diff --git a/docs/examples/code/code_optimization.ipynb b/docs/examples/code/code_optimization.ipynb index 90f195ce..66028c55 100644 --- a/docs/examples/code/code_optimization.ipynb +++ b/docs/examples/code/code_optimization.ipynb @@ -35,7 +35,7 @@ "import numpy as np\n", "from datetime import datetime\n", "import opto.trace as trace\n", - "from opto.optimizers import FunctionOptimizerV2Memory\n", + "from opto.optimizers import OptoPrime\n", "from opto.trace.bundle import ExceptionNode\n", "from opto.trace.errors import ExecutionError" ] @@ -139,7 +139,7 @@ " try:\n", " action = controller(controller_input)\n", " next_obs, reward, termination, truncation, info = env.step(action)\n", - " except trace.TraceExecutionError as e:\n", + " except ExecutionError as e:\n", " error = e\n", " break\n", "\n", @@ -189,7 +189,7 @@ " \"\"\"A feedback controller that computes the action based on the observation.\"\"\"\n", " return [0, 0, 0, 0]\n", "\n", - " optimizer = FunctionOptimizerV2Memory(controller.parameters(), config_list=config_list_from_json(\"OAI_CONFIG_LIST\"))\n", + " optimizer = OptoPrime(controller.parameters(), config_list=config_list_from_json(\"OAI_CONFIG_LIST\"))\n", "\n", " env = TracedEnv(env_name, seed=seed, relative=relative)\n", "\n", @@ -203,8 +203,8 @@ " target = traj[\"observation\"][-1][\"observation\"]\n", " returns = [sum(traj[\"reward\"]) for _ in range(n_episodes)]\n", " else:\n", - " feedback = str(error)\n", " target = error.exception_node\n", + " feedback = target.data\n", "\n", " optimizer.objective = f\"The goal is to optimize the pick-and-place task. {optimizer.default_objective}\"\n", " optimizer.zero_feedback()\n", diff --git a/docs/examples/game/joint_code_optimization.ipynb b/docs/examples/game/joint_code_optimization.ipynb index 0cd0c512..15a394d1 100644 --- a/docs/examples/game/joint_code_optimization.ipynb +++ b/docs/examples/game/joint_code_optimization.ipynb @@ -17,14 +17,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import random\n", "import numpy as np\n", - "from opto.trace import bundle, node, Module\n", - "from opto.trace.errors import ExecutionError" + "from opto.trace import bundle, node, Module, GRAPH\n", + "from opto.trace.errors import ExecutionError\n", + "from opto.trace.bundle import ExceptionNode\n", + "from opto.optimizers import OptoPrime\n", + "from autogen import config_list_from_json" ] }, { @@ -38,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +156,7 @@ " return is_hit\n", "\n", " def check_terminate(self):\n", - " return self.hits == sum(self.ships.values())\n", + " return (self.hits >= sum(self.ships.values())) or (self.misses + self.hits >= self.width * self.height)\n", "\n", " def get_board(self):\n", " return self.board\n", @@ -205,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -248,10 +251,21 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ + "# Function to get user feedback for placing shot\n", + "def user_fb_for_placing_shot(board, coords):\n", + " try:\n", + " reward = board.check_shot(coords[0], coords[1])\n", + " new_map = board.get_shots()\n", + " terminal = board.check_terminate()\n", + " return new_map, reward, terminal, f\"Got {int(reward)} reward.\"\n", + " except Exception as e:\n", + " board.misses += 1\n", + " return board.get_shots(), 0, False, str(e)\n", + " \n", "# Function to rollout policy\n", "def rollout(policy, board):\n", " rewards = []\n", @@ -274,43 +288,592 @@ " scores.append(rewards.mean())\n", " scores = np.array(scores)\n", " print(f\"Scores: {scores.mean()} ({scores.std()})\")\n", - " return scores\n", - "\n", - "# Function to get user feedback for placing shot\n", - "def user_fb_for_placing_shot(board, coords):\n", - " try:\n", - " reward = board.check_shot(coords[0], coords[1])\n", - " new_map = board.get_shots()\n", - " terminal = board.check_terminate()\n", - " return new_map, reward, terminal, f\"Got {int(reward)} reward.\"\n", - " except Exception as e:\n", - " return board.get_shots(), 0, False, str(e)" + " return scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Putting it all together\n", + "# Creating the initial policy\n", "\n", - "Finally, create a `Policy` object and evaluate the online optimization of the produced codes." + "Finally, create a `Policy` object and evaluate the performance of the initial code." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scores: 0.0 (0.0)\n", + "Initial scores: [0. 0. 0.]\n" + ] + } + ], "source": [ "# Set parameters\n", "board_size = 5\n", "num_each_type = 1\n", "exclude_ships = ['C']\n", - "n_eval_episodes = 5\n", + "n_eval_episodes = 3\n", "\n", "# Create policy and evaluate\n", "policy = Policy()\n", - "eval_policy(policy, board_size, num_each_type, exclude_ships, n_eval_episodes)" + "init_scores = eval_policy(policy, board_size, num_each_type, exclude_ships, n_eval_episodes)\n", + "print(\"Initial scores:\", init_scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Putting it all together\n", + "\n", + "Create an optimizer and evaluate the online optimization of the produced codes." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prompt\n", + " \n", + "You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.\n", + "\n", + "Specifically, a problem will be composed of the following parts:\n", + "- #Instruction: the instruction which describes the things you need to do or the question you should answer.\n", + "- #Code: the code defined in the problem.\n", + "- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.\n", + "- #Variables: the input variables that you can change.\n", + "- #Constraints: the constraints or descriptions of the variables in #Variables.\n", + "- #Inputs: the values of other inputs to the code, which are not changeable.\n", + "- #Others: the intermediate values created through the code execution.\n", + "- #Outputs: the result of the code output.\n", + "- #Feedback: the feedback about the code's execution result.\n", + "\n", + "In #Variables, #Inputs, #Outputs, and #Others, the format is:\n", + "\n", + " = \n", + "\n", + "If is (code), it means is the source code of a python code, which may include docstring and definitions.\n", + "\n", + "Output_format: Your output should be in the following json format, satisfying the json syntax:\n", + "\n", + "{{\n", + "\"reasoning\": ,\n", + "\"answer\": ,\n", + "\"suggestion\": {{\n", + " : ,\n", + " : ,\n", + "}}\n", + "}}\n", + "\n", + "In \"reasoning\", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.\n", + "\n", + "If #Instruction asks for an answer, write it down in \"answer\".\n", + "\n", + "If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", + "\n", + "If no changes or answer are needed, just output TERMINATE.\n", + "\n", + "Now you see problem instance:\n", + "\n", + "================================\n", + "\n", + "#Instruction\n", + "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", + "\n", + "#Code\n", + "Policy.reason0 = eval(self=Policy0, map=list150, __code=__code1)\n", + "Policy.act0 = eval(self=Policy1, map=list150, plan=Policy.reason0, __code=__code0)\n", + "\n", + "#Documentation\n", + "[Policy.act] Given a map, select a target coordinate in a game.\n", + "X denotes hits, O denotes misses, and . denotes unknown positions..\n", + "\n", + "#Variables\n", + "(code) __code1:def reason(self, map):\n", + " \"\"\"\n", + " Given a map, analyze the board in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + " return\n", + "(code) __code0:def act(self, map, plan):\n", + " \"\"\"\n", + " Given a map, select a target coordinate in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + " return\n", + "\n", + "#Constraints\n", + "(code) __code1: The code should start with:\n", + "def reason(self, map):\n", + " \"\"\"\n", + " Given a map, analyze the board in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + "(code) __code0: The code should start with:\n", + "def act(self, map, plan):\n", + " \"\"\"\n", + " Given a map, select a target coordinate in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + "\n", + "#Inputs\n", + "(Policy) Policy1=<__main__.Policy object at 0x000002271BB641F0>\n", + "(Policy) Policy0=<__main__.Policy object at 0x000002271BB641F0>\n", + "(list) list150=[['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]\n", + "\n", + "#Others\n", + "(NoneType) Policy.reason0=None\n", + "\n", + "#Outputs\n", + "(NoneType) Policy.act0=None\n", + "\n", + "#Feedback:\n", + "'NoneType' object is not subscriptable\n", + "\n", + "================================\n", + "\n", + "\n", + "Your response:\n", + "\n", + "LLM response:\n", + " {\n", + "\"reasoning\": \"The issue here is that the provided functions '__code1' and '__code0' for 'reason' and 'act' methods respectively are empty and return nothing (implicitly None). This becomes problematic when 'Policy.act0' tries to use the result of 'Policy.reason0' as part of its logic. If 'Policy.reason0' is None, it can't be used in the expected manner by 'Policy.act0', which likely attempts to subscript it or perform some operation that is not valid on a NoneType, leading to the 'NoneType' object is not subscriptable error. To improve the output, the '__code1' and '__code0' functions need to be correctly implemented so that 'reason' returns a valid plan that 'act' can use. For instance, 'reason' should analyze the given map to determine a strategic action plan (e.g., a coordinate to hit next), and 'act' should take this plan and execute a specific action based on it.\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {\n", + " \"__code1\": \"def reason(self, map):\\n \\\"\\\"\\\"\\n Given a map, analyze the board in a game.\\n X denotes hits, O denotes misses, and . denotes unknown positions.\\n \\\"\\\"\\\"\\n # Example implementation:\\n # Analyze the map to find the first unknown position\\n for i, row in enumerate(map):\\n for j, cell in enumerate(row):\\n if cell == '.':\\n return (i, j)\\n return None\",\n", + " \"__code0\": \"def act(self, map, plan):\\n \\\"\\\"\\\"\\n Given a map, select a target coordinate in a game.\\n X denotes hits, O denotes misses, and . denotes unknown positions.\\n \\\"\\\"\\\"\\n # Example implementation:\\n # Return the plan as is, assuming it's a valid target\\n return plan\\n\"\n", + "}\n", + "}\n", + "Scores: 0.4801587301587302 (0.030865772194650906)\n", + "Iteration 0 returns: [0.45833333 0.45833333 0.52380952]\n", + "Prompt\n", + " \n", + "You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.\n", + "\n", + "Specifically, a problem will be composed of the following parts:\n", + "- #Instruction: the instruction which describes the things you need to do or the question you should answer.\n", + "- #Code: the code defined in the problem.\n", + "- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.\n", + "- #Variables: the input variables that you can change.\n", + "- #Constraints: the constraints or descriptions of the variables in #Variables.\n", + "- #Inputs: the values of other inputs to the code, which are not changeable.\n", + "- #Others: the intermediate values created through the code execution.\n", + "- #Outputs: the result of the code output.\n", + "- #Feedback: the feedback about the code's execution result.\n", + "\n", + "In #Variables, #Inputs, #Outputs, and #Others, the format is:\n", + "\n", + " = \n", + "\n", + "If is (code), it means is the source code of a python code, which may include docstring and definitions.\n", + "\n", + "Output_format: Your output should be in the following json format, satisfying the json syntax:\n", + "\n", + "{{\n", + "\"reasoning\": ,\n", + "\"answer\": ,\n", + "\"suggestion\": {{\n", + " : ,\n", + " : ,\n", + "}}\n", + "}}\n", + "\n", + "In \"reasoning\", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.\n", + "\n", + "If #Instruction asks for an answer, write it down in \"answer\".\n", + "\n", + "If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", + "\n", + "If no changes or answer are needed, just output TERMINATE.\n", + "\n", + "Now you see problem instance:\n", + "\n", + "================================\n", + "\n", + "#Instruction\n", + "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", + "\n", + "#Code\n", + "eval0 = eval(self=Policy0, map=list0, __code=__code1)\n", + "eval1 = eval(self=Policy1, map=list1, plan=eval0, __code=__code0)\n", + "\n", + "#Documentation\n", + "[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).\n", + "\n", + "#Variables\n", + "(code) __code1:def reason(self, map):\n", + " \"\"\"\n", + " Given a map, analyze the board in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + " # Example implementation:\n", + " # Analyze the map to find the first unknown position\n", + " for i, row in enumerate(map):\n", + " for j, cell in enumerate(row):\n", + " if cell == '.':\n", + " return (i, j)\n", + " return None\n", + "(code) __code0:def act(self, map, plan):\n", + " \"\"\"\n", + " Given a map, select a target coordinate in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + " # Example implementation:\n", + " # Return the plan as is, assuming it's a valid target\n", + " return plan\n", + "\n", + "\n", + "#Constraints\n", + "(code) __code1: The code should start with:\n", + "def reason(self, map):\n", + " \"\"\"\n", + " Given a map, analyze the board in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + "(code) __code0: The code should start with:\n", + "def act(self, map, plan):\n", + " \"\"\"\n", + " Given a map, select a target coordinate in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + "\n", + "#Inputs\n", + "(Policy) Policy1=<__main__.Policy object at 0x000002271BB641F0>\n", + "(list) list1=[['X', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]\n", + "(Policy) Policy0=<__main__.Policy object at 0x000002271BB641F0>\n", + "(list) list0=[['X', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]\n", + "\n", + "#Others\n", + "(tuple) eval0=(0, 0)\n", + "\n", + "#Outputs\n", + "(tuple) eval1=(0, 0)\n", + "\n", + "#Feedback:\n", + "Got 1 reward.\n", + "\n", + "================================\n", + "\n", + "\n", + "Your response:\n", + "\n", + "LLM response:\n", + " {\n", + "\"reasoning\": \"According to the instruction, we need to improve the output based on the feedback which states 'Got 1 reward'. This would suggest that the actions taken are correct but possibly not optimal, given the context provided is not sufficiently detailed regarding how rewards are obtained (e.g., hitting targets in a game represented by a grid). The usage of the eval() function in the code executes defined logic within __code1 and __code0 blocks for analyzing a game board and selecting a target coordinate, respectively. The __code1 block is correctly identifying the first unknown ('.') position, which is (0, 1) but returns (0, 0) mistakenly due to the incorrect starting index of eval0 in #Others. The __code0 block simply returns the 'plan' (a coordinate), demonstrating it's assumed to be a valid next action but seems misaligned with the intent of dynamically selecting the next strategy based on map analysis. To align the output with what might be considered a better strategic move or to improve the obtained reward from the action, adjustments are suggested in the logic for __code1 (initial analysis) and potentially __code nd __code1 (1, retrieving it correctly from the game analysis) while ensuring no syntax errors are introduced and the code suggestions abide by the given constraints.\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {\n", + "\"__code1\": \"def reason(self, map):\\n \\\"\\\"\\\"\\n Given a map, analyze the board in a game.\\n X denotes hits, O denotes misses, and . denotes unknown positions.\\n \\\"\\\"\\\"\\n # Example implementation:\\n # Analyze the map to find the first unknown position\\n for i, row in enumerate(map):\\n for j, cell in enumerate(row):\\n if cell == '.':\\n return (i, j)\\n return None\",\n", + "\"__code0\": \"def act(self, map, plan):\\n \\\"\\\"\\\"\\n Given a map, select a target coordinate in a game.\\n X denotes hits, O denotes misses, and . denotes unknown positions.\\n \\\"\\\"\\\"\\n # Example implementation:\\n # Check if the plan is a valid target, if not, select the first unknown ('.') position\\n for i, row in enumerate(map):\\n for j, cell in enumerate(row):\\n if cell == '.':\\n return (i, j)\\n return plan\"\n", + "}\n", + "}\n", + "Scores: 0.4716183574879227 (0.00939393066793724)\n", + "Iteration 1 returns: [0.45833333 0.47826087 0.47826087]\n", + "Prompt\n", + " \n", + "You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.\n", + "\n", + "Specifically, a problem will be composed of the following parts:\n", + "- #Instruction: the instruction which describes the things you need to do or the question you should answer.\n", + "- #Code: the code defined in the problem.\n", + "- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.\n", + "- #Variables: the input variables that you can change.\n", + "- #Constraints: the constraints or descriptions of the variables in #Variables.\n", + "- #Inputs: the values of other inputs to the code, which are not changeable.\n", + "- #Others: the intermediate values created through the code execution.\n", + "- #Outputs: the result of the code output.\n", + "- #Feedback: the feedback about the code's execution result.\n", + "\n", + "In #Variables, #Inputs, #Outputs, and #Others, the format is:\n", + "\n", + " = \n", + "\n", + "If is (code), it means is the source code of a python code, which may include docstring and definitions.\n", + "\n", + "Output_format: Your output should be in the following json format, satisfying the json syntax:\n", + "\n", + "{{\n", + "\"reasoning\": ,\n", + "\"answer\": ,\n", + "\"suggestion\": {{\n", + " : ,\n", + " : ,\n", + "}}\n", + "}}\n", + "\n", + "In \"reasoning\", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.\n", + "\n", + "If #Instruction asks for an answer, write it down in \"answer\".\n", + "\n", + "If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", + "\n", + "If no changes or answer are needed, just output TERMINATE.\n", + "\n", + "Now you see problem instance:\n", + "\n", + "================================\n", + "\n", + "#Instruction\n", + "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", + "\n", + "#Code\n", + "eval0 = eval(self=Policy0, map=list0, __code=__code1)\n", + "eval1 = eval(self=Policy1, map=list1, plan=eval0, __code=__code0)\n", + "\n", + "#Documentation\n", + "[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).\n", + "\n", + "#Variables\n", + "(code) __code1:def reason(self, map):\n", + " \"\"\"\n", + " Given a map, analyze the board in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + " # Example implementation:\n", + " # Analyze the map to find the first unknown position\n", + " for i, row in enumerate(map):\n", + " for j, cell in enumerate(row):\n", + " if cell == '.':\n", + " return (i, j)\n", + " return None\n", + "(code) __code0:def act(self, map, plan):\n", + " \"\"\"\n", + " Given a map, select a target coordinate in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + " # Example implementation:\n", + " # Check if the plan is a valid target, if not, select the first unknown ('.') position\n", + " for i, row in enumerate(map):\n", + " for j, cell in enumerate(row):\n", + " if cell == '.':\n", + " return (i, j)\n", + " return plan\n", + "\n", + "#Constraints\n", + "(code) __code1: The code should start with:\n", + "def reason(self, map):\n", + " \"\"\"\n", + " Given a map, analyze the board in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + "(code) __code0: The code should start with:\n", + "def act(self, map, plan):\n", + " \"\"\"\n", + " Given a map, select a target coordinate in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + "\n", + "#Inputs\n", + "(Policy) Policy1=<__main__.Policy object at 0x000002271BB641F0>\n", + "(list) list1=[['X', 'O', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]\n", + "(Policy) Policy0=<__main__.Policy object at 0x000002271BB641F0>\n", + "(list) list0=[['X', 'O', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]\n", + "\n", + "#Others\n", + "(tuple) eval0=(0, 1)\n", + "\n", + "#Outputs\n", + "(tuple) eval1=(0, 1)\n", + "\n", + "#Feedback:\n", + "Got 0 reward.\n", + "\n", + "================================\n", + "\n", + "\n", + "Your response:\n", + "\n", + "LLM response:\n", + " {\n", + "\"reasoning\": \"The issue seems to lie in the act function's logic. According to the instructions, the act function should select a target coordinate in a game where 'X' denotes hits, 'O' denotes misses, and '.' denotes unknown positions. The provided __code0 for the act function incorrectly assumes that the plan (eval0) is always a valid target, but it should actually check if the target is an unknown ('.') position, and if not, it should find the next available unknown position. In this case, since eval0 returns (0, 1), and the map shows that position (0, 2) is the first unknown after (0, 1), the act function should be returning (0, 2) to improve the game outcome and possibly get a reward. However, it incorrectly just checks for the first '.' and returns (0, 1), which is not the desired logic for selecting the next target.\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {\n", + " \"__code0\": \"def act(self, map, plan):\\n \\\"\\\"\\\"\\n Given a map, select a target coordinate in a game.\\n X denotes hits, O denotes misses, and . denotes unknown positions.\\n \\\"\\\"\\\"\\n # Check if the plan points to an unknown position; otherwise, select the next.\\n i, j = plan\\n if map[i][j] == '.':\\n return (i, j + 1)\\n else:\\n for x, row in enumerate(map):\\n for y, cell in enumerate(row):\\n if cell == '.':\\n return (x, y)\\n return plan\"\n", + "}\n", + "}\n", + "Scores: 0.013888888888888888 (0.019641855032959652)\n", + "Iteration 2 returns: [0.04166667 0. 0. ]\n", + "Prompt\n", + " \n", + "You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.\n", + "\n", + "Specifically, a problem will be composed of the following parts:\n", + "- #Instruction: the instruction which describes the things you need to do or the question you should answer.\n", + "- #Code: the code defined in the problem.\n", + "- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.\n", + "- #Variables: the input variables that you can change.\n", + "- #Constraints: the constraints or descriptions of the variables in #Variables.\n", + "- #Inputs: the values of other inputs to the code, which are not changeable.\n", + "- #Others: the intermediate values created through the code execution.\n", + "- #Outputs: the result of the code output.\n", + "- #Feedback: the feedback about the code's execution result.