = program.execute_program() + # not executable + if answer is None: + answer = self.random_backup() + return answer, 'execution error', error_message + # successfully executed + answer = program.answer_mapping(answer) + return answer, 'success', '' diff --git a/evaluation/logic_reasoning/run_infer.py b/evaluation/logic_reasoning/run_infer.py new file mode 100644 index 0000000000..862c327bd6 --- /dev/null +++ b/evaluation/logic_reasoning/run_infer.py @@ -0,0 +1,436 @@ +import asyncio +import json +import logging +import multiprocessing as mp +import os +import pathlib +import shutil +import time +from concurrent.futures import ProcessPoolExecutor + +from datasets import load_dataset +from tqdm import tqdm + +from evaluation.swe_bench.swe_env_box import DockerSSHBox +from opendevin.controller.state.state import State +from opendevin.core.config import config, get_llm_config_arg, get_parser +from opendevin.core.logger import get_console_handler +from opendevin.core.logger import opendevin_logger as logger +from opendevin.core.main import main +from opendevin.events.action import MessageAction +from opendevin.events.serialization.event import event_to_dict + + +def cleanup(): + logger.info('Cleaning up child processes...') + for process in mp.active_children(): + logger.info(f'Terminating child process: {process.name}') + process.terminate() + process.join() + + +def codeact_user_response(state: State) -> str: + msg = ( + 'Please continue working on the task on whatever approach you think is suitable.\n' + 'If you think you have solved the task, please run the following command: exit .\n' + 'IMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP OR USE THE INTERNET TO SOLVE THIS TASK.\n' + ) + if state.history: + user_msgs = [ + action + for action, _ in state.history + if isinstance(action, MessageAction) and action.source == 'user' + ] + if len(user_msgs) >= 2: + # let the agent know that it can give up when it has tried 3 times + return ( + msg + + 'If you want to give up, run: exit .\n' + ) + return msg + + +def monologue_user_response(state: State) -> str: + raise NotImplementedError('MonologueAgent should never ask for user responses.') + + +AGENT_CLS_TO_FAKE_USER_RESPONSE_FN = { + 'CodeActAgent': codeact_user_response, + 'MonologueAgent': monologue_user_response, +} + +AGENT_CLS_TO_INST_SUFFIX = { + 'CodeActAgent': 'When you think you have solved the question, please first send your answer to user through message and then exit.\n' +} + + +def get_choice(answer_str): + choices = [ + 'A', + 'B', + 'C', + 'D', + 'E', + 'F', + 'G', + 'H', + 'A)', + 'B)', + 'C)', + 'D)', + 'E)', + 'F)', + 'G)', + 'H)', + 'A.', + 'B.', + 'C.', + 'D.', + 'E.', + 'F.', + 'G.', + 'H.', + ] + for c in choices: + if answer_str.startswith(c): + return c.replace(')', '') + + if answer_str.startswith(':'): + return answer_str.replace(':', '').replace('.', '').strip() + return None + + +def get_test_result( + model_answer: str, + ground_truth: str, +) -> bool: + gold_answer = ground_truth.replace('(', '').replace(')', '').strip() + answer_str = model_answer if model_answer is not None else '' + prediction = get_choice(answer_str) + + indicators = [ + 'the correct option is', + 'the correct answer is', + 'The correct answer is', + 'The correct option is', + 'Thus, the answer is', + ] + if prediction is None: + for indicator in indicators: + if answer_str.find(indicator) >= 0: + answer_str = answer_str.split(indicator)[1].strip() + prediction = get_choice(answer_str) + break + + isTrue = prediction == gold_answer + test_result = {'result': isTrue} + return test_result + + +def process_instance( + instance, + agent_class, + # metadata, + dataset_name, + skip_workspace_mount, + eval_output_dir, + reset_logger: bool = True, +): + old_workspace_mount_path = config.workspace_mount_path + old_workspace_base = config.workspace_base + workspace_mount_path = os.path.join(config.workspace_mount_path, '_eval_workspace') + # create process-specific workspace dir + # if `not skip_workspace_mount` - we will create a workspace directory for EACH process + # so that different agent don't interfere with each other. + if not skip_workspace_mount: + workspace_mount_path = os.path.join(workspace_mount_path, str(os.getpid())) + pathlib.Path(workspace_mount_path).mkdir(parents=True, exist_ok=True) + + # reset workspace to config + config.workspace_base = workspace_mount_path + config.workspace_mount_path = workspace_mount_path + + # Setup the logger properly, so you can run multi-processing to parallize the evaluation + if reset_logger: + # Set up logger + log_file = os.path.join( + eval_output_dir, 'logs', f'instance_{instance["id"]}.log' + ) + # Remove all existing handlers from logger + for handler in logger.handlers[:]: + logger.removeHandler(handler) + # add back the console handler to print ONE line + logger.addHandler(get_console_handler()) + logger.info( + f'Starting evaluation for instance {instance["id"]}.\nLOG: tail -f {log_file}' + ) + # Remove all existing handlers from logger + for handler in logger.handlers[:]: + logger.removeHandler(handler) + file_handler = logging.FileHandler(log_file) + file_handler.setFormatter( + logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') + ) + logger.addHandler(file_handler) + + if not skip_workspace_mount: + logger.info(f'Process-specific workspace mounted at {workspace_mount_path}') + + # sandbox = DockerSSHBox() + logic_inference_path = os.path.join(workspace_mount_path, 'logic_inference.py') + if not os.path.exists(logic_inference_path): + shutil.copyfile( + './evaluation/logic_reasoning/logic_inference.py', logic_inference_path + ) + logger.info(f'logic_inference.py copied to {workspace_mount_path}') + + cache_dir = os.path.join(workspace_mount_path, '.cache_program') + if not os.path.exists(cache_dir): + os.makedirs(cache_dir) + + # Prepare instruction + + with open('./evaluation/logic_reasoning/instruction.txt', 'r') as f: + instruction = f.read() + + instance_logic_programs = instance['raw_logic_programs'][0].strip() + instruction = instruction.replace('[[dataset_name]]', dataset_name) + instruction = instruction.replace('[[logic_programs]]', instance_logic_programs) + instruction = instruction.replace( + '[[logic_inference_path.py]]', logic_inference_path + ) + + # NOTE: You can actually set slightly different instruction for different agents + instruction += AGENT_CLS_TO_INST_SUFFIX.get(agent_class, '') + + sandbox = DockerSSHBox() + exit_code, command_output = sandbox.execute(f'pip install scitools-pyke') + + # Here's how you can run the agent (similar to the `main` function) and get the final task state + state: State = asyncio.run( + main( + instruction, + fake_user_response_fn=AGENT_CLS_TO_FAKE_USER_RESPONSE_FN.get(agent_class), + sandbox=sandbox, + ) + ) + # ======= Attempt to evaluate the agent's edits ======= + # If you are working on simplier benchmark that only evaluates the final model output (e.g., in a MessageAction) + # You can simply get the LAST `MessageAction` from the returned `state.history` and parse it for evaluation. + + if state is None: + raise ValueError('State should not be None.') + + final_message = '' + messages = [] + for action, obs in reversed(state.history): + # if isinstance(act, MessageAction): + messages.append(obs.content) + # print("obs.content:", obs.content) + if str(obs.content) in ["'A'", "'B'", "'C'"]: + final_message = obs.content + break + + final_message = final_message.strip("'") + logger.info(f'Predicted answer: {final_message}, Ground truth: {instance["answer"]}') + + test_result = get_test_result( + model_answer=final_message, ground_truth=instance['answer'] + ) + + # Save