\n", + "\n", + "In #Variables, #Inputs, #Outputs, and #Others, the format is:\n", + "\n", + " = \n", + "\n", + "If is (code), it means is the source code of a python code, which may include docstring and definitions.\n", + "\n", + "Output_format: Your output should be in the following json format, satisfying the json syntax:\n", + "\n", + "{{\n", + "\"reasoning\": ,\n", + "\"answer\": ,\n", + "\"suggestion\": {{\n", + " : ,\n", + " : ,\n", + "}}\n", + "}}\n", + "\n", + "In \"reasoning\", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.\n", + "\n", + "If #Instruction asks for an answer, write it down in \"answer\".\n", + "\n", + "If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", + "\n", + "If no changes or answer are needed, just output TERMINATE.\n", + "\n", + "Now you see problem instance:\n", + "\n", + "================================\n", + "\n", + "#Instruction\n", + "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", + "\n", + "#Code\n", + "eval0 = eval(self=Policy0, map=list0, __code=__code1)\n", + "eval1 = eval(self=Policy1, map=list1, plan=eval0, __code=__code0)\n", + "\n", + "#Documentation\n", + "[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).\n", + "\n", + "#Variables\n", + "(code) __code1:def reason(self, map):\n", + " \"\"\"\n", + " Given a map, analyze the board in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + " # Example implementation:\n", + " # Analyze the map to find the first unknown position\n", + " for i, row in enumerate(map):\n", + " for j, cell in enumerate(row):\n", + " if cell == '.':\n", + " return (i, j)\n", + " return None\n", + "(code) __code0:def act(self, map, plan):\n", + " \"\"\"\n", + " Given a map, select a target coordinate in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + " # Check if the plan points to an unknown position; otherwise, select the next.\n", + " i, j = plan\n", + " if map[i][j] == '.':\n", + " return (i, j + 1)\n", + " else:\n", + " for x, row in enumerate(map):\n", + " for y, cell in enumerate(row):\n", + " if cell == '.':\n", + " return (x, y)\n", + " return plan\n", + "\n", + "#Constraints\n", + "(code) __code1: The code should start with:\n", + "def reason(self, map):\n", + " \"\"\"\n", + " Given a map, analyze the board in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + "(code) __code0: The code should start with:\n", + "def act(self, map, plan):\n", + " \"\"\"\n", + " Given a map, select a target coordinate in a game.\n", + " X denotes hits, O denotes misses, and . denotes unknown positions.\n", + " \"\"\"\n", + "\n", + "#Inputs\n", + "(Policy) Policy1=<__main__.Policy object at 0x000002271BB641F0>\n", + "(list) list1=[['X', 'O', '.', 'O', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]\n", + "(Policy) Policy0=<__main__.Policy object at 0x000002271BB641F0>\n", + "(list) list0=[['X', 'O', '.', 'O', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]\n", + "\n", + "#Others\n", + "(tuple) eval0=(0, 2)\n", + "\n", + "#Outputs\n", + "(tuple) eval1=(0, 3)\n", + "\n", + "#Feedback:\n", + "Got 0 reward.\n", + "\n", + "================================\n", + "\n", + "\n", + "Your response:\n", + "\n", + "LLM response:\n", + " {\n", + "\"reasoning\": \"The instruction implies that the issue lies with the actions defined in __code1 and __code0. Given the maps and the evaluation output, it looks like the evaluation is not meeting the intended outcome, which is likely to find the next unknown position to target and get a reward. __code1 correctly identifies the first unknown position on the map, and eval0 reflects this with (0, 2). However, __code0 should select the target coordinate based on some criteria of success, but it currently just moves to the next coordinate from the plan given by eval0, without checking if the move makes strategic sense or leads to a reward. Given the feedback 'Got 0 reward', the move made by __code0 from (0, 2) to (0, 3) might not be an optimal strategy. It seems __code0 fails to define a strategic move after identifying the first unknown position. A better strategy might involve checking if moving to a certain position increases the chance of hitting a target ('X') or avoiding misses ('O').\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {\n", + "\"__code0\": \"def act(self, map, plan):\\n \\\"\\\"\\\"\\n Given a map, select a target coordinate in a game.\\n X denotes hits, O denotes misses, and . denotes unknown positions.\\n \\\"\\\"\\\"\\n i, j = plan\\n # Implement a smarter strategy to find the next best move.\\n # This involves looking for the next '.' in a row wise manner.\\n for x in range(i, len(map)):\\n if '.' in map[x]:\\n for y in range(len(map[x])):\\n if map[x][y] == '.':\\n return (x, y)\\n return plan\"\n", + "}\n", + "}\n", + "Scores: 0.4583333333333333 (0.0)\n", + "Iteration 3 returns: [0.45833333 0.45833333 0.45833333]\n" + ] + } + ], + "source": [ + "optimizer = OptoPrime(policy.parameters(), memory_size=0, config_list=config_list_from_json(\"OAI_CONFIG_LIST\"))\n", + "\n", + "\n", + "feedback = \"\"\n", + "# This is an online optimization problem. we have the opportunity to\n", + "# keep changing the function with each round of interaction\n", + "board = BattleshipBoard(board_size, board_size, num_each_type=num_each_type, exclude_ships=exclude_ships)\n", + "obs = node(board.get_shots()) # init observation\n", + "i = 0\n", + "while i < 4:\n", + " GRAPH.clear()\n", + "\n", + " try:\n", + " output = policy.select_coordinate(obs)\n", + " obs, reward, terminal, feedback = user_fb_for_placing_shot(board, output.data) # not traced\n", + " except ExecutionError as e: # this is a retry\n", + " output = e.exception_node\n", + " feedback = output.data\n", + " reward, terminal = 0, False\n", + "\n", + " if terminal:\n", + " board = BattleshipBoard(board_size, board_size, num_each_type=num_each_type, exclude_ships=exclude_ships)\n", + " obs = node(board.get_shots()) # init observation\n", + "\n", + " # Update\n", + " optimizer.zero_feedback()\n", + " optimizer.backward(output, feedback)\n", + " optimizer.step(verbose=True)\n", + "\n", + " # Logging\n", + " if not isinstance(output, ExceptionNode):\n", + " try:\n", + " returns = eval_policy(policy, board_size, num_each_type, exclude_ships, n_eval_episodes)\n", + " print(\"Iteration\", i, \"returns:\", returns)\n", + " except Exception:\n", + " pass\n", + "\n", + " i += 1\n", + " " ] }, { diff --git a/docs/examples/game/joint_prompt_optimization.ipynb b/docs/examples/game/joint_prompt_optimization.ipynb index 42bfcdc9..fbaf4f21 100644 --- a/docs/examples/game/joint_prompt_optimization.ipynb +++ b/docs/examples/game/joint_prompt_optimization.ipynb @@ -27,7 +27,7 @@ "import json\n", "\n", "import opto.trace as trace\n", - "from opto.optimizers import FunctionOptimizerV2Memory\n", + "from opto.optimizers import OptoPrime\n", "from autogen import config_list_from_json\n", "\n", "config = config_list_from_json(\"OAI_CONFIG_LIST\")\n", @@ -286,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -294,41 +294,47 @@ "output_type": "stream", "text": [ "ITERATION 1\n", - "Alice STOCKPILE THESE RESOURCES: WOOD\n", - "Bob STOCKPILE THESE RESOURCES: GOLD\n", - "The game has ended. Alice has inventory with value of 13 and Bob has inventory with value of 11.\n", - "OVERALL SCORE: 24\n", + "Alice STOCKPILE THESE RESOURCES: N/A\n", + "Bob STOCKPILE THESE RESOURCES: N/A\n", + "The game has ended. Alice has inventory with value of 13 and Bob has inventory with value of 15.\n", + "OVERALL SCORE: 28\n", "OVERALL SCORE is less than optimal. Find better trades to increase the OVERALL SCORE.\n", "ITERATION 2\n", - "Alice STOCKPILE THESE RESOURCES: STONE\n", - "Bob STOCKPILE THESE RESOURCES: WOOD\n", - "The game has ended. Alice has inventory with value of 13 and Bob has inventory with value of 12.\n", - "OVERALL SCORE: 25\n", + "Alice STOCKPILE THESE RESOURCES: GOLD, STONE\n", + "Bob STOCKPILE THESE RESOURCES: GOLD, WOOD\n", + "The game has ended. Alice has inventory with value of 13 and Bob has inventory with value of 15.\n", + "OVERALL SCORE: 28\n", "OVERALL SCORE is less than optimal. Find better trades to increase the OVERALL SCORE.\n", "ITERATION 3\n", - "Alice STOCKPILE THESE RESOURCES: GOLD\n", - "Bob STOCKPILE THESE RESOURCES: GOLD\n", - "The game has ended. Alice has inventory with value of 15 and Bob has inventory with value of 34.\n", - "OVERALL SCORE: 49\n", + "Alice STOCKPILE THESE RESOURCES: GOLD, WOOD\n", + "Bob STOCKPILE THESE RESOURCES: GOLD, STONE\n", + "The game has ended. Alice has inventory with value of 13 and Bob has inventory with value of 22.\n", + "OVERALL SCORE: 35\n", "OVERALL SCORE is less than optimal. Find better trades to increase the OVERALL SCORE.\n", + "Cannot extract suggestion from LLM's response:\n", + "{\n", + "\"reasoning\": \"The feedback indicates that the sum of values in the players' inventories at the end of the game is not optimal. The goal is to increase the OVERALL SCORE by making better trade decisions through the chat interactions modeled by the prompts and the responses in the chat variables (chat8, chat9, etc.). The trading decisions are based on the prompts 'STOCKPILE THESE RESOURCES: GOLD, WOOD' for Alice and 'STOCKPILE THESE RESOURCES: GOLD, STONE' for Bob. The trading actions 'TRADE', 'REJECT', and 'ACCEPT' suggest whether a proposed trade between Alice and Bob is successful or not. To optimize the overall score, we need to adjust the trading strategy, which could involve modifying the resources Alice and Bob are aiming to stockpile, to encourage more successful and beneficial trades. Since the only variables we can adjust are str2 and str3, which define the resources each player is trying to accumulate, changing these could potentially lead to better trade outcomes, increasing the overall value of the inventories. However, the instructions and feedback suggest that the strategy and prompts should be changed, rather than specific values. Without specific instructions on what values to change to what new values, there is no direct recommendation to improve the results other than considering changing the trading strategies.\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {}\n", + "}\n", "ITERATION 4\n", - "Alice STOCKPILE A VARIETY OF RESOURCES\n", - "Bob STOCKPILE THESE RESOURCES: STONE\n", - "The game has ended. Alice has inventory with value of 13 and Bob has inventory with value of 15.\n", - "OVERALL SCORE: 28\n", - "OVERALL SCORE is less than optimal. Find better trades to increase the OVERALL SCORE.\n", - "ITERATION 5\n", - "Alice SUGGESTED_CHANGE\n", - "Bob SUGGESTED_CHANGE\n", + "Alice STOCKPILE THESE RESOURCES: GOLD, WOOD\n", + "Bob STOCKPILE THESE RESOURCES: GOLD, STONE\n", "The game has ended. Alice has inventory with value of 13 and Bob has inventory with value of 22.\n", "OVERALL SCORE: 35\n", + "OVERALL SCORE is less than optimal. Find better trades to increase the OVERALL SCORE.\n", + "ITERATION 5\n", + "Alice STOCKPILE THESE RESOURCES: STONE, GOLD\n", + "Bob STOCKPILE THESE RESOURCES: WOOD, GOLD\n", + "The game has ended. Alice has inventory with value of 13 and Bob has inventory with value of 12.\n", + "OVERALL SCORE: 25\n", "OVERALL SCORE is less than optimal. Find better trades to increase the OVERALL SCORE.\n" ] } ], "source": [ "# Initialize optimizer\n", - "optimizer = FunctionOptimizerV2Memory(\n", + "optimizer = OptoPrime(\n", " [p1_prompt, p2_prompt], memory_size=0, config_list=config_list_from_json(\"OAI_CONFIG_LIST\")\n", " )\n", "\n", diff --git a/docs/examples/joint/joint_code_prompt_optimization.ipynb b/docs/examples/joint/joint_code_prompt_optimization.ipynb index d669637e..28da9ad5 100644 --- a/docs/examples/joint/joint_code_prompt_optimization.ipynb +++ b/docs/examples/joint/joint_code_prompt_optimization.ipynb @@ -33,7 +33,7 @@ "# Import necessary libraries\n", "import autogen\n", "from opto.trace.nodes import node, GRAPH, ParameterNode\n", - "from opto.optimizers import FunctionOptimizerV2\n", + "from opto.optimizers import OptoPrime\n", "from datasets import load_dataset\n", "from textwrap import dedent\n", "from opto.trace.bundle import bundle\n", @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2024-07-19T21:19:04.970205Z", @@ -189,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2024-07-19T21:19:06.148198Z", @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2024-07-19T21:19:21.867979Z", @@ -252,14 +252,14 @@ "Training on a few examples:\n", "Question: Is the following sentence plausible? \"Elias Lindholm beat the buzzer.\"\n", "Expected answer: no\n", - "Answer: MessageNode: (eval:1, dtype=, data=Yes, the sentence \"Elias Lindholm beat the buzzer.\" is plausible. It suggests that Elias Lindholm, likely a sports player, scored or accomplished something significant just before a deadline or timer expired in a game.)\n", - "Output: MessageNode: (eval:1, dtype=, data=Yes, the sentence \"Elias Lindholm beat the buzzer.\" is plausible. It suggests that Elias Lindholm, likely a sports player, scored or accomplished something significant just before a deadline or timer expired in a game.), Feedback: The answer is wrong. We expect the output of your answer to be \"no\". Please modify the prompt and relevant parts of the program to help LLM produce the right answer., Variables:\n", + "Answer: MessageNode: (eval:1, dtype=, data=Yes)\n", + "Output: MessageNode: (eval:1, dtype=, data=Yes), Feedback: The answer is wrong. We expect the output of your answer to be \"no\". Please modify the prompt and relevant parts of the program to help LLM produce the right answer., Variables:\n", "__code:1 def create_prompt(self, prompt_template, question):\n", " return prompt_template.format(question)\n", "__code:0 def extract_answer(self, prompt_template, question, response):\n", " answer = response.split(\"Answer:\")[1].strip()\n", " return answer\n", - "str:1 \n", + "str:0 \n", "Given the fields `question`, produce the fields `answer`.\n", "\n", "---\n", @@ -273,7 +273,7 @@ "Question: {}\n", "Answer:\n", "\n", - "str:1 \n", + "str:0 \n", "Given the fields `question`, produce the fields `answer`.\n", "\n", "---\n", @@ -335,28 +335,16 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "eval0 = eval(self=ModelWrapper0, prompt_template=str1, question=str0, __code=__code1)\n", + "eval0 = eval(self=ModelWrapper0, prompt_template=str0, question=str0, __code=__code1)\n", "LLMCallable.call_llm0 = LLMCallable.call_llm(self=ModelWrapper1, user_prompt=eval0)\n", + "eval1 = eval(self=ModelWrapper2, prompt_template=str0, question=str1, response=LLMCallable.call_llm0, __code=__code0)\n", "\n", "#Documentation\n", "[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).\n", "[LLMCallable.call_llm] .\n", "\n", "#Variables\n", - "(str) str1=\n", - "Given the fields `question`, produce the fields `answer`.\n", - "\n", - "---\n", - "\n", - "Follow the following format.\n", - "\n", - "Question: \n", - "Answer: \n", - "\n", - "---\n", - "Question: {}\n", - "Answer:\n", - "\n", + "(str) str0=Is the following sentence plausible? \"Elias Lindholm beat the buzzer.\"\n", "(code) __code1:def create_prompt(self, prompt_template, question):\n", " return prompt_template.format(question)\n", "(code) __code0:def extract_answer(self, prompt_template, question, response):\n", @@ -370,11 +358,10 @@ "def extract_answer(self, prompt_template, question, response):\n", "\n", "#Inputs\n", - "(ModelWrapper) ModelWrapper2=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(ModelWrapper) ModelWrapper1=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(ModelWrapper) ModelWrapper0=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(str) str0=Is the following sentence plausible? \"Elias Lindholm beat the buzzer.\"\n", - "(str) eval1=Yes, the sentence \"Elias Lindholm beat the buzzer.\" is plausible. It suggests that Elias Lindholm, likely a sports player, scored or accomplished something significant just before a deadline or timer expired in a game.\n", + "(ModelWrapper) ModelWrapper2=.ModelWrapper object at 0x000002706C4DABB0>\n", + "(str) str1=Is the following sentence plausible? \"Elias Lindholm beat the buzzer.\"\n", + "(ModelWrapper) ModelWrapper1=.ModelWrapper object at 0x000002706C4DABB0>\n", + "(ModelWrapper) ModelWrapper0=.ModelWrapper object at 0x000002706C4DABB0>\n", "\n", "#Others\n", "(str) eval0=\n", @@ -392,10 +379,10 @@ "Answer:\n", "\n", "(str) LLMCallable.call_llm0=Question: Is the following sentence plausible? \"Elias Lindholm beat the buzzer.\"\n", - "Answer: Yes, the sentence \"Elias Lindholm beat the buzzer.\" is plausible. It suggests that Elias Lindholm, likely a sports player, scored or accomplished something significant just before a deadline or timer expired in a game.\n", + "Answer: Yes\n", "\n", "#Outputs\n", - "\n", + "(str) eval1=Yes\n", "\n", "#Feedback:\n", "The answer is wrong. We expect the output of your answer to be \"no\". Please modify the prompt and relevant parts of the program to help LLM produce the right answer.\n", @@ -407,122 +394,30 @@ "\n", "LLM response:\n", " {\n", - " \"reasoning\": \"The task is to improve the output based on the feedback provided, where 'no' is expected as the answer rather than 'yes'. Looking at the existing code and templates, the issue likely originates from the prompt formatting and instructions given to the LLM for generating an answer. Currently, the prompt given to the LLM consists of an empty template formatted to include the 'str0' question. However, it is evident that the formatting and guidance within the prompt template are insufficient or incorrectly guiding the LLM to deliver the desired answer ('no'). Modifying the prompt template (str1) to explicitly guide the model on assessing the plausibility factually rather than inferring general plausibility based on context will likely yield better results. Adjusting the prompt to explicitly guide towards a factual assessment of plausibility promotes a straightforward analysis by the LLM, hence increasing the likelihood of receiving the correct answer.\",\n", - " \"answer\": \"\",\n", - " \"suggestion\": {\n", - " \"str1\": \"Given the fields `question`, produce the fields `answer`\\\\n\\\\n---\\\\n\\\\nFollow the fact-checking format and validate information preciseness.\\\\n\\\\nQuestion: {question}\\\\nAnswer: \"\n", - " }\n", + "\"reasoning\": \"The feedback indicates that the current settings and prompt formulation lead to an incorrect answer of 'Yes' from the language model (LLM) regarding the question's plausibility. The question was whether it's plausible for 'Elias Lindholm beat the buzzer.' The output we need from this model is 'no'. To address this, we need to adjust either how the prompt is being constructed (__code1) or how the response is being handled (__code0). However, the issue seems more aligned with how we form the question to the model rather than how we extract the answer, since the model outputs 'Yes', which means it confirms plausibility based on the question's current phrasing or context. Since the issue is about generating the right context for the LLM to produce 'no', the suggestion is to modify the prompt template or the way the prompt is constructed to provide more contextual clues to the LLM that could push it towards a 'no' answer. There were no negative remarks on the code's ability to call the LLM or extract the answer, suggesting the logic for these operations is correct. Thus, we'll adjust the question's phrasing or add to the template to help give the model context that leads to realizing the scenario in the question is implausible.\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {\n", + "\"str0\": \"Considering known historical contexts and logical sequences, is the following sentence plausible? \\\"Elias Lindhorn beat the buzzer.\\\"\",\n", + "\"__code1\": \"def create_prompt(self, prompt_template, question):\\n return f'{prompt_template} With a focus on the likely scenarios, evaluate: {question}'\"\n", "}\n", - "Question: Is the following sentence plausible? \"John Carlson scored in the third period.\"\n", - "Expected answer: yes\n", - "Answer: MessageNode: (exception_eval:0, dtype=, data=(KeyError) 'question')\n", - "Output: MessageNode: (exception_eval:0, dtype=, data=(KeyError) 'question'), Feedback: (KeyError) 'question', Variables:\n", - "__code:1 def create_prompt(self, prompt_template, question):\n", - " return prompt_template.format(question)\n", - "__code:0 def extract_answer(self, prompt_template, question, response):\n", - " answer = response.split(\"Answer:\")[1].strip()\n", - " return answer\n", - "str:1 Given the fields `question`, produce the fields `answer`\\n\\n---\\n\\nFollow the fact-checking format and validate information preciseness.\\n\\nQuestion: {question}\\nAnswer: \n", - "str:1 Given the fields `question`, produce the fields `answer`\\n\\n---\\n\\nFollow the fact-checking format and validate information preciseness.\\n\\nQuestion: {question}\\nAnswer: \n", - "Prompt\n", - " \n", - "You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.\n", - "\n", - "Specifically, a problem will be composed of the following parts:\n", - "- #Instruction: the instruction which describes the things you need to do or the question you should answer.\n", - "- #Code: the code defined in the problem.\n", - "- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.\n", - "- #Variables: the input variables that you can change.\n", - "- #Constraints: the constraints or descriptions of the variables in #Variables.\n", - "- #Inputs: the values of other inputs to the code, which are not changeable.\n", - "- #Others: the intermediate values created through the code execution.\n", - "- #Outputs: the result of the code output.\n", - "- #Feedback: the feedback about the code's execution result.\n", - "\n", - "In #Variables, #Inputs, #Outputs, and #Others, the format is:\n", - "\n", - " = \n", - "\n", - "If is (code), it means is the source code of a python code, which may include docstring and definitions.\n", - "\n", - "Output_format: Your output should be in the following json format, satisfying the json syntax:\n", - "\n", - "{{\n", - "\"reasoning\": ,\n", - "\"answer\": ,\n", - "\"suggestion\": {{\n", - " : ,\n", - " : ,\n", - "}}\n", - "}}\n", - "\n", - "In \"reasoning\", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.\n", - "\n", - "If #Instruction asks for an answer, write it down in \"answer\".\n", - "\n", - "If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", - "\n", - "If no changes or answer are needed, just output TERMINATE.\n", - "\n", - "Now you see problem instance:\n", - "\n", - "================================\n", - "\n", - "#Instruction\n", - "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", - "\n", - "#Code\n", - "exception_eval0 = eval(self=ModelWrapper0, prompt_template=str1, question=str0, __code=__code1)\n", - "\n", - "#Documentation\n", - "[exception] The operator eval raises an exception.\n", - "\n", - "#Variables\n", - "(str) str1=Given the fields `question`, produce the fields `answer`\\n\\n---\\n\\nFollow the fact-checking format and validate information preciseness.\\n\\nQuestion: {question}\\nAnswer: \n", - "(code) __code1:def create_prompt(self, prompt_template, question):\n", - " return prompt_template.format(question)\n", - "\n", - "#Constraints\n", - "(code) __code1: The code should start with:\n", - "def create_prompt(self, prompt_template, question):\n", - "\n", - "#Inputs\n", - "(ModelWrapper) ModelWrapper0=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(str) str0=Is the following sentence plausible? \"John Carlson scored in the third period.\"\n", - "\n", - "#Others\n", - "\n", - "\n", - "#Outputs\n", - "(str) exception_eval0=(KeyError) 'question'\n", - "\n", - "#Feedback:\n", - "(KeyError) 'question'\n", - "\n", - "================================\n", - "\n", - "\n", - "Your response:\n", - "\n", - "LLM response:\n", - " {\n", - " \"reasoning\": \"The KeyError 'question' arises due to the inappropriate use of the eval function in the provided code. The eval function is attempting to execute the function create_prompt using syntax where `self`, `prompt_template`, and `question` are named explicitly in the eval call with the `__code` parameter, which seems to be incorrect or not supported in the current context. From the documentation and the error output, it appears that kwargs like `self=ModelWrapper0` or `prompt_template=str1` are causing the function to not recognize or improperly handle the 'question' key passed to the create_prompt function. This misconfiguration in the eval function leads to failure in string formatting, as the key 'question' does not exist in the function's execution context leading to the KeyError. Adjusting the __code1 to use proper positional or keyword arguments in the function or altering its calling convention is necessary.\",\n", - " \"answer\": \"\",\n", - " \"suggestion\": {\n", - " \"__code1\": \"def create_prompt(self, prompt_template, question):\\n\\t\\treturn prompt_template.format(question=question)\"\n", - " }\n", "}\n", - "Question: Is the following sentence plausible? \"Marcelo got on the end of a through ball.\"\n", + "Question: Is the following sentence plausible? \"John Carlson scored in the third period.\"\n", "Expected answer: yes\n", - "Answer: MessageNode: (eval:1, dtype=, data=Yes, the sentence \"Marcelo got on the end of a through ball\" is plausible. The phrase \"got on the end of a through ball\" typically refers to a soccer scenario where a player reaches a pass that has been sent through the opponents' defensive line, potentially putting the player in a good position to score or create a scoring opportunity. Marcelo, being a common soccer player's name, fits well within this context. Hence, the sentence makes sense in a football (soccer) setting.)\n", - "Output: MessageNode: (eval:1, dtype=, data=Yes, the sentence \"Marcelo got on the end of a through ball\" is plausible. The phrase \"got on the end of a through ball\" typically refers to a soccer scenario where a player reaches a pass that has been sent through the opponents' defensive line, potentially putting the player in a good position to score or create a scoring opportunity. Marcelo, being a common soccer player's name, fits well within this context. Hence, the sentence makes sense in a football (soccer) setting.), Feedback: The answer is wrong. We expect the output of your answer to be \"yes\". Please modify the prompt and relevant parts of the program to help LLM produce the right answer., Variables:\n", + "Answer: MessageNode: (exception_eval:0, dtype=, data=def extract_answer(self, prompt_template, question, response):\n", + " answer = response.split(\"Answer:\")[1].strip() <--- (IndexError) list index out of range\n", + " return answer)\n", + "Output: MessageNode: (exception_eval:0, dtype=, data=def extract_answer(self, prompt_template, question, response):\n", + " answer = response.split(\"Answer:\")[1].strip() <--- (IndexError) list index out of range\n", + " return answer), Feedback: def extract_answer(self, prompt_template, question, response):\n", + " answer = response.split(\"Answer:\")[1].strip() <--- (IndexError) list index out of range\n", + " return answer, Variables:\n", "__code:1 def create_prompt(self, prompt_template, question):\n", - "\t\treturn prompt_template.format(question=question)\n", + " return f'{prompt_template} With a focus on the likely scenarios, evaluate: {question}'\n", "__code:0 def extract_answer(self, prompt_template, question, response):\n", " answer = response.split(\"Answer:\")[1].strip()\n", " return answer\n", - "str:1 Given the fields `question`, produce the fields `answer`\\n\\n---\\n\\nFollow the fact-checking format and validate information preciseness.\\n\\nQuestion: {question}\\nAnswer: \n", - "str:1 Given the fields `question`, produce the fields `answer`\\n\\n---\\n\\nFollow the fact-checking format and validate information preciseness.\\n\\nQuestion: {question}\\nAnswer: \n", + "str:0 Considering known historical contexts and logical sequences, is the following sentence plausible? \"Elias Lindhorn beat the buzzer.\"\n", + "str:0 Considering known historical contexts and logical sequences, is the following sentence plausible? \"Elias Lindhorn beat the buzzer.\"\n", "Prompt\n", " \n", "You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.\n", @@ -571,17 +466,18 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "eval0 = eval(self=ModelWrapper0, prompt_template=str1, question=str0, __code=__code1)\n", + "eval0 = eval(self=ModelWrapper0, prompt_template=str0, question=str0, __code=__code1)\n", "LLMCallable.call_llm0 = LLMCallable.call_llm(self=ModelWrapper1, user_prompt=eval0)\n", + "exception_eval0 = eval(self=ModelWrapper2, prompt_template=str0, question=str1, response=LLMCallable.call_llm0, __code=__code0)\n", "\n", "#Documentation\n", - "[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).\n", + "[exception] The operator eval raises an exception.\n", "[LLMCallable.call_llm] .\n", "\n", "#Variables\n", - "(str) str1=Given the fields `question`, produce the fields `answer`\\n\\n---\\n\\nFollow the fact-checking format and validate information preciseness.\\n\\nQuestion: {question}\\nAnswer: \n", + "(str) str0=Is the following sentence plausible? \"John Carlson scored in the third period.\"\n", "(code) __code1:def create_prompt(self, prompt_template, question):\n", - "\t\treturn prompt_template.format(question=question)\n", + " return f'{prompt_template} With a focus on the likely scenarios, evaluate: {question}'\n", "(code) __code0:def extract_answer(self, prompt_template, question, response):\n", " answer = response.split(\"Answer:\")[1].strip()\n", " return answer\n", @@ -593,21 +489,34 @@ "def extract_answer(self, prompt_template, question, response):\n", "\n", "#Inputs\n", - "(ModelWrapper) ModelWrapper2=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(ModelWrapper) ModelWrapper1=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(ModelWrapper) ModelWrapper0=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(str) str0=Is the following sentence plausible? \"Marcelo got on the end of a through ball.\"\n", - "(str) eval1=Yes, the sentence \"Marcelo got on the end of a through ball\" is plausible. The phrase \"got on the end of a through ball\" typically refers to a soccer scenario where a player reaches a pass that has been sent through the opponents' defensive line, potentially putting the player in a good position to score or create a scoring opportunity. Marcelo, being a common soccer player's name, fits well within this context. Hence, the sentence makes sense in a football (soccer) setting.\n", + "(ModelWrapper) ModelWrapper2=.ModelWrapper object at 0x000002706C4DABB0>\n", + "(str) str1=Is the following sentence plausible? \"John Carlson scored in the third period.\"\n", + "(ModelWrapper) ModelWrapper1=.ModelWrapper object at 0x000002706C4DABB0>\n", + "(ModelWrapper) ModelWrapper0=.ModelWrapper object at 0x000002706C4DABB0>\n", "\n", "#Others\n", - "(str) eval0=Given the fields `question`, produce the fields `answer`\\n\\n---\\n\\nFollow the fact-checking format and validate information preciseness.\\n\\nQuestion: Is the following sentence plausible? \"Marcelo got on the end of a through ball.\"\\nAnswer: \n", - "(str) LLMCallable.call_llm0=Answer: Yes, the sentence \"Marcelo got on the end of a through ball\" is plausible. The phrase \"got on the end of a through ball\" typically refers to a soccer scenario where a player reaches a pass that has been sent through the opponents' defensive line, potentially putting the player in a good position to score or create a scoring opportunity. Marcelo, being a common soccer player's name, fits well within this context. Hence, the sentence makes sense in a football (soccer) setting.\n", + "(str) eval0=Considering known historical contexts and logical sequences, is the following sentence plausible? \"Elias Lindhorn beat the buzzer.\" With a focus on the likely scenarios, evaluate: Is the following sentence plausible? \"John Carlson scored in the third period.\"\n", + "(str) LLMCallable.call_llm0=To evaluate the plausibility of both sentences, let's break them down considering the contexts in which they might occur.\n", "\n", - "#Outputs\n", + "1. **\"Elias Lindhorn beat the buzzer.\"**\n", + " - Plausibility: Yes, this sentence is plausible.\n", + " - Context: This sentence likely refers to a situation in a basketball game or any sport where a buzzer marks the end of a period, quarter, or game. \"Beating the buzzer\" means scoring just before the game or period ends. It's a common phrase in sports commentary. Assuming Elias Lindhorn is a fictional or actual athlete in a sport that features a buzzer, this scenario is very plausible.\n", + "\n", + "2. **\"John Carlson scored in the third period.\"**\n", + " - Plausibility: Yes, this sentence is also plausible.\n", + " - Context: This sentence is likely referring to a hockey game since hockey matches are divided into three periods and John Carlson is a name that could be associated with a hockey player. Scoring in the third period is a straightforward event that happens regularly in hockey games. Given that \"John Carlson\" could also refer to a real-life NHL defenseman known for his offensive skills, this makes the sentence not just plausible but likely in the context of a hockey match.\n", "\n", + "In summary, both sentences are plausible within their respective sports contexts (basketball or other buzzer sports for the first, and hockey for the second), and they logically fit into the narrative flows of those sports.\n", + "\n", + "#Outputs\n", + "(str) exception_eval0=def extract_answer(self, prompt_template, question, response):\n", + " answer = response.split(\"Answer:\")[1].strip() <--- (IndexError) list index out of range\n", + " return answer\n", "\n", "#Feedback:\n", - "The answer is wrong. We expect the output of your answer to be \"yes\". Please modify the prompt and relevant parts of the program to help LLM produce the right answer.\n", + "def extract_answer(self, prompt_template, question, response):\n", + " answer = response.split(\"Answer:\")[1].strip() <--- (IndexError) list index out of range\n", + " return answer\n", "\n", "================================\n", "\n", @@ -616,27 +525,26 @@ "\n", "LLM response:\n", " {\n", - " \"reasoning\": \"The task is to modify the variables such that the large language model (LLM) produces the correct answer format, namely 'yes' to indicate plausibility, instead of a detailed explanation. Looking at the current setup: \\n\\n1. The `str1` variable is used to create a structured prompt that includes instructions for fact-checking and preciseness, and then formulating a question-answer format. The current format in `str1` inherently encourages a detailed response because it explicitly asks for a validation of information's preciseness, which might have influenced the detailed explanation from the LLM.\\n\\n2. The `__code1` function formats the prompt to include this question, correctly formatted per `str1`. Given this setup, a straightforward 'yes' or 'no' response might be impossible to achieve unless the initial prompt (defined in `str1`) is altered to specifically ask for such a response. Therefore, modifying `str1` to a simpler question format and explicitly requesting a binary (yes/no) answer should guide the LLM better.\\n\\n3. No changes to `__code0` are suggested as it seems to function correctly in extracting the part of the LLM's response following 'Answer:', although it is not actively used in the provided code excerpts.\",\n", - " \"answer\": \"\",\n", - " \"suggestion\": {\n", - " \"str1\": \"Is the following statement plausible? Answer with 'yes' or 'no' only.\\n\\nQuestion: {question}\"\n", - " }\n", + "\"reasoning\": \"The function extract_answer is designed to split the response by 'Answer:' and then select the part after this split to extract the answer. However, the IndexError exception indicates that the response string does not contain the substring 'Answer:', leading to an attempt to access a list index that does not exist, since splitting by 'Answer:' does not generate a second element in the list. To correct this, we either need to ensure the response includes the term 'Answer:' followed by the actual answer or modify the extract_answer function to handle cases when 'Answer:' is not present. Considering that changing the response output of LLMCallable.call_llm would be external to this specific task and could affect other dependencies, adjusting the __code0 function to correctly handle scenarios where 'Answer:' is not present would be a more isolated and safer change.\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {\n", + "\"__code0\": \"def extract_answer(self, prompt_template, question, response):\\n if 'Answer:' in response:\\n answer = response.split('Answer:')[1].strip()\\n else:\\n answer = 'The response does not contain a clear answer.'\\n return answer\"\n", "}\n", - "Question: Is the following sentence plausible? \"Deshaun Watson was called for the goal tend in the Eastern Conference Finals.\"\n", - "Expected answer: no\n", - "Answer: MessageNode: (exception_eval:0, dtype=, data=(IndexError) list index out of range)\n", - "Output: MessageNode: (exception_eval:0, dtype=, data=(IndexError) list index out of range), Feedback: (IndexError) list index out of range, Variables:\n", + "}\n", + "Question: Is the following sentence plausible? \"Marcelo got on the end of a through ball.\"\n", + "Expected answer: yes\n", + "Answer: MessageNode: (eval:1, dtype=, data=The response does not contain a clear answer.)\n", + "Output: MessageNode: (eval:1, dtype=, data=The response does not contain a clear answer.), Feedback: The answer is wrong. We expect the output of your answer to be \"yes\". Please modify the prompt and relevant parts of the program to help LLM produce the right answer., Variables:\n", "__code:1 def create_prompt(self, prompt_template, question):\n", - "\t\treturn prompt_template.format(question=question)\n", + " return f'{prompt_template} With a focus on the likely scenarios, evaluate: {question}'\n", "__code:0 def extract_answer(self, prompt_template, question, response):\n", - " answer = response.split(\"Answer:\")[1].strip()\n", - " return answer\n", - "str:1 Is the following statement plausible? Answer with 'yes' or 'no' only.\n", - "\n", - "Question: {question}\n", - "str:1 Is the following statement plausible? Answer with 'yes' or 'no' only.\n", - "\n", - "Question: {question}\n", + " if 'Answer:' in response:\n", + " answer = response.split('Answer:')[1].strip()\n", + " else:\n", + " answer = 'The response does not contain a clear answer.'\n", + " return answer\n", + "str:0 Considering known historical contexts and logical sequences, is the following sentence plausible? \"Elias Lindhorn beat the buzzer.\"\n", + "str:0 Considering known historical contexts and logical sequences, is the following sentence plausible? \"Elias Lindhorn beat the buzzer.\"\n", "Prompt\n", " \n", "You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.\n", @@ -685,22 +593,24 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "eval0 = eval(self=ModelWrapper0, prompt_template=str1, question=str0, __code=__code1)\n", + "eval0 = eval(self=ModelWrapper0, prompt_template=str0, question=str0, __code=__code1)\n", "LLMCallable.call_llm0 = LLMCallable.call_llm(self=ModelWrapper1, user_prompt=eval0)\n", + "eval1 = eval(self=ModelWrapper2, prompt_template=str0, question=str1, response=LLMCallable.call_llm0, __code=__code0)\n", "\n", "#Documentation\n", "[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).\n", "[LLMCallable.call_llm] .\n", "\n", "#Variables\n", - "(str) str1=Is the following statement plausible? Answer with 'yes' or 'no' only.\n", - "\n", - "Question: {question}\n", + "(str) str0=Is the following sentence plausible? \"Marcelo got on the end of a through ball.\"\n", "(code) __code1:def create_prompt(self, prompt_template, question):\n", - "\t\treturn prompt_template.format(question=question)\n", + " return f'{prompt_template} With a focus on the likely scenarios, evaluate: {question}'\n", "(code) __code0:def extract_answer(self, prompt_template, question, response):\n", - " answer = response.split(\"Answer:\")[1].strip()\n", - " return answer\n", + " if 'Answer:' in response:\n", + " answer = response.split('Answer:')[1].strip()\n", + " else:\n", + " answer = 'The response does not contain a clear answer.'\n", + " return answer\n", "\n", "#Constraints\n", "(code) __code1: The code should start with:\n", @@ -709,23 +619,26 @@ "def extract_answer(self, prompt_template, question, response):\n", "\n", "#Inputs\n", - "(ModelWrapper) ModelWrapper2=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(ModelWrapper) ModelWrapper1=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(ModelWrapper) ModelWrapper0=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(str) str0=Is the following sentence plausible? \"Deshaun Watson was called for the goal tend in the Eastern Conference Finals.\"\n", - "(str) exception_eval0=(IndexError) list index out of range\n", + "(ModelWrapper) ModelWrapper2=.ModelWrapper object at 0x000002706C4DABB0>\n", + "(str) str1=Is the following sentence plausible? \"Marcelo got on the end of a through ball.\"\n", + "(ModelWrapper) ModelWrapper1=.ModelWrapper object at 0x000002706C4DABB0>\n", + "(ModelWrapper) ModelWrapper0=.ModelWrapper object at 0x000002706C4DABB0>\n", "\n", "#Others\n", - "(str) eval0=Is the following statement plausible? Answer with 'yes' or 'no' only.\n", + "(str) eval0=Considering known historical contexts and logical sequences, is the following sentence plausible? \"Elias Lindhorn beat the buzzer.\" With a focus on the likely scenarios, evaluate: Is the following sentence plausible? \"Marcelo got on the end of a through ball.\"\n", + "(str) LLMCallable.call_llm0=To analyze the plausibility of these sentences considering known historical contexts and logical sequences, let's break them down based on the contexts implied by their activities and the specific phrasing used.\n", "\n", - "Question: Is the following sentence plausible? \"Deshaun Watson was called for the goal tend in the Eastern Conference Finals.\"\n", - "(str) LLMCallable.call_llm0=No.\n", + "1. **\"Elias Lindhorn beat the buzzer.\"**: This sentence is typical in the context of sports, particularly those played with a clock, such as basketball, where the term \"beat the buzzer\" implies making a score just before the game or quarter clock expires. There's nothing inherently implausible about this statement without additional context. The name \"Elias Lindhorn,\" while not immediately recognizable as a famous athlete from history as of my last knowledge update, does not detract from the plausibility. Sports players regularly achieve significant moments, and new talents are constantly emerging. Therefore, without specific historical or logical contradictions, this sentence is plausible.\n", "\n", - "#Outputs\n", + "2. **\"Marcelo got on the end of a through ball.\"**: This sentence fits well within the context of soccer (football), where a \"through ball\" is a pass sent to a player in a position to score or advance the ball significantly, often bypassing defenders. The name \"Marcelo\" is commonly associated with soccer, notably with Marcelo Vieira da Silva Júnior, a Brazilian footballer known for his tenure at Real Madrid among other achievements. Even without referencing this specific Marcelo, the scenario described is a routine part of a soccer match, making the sentence highly plausible. Soccer is the most popular sport globally, and the tactics described align with common gameplay. \n", + "\n", + "In conclusion, both sentences are plausible within the contexts of their respective sports, assuming no extraordinary circumstances or inaccuracies in the details provided.\n", "\n", + "#Outputs\n", + "(str) eval1=The response does not contain a clear answer.\n", "\n", "#Feedback:\n", - "(IndexError) list index out of range\n", + "The answer is wrong. We expect the output of your answer to be \"yes\". Please modify the prompt and relevant parts of the program to help LLM produce the right answer.\n", "\n", "================================\n", "\n", @@ -734,22 +647,26 @@ "\n", "LLM response:\n", " {\n", - " \"reasoning\": \"The IndexError 'list index out of range' in the feedback refers to an issue occurring somewhere involving list access. Upon examining the functions and structures provided, the first thing to notice is that the eval function successfully creates a formatted prompt without issues, indicated by 'eval0' being properly set in #Others. The error must then be happening after this point, which would involve the response being generated and then parsed.\\n\\nLooking at the __code0 function for 'extract_answer', the function attempts to split the response on 'Answer:' and take the second element from this split operation. The error comes from the fact that the response returned, 'No.', does't contain the word 'Answer:' leading to the split result being a list of length 1 and subsequently accessing the second element [1] is causing the IndexError.