the output + output = { + 'id': instance['id'], + 'instance': instance, + 'instruction': instruction, + # 'metadata': metadata, + 'history': [ + (event_to_dict(action), event_to_dict(obs)) for action, obs in state.history + ], + 'final_message': final_message, + 'messages': messages, + 'error': state.error if state and state.error else None, + 'test_result': test_result, + } + config.workspace_mount_path = old_workspace_mount_path + config.workspace_base = old_workspace_base + + # Close the sandbox + sandbox.close() + + return output + + +if __name__ == '__main__': + parser = get_parser() + parser.add_argument( + '--dataset', + type=str, + help='the logic reasoning dataset to evaluate on {ProntoQA, ProofWriter}', + default='ProntoQA', + ) + parser.add_argument( + '--data_split', + type=str, + help='data split to evaluate on {validation}', # right now we only support validation split + default='validation', + ) + + args, _ = parser.parse_known_args() + if args.directory: + config.workspace_base = os.path.abspath(args.directory) + print(f'Setting workspace base to {config.workspace_base}') + # NOTE: It is preferable to load datasets from huggingface datasets and perform post-processing + # so we don't need to manage file uploading to OpenDevin's repo + + dataset_name = args.dataset + data_split = args.data_split + dataset = load_dataset(f'renma/{dataset_name}') + logic_reasoning_tests = dataset[data_split] + logger.info(f'Evaluating logic reasoning dataset {dataset_name} {data_split} split') + + # Check https://github.com/OpenDevin/OpenDevin/blob/main/evaluation/swe_bench/README.md#configure-opendevin-and-your-llm + # for details of how to set `llm_config` + if args.llm_config: + specified_llm_config = get_llm_config_arg(args.llm_config) + if specified_llm_config: + config.llm = specified_llm_config + logger.info(f'Config for evaluation: {config}') + + # TEST METADATA + agent_class = args.agent_cls + assert ( + agent_class in AGENT_CLS_TO_FAKE_USER_RESPONSE_FN + ), f'Unsupported agent class: {agent_class}' + model_name = config.llm.model.split('/')[-1] + max_iterations = args.max_iterations + eval_note = '' + if args.eval_note is not None: + eval_note += '_N_' + args.eval_note + + eval_output_dir = os.path.join( + args.eval_output_dir, + 'logic_reasoning', + agent_class, + dataset_name, + model_name + '_maxiter_' + str(max_iterations) + eval_note + ) + + pathlib.Path(eval_output_dir).mkdir(parents=True, exist_ok=True) + pathlib.Path(os.path.join(eval_output_dir, 'logs')).mkdir( + parents=True, exist_ok=True + ) + logger.info(f'Using evaluation output directory: {eval_output_dir}') + + # LIMIT EVALUATION + eval_n_limit = args.eval_n_limit + if eval_n_limit: + logic_reasoning_tests = logic_reasoning_tests.select(list(range(eval_n_limit))) + logger.info(f'Limiting evaluation to first {eval_n_limit} instances.') + + start_time = time.strftime('%Y-%m-%d %H:%M:%S') + + # OUTPUT FILE + output_file = os.path.join(eval_output_dir, 'output.jsonl') + logger.info(f'Writing evaluation output to {output_file}') + finished_task_ids = set() + if os.path.exists(output_file): + with open(output_file, 'r') as f: + for line in f: + data = json.loads(line) + finished_task_ids.add(data['id']) + logger.warning( + f'Output file {output_file} already exists. Loaded {len(finished_task_ids)} finished instances.' + ) + output_fp = open(output_file, 'a') + + logger.info( + f'Evaluation started with Agent {agent_class}, model {model_name}, max iterations {max_iterations}.' + ) + + # ============================================= + # filter out finished instances + new_logic_reasoning_tests = [] + for instance in logic_reasoning_tests: + if instance['id'] in finished_task_ids: + logger.info( + f'Skipping instance {instance["id"]} as it is already