\\n\\nConsidering this, the proper rectification would involve adjusting the output expectation within the LLM responses to match the format expected by 'extract_answer'. This means that the generation performed by LLM needs to include the word 'Answer:' in its response, which according to the provided code, happens before the 'extract_answer' function is used. However, making changes to that directly isn't an option here due to constraints. Thus, the available modification lies in the prompt template 'str1' to possibly cue the LLM towards the expected response format.\",\n", - " \"suggestion\": {\n", - " \"str1\": \"Is the following statement plausible? Please prefix your answer with 'Answer: ' and reply with 'yes' or 'no' only.\\\\n\\\\nQuestion: {question}\"\n", - " }\n", + "\"reasoning\": \"From the given problem, the output indicates that the function extract_answer could not find a clear and definite 'Answer:' formulated within the response provided by LLMCallable.call_llm0. However, the response from call_llm0 does analyze the plausibility of the sentences in detail, concluding that both are plausible within their contexts. The issue seems to be that the provided response does not adhere to a format expected by the extract_answer function, which expects the response to include a specific 'Answer:' preamble followed by the conclusion. Since the feedback mentions that the answer should be 'yes', the prompt creation code (__code1) could be adapted to direct the Large Language Model (LLM) to formulate responses in a way that includes a definitive 'Answer: yes' or 'Answer: no', based on the analysis of sentence plausibility. Specifically, modifying the create_prompt function to include guidelines for response formatting may help the LLM to generate an answer that fits the expected format by the extract_answer function.\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {\n", + " \"__code1\": \"def create_prompt(self, prompt_template, question):\\n return f'{prompt_template} With a focus on the likely scenarios, evaluate: {question} Please provide your conclusion with a definitive \\\"Answer: yes\\\" or \\\"Answer: no\\\".'\"\n", "}\n", - "Question: Is the following sentence plausible? \"Mookie Betts skated behind the net.\"\n", + "}\n", + "Question: Is the following sentence plausible? \"Deshaun Watson was called for the goal tend in the Eastern Conference Finals.\"\n", "Expected answer: no\n", "Answer: MessageNode: (eval:1, dtype=, data=yes)\n", "Output: MessageNode: (eval:1, dtype=, data=yes), Feedback: The answer is wrong. We expect the output of your answer to be \"no\". Please modify the prompt and relevant parts of the program to help LLM produce the right answer., Variables:\n", "__code:1 def create_prompt(self, prompt_template, question):\n", - "\t\treturn prompt_template.format(question=question)\n", + " return f'{prompt_template} With a focus on the likely scenarios, evaluate: {question} Please provide your conclusion with a definitive \"Answer: yes\" or \"Answer: no\".'\n", "__code:0 def extract_answer(self, prompt_template, question, response):\n", - " answer = response.split(\"Answer:\")[1].strip()\n", - " return answer\n", - "str:1 Is the following statement plausible? Please prefix your answer with 'Answer: ' and reply with 'yes' or 'no' only.\\n\\nQuestion: {question}\n", - "str:1 Is the following statement plausible? Please prefix your answer with 'Answer: ' and reply with 'yes' or 'no' only.\\n\\nQuestion: {question}\n", + " if 'Answer:' in response:\n", + " answer = response.split('Answer:')[1].strip()\n", + " else:\n", + " answer = 'The response does not contain a clear answer.'\n", + " return answer\n", + "str:0 Considering known historical contexts and logical sequences, is the following sentence plausible? \"Elias Lindhorn beat the buzzer.\"\n", + "str:0 Considering known historical contexts and logical sequences, is the following sentence plausible? \"Elias Lindhorn beat the buzzer.\"\n", "Prompt\n", " \n", "You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.\n", @@ -798,20 +715,24 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "eval0 = eval(self=ModelWrapper0, prompt_template=str1, question=str0, __code=__code1)\n", + "eval0 = eval(self=ModelWrapper0, prompt_template=str0, question=str0, __code=__code1)\n", "LLMCallable.call_llm0 = LLMCallable.call_llm(self=ModelWrapper1, user_prompt=eval0)\n", + "eval1 = eval(self=ModelWrapper2, prompt_template=str0, question=str1, response=LLMCallable.call_llm0, __code=__code0)\n", "\n", "#Documentation\n", "[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).\n", "[LLMCallable.call_llm] .\n", "\n", "#Variables\n", - "(str) str1=Is the following statement plausible? Please prefix your answer with 'Answer: ' and reply with 'yes' or 'no' only.\\n\\nQuestion: {question}\n", + "(str) str0=Is the following sentence plausible? \"Deshaun Watson was called for the goal tend in the Eastern Conference Finals.\"\n", "(code) __code1:def create_prompt(self, prompt_template, question):\n", - "\t\treturn prompt_template.format(question=question)\n", + " return f'{prompt_template} With a focus on the likely scenarios, evaluate: {question} Please provide your conclusion with a definitive \"Answer: yes\" or \"Answer: no\".'\n", "(code) __code0:def extract_answer(self, prompt_template, question, response):\n", - " answer = response.split(\"Answer:\")[1].strip()\n", - " return answer\n", + " if 'Answer:' in response:\n", + " answer = response.split('Answer:')[1].strip()\n", + " else:\n", + " answer = 'The response does not contain a clear answer.'\n", + " return answer\n", "\n", "#Constraints\n", "(code) __code1: The code should start with:\n", @@ -820,18 +741,17 @@ "def extract_answer(self, prompt_template, question, response):\n", "\n", "#Inputs\n", - "(ModelWrapper) ModelWrapper2=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(ModelWrapper) ModelWrapper1=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(ModelWrapper) ModelWrapper0=.ModelWrapper object at 0x7fd3b0f34d90>\n", - "(str) str0=Is the following sentence plausible? \"Mookie Betts skated behind the net.\"\n", - "(str) eval1=yes\n", + "(ModelWrapper) ModelWrapper2=.ModelWrapper object at 0x000002706C4DABB0>\n", + "(str) str1=Is the following sentence plausible? \"Deshaun Watson was called for the goal tend in the Eastern Conference Finals.\"\n", + "(ModelWrapper) ModelWrapper1=.ModelWrapper object at 0x000002706C4DABB0>\n", + "(ModelWrapper) ModelWrapper0=.ModelWrapper object at 0x000002706C4DABB0>\n", "\n", "#Others\n", - "(str) eval0=Is the following statement plausible? Please prefix your answer with 'Answer: ' and reply with 'yes' or 'no' only.\\n\\nQuestion: Is the following sentence plausible? \"Mookie Betts skated behind the net.\"\n", + "(str) eval0=Considering known historical contexts and logical sequences, is the following sentence plausible? \"Elias Lindhorn beat the buzzer.\" With a focus on the likely scenarios, evaluate: Is the following sentence plausible? \"Deshaun Watson was called for the goal tend in the Eastern Conference Finals.\" Please provide your conclusion with a definitive \"Answer: yes\" or \"Answer: no\".\n", "(str) LLMCallable.call_llm0=Answer: yes\n", "\n", "#Outputs\n", - "\n", + "(str) eval1=yes\n", "\n", "#Feedback:\n", "The answer is wrong. We expect the output of your answer to be \"no\". Please modify the prompt and relevant parts of the program to help LLM produce the right answer.\n", @@ -843,16 +763,20 @@ "\n", "LLM response:\n", " {\n", - " \"reasoning\": \"The task at hand involves modifying certain elements of the prompt to guide the Large Language Model (LLM) to answer 'no' rather than 'yes' to the statement about Mookie Betts, who is known primarily for his skills in baseball, not ice hockey. Currently, the prompt provided to the LLM reads: 'Is the following statement plausible?' followed by the question concerning Mookie Betts skating. This is neutral and does not guide the LLM toward understanding why the question might be implausible given Mookie Betts' actual sporting background. Adjusting the wording in the prompt to not only inquire about the plausibility of the statement but to also implicitly point towards the oddity of the statement (mentioning his actual sports) could guide the LLM to produce the expected 'no' answer.\",\n", - " \"suggestion\": {\n", - " \"str1\": \"Is the following statement plausible given that Mookie Betts is primarily known as a baseball player for the Los Angeles Dodgers? Please prefix your answer with 'Answer: ' and reply with 'yes' or 'no' only.\\n\\nQuestion: {question}\"\n", - " }\n", + "\"reasoning\": \"The instruction indicates that the output of our program is incorrect; specifically, the final evaluation ('eval1') of whether Deshaun Watson being called for the goal tend in the Eastern Conference Finals is plausible produced an incorrect 'yes' answer when the expected answer is 'no'. The problem lies within the formulation of the prompt passed to the language model. The prompt is generated by the '__code1' function and is used to ask the language model for an evaluation. Given the feedback, it's clear that the generated prompt does not correctly guide the language model to consider the implausibility of Deshaun Watson being called for a 'goal tend' which is a term from basketball, not applicable in contexts where Watson, a football player, would be involved. Therefore, the corrective action involves refining the '__code1' function to ensure that the language model not only focuses on 'likely scenarios' but also has enough context to understand and analyze the scenario's plausibility accurately, especially regarding sports context.\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {\n", + " \"__code1\": \"def create_prompt(self, prompt_template, question):\\n return f'{prompt_template} Considering the context of the scenario, evaluate the plausibility based on the involved sports figures and terms: {question} Your response should conclude with a definitive \\\"Answer: yes\\\" or \\\"Answer: no\\\".'\"\n", + "}\n", "}\n", + "Question: Is the following sentence plausible? \"Mookie Betts skated behind the net.\"\n", + "Expected answer: no\n", + "Answer: MessageNode: (eval:1, dtype=, data=no)\n", "\n", "Testing on new examples:\n", "Question: Is the following sentence plausible? \"John Tavares earned a trip to the penalty box in the Stanley Cup.\"\n", "Expected answer: yes\n", - "Answer: MessageNode: (eval:3, dtype=, data=yes)\n" + "Answer: yes\n" ] } ], @@ -862,7 +786,7 @@ "examples = [{\"question\": r[\"input\"], \"answer\": r[\"target\"]} for r in train]\n", "\n", "dp = Predict()\n", - "optimizer = FunctionOptimizerV2(dp.parameters() + [dp.prompt_template],\n", + "optimizer = OptoPrime(dp.parameters() + [dp.prompt_template],\n", " config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", "\n", "print(\"Training on a few examples:\")\n", @@ -870,10 +794,15 @@ " \n", "print(\"\\nTesting on new examples:\")\n", "for example in examples[5:6]:\n", - " response = dp.forward(example[\"question\"])\n", - " print(\"Question:\", example[\"question\"])\n", - " print(\"Expected answer:\", example[\"answer\"])\n", - " print(\"Answer:\", response)" + " try:\n", + " response = dp.forward(example[\"question\"])\n", + " print(\"Question:\", example[\"question\"])\n", + " print(\"Expected answer:\", example[\"answer\"])\n", + " print(\"Answer:\", response.data)\n", + " except ExecutionError as e:\n", + " print(\"Question:\", example[\"question\"])\n", + " print(\"Expected answer:\", example[\"answer\"])\n", + " print(\"Error:\", e.exception_node.data)" ] }, { @@ -886,9 +815,9 @@ ], "metadata": { "kernelspec": { - "display_name": "verbal-gym", + "display_name": "newtrace", "language": "python", - "name": "verbal-gym" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -900,7 +829,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.18" + "version": "3.9.19" } }, "nbformat": 4, diff --git a/docs/examples/numerical/numerical_optimization.ipynb b/docs/examples/numerical/numerical_optimization.ipynb index 48327b16..70a88fc3 100644 --- a/docs/examples/numerical/numerical_optimization.ipynb +++ b/docs/examples/numerical/numerical_optimization.ipynb @@ -25,23 +25,14 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\adswamin\\AppData\\Local\\miniconda3\\envs\\newtrace\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import uxsim as ux\n", "import itertools\n", "import opto\n", "import opto.trace as trace\n", - "from opto.optimizers import FunctionOptimizerV2Memory\n", + "from opto.optimizers import OptoPrime\n", "from opto.trace.bundle import ExceptionNode\n", "from autogen import config_list_from_json" ] @@ -199,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -225,7 +216,7 @@ "\n", " EW_x = trace.node(MIN_GREEN_TIME, trainable=True, constraint=f\"[{MIN_GREEN_TIME},{MAX_GREEN_TIME}]\")\n", " NS_x = trace.node(MIN_GREEN_TIME, trainable=True, constraint=f\"[{MIN_GREEN_TIME},{MAX_GREEN_TIME}]\")\n", - " optimizer = FunctionOptimizerV2Memory(\n", + " optimizer = OptoPrime(\n", " [EW_x, NS_x], memory_size=trace_memory, config_list=config_list_from_json(trace_config)\n", " )\n", "\n", @@ -269,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -329,29 +320,29 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "create_world11 = create_world(EW_time=int4, NS_time=int5)\n", - "analyze_world11 = analyze_world(W=create_world11)\n", + "create_world0 = create_world(EW_time=int0, NS_time=int1)\n", + "analyze_world0 = analyze_world(W=create_world0)\n", "\n", "#Documentation\n", "[create_world] .\n", "[analyze_world] .\n", "\n", "#Variables\n", - "(int) int4=15\n", - "(int) int5=15\n", + "(int) int0=15\n", + "(int) int1=15\n", "\n", "#Constraints\n", - "(int) int4: [15,90]\n", - "(int) int5: [15,90]\n", + "(int) int0: [15,90]\n", + "(int) int1: [15,90]\n", "\n", "#Inputs\n", "\n", "\n", "#Others\n", - "(World) create_world11=\n", + "(World) create_world0=\n", "\n", "#Outputs\n", - "(dict) analyze_world11={'Avg. Delay': 10.710565476190474, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 52.017699115044245}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 47.59047619047619}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 33.69767441860465}, (, ): {'Trips attempted': 238, 'Trips completed': 189, 'Time lost per vehicle': 90.74369747899159}, (, ): {'Trips attempted': 104, 'Trips completed': 94, 'Time lost per vehicle': 82.59615384615384}, (, ): {'Trips attempted': 93, 'Trips completed': 82, 'Time lost per vehicle': 78.68817204301075}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 19.551020408163264}, (, ): {'Trips attempted': 100, 'Trips completed': 95, 'Time lost per vehicle': 21.53}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 22.5}, (, ): {'Trips attempted': 111, 'Trips completed': 104, 'Time lost per vehicle': 22.126126126126128}, (, ): {'Trips attempted': 93, 'Trips completed': 87, 'Time lost per vehicle': 22.010752688172044}, (, ): {'Trips attempted': 104, 'Trips completed': 101, 'Time lost per vehicle': 21.317307692307693}, 'Best-Case Estimated Delay': 48.17994505494506, 'OVERALL SCORE': 75.57083282443965}\n", + "(dict) analyze_world0={'Avg. Delay': 10.710565476190474, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 52.017699115044245}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 47.59047619047619}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 33.69767441860465}, (, ): {'Trips attempted': 238, 'Trips completed': 189, 'Time lost per vehicle': 90.74369747899159}, (, ): {'Trips attempted': 104, 'Trips completed': 94, 'Time lost per vehicle': 82.59615384615384}, (, ): {'Trips attempted': 93, 'Trips completed': 82, 'Time lost per vehicle': 78.68817204301075}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 19.551020408163264}, (, ): {'Trips attempted': 100, 'Trips completed': 95, 'Time lost per vehicle': 21.53}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 22.5}, (, ): {'Trips attempted': 111, 'Trips completed': 104, 'Time lost per vehicle': 22.126126126126128}, (, ): {'Trips attempted': 93, 'Trips completed': 87, 'Time lost per vehicle': 22.010752688172044}, (, ): {'Trips attempted': 104, 'Trips completed': 101, 'Time lost per vehicle': 21.317307692307693}, 'Best-Case Estimated Delay': 48.17994505494506, 'OVERALL SCORE': 75.57083282443965}\n", "\n", "#Feedback:\n", "OVERALL SCORE: 75.57083282443965\n", @@ -364,11 +355,11 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"The instruction is to optimize the green light durations (East-West and North-South) to minimize the OVERALL SCORE, which represents the efficiency of traffic flow through the intersection. The initial values for East-West (EW) and North-South (NS) green light durations are both set to 15. Given the outputs, particularly the OVERALL SCORE of 75.57083282443965, there's room for improvement to find a more balanced configuration that minimizes delays and thus improves the intersection's score. Adjustments to these durations should consider the trade-off between minimizing waiting times for one direction without disproportionately increasing them for the other direction. The feedback implies that the current settings are not optimal. To optimize, we must alter the durations within the constraints of 15 to 90 seconds. Analyzing the output, we notice high 'Time lost per vehicle' for East-West movements compared to North-South, which suggests a potential imbalance in green light durations favoring one set of movements over the other. To improve, we might slightly increase the duration for East-West movement (int4) and decrease the duration for North-South movement (int5), aiming for a more balanced traffic flow that could lead to a reduced OVERALL SCORE.\",\n", + "\"reasoning\": \"To minimize the OVERALL SCORE, we need to adjust the green light durations (int0 and int1) for East-West (EW) and North-South (NS) respectively. Given that the initial values for both directions are equal (15 seconds each), and the feedback suggests optimizing further, we need to consider the traffic patterns indicated by 'Time lost per vehicle' for each direction. High 'Time lost per vehicle' values suggest longer green light durations might be required to reduce vehicle delays in that direction. For instance, East to West and East to North directions have considerably high time lost per vehicle, indicating that the East-West green light duration might need to be increased to accommodate the heavier traffic flow from East. Similarly, while North to West, North to East, and South to East have lower time lost per vehicle, suggesting balanced or slightly increased green light duration for North-South could help, we should also avoid reducing the North-South time significantly as it seems already balanced. Therefore, a slight increase in EW time and a smaller adjustment or maintaining the NS time could help in balancing the delays further and reducing the overall score.\",\n", "\"answer\": \"\",\n", "\"suggestion\": {\n", - " \"int4\": 20,\n", - " \"int5\": 10\n", + " \"int0\": 30,\n", + " \"int1\": 20\n", "}\n", "}\n", "Prompt\n", @@ -424,30 +415,33 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "exception_step2 = exception_step(EW_green_time=int4, NS_green_time=int5)\n", + "create_world1 = create_world(EW_time=int0, NS_time=int1)\n", + "analyze_world1 = analyze_world(W=create_world1)\n", "\n", "#Documentation\n", - "[exception] Simulation raises an exception with these inputs.\n", + "[create_world] .\n", + "[analyze_world] .\n", "\n", "#Variables\n", - "(int) int4=20\n", - "(int) int5=10\n", + "(int) int0=30\n", + "(int) int1=20\n", "\n", "#Constraints\n", - "(int) int4: [15,90]\n", - "(int) int5: [15,90]\n", + "(int) int0: [15,90]\n", + "(int) int1: [15,90]\n", "\n", "#Inputs\n", "\n", "\n", "#Others\n", - "\n", + "(World) create_world1=\n", "\n", "#Outputs\n", - "(ExecutionError) exception_step2=ExecutionError: (AssertionError) NS_time out of bounds.\n", + "(dict) analyze_world1={'Avg. Delay': 11.085356880058868, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 31.23008849557522}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 29.98095238095238}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 26.96511627906977}, (, ): {'Trips attempted': 238, 'Trips completed': 201, 'Time lost per vehicle': 47.436974789915965}, (, ): {'Trips attempted': 104, 'Trips completed': 99, 'Time lost per vehicle': 40.99038461538461}, (, ): {'Trips attempted': 93, 'Trips completed': 87, 'Time lost per vehicle': 40.376344086021504}, (, ): {'Trips attempted': 98, 'Trips completed': 91, 'Time lost per vehicle': 39.724489795918366}, (, ): {'Trips attempted': 100, 'Trips completed': 93, 'Time lost per vehicle': 42.53}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 41.36734693877551}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 39.549549549549546}, (, ): {'Trips attempted': 93, 'Trips completed': 85, 'Time lost per vehicle': 39.505376344086024}, (, ): {'Trips attempted': 104, 'Trips completed': 100, 'Time lost per vehicle': 42.58653846153846}, 'Best-Case Estimated Delay': 38.8228021978022, 'OVERALL SCORE': 44.98937866274704}\n", "\n", "#Feedback:\n", - "ExecutionError: (AssertionError) NS_time out of bounds.\n", + "OVERALL SCORE: 44.98937866274704\n", + "Please try to optimize the intersection further. If you are certain that you have found the optimal solution, please suggest it again.\n", "\n", "================================\n", "\n", @@ -456,11 +450,11 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"Given the problem instruction, we are to adjust the green light duration for East-West (EW) and North-South (NS) directions to minimize the overall intersection score. The goal is to balance the green light durations to prevent vehicle queues and delays in either direction. The feedback indicates that the provided NS green time of 10 is causing an 'NS_time out of bounds' exception, suggesting it does not meet the simulation's constraints for valid green light durations. Given the constraint that both int4 (EW_green_time) and int5 (NS_green_time) must be within the range of 15 to 90, an NS green time of 10 does not satisfy these conditions. Therefore, to solve the problem, we need to adjust int5 (NS_green_time) to be within the specified boundaries. Given that int4 is already within bounds, but there could still be an optimization needed based on the balance requirement, int4 might also need slight adjustments.\",\n", + "\"reasoning\": \"The instruction requires finding a balance between the East-West (EW) and North-South (NS) green light durations to minimize the overall intersection score, which is a measure of vehicle delay. The current settings result in an overall score of approximately 44.99, indicating that there may be room for optimization in the light durations to achieve a more balanced and efficient flow of traffic. The feedback prompts for further optimization, implying that the current settings are not yet optimal. Given that both `int0` (EW_time) and `int1` (NS_time) are within their constraints but may not be optimally set, the strategy should involve adjusting these durations to attempt to balance the waiting times and reduce the overall score. The ideal values likely exist between the extremes of their defined ranges, since too high or too low values for either direction would cause imbalances in traffic flow, as mentioned in the instructions. The values to try next should consider the feedback that the current settings are not optimal and attempt to reduce the 'Time lost per vehicle' for each direction, ultimately reducing the 'OVERALL SCORE'. Adjusting these values with a focus on minor increments could help identify a more optimal balance.\",\n", "\"answer\": \"\",\n", "\"suggestion\": {\n", - " \"int4\": 25,\n", - " \"int5\": 25\n", + "\"int0\": 25,\n", + "\"int1\": 25\n", "}\n", "}\n", "Prompt\n", @@ -516,29 +510,29 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "create_world12 = create_world(EW_time=int4, NS_time=int5)\n", - "analyze_world12 = analyze_world(W=create_world12)\n", + "create_world2 = create_world(EW_time=int0, NS_time=int1)\n", + "analyze_world2 = analyze_world(W=create_world2)\n", "\n", "#Documentation\n", "[create_world] .\n", "[analyze_world] .\n", "\n", "#Variables\n", - "(int) int4=25\n", - "(int) int5=25\n", + "(int) int0=25\n", + "(int) int1=25\n", "\n", "#Constraints\n", - "(int) int4: [15,90]\n", - "(int) int5: [15,90]\n", + "(int) int0: [15,90]\n", + "(int) int1: [15,90]\n", "\n", "#Inputs\n", "\n", "\n", "#Others\n", - "(World) create_world12=\n", + "(World) create_world2=\n", "\n", "#Outputs\n", - "(dict) analyze_world12={'Avg. Delay': 14.71833084947839, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 66.46902654867256}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 62.65714285714286}, (, ): {'Trips attempted': 86, 'Trips completed': 79, 'Time lost per vehicle': 50.13953488372093}, (, ): {'Trips attempted': 238, 'Trips completed': 189, 'Time lost per vehicle': 100.60924369747899}, (, ): {'Trips attempted': 104, 'Trips completed': 94, 'Time lost per vehicle': 89.82692307692308}, (, ): {'Trips attempted': 93, 'Trips completed': 82, 'Time lost per vehicle': 83.3225806451613}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 31.8265306122449}, (, ): {'Trips attempted': 100, 'Trips completed': 96, 'Time lost per vehicle': 34.81}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 33.44897959183673}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 32.62162162162162}, (, ): {'Trips attempted': 93, 'Trips completed': 86, 'Time lost per vehicle': 33.903225806451616}, (, ): {'Trips attempted': 104, 'Trips completed': 101, 'Time lost per vehicle': 36.26923076923077}, 'Best-Case Estimated Delay': 60.00892857142857, 'OVERALL SCORE': 85.8145253129838}\n", + "(dict) analyze_world2={'Avg. Delay': 14.71833084947839, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 66.46902654867256}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 62.65714285714286}, (, ): {'Trips attempted': 86, 'Trips completed': 79, 'Time lost per vehicle': 50.13953488372093}, (, ): {'Trips attempted': 238, 'Trips completed': 189, 'Time lost per vehicle': 100.60924369747899}, (, ): {'Trips attempted': 104, 'Trips completed': 94, 'Time lost per vehicle': 89.82692307692308}, (, ): {'Trips attempted': 93, 'Trips completed': 82, 'Time lost per vehicle': 83.3225806451613}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 31.8265306122449}, (, ): {'Trips attempted': 100, 'Trips completed': 96, 'Time lost per vehicle': 34.81}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 33.44897959183673}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 32.62162162162162}, (, ): {'Trips attempted': 93, 'Trips completed': 86, 'Time lost per vehicle': 33.903225806451616}, (, ): {'Trips attempted': 104, 'Trips completed': 101, 'Time lost per vehicle': 36.26923076923077}, 'Best-Case Estimated Delay': 60.00892857142857, 'OVERALL SCORE': 85.8145253129838}\n", "\n", "#Feedback:\n", "OVERALL SCORE: 85.8145253129838\n", @@ -551,11 +545,11 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"The task is to adjust the duration of green lights for both East-West (EW) and North-South (NS) directions to minimize the overall score, which represents delays at the intersection. From the feedback, an overall score of 85.8145253129838 suggests there is room for improvement. The given durations for both directions are equal at 25. Given the instructions, this implies that neither direction is currently optimized. To improve the overall score, one needs to find a balance where the duration of green lights for each direction minimizes the average delay for all vehicles. Looking at the feedback and output, it seems like the 'Time lost per vehicle' is high for certain directions, especially from East to West and vice versa, suggesting a need to possibly redistribute the green light duration between the two directions. Adjusting these durations while staying within the constraints ([15, 90] for both int4 and int5) could help achieve a lower overall score. The specific values to choose for int4 and int5 depend on a combination of the analysis provided in the output and logical deduction on how changes to these values influence delay times and overall scores. Without precise simulation results accessible, a balanced yet somewhat experimental approach would suggest slightly adjusting these values to either increase or decrease and observing the impact on the overall score.\",\n", + "\"reasoning\": \"The instruction specifies the need to balance the green light durations for East-West (EW) and North-South (NS) directions to minimize the OVERALL SCORE, which represents the efficiency and effectiveness of the intersection management. Given that both int0 (EW_time) and int1 (NS_time) were initially set to equal durations (25 seconds each), the OVERALL SCORE resulted in 85.8145253129838, which is indicated as not optimal according to the feedback received. The goal is to adjust these times within their respective constraints ([15, 90] seconds) to find a more balanced approach that reduces the average delay and overall intersection score. The trips attempted, completed, and time lost per vehicle data indicate varying levels of congestion in different directions, suggesting that adjusting the balance between EW and NS green times could address these imbalances. Decreasing or increasing the duration of green lights for either direction can affect the flow of traffic, potentially reducing queue times and the overall score. Considering these factors, it is logical to experiment with adjusting the durations slightly but not too drastically, observing the constraints. A slight increase in one direction and a slight decrease or maintenance in the other may find a better balance, reducing delays.\",\n", "\"answer\": \"\",\n", "\"suggestion\": {\n", - " \"int4\": 30,\n", - " \"int5\": 20\n", + " \"int0\": 23,\n", + " \"int1\": 27\n", "}\n", "}\n", "Prompt\n", @@ -611,32 +605,32 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "create_world13 = create_world(EW_time=int4, NS_time=int5)\n", - "analyze_world13 = analyze_world(W=create_world13)\n", + "create_world3 = create_world(EW_time=int0, NS_time=int1)\n", + "analyze_world3 = analyze_world(W=create_world3)\n", "\n", "#Documentation\n", "[create_world] .\n", "[analyze_world] .\n", "\n", "#Variables\n", - "(int) int4=30\n", - "(int) int5=20\n", + "(int) int0=23\n", + "(int) int1=27\n", "\n", "#Constraints\n", - "(int) int4: [15,90]\n", - "(int) int5: [15,90]\n", + "(int) int0: [15,90]\n", + "(int) int1: [15,90]\n", "\n", "#Inputs\n", "\n", "\n", "#Others\n", - "(World) create_world13=\n", + "(World) create_world3=\n", "\n", "#Outputs\n", - "(dict) analyze_world13={'Avg. Delay': 11.085356880058868, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 31.23008849557522}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 29.98095238095238}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 26.96511627906977}, (, ): {'Trips attempted': 238, 'Trips completed': 201, 'Time lost per vehicle': 47.436974789915965}, (, ): {'Trips attempted': 104, 'Trips completed': 99, 'Time lost per vehicle': 40.99038461538461}, (, ): {'Trips attempted': 93, 'Trips completed': 87, 'Time lost per vehicle': 40.376344086021504}, (, ): {'Trips attempted': 98, 'Trips completed': 91, 'Time lost per vehicle': 39.724489795918366}, (, ): {'Trips attempted': 100, 'Trips completed': 93, 'Time lost per vehicle': 42.53}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 41.36734693877551}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 39.549549549549546}, (, ): {'Trips attempted': 93, 'Trips completed': 85, 'Time lost per vehicle': 39.505376344086024}, (, ): {'Trips attempted': 104, 'Trips completed': 100, 'Time lost per vehicle': 42.58653846153846}, 'Best-Case Estimated Delay': 38.8228021978022, 'OVERALL SCORE': 44.98937866274704}\n", + "(dict) analyze_world3={'Avg. Delay': 18.505263157894735, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 104.43362831858407}, (, ): {'Trips attempted': 105, 'Trips completed': 102, 'Time lost per vehicle': 96.65714285714286}, (, ): {'Trips attempted': 86, 'Trips completed': 79, 'Time lost per vehicle': 66.09302325581395}, (, ): {'Trips attempted': 238, 'Trips completed': 179, 'Time lost per vehicle': 140.109243697479}, (, ): {'Trips attempted': 104, 'Trips completed': 91, 'Time lost per vehicle': 126.99038461538461}, (, ): {'Trips attempted': 93, 'Trips completed': 81, 'Time lost per vehicle': 110.84946236559139}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 28.775510204081634}, (, ): {'Trips attempted': 100, 'Trips completed': 96, 'Time lost per vehicle': 31.68}, (, ): {'Trips attempted': 98, 'Trips completed': 94, 'Time lost per vehicle': 30.581632653061224}, (, ): {'Trips attempted': 111, 'Trips completed': 104, 'Time lost per vehicle': 28.765765765765767}, (, ): {'Trips attempted': 93, 'Trips completed': 87, 'Time lost per vehicle': 31.06451612903226}, (, ): {'Trips attempted': 104, 'Trips completed': 101, 'Time lost per vehicle': 33.13461538461539}, 'Best-Case Estimated Delay': 78.85302197802197, 'OVERALL SCORE': 122.93479991296886}\n", "\n", "#Feedback:\n", - "OVERALL SCORE: 44.98937866274704\n", + "OVERALL SCORE: 122.93479991296886\n", "Please try to optimize the intersection further. If you are certain that you have found the optimal solution, please suggest it again.\n", "\n", "================================\n", @@ -646,11 +640,11 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"The given instruction asks for setting the green light durations (East-West and North-South directions) to minimize the overall score of the intersection, suggesting there's a need to balance these durations to reduce vehicles' waiting time and improve traffic flow. The initial values of int4 (East-West duration) and int5 (North-South duration) are 30 and 20, respectively, resulting in an OVERALL SCORE of 44.98937866274704, as per the outputs. This score is indicative of the performance or efficiency of the intersection under the current settings. The goal is to adjust these durations in such a manner that the overall score is minimized, implying a more efficient traffic management at the intersection. Given the constraints that both variables must be within the range of 15 to 90, a plausible way to optimize is to slightly tweak these values, based on the feedback that indicates the need for further optimization beyond the current score. Adjusting the timing could either decrease or redistribute the time lost per vehicle across directions, leading to potential improvements in the OVERALL SCORE. It appears that an increase in either or both durations might achieve better balance, considering the current score and feedback to further optimize the intersection.\",\n", + "\"reasoning\": \"The instruction is to suggest values for the 'East-West' (EW) and 'North-South' (NS) green light durations that minimize the overall score of the intersection, where the overall score is influenced by the delays experienced by the vehicles. The goal is to find a balance between these two durations to ensure that delays for all directions are minimized, enhancing traffic flow. The current values of int0=23 and int1=27 yield an OVERALL SCORE of 122.93479991296886, indicating there is room for improvement. To optimize further, consider that the ideal values should efficiently balance the delays for North-South and East-West directions. Based on the output, it's clear that while some delays are reasonably low (e.g., for N1-W1, N1-E1, N1-S1, S1-W1, S1-E1, and S1-N218), others, especially involving E1 as a starting node, are quite high. This suggests that the balance is off, possibly requiring a reduction in the East-West duration to allow more time for the North-South direction or vice versa. Given that constraints allow for significant adjustment within a range of [15,90] for both directions, we have flexibility in our adjustments.\",\n", "\"answer\": \"\",\n", "\"suggestion\": {\n", - "\"int4\": 35,\n", - "\"int5\": 25\n", + " \"int0\": 20,\n", + " \"int1\": 30\n", "}\n", "}\n", "Prompt\n", @@ -706,32 +700,32 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "create_world14 = create_world(EW_time=int4, NS_time=int5)\n", - "analyze_world14 = analyze_world(W=create_world14)\n", + "create_world4 = create_world(EW_time=int0, NS_time=int1)\n", + "analyze_world4 = analyze_world(W=create_world4)\n", "\n", "#Documentation\n", "[create_world] .\n", "[analyze_world] .\n", "\n", "#Variables\n", - "(int) int4=35\n", - "(int) int5=25\n", + "(int) int0=20\n", + "(int) int1=30\n", "\n", "#Constraints\n", - "(int) int4: [15,90]\n", - "(int) int5: [15,90]\n", + "(int) int0: [15,90]\n", + "(int) int1: [15,90]\n", "\n", "#Inputs\n", "\n", "\n", "#Others\n", - "(World) create_world14=\n", + "(World) create_world4=\n", "\n", "#Outputs\n", - "(dict) analyze_world14={'Avg. Delay': 12.480882352941176, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 40.084070796460175}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 37.08571428571429}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 33.81395348837209}, (, ): {'Trips attempted': 238, 'Trips completed': 199, 'Time lost per vehicle': 51.357142857142854}, (, ): {'Trips attempted': 104, 'Trips completed': 98, 'Time lost per vehicle': 47.66346153846154}, (, ): {'Trips attempted': 93, 'Trips completed': 87, 'Time lost per vehicle': 48.53763440860215}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 43.42857142857143}, (, ): {'Trips attempted': 100, 'Trips completed': 95, 'Time lost per vehicle': 46.35}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 46.98979591836735}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 46.648648648648646}, (, ): {'Trips attempted': 93, 'Trips completed': 86, 'Time lost per vehicle': 47.69892473118279}, (, ): {'Trips attempted': 104, 'Trips completed': 100, 'Time lost per vehicle': 48.25}, 'Best-Case Estimated Delay': 45.11195054945055, 'OVERALL SCORE': 50.16598605909516}\n", + "(dict) analyze_world4={'Avg. Delay': 31.78386605783866, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 240.39380530973452}, (, ): {'Trips attempted': 105, 'Trips completed': 102, 'Time lost per vehicle': 221.52380952380952}, (, ): {'Trips attempted': 86, 'Trips completed': 79, 'Time lost per vehicle': 190.2093023255814}, (, ): {'Trips attempted': 238, 'Trips completed': 165, 'Time lost per vehicle': 174.98739495798318}, (, ): {'Trips attempted': 104, 'Trips completed': 89, 'Time lost per vehicle': 163.32692307692307}, (, ): {'Trips attempted': 93, 'Trips completed': 80, 'Time lost per vehicle': 151.16129032258064}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 25.346938775510203}, (, ): {'Trips attempted': 100, 'Trips completed': 97, 'Time lost per vehicle': 26.62}, (, ): {'Trips attempted': 98, 'Trips completed': 94, 'Time lost per vehicle': 26.5}, (, ): {'Trips attempted': 111, 'Trips completed': 104, 'Time lost per vehicle': 28.38738738738739}, (, ): {'Trips attempted': 93, 'Trips completed': 87, 'Time lost per vehicle': 26.032258064516128}, (, ): {'Trips attempted': 104, 'Trips completed': 101, 'Time lost per vehicle': 28.692307692307693}, 'Best-Case Estimated Delay': 125.64354395604396, 'OVERALL SCORE': 212.4335264055084}\n", "\n", "#Feedback:\n", - "OVERALL SCORE: 50.16598605909516\n", + "OVERALL SCORE: 212.4335264055084\n", "Please try to optimize the intersection further. If you are certain that you have found the optimal solution, please suggest it again.\n", "\n", "================================\n", @@ -741,10 +735,11 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"The instruction asks to optimize the green light durations (East-West and North-South) to minimize the overall score. This involves finding a balance where neither direction has its vehicles queuing up disproportionately, causing significant delays. Based on the given feedback, the current settings of int4=35 and int5=25 result in an OVERALL SCORE of 50.16598605909516, which is deemed suboptimal. Thus, adjustments need to be made to either increase or decrease the green light durations for both directions without exceeding the constraints of [15,90] for both variables. Considering that both values are relatively close to the middle of their constraints, slight adjustments in both timings can be experimented with to reach a better balance and potentially reduce the overall score. It might be beneficial to slightly increase the duration for the North-South direction (int5) as this could balance the wait times between the two directions more effectively. Conversely, considering tweaking the East-West direction (int4) timing slightly could also be an approach to find an optimal balance.\",\n", + "\"reasoning\": \"The instruction requires finding a balance between the green light durations for East-West and North-South directions to minimize the overall intersection score. Given the feedback, the initial settings of 20 seconds for East-West (EW_time=int0) and 30 seconds for North-South (NS_time=int1) resulted in an overall score of 212.4335264055084, which suggests there's room for improvement. The goal is to adjust these durations within the constraints (15 to 90 seconds) to achieve a lower overall score. Since the feedback implies that the current settings may not be optimal, adjusting these values could help find a balance that reduces vehicle queuing and delays in all directions. The detailed delay information for each direction suggests that both North-South and East-West directions are not perfectly balanced, with some directions experiencing significantly higher time lost per vehicle than others. This indicates that tweaking the green light durations could indeed optimize traffic flow. Since the feedback specifically asks for further optimization without indicating whether the current distribution leans heavily towards favoring either East-West or North-South traffic, an educated guess towards achieving a better balance could be to slightly reduce the time for the direction that has a longer green phase (in this case, North-South) and increase the time for the other direction (East-West), aiming for a more equitable distribution of green light duration, thereby potentially reducing the overall delay.\",\n", + "\"answer\": \"\",\n", "\"suggestion\": {\n", - " \"int4\": 33,\n", - " \"int5\": 27\n", + " \"int0\": 25,\n", + " \"int1\": 25\n", "}\n", "}\n", "Prompt\n", @@ -800,32 +795,32 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "create_world15 = create_world(EW_time=int4, NS_time=int5)\n", - "analyze_world15 = analyze_world(W=create_world15)\n", + "create_world5 = create_world(EW_time=int0, NS_time=int1)\n", + "analyze_world5 = analyze_world(W=create_world5)\n", "\n", "#Documentation\n", "[create_world] .\n", "[analyze_world] .\n", "\n", "#Variables\n", - "(int) int4=33\n", - "(int) int5=27\n", + "(int) int0=25\n", + "(int) int1=25\n", "\n", "#Constraints\n", - "(int) int4: [15,90]\n", - "(int) int5: [15,90]\n", + "(int) int0: [15,90]\n", + "(int) int1: [15,90]\n", "\n", "#Inputs\n", "\n", "\n", "#Others\n", - "(World) create_world15=\n", + "(World) create_world5=\n", "\n", "#Outputs\n", - "(dict) analyze_world15={'Avg. Delay': 13.742222222222223, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 49.91150442477876}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 45.78095238095238}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 41.18604651162791}, (, ): {'Trips attempted': 238, 'Trips completed': 193, 'Time lost per vehicle': 74.61344537815125}, (, ): {'Trips attempted': 104, 'Trips completed': 96, 'Time lost per vehicle': 68.66346153846153}, (, ): {'Trips attempted': 93, 'Trips completed': 85, 'Time lost per vehicle': 66.87096774193549}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 41.38775510204081}, (, ): {'Trips attempted': 100, 'Trips completed': 95, 'Time lost per vehicle': 43.14}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 43.826530612244895}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 43.234234234234236}, (, ): {'Trips attempted': 93, 'Trips completed': 86, 'Time lost per vehicle': 44.27956989247312}, (, ): {'Trips attempted': 104, 'Trips completed': 100, 'Time lost per vehicle': 44.91346153846154}, 'Best-Case Estimated Delay': 52.88461538461539, 'OVERALL SCORE': 65.44882059721279}\n", + "(dict) analyze_world5={'Avg. Delay': 14.71833084947839, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 66.46902654867256}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 62.65714285714286}, (, ): {'Trips attempted': 86, 'Trips completed': 79, 'Time lost per vehicle': 50.13953488372093}, (, ): {'Trips attempted': 238, 'Trips completed': 189, 'Time lost per vehicle': 100.60924369747899}, (, ): {'Trips attempted': 104, 'Trips completed': 94, 'Time lost per vehicle': 89.82692307692308}, (, ): {'Trips attempted': 93, 'Trips completed': 82, 'Time lost per vehicle': 83.3225806451613}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 31.8265306122449}, (, ): {'Trips attempted': 100, 'Trips completed': 96, 'Time lost per vehicle': 34.81}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 33.44897959183673}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 32.62162162162162}, (, ): {'Trips attempted': 93, 'Trips completed': 86, 'Time lost per vehicle': 33.903225806451616}, (, ): {'Trips attempted': 104, 'Trips completed': 101, 'Time lost per vehicle': 36.26923076923077}, 'Best-Case Estimated Delay': 60.00892857142857, 'OVERALL SCORE': 85.8145253129838}\n", "\n", "#Feedback:\n", - "OVERALL SCORE: 65.44882059721279\n", + "OVERALL SCORE: 85.8145253129838\n", "Please try to optimize the intersection further. If you are certain that you have found the optimal solution, please suggest it again.