finished.' + ) + continue + new_logic_reasoning_tests.append(instance) + + logic_reasoning_tests = new_logic_reasoning_tests + logger.info( + f'Finished instances: {len(finished_task_ids)}, Remaining instances: {len(logic_reasoning_tests)}' + ) + # ============================================= + + pbar = tqdm(total=len(logic_reasoning_tests)) + + # This function tracks the progress AND write the output to a JSONL file + def update_progress(future): + pbar.update(1) + output = future.result() + pbar.set_description(f'Instance {output["id"]}') + pbar.set_postfix_str(f'Test Result: {output["test_result"]["result"]}') + logger.info( + f'Finished evaluation for instance {output["id"]}: {output["test_result"]["result"]}' + ) + output_fp.write(json.dumps(output) + '\n') + # json.dump(output, output_fp, indent=4) + output_fp.flush() + + # This sets the multi-processing + num_workers = args.eval_num_workers + # num_workers = 1 + logger.info(f'Using {num_workers} workers for evaluation.') + + # This is SWE-Bench specific - CodeActAgent don't requires mounted workspace to work + skip_workspace_mount = False + logger.info(f'Skipping workspace mount: {skip_workspace_mount}') + + try: + with ProcessPoolExecutor(num_workers) as executor: + futures = [] + # This is how we perform multi-processing + for instance in logic_reasoning_tests: + future = executor.submit( + process_instance, + instance, + agent_class, + dataset_name, + skip_workspace_mount, + eval_output_dir, + reset_logger=bool(num_workers > 1), + ) + future.add_done_callback(update_progress) + futures.append(future) + + # Wait for all futures to complete + for future in futures: + future.result() + except KeyboardInterrupt: + print('KeyboardInterrupt received. Cleaning up...') + cleanup() + + output_fp.close() + + with open(output_file, 'r') as f: + test_result = [(json.loads(line))["test_result"]["result"] for line in f] + + metadata = { + "Dataset": dataset_name, + "Data split": data_split, + "Number of Samples": len(test_result), + 'Agent class': agent_class, + 'Model name': model_name, + 'Start_time': start_time, + "End_time": time.strftime('%Y-%m-%d %H:%M:%S'), + "Final Accuracy": f"{sum(test_result)/len(test_result):.2f}", + } + + with open(os.path.join(eval_output_dir, 'metadata.json'), 'w') as f: + json.dump(metadata, f, indent=4) + + logger.info(f'Metadata: {json.dumps(metadata, indent=4)}') + logger.info(f'Evaluation finished. Metadata saved to {eval_output_dir}/metadata.json') diff --git a/evaluation/logic_reasoning/scripts/run_infer.sh b/evaluation/logic_reasoning/scripts/run_infer.sh new file mode 100755 index 0000000000..6838e8e089 --- /dev/null +++ b/evaluation/logic_reasoning/scripts/run_infer.sh @@ -0,0 +1,37 @@ +#!/bin/bash +DATASET=$1 +MODEL_CONFIG=$2 +EVAL_LIMIT=$3 +AGENT=$4 + +# ################################################################################ + +if [ -z "$AGENT" ]; then + echo "Agent not specified, use default CodeActAgent" + AGENT="CodeActAgent" +fi + +# IMPORTANT: Because Agent's prompt changes fairly often in the rapidly evolving codebase of OpenDevin +# We need to track the version of Agent in the evaluation to make sure results are comparable +AGENT_VERSION=v$(poetry run python -c "import agenthub; from opendevin.controller.agent import Agent; print(Agent.get_cls('$AGENT').VERSION)") + +echo "AGENT: $AGENT" +echo "AGENT_VERSION: $AGENT_VERSION" +echo "MODEL_CONFIG: $MODEL_CONFIG" + +COMMAND="poetry run python evaluation/logic_reasoning/run_infer.py \ + --agent-cls $AGENT \ + --llm-config $MODEL_CONFIG \ + --dataset $DATASET \ + --max-iterations 10 \ + --max-chars 10000000 \ + --eval-num-workers 1 \ + --eval-note $AGENT_VERSION" + +if [ -n "$EVAL_LIMIT" ]; then + echo "EVAL_LIMIT: $EVAL_LIMIT" + COMMAND="$COMMAND --eval-n-limit $EVAL_LIMIT" +fi + +# Run the command +eval $COMMAND