\n", "\n", "================================\n", @@ -835,11 +830,11 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"From the feedback, we identify that the OVERALL SCORE is 65.44882059721279, and the instruction suggests optimizing the intersection further to reduce this score. The goal is to minimize the OVERALL SCORE which is influenced by the green light durations for East-West (EW_time) and North-South (NS_time) directions. The current values for int4 (East-West duration) is 33 and int5 (North-South duration) is 27. To optimize the intersection, looking at how these durations affect the overall score, adjustments to these durations are essential to find a better balance that results in minimizing the average delay and thus lowering the overall score. Given the feedback and the current setting, it is suggested to adjust the green light durations slightly to test if a better balance can be achieved. Since the documentation does not specify the impact of increasing or decreasing these values explicitly, empirical adjustments based on the feedback are necessary. Adjusting the durations closer to a balanced middle point within the allowed range might distribute the traffic flow more evenly across directions, potentially leading to a lower overall score. Hence, a slight decrease in East-West duration and an increment in North-South duration is proposed to see if it positively affects the overall score.\",\n", - "\"answer\": \"\",\n", + "\"reasoning\": \"To minimize the OVERALL SCORE, we need to find a balance between the green light durations for East-West (EW) and North-South (NS) directions. The feedback suggests that the chosen values of 25 seconds for both directions have led to an OVERALL SCORE of 85.8145253129838, which indicates room for improvement. To achieve a lower score, we could try adjusting the values to respond more effectively to the specific traffic patterns indicated by the feedback. For example, it appears that trips from E1 to W1 have a significantly higher 'Time lost per vehicle' compared to trips from N1 to W1, indicating potential over-prioritization of the NS direction. Similarly, the 'Time lost per vehicle' from E1 to N1 and E1 to S1 suggest that the EW direction might be under-prioritized. We can infer that adjusting the EW and NS times to better accommodate these traffic patterns could lead to a lower overall delay and thus a lower OVERALL SCORE. A possible direction is to slightly increase EW time to alleviate the high delays seen in east-west movements and slightly decrease NS time, provided we keep within the constraints of 15-90 seconds for both.\",\n", + "\"answer\": \"Reduce OVERALL SCORE\",\n", "\"suggestion\": {\n", - " \"int4\": 31,\n", - " \"int5\": 29\n", + " \"int0\": 30,\n", + " \"int1\": 20\n", "}\n", "}\n", "Prompt\n", @@ -895,32 +890,32 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "create_world16 = create_world(EW_time=int4, NS_time=int5)\n", - "analyze_world16 = analyze_world(W=create_world16)\n", + "create_world6 = create_world(EW_time=int0, NS_time=int1)\n", + "analyze_world6 = analyze_world(W=create_world6)\n", "\n", "#Documentation\n", "[create_world] .\n", "[analyze_world] .\n", "\n", "#Variables\n", - "(int) int4=31\n", - "(int) int5=29\n", + "(int) int0=30\n", + "(int) int1=20\n", "\n", "#Constraints\n", - "(int) int4: [15,90]\n", - "(int) int5: [15,90]\n", + "(int) int0: [15,90]\n", + "(int) int1: [15,90]\n", "\n", "#Inputs\n", "\n", "\n", "#Others\n", - "(World) create_world16=\n", + "(World) create_world6=\n", "\n", "#Outputs\n", - "(dict) analyze_world16={'Avg. Delay': 15.268801191362623, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 65.93362831858407}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 59.95238095238095}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 47.848837209302324}, (, ): {'Trips attempted': 238, 'Trips completed': 190, 'Time lost per vehicle': 98.60504201680672}, (, ): {'Trips attempted': 104, 'Trips completed': 95, 'Time lost per vehicle': 88.375}, (, ): {'Trips attempted': 93, 'Trips completed': 82, 'Time lost per vehicle': 72.64516129032258}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 38.19387755102041}, (, ): {'Trips attempted': 100, 'Trips completed': 95, 'Time lost per vehicle': 39.79}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 41.41836734693877}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 39.288288288288285}, (, ): {'Trips attempted': 93, 'Trips completed': 86, 'Time lost per vehicle': 41.354838709677416}, (, ): {'Trips attempted': 104, 'Trips completed': 100, 'Time lost per vehicle': 40.96153846153846}, 'Best-Case Estimated Delay': 61.10851648351648, 'OVERALL SCORE': 83.20811075335004}\n", + "(dict) analyze_world6={'Avg. Delay': 11.085356880058868, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 31.23008849557522}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 29.98095238095238}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 26.96511627906977}, (, ): {'Trips attempted': 238, 'Trips completed': 201, 'Time lost per vehicle': 47.436974789915965}, (, ): {'Trips attempted': 104, 'Trips completed': 99, 'Time lost per vehicle': 40.99038461538461}, (, ): {'Trips attempted': 93, 'Trips completed': 87, 'Time lost per vehicle': 40.376344086021504}, (, ): {'Trips attempted': 98, 'Trips completed': 91, 'Time lost per vehicle': 39.724489795918366}, (, ): {'Trips attempted': 100, 'Trips completed': 93, 'Time lost per vehicle': 42.53}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 41.36734693877551}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 39.549549549549546}, (, ): {'Trips attempted': 93, 'Trips completed': 85, 'Time lost per vehicle': 39.505376344086024}, (, ): {'Trips attempted': 104, 'Trips completed': 100, 'Time lost per vehicle': 42.58653846153846}, 'Best-Case Estimated Delay': 38.8228021978022, 'OVERALL SCORE': 44.98937866274704}\n", "\n", "#Feedback:\n", - "OVERALL SCORE: 83.20811075335004\n", + "OVERALL SCORE: 44.98937866274704\n", "Please try to optimize the intersection further. If you are certain that you have found the optimal solution, please suggest it again.\n", "\n", "================================\n", @@ -930,11 +925,11 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"To optimize the intersection further and minimize the overall score, we need to adjust the green light durations for East-West (EW_time) and North-South (NS_time) directions. The current OVERALL SCORE is 83.20811075335004, with green light durations set to 31 seconds for EW and 29 seconds for NS. The goal is to find a balance that reduces vehicle queue times for all directions, thus lowering the overall score. Adjusting the green light durations slightly up or down from their current values might help find a better balance between the directions. Given the constraints for int4 and int5 are between 15 and 90 seconds, there's room for adjustment. We should consider the time lost per vehicle for different routes. It seems that there's significant time lost, especially for vehicles moving from E1 to W1 (98.60504201680672 seconds lost per vehicle), indicating potential room for improvement in the green light duration balance. Finding a better balance might involve increasing the duration for one direction slightly while decreasing for the other, or adjusting both in a way that vehicles in all directions experience less delay. Since the exact optimal values are not clear without further analysis or simulation, reasonable adjustments need to be suggested based on the given feedback and observed delay patterns.\",\n", + "\"reasoning\": \"The aim is to find an optimum balance between the East-West (EW) and North-South (NS) green light durations to minimize the overall score, which indicates delay at the intersection. The given scores suggest that while there has been some success in managing delays (with an average delay of 11.085356880058868), there's significant room for improvement, given that the feedback asks for further optimization. We notice that the time lost per vehicle for East to West and vice versa is comparatively high, indicating potentially excessive green light duration in one direction causing delays in the other. Adjusting the green light duration closer to a balanced value might reduce the overall delays experienced by vehicles from all directions, thus lowering the overall score. Considering the constraints that the duration can only be between 15 to 90 seconds, and aiming to balance the durations more effectively without favoring one direction excessively over the others, we could try incrementally adjusting both values to study the impact on the overall score. Since the initial set values are int0=30 for East-West and int1=20 for North-South, and the feedback suggests optimization, we could experiment by slightly increasing the NS duration and/or adjusting the EW duration to achieve a better balance.\",\n", "\"answer\": \"\",\n", "\"suggestion\": {\n", - " \"int4\": 33,\n", - " \"int5\": 31\n", + "\"int0\": 25,\n", + "\"int1\": 25\n", "}\n", "}\n", "Prompt\n", @@ -990,32 +985,32 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "create_world17 = create_world(EW_time=int4, NS_time=int5)\n", - "analyze_world17 = analyze_world(W=create_world17)\n", + "create_world7 = create_world(EW_time=int0, NS_time=int1)\n", + "analyze_world7 = analyze_world(W=create_world7)\n", "\n", "#Documentation\n", "[create_world] .\n", "[analyze_world] .\n", "\n", "#Variables\n", - "(int) int4=33\n", - "(int) int5=31\n", + "(int) int0=25\n", + "(int) int1=25\n", "\n", "#Constraints\n", - "(int) int4: [15,90]\n", - "(int) int5: [15,90]\n", + "(int) int0: [15,90]\n", + "(int) int1: [15,90]\n", "\n", "#Inputs\n", "\n", "\n", "#Others\n", - "(World) create_world17=\n", + "(World) create_world7=\n", "\n", "#Outputs\n", - "(dict) analyze_world17={'Avg. Delay': 17.069506726457398, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 67.40265486725664}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 60.24761904761905}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 50.604651162790695}, (, ): {'Trips attempted': 238, 'Trips completed': 191, 'Time lost per vehicle': 103.76470588235294}, (, ): {'Trips attempted': 104, 'Trips completed': 95, 'Time lost per vehicle': 92.28846153846153}, (, ): {'Trips attempted': 93, 'Trips completed': 83, 'Time lost per vehicle': 83.58064516129032}, (, ): {'Trips attempted': 98, 'Trips completed': 90, 'Time lost per vehicle': 43.92857142857143}, (, ): {'Trips attempted': 100, 'Trips completed': 93, 'Time lost per vehicle': 43.87}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 42.234693877551024}, (, ): {'Trips attempted': 111, 'Trips completed': 102, 'Time lost per vehicle': 45.711711711711715}, (, ): {'Trips attempted': 93, 'Trips completed': 85, 'Time lost per vehicle': 45.25806451612903}, (, ): {'Trips attempted': 104, 'Trips completed': 99, 'Time lost per vehicle': 43.32692307692308}, 'Best-Case Estimated Delay': 64.97115384615384, 'OVERALL SCORE': 87.72815097810462}\n", + "(dict) analyze_world7={'Avg. Delay': 14.71833084947839, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 66.46902654867256}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 62.65714285714286}, (, ): {'Trips attempted': 86, 'Trips completed': 79, 'Time lost per vehicle': 50.13953488372093}, (, ): {'Trips attempted': 238, 'Trips completed': 189, 'Time lost per vehicle': 100.60924369747899}, (, ): {'Trips attempted': 104, 'Trips completed': 94, 'Time lost per vehicle': 89.82692307692308}, (, ): {'Trips attempted': 93, 'Trips completed': 82, 'Time lost per vehicle': 83.3225806451613}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 31.8265306122449}, (, ): {'Trips attempted': 100, 'Trips completed': 96, 'Time lost per vehicle': 34.81}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 33.44897959183673}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 32.62162162162162}, (, ): {'Trips attempted': 93, 'Trips completed': 86, 'Time lost per vehicle': 33.903225806451616}, (, ): {'Trips attempted': 104, 'Trips completed': 101, 'Time lost per vehicle': 36.26923076923077}, 'Best-Case Estimated Delay': 60.00892857142857, 'OVERALL SCORE': 85.8145253129838}\n", "\n", "#Feedback:\n", - "OVERALL SCORE: 87.72815097810462\n", + "OVERALL SCORE: 85.8145253129838\n", "Please try to optimize the intersection further. If you are certain that you have found the optimal solution, please suggest it again.\n", "\n", "================================\n", @@ -1025,11 +1020,11 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"The instruction asks to set values for East-West (EW_time) and North-South (NS_time) green light durations to minimize the OVERALL SCORE, which represents the efficiency of traffic management at an intersection. The current OVERALL SCORE of 87.72815097810462 suggests that there is an imbalance in the green light durations for the two directions, causing either excessive waiting times or underutilization of one direction. Adjusting these durations can better balance traffic flow, reduce vehicle delays, and thus lower the OVERALL SCORE. Given the feedback and the constraints for int4 and int5, which are between 15 and 90 seconds, the goal is to experiment with different durations within these ranges to find a more balanced setting that minimizes traffic congestion and delay. Based on the outputs, there seems to be significant delays for certain directions, most notably from E1 to W1 which indicates that the East-West duration might be too short, preventing adequate clearance of eastbound traffic. Hence, attempts to adjust the variables should consider balancing the reduction of high time lost per vehicle in directions with heavy traffic, while also mitigating increased times for less congested routes. To improve the OVERALL SCORE, an equal or slightly higher duration allocation might be experimented for the East-West direction (int4) while finely adjusting the North-South direction (int5) to achieve a better overall traffic flow.\",\n", + "\"reasoning\": \"The instruction indicates the goal is to find a balance for green light durations in each direction (East-West and North-South) to minimize the overall intersection score. The initial values for both directions (int0 for East-West and int1 for North-South) are set to 25. Given that the OVERALL SCORE is 85.8145253129838, there is a need to adjust these values to find a balance that reduces this score. The overall score likely represents a combination of the average delay, the number of trips completed versus attempted, and the time lost per vehicle. Since we don't have specific information on the traffic distribution or how the scoring is precisely calculated, the optimization will involve adjusting these values to strike a balance between waiting times across all directions. We can infer from general traffic management principles that if one direction consistently has higher waiting times or loss times, reducing its green light duration (and potentially increasing it for the opposing direction) can help balance the flow. However, extreme balances should be avoided; both values must stay within the constraints of 15 to 90 seconds. Based on the feedback, adjusting the green light durations may help improve traffic flow efficiency and reduce the OVERALL SCORE.\",\n", "\"answer\": \"\",\n", "\"suggestion\": {\n", - " \"int4\": 35,\n", - " \"int5\": 33\n", + " \"int0\": 30,\n", + " \"int1\": 30\n", "}\n", "}\n", "Prompt\n", @@ -1085,32 +1080,32 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "create_world18 = create_world(EW_time=int4, NS_time=int5)\n", - "analyze_world18 = analyze_world(W=create_world18)\n", + "create_world8 = create_world(EW_time=int0, NS_time=int1)\n", + "analyze_world8 = analyze_world(W=create_world8)\n", "\n", "#Documentation\n", "[create_world] .\n", "[analyze_world] .\n", "\n", "#Variables\n", - "(int) int4=35\n", - "(int) int5=33\n", + "(int) int0=30\n", + "(int) int1=30\n", "\n", "#Constraints\n", - "(int) int4: [15,90]\n", - "(int) int5: [15,90]\n", + "(int) int0: [15,90]\n", + "(int) int1: [15,90]\n", "\n", "#Inputs\n", "\n", "\n", "#Others\n", - "(World) create_world18=\n", + "(World) create_world8=\n", "\n", "#Outputs\n", - "(dict) analyze_world18={'Avg. Delay': 16.45134730538922, (, ): {'Trips attempted': 226, 'Trips completed': 221, 'Time lost per vehicle': 67.82743362831859}, (, ): {'Trips attempted': 105, 'Trips completed': 101, 'Time lost per vehicle': 64.5904761904762}, (, ): {'Trips attempted': 86, 'Trips completed': 79, 'Time lost per vehicle': 52.906976744186046}, (, ): {'Trips attempted': 238, 'Trips completed': 185, 'Time lost per vehicle': 101.02941176470588}, (, ): {'Trips attempted': 104, 'Trips completed': 93, 'Time lost per vehicle': 93.8173076923077}, (, ): {'Trips attempted': 93, 'Trips completed': 82, 'Time lost per vehicle': 79.44086021505376}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 43.06122448979592}, (, ): {'Trips attempted': 100, 'Trips completed': 97, 'Time lost per vehicle': 44.77}, (, ): {'Trips attempted': 98, 'Trips completed': 94, 'Time lost per vehicle': 46.97959183673469}, (, ): {'Trips attempted': 111, 'Trips completed': 104, 'Time lost per vehicle': 43.85585585585586}, (, ): {'Trips attempted': 93, 'Trips completed': 87, 'Time lost per vehicle': 45.086021505376344}, (, ): {'Trips attempted': 104, 'Trips completed': 101, 'Time lost per vehicle': 46.50961538461539}, 'Best-Case Estimated Delay': 65.2815934065934, 'OVERALL SCORE': 86.74869840201336}\n", + "(dict) analyze_world8={'Avg. Delay': 16.917664670658684, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 73.34955752212389}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 66.81904761904762}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 51.05813953488372}, (, ): {'Trips attempted': 238, 'Trips completed': 185, 'Time lost per vehicle': 115.21428571428571}, (, ): {'Trips attempted': 104, 'Trips completed': 93, 'Time lost per vehicle': 107.52884615384616}, (, ): {'Trips attempted': 93, 'Trips completed': 82, 'Time lost per vehicle': 98.95698924731182}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 36.8469387755102}, (, ): {'Trips attempted': 100, 'Trips completed': 95, 'Time lost per vehicle': 38.45}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 39.183673469387756}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 37.81981981981982}, (, ): {'Trips attempted': 93, 'Trips completed': 86, 'Time lost per vehicle': 39.60215053763441}, (, ): {'Trips attempted': 104, 'Trips completed': 100, 'Time lost per vehicle': 39.35576923076923}, 'Best-Case Estimated Delay': 68.0364010989011, 'OVERALL SCORE': 98.35876415688605}\n", "\n", "#Feedback:\n", - "OVERALL SCORE: 86.74869840201336\n", + "OVERALL SCORE: 98.35876415688605\n", "Please try to optimize the intersection further. If you are certain that you have found the optimal solution, please suggest it again.\n", "\n", "================================\n", @@ -1120,11 +1115,11 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"The instruction indicates that the goal is to adjust the green light duration for East-West (EW) and North-South (NS) directions such that the overall score, representing the average delay and efficiency of the intersection, is minimized. The current settings of 35 seconds for EW and 33 seconds for NS result in an overall score of 86.74869840201336, which is considered suboptimal. To optimize, adjustments to these durations should aim at achieving a better balance in traffic flow, possibly by reducing the time lost per vehicle in directions where delays are most significant (notably from East to West, and for other directions as well), ensuring that traffic in all directions flows more smoothly with fewer delays. Since directions involving East to West and North to South show high delays, it might be beneficial to slightly adjust the green light duration to accommodate traffic demand more evenly without excessively favoring one direction over the other. Given the constraints of 15 to 90 seconds for both durations, subtle tweaks are suggested to test if these new settings can reduce the overall score.\",\n", + "\"reasoning\": \"The instruction requires us to adjust the green light durations for East-West (EW) and North-South (NS) directions to minimize the OVERALL SCORE of the intersection, which is indicative of the intersection's efficiency. The OVERALL SCORE takes into account the average delay, time lost per vehicle, and other performance metrics. Given that both EW and NS durations are set equitably at 30, and observing that the overall score is deemed sub-optimal according to the feedback, it's clear a trade-off needs to be found that better accommodates the actual traffic flow patterns in all directions. Ideally, adjusting the durations would strike a balance that minimizes delays and maximizes the number of trips completed in all directions. The particular values for adjustment should consider the constraints provided (between 15 to 90 seconds) and aim at fine-tuning the distribution of green light time to reflect the demand revealed in the outputs (for example, areas with higher trips attempted and lower trips completed might benefit from longer green times). Without specific data on traffic patterns from the outputs, an educated trial to improve scores can be made by slightly adjusting the durations to favor the direction showing higher delays or incomplete trips.\",\n", "\"answer\": \"\",\n", "\"suggestion\": {\n", - " \"int4\": 30,\n", - " \"int5\": 35\n", + "\"int0\": 35,\n", + "\"int1\": 25\n", "}\n", "}\n", "Prompt\n", @@ -1180,32 +1175,32 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "create_world19 = create_world(EW_time=int4, NS_time=int5)\n", - "analyze_world19 = analyze_world(W=create_world19)\n", + "create_world9 = create_world(EW_time=int0, NS_time=int1)\n", + "analyze_world9 = analyze_world(W=create_world9)\n", "\n", "#Documentation\n", "[create_world] .\n", "[analyze_world] .\n", "\n", "#Variables\n", - "(int) int4=30\n", - "(int) int5=35\n", + "(int) int0=35\n", + "(int) int1=25\n", "\n", "#Constraints\n", - "(int) int4: [15,90]\n", - "(int) int5: [15,90]\n", + "(int) int0: [15,90]\n", + "(int) int1: [15,90]\n", "\n", "#Inputs\n", "\n", "\n", "#Others\n", - "(World) create_world19=\n", + "(World) create_world9=\n", "\n", "#Outputs\n", - "(dict) analyze_world19={'Avg. Delay': 23.134441087613293, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 139.73008849557522}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 124.87619047619047}, (, ): {'Trips attempted': 86, 'Trips completed': 79, 'Time lost per vehicle': 105.04651162790698}, (, ): {'Trips attempted': 238, 'Trips completed': 176, 'Time lost per vehicle': 151.03361344537817}, (, ): {'Trips attempted': 104, 'Trips completed': 90, 'Time lost per vehicle': 120.53846153846153}, (, ): {'Trips attempted': 93, 'Trips completed': 81, 'Time lost per vehicle': 129.9784946236559}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 36.255102040816325}, (, ): {'Trips attempted': 100, 'Trips completed': 96, 'Time lost per vehicle': 34.54}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 38.01020408163265}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 40.126126126126124}, (, ): {'Trips attempted': 93, 'Trips completed': 86, 'Time lost per vehicle': 41.40860215053763}, (, ): {'Trips attempted': 104, 'Trips completed': 101, 'Time lost per vehicle': 42.43269230769231}, 'Best-Case Estimated Delay': 94.60508241758242, 'OVERALL SCORE': 142.88579745966376}\n", + "(dict) analyze_world9={'Avg. Delay': 12.480882352941176, (, ): {'Trips attempted': 226, 'Trips completed': 224, 'Time lost per vehicle': 40.084070796460175}, (, ): {'Trips attempted': 105, 'Trips completed': 103, 'Time lost per vehicle': 37.08571428571429}, (, ): {'Trips attempted': 86, 'Trips completed': 80, 'Time lost per vehicle': 33.81395348837209}, (, ): {'Trips attempted': 238, 'Trips completed': 199, 'Time lost per vehicle': 51.357142857142854}, (, ): {'Trips attempted': 104, 'Trips completed': 98, 'Time lost per vehicle': 47.66346153846154}, (, ): {'Trips attempted': 93, 'Trips completed': 87, 'Time lost per vehicle': 48.53763440860215}, (, ): {'Trips attempted': 98, 'Trips completed': 92, 'Time lost per vehicle': 43.42857142857143}, (, ): {'Trips attempted': 100, 'Trips completed': 95, 'Time lost per vehicle': 46.35}, (, ): {'Trips attempted': 98, 'Trips completed': 93, 'Time lost per vehicle': 46.98979591836735}, (, ): {'Trips attempted': 111, 'Trips completed': 103, 'Time lost per vehicle': 46.648648648648646}, (, ): {'Trips attempted': 93, 'Trips completed': 86, 'Time lost per vehicle': 47.69892473118279}, (, ): {'Trips attempted': 104, 'Trips completed': 100, 'Time lost per vehicle': 48.25}, 'Best-Case Estimated Delay': 45.11195054945055, 'OVERALL SCORE': 50.16598605909516}\n", "\n", "#Feedback:\n", - "OVERALL SCORE: 142.88579745966376\n", + "OVERALL SCORE: 50.16598605909516\n", "Please try to optimize the intersection further. If you are certain that you have found the optimal solution, please suggest it again.\n", "\n", "================================\n", @@ -1215,22 +1210,23 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"To minimize the OVERALL SCORE, we need to adjust the EW_time and NS_time to find a balanced configuration that reduces the average delay per vehicle and the total delay to achieve a lower OVERALL SCORE. The feedback indicates that the current settings of 30 for EW_time and 35 for NS_time resulted in an OVERALL SCORE of 142.88579745966376, which suggests there is room for optimization. The 'Time lost per vehicle' values suggest that the East-West trips (both from W1->E1 and E1->W1) experience higher delays compared to North-South trips. This may imply that reducing the EW_time slightly and increasing NS_time might help balance the delays and potentially reduce the OVERALL SCORE. Therefore, adjusting EW_time to a slightly lower value to decrease the time lost for vehicles traveling East-West, and increasing NS_time to a slightly higher value to maintain or slightly increase the flow for North-South traffic without causing excessive waiting times, could bring a more balanced outcome.\",\n", + "\"reasoning\": \"The instruction points towards finding an optimal balance between the green light durations for East-West (EW) and North-South (NS) directions to minimize the overall intersection score, which signifies the efficiency of traffic flow through the intersection. An OVERALL SCORE of 50.16598605909516 suggests there is room for improvement in the traffic light timing to either reduce or better balance the time vehicles spend waiting, thereby optimizing the overall traffic flow and reducing the score. Given that adjustments to 'int0' and 'int1' values influence the duration vehicles in each direction are allowed to move, the key is to find a set of values that minimizes delays for all directions effectively. The current settings of 'int0=35' and 'int1=25' suggest a longer green light duration for the East-West direction compared to the North-South direction, potentially indicating a misbalance in favor of East-West trips. To improve the OVERALL SCORE, a reevaluation of these durations based on the observed delays and completed trips for each direction is necessary. It might be beneficial to reduce the East-West green light time slightly while increasing the North-South duration to provide a more balanced approach, taking into account the constraints of [15,90] for both variables. This can potentially reduce queuing and delays, hence improving the OVERALL SCORE.\",\n", + "\"answer\": \"\",\n", "\"suggestion\": {\n", - " \"int4\": 27,\n", - " \"int5\": 38\n", + " \"int0\": 30,\n", + " \"int1\": 30\n", "}\n", "}\n", "[[ 15. 15. 75.57083282]\n", - " [ 20. 10. inf]\n", + " [ 30. 20. 44.98937866]\n", + " [ 25. 25. 85.81452531]\n", + " [ 23. 27. 122.93479991]\n", + " [ 20. 30. 212.43352641]\n", " [ 25. 25. 85.81452531]\n", " [ 30. 20. 44.98937866]\n", - " [ 35. 25. 50.16598606]\n", - " [ 33. 27. 65.4488206 ]\n", - " [ 31. 29. 83.20811075]\n", - " [ 33. 31. 87.72815098]\n", - " [ 35. 33. 86.7486984 ]\n", - " [ 30. 35. 142.88579746]]\n" + " [ 25. 25. 85.81452531]\n", + " [ 30. 30. 98.35876416]\n", + " [ 35. 25. 50.16598606]]\n" ] } ], diff --git a/docs/tutorials/basic_tutorial.ipynb b/docs/tutorials/basic_tutorial.ipynb index dcce2490..cd98e345 100644 --- a/docs/tutorials/basic_tutorial.ipynb +++ b/docs/tutorials/basic_tutorial.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -24,7 +24,7 @@ "string\n", "[1, 2, 3]\n", "{'a': 1, 'b': 2}\n", - "<__main__.Foo object at 0x0000011D09D253A0>\n" + "<__main__.Foo object at 0x00000233CF6986D0>\n" ] } ], @@ -68,14 +68,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "<__main__.Foo object at 0x0000011D0A45CC70>\n" + "<__main__.Foo object at 0x00000233CF901F40>\n" ] } ], @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -212,19 +212,19 @@ "output_type": "stream", "text": [ "output: MessageNode: (call:1, dtype=, data=2)\n", - "parents [('function:0', ), ('int:4', 1)]\n", + "parents [('function:0', ), ('int:3', 1)]\n", "\n", "\n", "\n", "The attribute of the wrapped object cannot be directly accessed. Instead use getattr() or call()\n", "foo_node: MessageNode: (node_getattr:2, dtype=, data=1)\n", - "parents [('Foo:2', <__main__.Foo object at 0x0000011D0A48F490>), ('str:5', 'node')]\n", + "parents [('Foo:2', <__main__.Foo object at 0x00000233CF730FA0>), ('str:5', 'node')]\n", "non_node: MessageNode: (node_getattr:3, dtype=, data=2)\n", - "parents [('Foo:2', <__main__.Foo object at 0x0000011D0A48F490>), ('str:6', 'non_node')]\n", + "parents [('Foo:2', <__main__.Foo object at 0x00000233CF730FA0>), ('str:6', 'non_node')]\n", "output: MessageNode: (call:2, dtype=, data=4)\n", - "parents [('node_getattr:4', >)]\n", + "parents [('node_getattr:4', >)]\n", "output: MessageNode: (call:4, dtype=, data=4)\n", - "parents [('node_getattr:6', >)]\n" + "parents [('node_getattr:6', >)]\n" ] } ], @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -389,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -408,7 +408,7 @@ "\n", "\n", "add0\n", - "\n", + "\n", "add0\n", "[add] This is an add operator of x and y. .\n", "3\n", @@ -416,7 +416,7 @@ "\n", "\n", "add2\n", - "\n", + "\n", "add2\n", "[add] This is an add operator of x and y. .\n", "5\n", @@ -430,7 +430,7 @@ "\n", "\n", "add1\n", - "\n", + "\n", "add1\n", "[add] This is an add operator of x and y. .\n", "2\n", @@ -444,59 +444,59 @@ "\n", "\n", "node_x0\n", - "\n", + "\n", "node_x0\n", "[Node] This is a node in a computational graph.\n", "1\n", "\n", "\n", - "\n", + "\n", "node_x0->add0\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "node_x0->add1\n", "\n", "\n", "\n", - "\n", - "\n", - "node_y0\n", - "\n", - "node_y0\n", - "[Node] This is a node in a computational graph.\n", - "2\n", - "\n", - "\n", - "\n", - "node_y0->add0\n", - "\n", - "\n", - "\n", "\n", - "\n", + "\n", "int0\n", - "\n", + "\n", "int0\n", "[Node] This is a node in a computational graph.\n", "1\n", "\n", "\n", - "\n", + "\n", "int0->add1\n", "\n", "\n", "\n", + "\n", + "\n", + "node_y0\n", + "\n", + "node_y0\n", + "[Node] This is a node in a computational graph.\n", + "2\n", + "\n", + "\n", + "\n", + "node_y0->add0\n", + "\n", + "\n", + "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -515,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -541,7 +541,7 @@ "\n", "\n", "int0\n", - "\n", + "\n", "int0\n", "[Node] This is a node in a computational graph.\n", "1\n", @@ -550,10 +550,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -571,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -590,7 +590,7 @@ "\n", "\n", "bool0\n", - "\n", + "\n", "bool0\n", "[Node] This is a node in a computational graph.\n", "True\n", @@ -598,7 +598,7 @@ "\n", "\n", "fun0\n", - "\n", + "\n", "fun0\n", "[fun] Return one if input x is True, otherwise return zero.\n", "1\n", @@ -613,10 +613,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -645,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -717,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -744,7 +744,7 @@ "\n", "\n", "to_list0\n", - "\n", + "\n", "to_list0\n", "[to_list] This converts x to a list.  .\n", "['arg2', 'arg1']\n", @@ -752,7 +752,7 @@ "\n", "\n", "getitem1\n", - "\n", + "\n", "getitem1\n", "[getitem] This is a getitem operator of x based on index. .\n", "arg1\n", @@ -766,7 +766,7 @@ "\n", "\n", "int15\n", - "\n", + "\n", "int15\n", "[Node] This is a node in a computational graph.\n", "1\n", @@ -780,7 +780,7 @@ "\n", "\n", "set0\n", - "\n", + "\n", "set0\n", "[Node] This is a node in a computational graph.\n", "{'arg2', 'arg1'}\n", @@ -795,10 +795,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -815,7 +815,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -842,7 +842,7 @@ "\n", "\n", "list0\n", - "\n", + "\n", "list0\n", "[Node] This is a node in a computational graph.\n", "[1, 2]\n", @@ -850,7 +850,7 @@ "\n", "\n", "getitem5\n", - "\n", + "\n", "getitem5\n", "[getitem] This is a getitem operator of x based on index. .\n", "2\n", @@ -864,7 +864,7 @@ "\n", "\n", "int19\n", - "\n", + "\n", "int19\n", "[Node] This is a node in a computational graph.\n", "1\n", @@ -879,10 +879,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -914,7 +914,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.19" + "version": "3.9.19" } }, "nbformat": 4, diff --git a/docs/tutorials/error_handling_tutorial.ipynb b/docs/tutorials/error_handling_tutorial.ipynb index 5d42ef15..5def8afc 100644 --- a/docs/tutorials/error_handling_tutorial.ipynb +++ b/docs/tutorials/error_handling_tutorial.ipynb @@ -113,7 +113,12 @@ "Your response:\n", "\n", "LLM response:\n", - " {\"reasoning\": \"The error is raised because the provided value for variable 'float0' is not meeting the condition inside the function 'check_input'. According to the error message, the input 'a' must be greater than 0.1, however the given 'float0' value is -1.0 which is less than 0.1 and that's why ValueError is being raised.\", \"answer\": \"\", \"suggestion\": {\"float0\": 0.2}}\n", + " {\n", + "\"reasoning\": \"The instruction requires adjusting the value of variable 'float0' to improve the code's output by avoiding the exception. According to the feedback provided, calling 'func_with_input_checking' with 'float0=-1.0' causes a 'ValueError' because of the condition in 'check_input' function where 'a' must be greater than 0.1. To meet this condition and avoid the error, 'float0' should be set to a value greater than 0.1.\",\n", + "\"suggestion\": {\n", + " \"float0\": 0.2\n", + "}\n", + "}\n", "\n", "Success, Parameter: 0.2\n" ] @@ -170,36 +175,32 @@ "Iter 0, Not satisfying constraint, Parameter -1.0\n", "\n", "\n", - "Iter 1, Objective 2.25, Parameter 0.5\n", + "Iter 1, Objective 1.2100000000000002, Parameter 0.1\n", "\n", "\n", - "Iter 2, Objective 1.2100000000000002, Parameter 0.1\n", + "Iter 2, Objective 1.2100220001, Parameter 0.10001\n", "\n", "\n", - "Iter 3, Objective 1.2321000000000002, Parameter 0.11\n", + "Iter 3, Objective 1.2100220001, Parameter 0.10001\n", "\n", "\n", "Iter 4, Objective 1.2100000000000002, Parameter 0.1\n", "\n", "\n", - "Iter 5, Objective 1.2122009999999999, Parameter 0.101\n", + "Iter 5, Objective 1.210002200001, Parameter 0.100001\n", "\n", "\n", - "Iter 6, Objective 1.2100000000000002, Parameter 0.1\n", + "Iter 6, Objective 1.210002200001, Parameter 0.100001\n", "\n", "\n", - "Iter 7, Not satisfying constraint, Parameter -1.0\n", + "Iter 7, Not satisfying constraint, Parameter -0.999999\n", "\n", "\n", - "Iter 8, Objective 1.2100000000000002, Parameter 0.1\n", + "Iter 8, Objective 1.210002200001, Parameter 0.100001\n", "\n", "\n", - "Iter 9, Objective 1.2100000000000002, Parameter 0.1\n", - "\n", - "Cannot extract suggestion from LLM's response:\n", - "{\"reasoning\": \"From the #Documentation of the code, we know that the function 'objective' computes (a+1)**2. The feedback indicates that we need to minimize the objective. The objective is minimal when a is as small as possible. Given the constraint that a must be greater than 0.1 (provided from the previous feedbacks), the smallest value we can give to a is 0.1 itself. Therefore, any changes in the #Variables would not lead to a minimization of the objective, and there is no issue with the existing value of float0 that is currently set to the minimum allowable value of 0.1.\",\n", - "\"answer\": \"\",\n", - "\"suggestion\": {}}\n" + "Iter 9, Objective 1.2100002200000097, Parameter 0.1000001\n", + "\n" ] } ], diff --git a/docs/tutorials/optimization_tutorial.ipynb b/docs/tutorials/optimization_tutorial.ipynb index ccf00340..33c68989 100644 --- a/docs/tutorials/optimization_tutorial.ipynb +++ b/docs/tutorials/optimization_tutorial.ipynb @@ -17,7 +17,7 @@ "source": [ "import opto\n", "from opto.trace import bundle, node\n", - "from opto.optimizers import FunctionOptimizer\n", + "from opto.optimizers import OptoPrime\n", "from opto.trace.nodes import GRAPH\n", "\n", "\n", @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -82,7 +82,7 @@ "\n", "\n", "bar0\n", - "\n", + "\n", "bar0\n", "[bar] This is a test function, which does negative scaling..\n", "2.0\n", @@ -90,7 +90,7 @@ "\n", "\n", "multiply0\n", - "\n", + "\n", "multiply0\n", "[multiply] This is a multiply operator of x and y. .\n", "6.0\n", @@ -104,7 +104,7 @@ "\n", "\n", "add0\n", - "\n", + "\n", "add0\n", "[add] This is an add operator of x and y. .\n", "3.0\n", @@ -124,7 +124,7 @@ "\n", "\n", "int0\n", - "\n", + "\n", "int0\n", "[Node] This is a node in a computational graph.\n", "1\n", @@ -135,17 +135,17 @@ "\n", "\n", "\n", - "\n", + "\n", "\n", - "float1\n", - "\n", - "float1\n", + "float0\n", + "\n", + "float0\n", "[ParameterNode] This is a ParameterNode in a computational graph.\n", "-1.0\n", "\n", - "\n", + "\n", "\n", - "float1->bar0\n", + "float0->bar0\n", "\n", "\n", "\n", @@ -153,10 +153,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -166,7 +166,7 @@ "\n", "# One-step optimization example\n", "x = node(-1.0, trainable=True)\n", - "optimizer = FunctionOptimizer([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", + "optimizer = OptoPrime([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", "output = foobar(x)\n", "feedback = user(output.data)\n", "optimizer.zero_feedback()\n", @@ -183,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -192,11 +192,11 @@ "text": [ "Function Feedback\n", "Graph:\n", - " 1: bar0 = bar(x=float1)\n", + " 1: bar0 = bar(x=float0)\n", " 2: add0 = add(x=bar0, y=int0)\n", " 3: multiply0 = multiply(x=bar0, y=add0)\n", "Roots:\n", - " float1: (-1.0, None)\n", + " float0: (-1.0, None)\n", " int0: (1, None)\n", "Others:\n", " bar0: (2.0, None)\n", @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -286,7 +286,11 @@ "}}\n", "}}\n", "\n", - "You should write down your thought process in \"reasoning\". If #Instruction asks for an answer, write it down in \"answer\". If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", + "In \"reasoning\", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.\n", + "\n", + "If #Instruction asks for an answer, write it down in \"answer\".\n", + "\n", + "If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", "\n", "If no changes or answer are needed, just output TERMINATE.\n", "\n", @@ -298,7 +302,7 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "bar0 = bar(x=float1)\n", + "bar0 = bar(x=float0)\n", "add0 = add(x=bar0, y=int0)\n", "multiply0 = multiply(x=bar0, y=add0)\n", "\n", @@ -308,7 +312,7 @@ "[multiply] This is a multiply operator of x and y. .\n", "\n", "#Variables\n", - "(float) float1=-1.0\n", + "(float) float0=-1.0\n", "\n", "#Constraints\n", "\n", @@ -333,10 +337,10 @@ "\n", "LLM response:\n", " {\n", - "\"reasoning\": \"Given the feedback that the number needs to be larger, we need to adjust the variables to ensure the output of 'multiply0' increases. The operation follows a sequence where first, 'float1' is used by the 'bar' function, which apparently scales the number (despite the documentation mentioning negative scaling, 'float1' of -1.0 leading to 'bar0' of 2.0 indicates either positive scaling or an incorrect documentation). Then, 'add0' adds 'bar0' and 'int0' (1), resulting in 'add0'=3.0. Finally, 'multiply' multiplies 'bar0' with 'add0', resulting in 'multiply0'=6.0. To increase 'multiply0', we can manipulate 'float1', which is the initial value that impacts all subsequent operations directly or indirectly. Increasing its magnitude should, based on the provided operations and their results, increase the output of 'multiply0', assuming 'bar' does indeed scale positively in this specific instance.\",\n", + "\"reasoning\": \"The given code performs a series of operations using the functions 'bar', 'add', and 'multiply', starting with a float 'float0' and an integer 'int0' as inputs. Initially, 'bar' function performs some operation (described as negative scaling, but the detail is unclear from the description) on 'float0', producing 'bar0'. Then, 'add' combines 'bar0' and 'int0' into 'add0', and finally 'multiply' combines 'bar0' and 'add0' into the final result 'multiply0'. The output of multiply0=6.0 is considered not large enough based on the feedback given. To make the number larger, we can manipulate the value of 'float0' since it's directly involved in the initial calculation and influences all subsequent results. Increasing 'float0' should increase 'bar0', which in turn should increase 'add0' and finally 'multiply0'. However, without more specific details on how 'bar' performs negative scaling, we can assume it reverses the sign and/or scales the magnitude, hence using a more negative 'float0' might actually result in a more positive 'bar0'. To increase the result of the 'multiply' operation, we aim to increase the value of 'float0' but in a manner consistent with producing a larger 'bar0'. If 'bar' makes the number more positive as suggested by the 'Others' section, we should try a smaller (more negative) starting value for 'float0'. However, given that 'bar0' is positive and feedback suggests needing a larger number, and without explicit knowledge of how 'bar' scales negatively, our approach should instead aim to increase 'float0' under the assumption it would inversely increase 'bar0' and thus 'multiply0'. Still, the 'bar' function's behavior contradicts this assumption given 'float0' is negative and 'bar0' ends up positive. Therefore, the suggested change may involve re-evaluating the understanding of 'bar's behavior or considering errors in the initial understanding.\",\n", "\"answer\": \"\",\n", "\"suggestion\": {\n", - " \"float1\": \"-2.0\"\n", + "\"float0\": \"-2.0\"\n", "}\n", "}\n", "\n", @@ -375,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -383,15 +387,19 @@ "output_type": "stream", "text": [ "variable=-1.0, output=6.0, feedback=The number needs to be larger.\n", - "variable=2.0, output=12.0, feedback=The number needs to be larger.\n", - "variable=4.0, output=56.0, feedback=Success.\n", + "variable=-2.0, output=20.0, feedback=The number needs to be larger.\n", + "variable=-4.0, output=72.0, feedback=Success.\n", "Cannot extract suggestion from LLM's response:\n", - "{\"reasoning\": \"Since the feedback indicates success, there is no need to change the value of 'float0'. The flow of operations and the output suggest that the operations bar, add, and multiply have been executed correctly and produced the expected outcome based on the given inputs and functions.\", \"answer\": \"No changes needed\", \"suggestion\": {}}\n", + "{\n", + "\"reasoning\": \"The given instruction was to change the value of the variables in #Variables to improve the output according to the given feedback. However, the feedback indicates success, suggesting that the modification made to the variables (in this case, the value of float0) has produced the correct or desired outcome. In the code, the variable float0 is used as the input for the function 'bar' which performs negative scaling (though its specific behavior is not detailed, the output suggests it might double the value and change the sign), then this result is added to int2 in the 'add' function, and finally, the result of 'bar' and 'add' is multiplied in the 'multiply' function. Based on the feedback, the results of these operations were deemed successful, therefore no changes are needed to the variables.\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {}\n", + "}\n", "History\n", " 0: -1.0\n", - " 1: 2.0\n", - " 2: 4.0\n", - " 3: 4.0\n" + " 1: -2.0\n", + " 2: -4.0\n", + " 3: -4.0\n" ] } ], @@ -399,7 +407,7 @@ "# A small example of how to use the optimizer in a loop\n", "GRAPH.clear()\n", "x = node(-1.0, trainable=True)\n", - "optimizer = FunctionOptimizer([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", + "optimizer = OptoPrime([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", "\n", "history = [x.data]\n", "feedback = \"\"\n", @@ -428,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -436,17 +444,17 @@ "output_type": "stream", "text": [ "variable=-1.0, output=6.0, feedback=The number needs to be larger.\n", - "variable=4.0, output=56.0, feedback=Success.\n", + "variable=5.0, output=90.0, feedback=Success.\n", "Cannot extract suggestion from LLM's response:\n", "{\n", - "\"reasoning\": \"Since the feedback indicates success and all the operations performed in the code correctly output the result based on the provided functions and inputs, there is no need to make any changes to the variables. The bar function correctly transforms the input float by applying negative scaling, leading to `bar1 = -8.0` when `float0 = 4.0`. The add function then adds `bar1` and `int1`, resulting in `add1 = -7.0`. Lastly, the multiply function multiplies `bar1` by `add1`, resulting in `multiply1 = 56.0`. All these operations align with the described behavior of the functions and meet the requirement as indicated in the feedback.\",\n", - "\"answer\": \"No changes needed.\",\n", + "\"reasoning\": \"Since the feedback indicates success and the instruction asks for improvement in output in accordance with the feedback, there is no need for further changes. The feedback suggests that the desired output was achieved with the current variable settings. Given the constraints and functioning of the code, the variables' values are resulting in the expected success as indicated by the feedback.\",\n", + "\"answer\": \"No changes are needed as the feedback indicates success.\",\n", "\"suggestion\": {}\n", "}\n", "History\n", " 0: -1.0\n", - " 1: 4.0\n", - " 2: 4.0\n" + " 1: 5.0\n", + " 2: 5.0\n" ] } ], @@ -454,7 +462,7 @@ "# A small example of how to include constraints on parameters\n", "GRAPH.clear()\n", "x = node(-1.0, trainable=True, constraint=\"The value should be greater than 2.0\")\n", - "optimizer = FunctionOptimizer([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", + "optimizer = OptoPrime([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", "\n", "history = [x.data]\n", "feedback = \"\"\n", @@ -483,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -561,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -569,20 +577,30 @@ "output_type": "stream", "text": [ "variable=negative point one, output=FAIL, feedback=The text cannot be converted to a number.\n", + "variable=one, output=2, feedback=The number needs to be larger.\n", + "variable=two, output=12, feedback=The number needs to be larger.\n", + "variable=three, output=30, feedback=The number needs to be larger.\n", "variable=ten, output=380, feedback=Success.\n", "Cannot extract suggestion from LLM's response:\n", - "{\"reasoning\": \"According to the feedback, the execution result was successful. The instructions required improving the output, but given that the feedback is positive with 'Success.', there seems to be no need for changes. The process of converting an English word to a number, applying a negative scaling, performing an addition, and then a multiplication, all follow as per the documentation and produce the specified output successfully. Therefore, there is no need to suggest any changes to the variable values in #Variables.\", \"answer\": \"No changes are needed.\", \"suggestion\": {}}\n", + "{\n", + "\"reasoning\": \"The instruction asked for a change in the variables to improve the output in accordance with the feedback. Since the feedback indicates success, it means that the output generated by the current values of the variables met the expected outcome. The sequence of functions converts an English string to a number, then a negative scaling is applied to it, followed by addition and multiplication operations. Given the feedback, there's no need to suggest any changes since the operations worked as intended with the existing variable.\",\n", + "\"answer\": \"No changes needed as the feedback indicates success.\",\n", + "\"suggestion\": {}\n", + "}\n", "History\n", " 0: negative point one\n", - " 1: ten\n", - " 2: ten\n" + " 1: one\n", + " 2: two\n", + " 3: three\n", + " 4: ten\n", + " 5: ten\n" ] } ], "source": [ "GRAPH.clear()\n", "x = node(\"negative point one\", trainable=True)\n", - "optimizer = FunctionOptimizer([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", + "optimizer = OptoPrime([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", "\n", "history = [x.data]\n", "feedback = \"\"\n", @@ -611,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -623,11 +641,11 @@ "__code:0 def my_fun(x):\n", " \"\"\"Test function\"\"\"\n", " return x**2 + 1\n", - "output=-2, feedback=Success., variables=\n", + "output=-3, feedback=Success., variables=\n", "\n", "__code:0 def my_fun(x):\n", " \"\"\"Test function\"\"\"\n", - " return (x**2 + 1) * -1\n" + " return x - 2\n" ] } ], @@ -652,7 +670,7 @@ "\n", "\n", "x = node(-1, trainable=False)\n", - "optimizer = FunctionOptimizer([my_fun.parameter], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", + "optimizer = OptoPrime([my_fun.parameter], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", "\n", "feedback = \"\"\n", "while feedback != \"Success.\":\n", @@ -680,18 +698,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\adswamin\\AppData\\Local\\miniconda3\\envs\\newtrace\\lib\\site-packages\\sklearn\\datasets\\_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n", - " warn(\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.datasets import fetch_openml\n", "from sklearn.linear_model import LogisticRegression\n", @@ -726,7 +735,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -756,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -768,132 +777,132 @@ "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", "\n", "Most recent guess: \n", - "Regularization Parameter: 0.0050, Score: 83.23%, Sparsity: 81.63%\n", + "Regularization Parameter: 0.0050, Score: 83.62%, Sparsity: 75.15%\n", "variable=0.01, feedback=\n", "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", "\n", "Most recent guess: \n", - "Regularization Parameter: 0.0100, Score: 85.63%, Sparsity: 61.07%\n", + "Regularization Parameter: 0.0100, Score: 85.67%, Sparsity: 66.26%\n", "\n", "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.23%, Sparsity: 81.63%\n", + "Regularization Parameter: 0.0050, Score: 83.62%, Sparsity: 75.15%\n", "variable=0.02, feedback=\n", "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", "\n", "Most recent guess: \n", - "Regularization Parameter: 0.0200, Score: 86.85%, Sparsity: 50.45%\n", + "Regularization Parameter: 0.0200, Score: 86.81%, Sparsity: 51.86%\n", "\n", "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.23%, Sparsity: 81.63%\n", - "Regularization Parameter: 0.0100, Score: 85.63%, Sparsity: 61.07%\n", + "Regularization Parameter: 0.0050, Score: 83.62%, Sparsity: 75.15%\n", + "Regularization Parameter: 0.0100, Score: 85.67%, Sparsity: 66.26%\n", "variable=0.03, feedback=\n", "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", "\n", "Most recent guess: \n", - "Regularization Parameter: 0.0300, Score: 86.94%, Sparsity: 37.76%\n", + "Regularization Parameter: 0.0300, Score: 87.17%, Sparsity: 39.44%\n", "\n", "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.23%, Sparsity: 81.63%\n", - "Regularization Parameter: 0.0100, Score: 85.63%, Sparsity: 61.07%\n", - "Regularization Parameter: 0.0200, Score: 86.85%, Sparsity: 50.45%\n", + "Regularization Parameter: 0.0050, Score: 83.62%, Sparsity: 75.15%\n", + "Regularization Parameter: 0.0100, Score: 85.67%, Sparsity: 66.26%\n", + "Regularization Parameter: 0.0200, Score: 86.81%, Sparsity: 51.86%\n", "variable=0.04, feedback=\n", "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", "\n", "Most recent guess: \n", - "Regularization Parameter: 0.0400, Score: 87.08%, Sparsity: 34.77%\n", + "Regularization Parameter: 0.0400, Score: 87.38%, Sparsity: 36.57%\n", "\n", "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.23%, Sparsity: 81.63%\n", - "Regularization Parameter: 0.0100, Score: 85.63%, Sparsity: 61.07%\n", - "Regularization Parameter: 0.0200, Score: 86.85%, Sparsity: 50.45%\n", - "Regularization Parameter: 0.0300, Score: 86.94%, Sparsity: 37.76%\n", + "Regularization Parameter: 0.0050, Score: 83.62%, Sparsity: 75.15%\n", + "Regularization Parameter: 0.0100, Score: 85.67%, Sparsity: 66.26%\n", + "Regularization Parameter: 0.0200, Score: 86.81%, Sparsity: 51.86%\n", + "Regularization Parameter: 0.0300, Score: 87.17%, Sparsity: 39.44%\n", "variable=0.05, feedback=\n", "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", "\n", "Most recent guess: \n", - "Regularization Parameter: 0.0500, Score: 87.28%, Sparsity: 31.79%\n", + "Regularization Parameter: 0.0500, Score: 87.49%, Sparsity: 33.83%\n", "\n", "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.23%, Sparsity: 81.63%\n", - "Regularization Parameter: 0.0100, Score: 85.63%, Sparsity: 61.07%\n", - "Regularization Parameter: 0.0200, Score: 86.85%, Sparsity: 50.45%\n", - "Regularization Parameter: 0.0300, Score: 86.94%, Sparsity: 37.76%\n", - "Regularization Parameter: 0.0400, Score: 87.08%, Sparsity: 34.77%\n", + "Regularization Parameter: 0.0050, Score: 83.62%, Sparsity: 75.15%\n", + "Regularization Parameter: 0.0100, Score: 85.67%, Sparsity: 66.26%\n", + "Regularization Parameter: 0.0200, Score: 86.81%, Sparsity: 51.86%\n", + "Regularization Parameter: 0.0300, Score: 87.17%, Sparsity: 39.44%\n", + "Regularization Parameter: 0.0400, Score: 87.38%, Sparsity: 36.57%\n", "variable=0.06, feedback=\n", "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", "\n", "Most recent guess: \n", - "Regularization Parameter: 0.0600, Score: 87.69%, Sparsity: 30.14%\n", + "Regularization Parameter: 0.0600, Score: 87.52%, Sparsity: 31.90%\n", "\n", "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.23%, Sparsity: 81.63%\n", - "Regularization Parameter: 0.0100, Score: 85.63%, Sparsity: 61.07%\n", - "Regularization Parameter: 0.0200, Score: 86.85%, Sparsity: 50.45%\n", - "Regularization Parameter: 0.0300, Score: 86.94%, Sparsity: 37.76%\n", - "Regularization Parameter: 0.0400, Score: 87.08%, Sparsity: 34.77%\n", - "Regularization Parameter: 0.0500, Score: 87.28%, Sparsity: 31.79%\n", + "Regularization Parameter: 0.0050, Score: 83.62%, Sparsity: 75.15%\n", + "Regularization Parameter: 0.0100, Score: 85.67%, Sparsity: 66.26%\n", + "Regularization Parameter: 0.0200, Score: 86.81%, Sparsity: 51.86%\n", + "Regularization Parameter: 0.0300, Score: 87.17%, Sparsity: 39.44%\n", + "Regularization Parameter: 0.0400, Score: 87.38%, Sparsity: 36.57%\n", + "Regularization Parameter: 0.0500, Score: 87.49%, Sparsity: 33.83%\n", "variable=0.07, feedback=\n", "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", "\n", "Most recent guess: \n", - "Regularization Parameter: 0.0700, Score: 87.76%, Sparsity: 30.47%\n", + "Regularization Parameter: 0.0700, Score: 87.58%, Sparsity: 29.74%\n", "\n", "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.23%, Sparsity: 81.63%\n", - "Regularization Parameter: 0.0100, Score: 85.63%, Sparsity: 61.07%\n", - "Regularization Parameter: 0.0200, Score: 86.85%, Sparsity: 50.45%\n", - "Regularization Parameter: 0.0300, Score: 86.94%, Sparsity: 37.76%\n", - "Regularization Parameter: 0.0400, Score: 87.08%, Sparsity: 34.77%\n", - "Regularization Parameter: 0.0500, Score: 87.28%, Sparsity: 31.79%\n", - "Regularization Parameter: 0.0600, Score: 87.69%, Sparsity: 30.14%\n", + "Regularization Parameter: 0.0050, Score: 83.62%, Sparsity: 75.15%\n", + "Regularization Parameter: 0.0100, Score: 85.67%, Sparsity: 66.26%\n", + "Regularization Parameter: 0.0200, Score: 86.81%, Sparsity: 51.86%\n", + "Regularization Parameter: 0.0300, Score: 87.17%, Sparsity: 39.44%\n", + "Regularization Parameter: 0.0400, Score: 87.38%, Sparsity: 36.57%\n", + "Regularization Parameter: 0.0500, Score: 87.49%, Sparsity: 33.83%\n", + "Regularization Parameter: 0.0600, Score: 87.52%, Sparsity: 31.90%\n", "variable=0.08, feedback=\n", "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", "\n", "Most recent guess: \n", - "Regularization Parameter: 0.0800, Score: 87.28%, Sparsity: 25.23%\n", + "Regularization Parameter: 0.0800, Score: 87.54%, Sparsity: 27.33%\n", "\n", "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.23%, Sparsity: 81.63%\n", - "Regularization Parameter: 0.0100, Score: 85.63%, Sparsity: 61.07%\n", - "Regularization Parameter: 0.0200, Score: 86.85%, Sparsity: 50.45%\n", - "Regularization Parameter: 0.0300, Score: 86.94%, Sparsity: 37.76%\n", - "Regularization Parameter: 0.0400, Score: 87.08%, Sparsity: 34.77%\n", - "Regularization Parameter: 0.0500, Score: 87.28%, Sparsity: 31.79%\n", - "Regularization Parameter: 0.0600, Score: 87.69%, Sparsity: 30.14%\n", - "Regularization Parameter: 0.0700, Score: 87.76%, Sparsity: 30.47%\n", + "Regularization Parameter: 0.0050, Score: 83.62%, Sparsity: 75.15%\n", + "Regularization Parameter: 0.0100, Score: 85.67%, Sparsity: 66.26%\n", + "Regularization Parameter: 0.0200, Score: 86.81%, Sparsity: 51.86%\n", + "Regularization Parameter: 0.0300, Score: 87.17%, Sparsity: 39.44%\n", + "Regularization Parameter: 0.0400, Score: 87.38%, Sparsity: 36.57%\n", + "Regularization Parameter: 0.0500, Score: 87.49%, Sparsity: 33.83%\n", + "Regularization Parameter: 0.0600, Score: 87.52%, Sparsity: 31.90%\n", + "Regularization Parameter: 0.0700, Score: 87.58%, Sparsity: 29.74%\n", "variable=0.09, feedback=\n", "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", "\n", "Most recent guess: \n", - "Regularization Parameter: 0.0900, Score: 87.31%, Sparsity: 25.20%\n", + "Regularization Parameter: 0.0900, Score: 87.53%, Sparsity: 26.11%\n", "\n", "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.23%, Sparsity: 81.63%\n", - "Regularization Parameter: 0.0100, Score: 85.63%, Sparsity: 61.07%\n", - "Regularization Parameter: 0.0200, Score: 86.85%, Sparsity: 50.45%\n", - "Regularization Parameter: 0.0300, Score: 86.94%, Sparsity: 37.76%\n", - "Regularization Parameter: 0.0400, Score: 87.08%, Sparsity: 34.77%\n", - "Regularization Parameter: 0.0500, Score: 87.28%, Sparsity: 31.79%\n", - "Regularization Parameter: 0.0600, Score: 87.69%, Sparsity: 30.14%\n", - "Regularization Parameter: 0.0700, Score: 87.76%, Sparsity: 30.47%\n", - "Regularization Parameter: 0.0800, Score: 87.28%, Sparsity: 25.23%\n", + "Regularization Parameter: 0.0050, Score: 83.62%, Sparsity: 75.15%\n", + "Regularization Parameter: 0.0100, Score: 85.67%, Sparsity: 66.26%\n", + "Regularization Parameter: 0.0200, Score: 86.81%, Sparsity: 51.86%\n", + "Regularization Parameter: 0.0300, Score: 87.17%, Sparsity: 39.44%\n", + "Regularization Parameter: 0.0400, Score: 87.38%, Sparsity: 36.57%\n", + "Regularization Parameter: 0.0500, Score: 87.49%, Sparsity: 33.83%\n", + "Regularization Parameter: 0.0600, Score: 87.52%, Sparsity: 31.90%\n", + "Regularization Parameter: 0.0700, Score: 87.58%, Sparsity: 29.74%\n", + "Regularization Parameter: 0.0800, Score: 87.54%, Sparsity: 27.33%\n", "Best regularization parameter: 0.07\n", - "Best score: 87.75500000000001\n" + "Best score: 87.58\n" ] } ], "source": [ "x = node(0.005, trainable=True)\n", - "optimizer = FunctionOptimizer([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", + "optimizer = OptoPrime([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", "\n", "history = []\n", "bestScore = None\n", @@ -932,7 +941,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.19" + "version": "3.9.19" } }, "nbformat": 4, diff --git a/docs/tutorials/quick_start.ipynb b/docs/tutorials/quick_start.ipynb index 96fe41ff..1505559f 100644 --- a/docs/tutorials/quick_start.ipynb +++ b/docs/tutorials/quick_start.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "id": "b36585d6-e814-4753-aa7b-eea4956fac9f", "metadata": {}, "outputs": [], @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "id": "1cab71b5-13f5-4395-9944-84f204c12fb2", "metadata": {}, "outputs": [ @@ -57,7 +57,7 @@ "[1, 2, 3, 4]" ] }, - "execution_count": 14, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "e9d5cfa1-209e-4cea-b832-f12e9f4eb324", "metadata": {}, "outputs": [ @@ -88,7 +88,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MessageNode: (eval:1, dtype=, data=[1, 2, 3, 4])\n" + "MessageNode: (eval:0, dtype=, data=[1, 2, 3, 4])\n" ] } ], @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "22dd3048-1ac8-41f3-8290-18afee21081c", "metadata": {}, "outputs": [ @@ -135,86 +135,85 @@ "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", "\n", - "%3\n", - "\n", + "\n", "\n", "\n", "eval0\n", - "\n", - "eval0\n", - "[eval] This operator eval...\n", - "[1, 2, 3, 4]\n", + "\n", + "eval0\n", + "[eval] This operator eval...\n", + "[1, 2, 3, 4]\n", "\n", "\n", "\n", "eq0\n", - "\n", - "eq0\n", - "[eq] This is an eq operat...\n", - "False\n", + "\n", + "eq0\n", + "[eq] This is an eq operat...\n", + "False\n", "\n", "\n", "\n", "eval0->eq0\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "list1\n", - "\n", - "list1\n", - "[Node] This is a node in ...\n", - "[1, 4, 2, 3]\n", + "\n", + "list1\n", + "[Node] This is a node in ...\n", + "[1, 4, 2, 3]\n", "\n", "\n", "\n", "list1->eq0\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "list0\n", - "\n", - "list0\n", - "[Node] This is a node in ...\n", - "[1, 2, 3, 4]\n", + "\n", + "list0\n", + "[Node] This is a node in ...\n", + "[1, 2, 3, 4]\n", "\n", "\n", "\n", "list0->eval0\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "__code0\n", - "\n", - "__code0\n", - "[ParameterNode] This is a...\n", - "def strange_sort_list(lst...\n", + "\n", + "__code0\n", + "[ParameterNode] This is a...\n", + "def strange_sort_list(lst...\n", "\n", "\n", "\n", "__code0->eval0\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -257,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "d38793a1-03ba-4897-b9f2-aee40ece76ed", "metadata": {}, "outputs": [], @@ -285,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "id": "81d783e1-462f-4938-9f2c-389b4b546a74", "metadata": {}, "outputs": [ @@ -325,7 +324,11 @@ "}}\n", "}}\n", "\n", - "You should write down your thought process in \"reasoning\". If #Instruction asks for an answer, write it down in \"answer\". If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", + "In \"reasoning\", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.\n", + "\n", + "If #Instruction asks for an answer, write it down in \"answer\".\n", + "\n", + "If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", "\n", "If no changes or answer are needed, just output TERMINATE.\n", "\n", @@ -337,15 +340,15 @@ "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", "\n", "#Code\n", - "eval6 = eval(lst=list11, __code=__code1)\n", - "eq5 = eq(x=eval6, y=list12)\n", + "eval1 = eval(lst=list2, __code=__code0)\n", + "eq1 = eq(x=eval1, y=list3)\n", "\n", "#Documentation\n", "[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).\n", "[eq] This is an eq operator of x and y. .\n", "\n", "#Variables\n", - "(code) __code1:def strange_sort_list(lst):\n", + "(code) __code0:def strange_sort_list(lst):\n", " '''\n", " Given list of integers, return list in strange order.\n", " Strange sorting, is when you start with the minimum value,\n", @@ -360,18 +363,18 @@ " return lst\n", "\n", "#Constraints\n", - "(code) __code1: The code should start with:\n", + "(code) __code0: The code should start with:\n", "def strange_sort_list(lst):\n", "\n", "#Inputs\n", - "(list) list11=[1, 2, 3, 4]\n", - "(list) list12=[1, 4, 2, 3]\n", + "(list) list2=[1, 2, 3, 4]\n", + "(list) list3=[1, 4, 2, 3]\n", "\n", "#Others\n", - "(list) eval6=[1, 2, 3, 4]\n", + "(list) eval1=[1, 2, 3, 4]\n", "\n", "#Outputs\n", - "(bool) eq5=False\n", + "(bool) eq1=False\n", "\n", "#Feedback:\n", "test case failed!\n", @@ -383,130 +386,25 @@ "\n", "LLM response:\n", " {\n", - " \"reasoning\": \"The function strange_sort_list is supposed to alternate between selecting minimum and maximum values from the input list but currently it only returns a sorted list. As seen from the example outputs, the expected functionality is to sort the list so that elements are picked alternatively from the list of sorted values starting with the smallest and then picking the largest. Since the function is called on list11 which is [1, 2, 3, 4], the desired output as per list12 is [1, 4, 2, 3]. However, the current code just sorts the list which results in [1, 2, 3, 4]. This results in the eq operator comparison between eval6 and list12 returning False, failing the test case as per the Feedback. To remedy this, the logic of strange_sort_list needs to be revised so that it indeed returns elements in the strange sorting order as described in its documentation.\",\n", - " \"answer\": \"\",\n", - " \"suggestion\": {\n", - " \"__code1\": \"def strange_sort_list(lst):\\n '''\\n Given list of integers, return list in strange order.\\n Strange sorting, is when you start with the minimum value,\\n then maximum of the remaining integers, then minimum and so on.\\n\\n Examples:\\n strange_sort_list([1, 2, 3, 4]) == [1, 4, 2, 3]\\n strange_sort_list([5, 5, 5, 5]) == [5, 5, 5, 5]\\n strange_sort_list([]) == []\\n '''\\n if not lst:\\n return lst\\n sorted_lst = sorted(lst)\\n result = []\\n while sorted_lst:\\n result.append(sorted_lst.pop(0))\\n if sorted_lst:\\n result.append(sorted_lst.pop())\\n return result\"\n", - " }\n", - "}\n", - "Prompt\n", - " \n", - "You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.\n", - "\n", - "Specifically, a problem will be composed of the following parts:\n", - "- #Instruction: the instruction which describes the things you need to do or the question you should answer.\n", - "- #Code: the code defined in the problem.\n", - "- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.\n", - "- #Variables: the input variables that you can change.\n", - "- #Constraints: the constraints or descriptions of the variables in #Variables.\n", - "- #Inputs: the values of other inputs to the code, which are not changeable.\n", - "- #Others: the intermediate values created through the code execution.\n", - "- #Outputs: the result of the code output.\n", - "- #Feedback: the feedback about the code's execution result.\n", - "\n", - "In #Variables, #Inputs, #Outputs, and #Others, the format is:\n", - "\n", - " = \n", - "\n", - "If is (code), it means is the source code of a python code, which may include docstring and definitions.\n", - "\n", - "Output_format: Your output should be in the following json format, satisfying the json syntax:\n", - "\n", - "{{\n", - "\"reasoning\": ,\n", - "\"answer\": ,\n", - "\"suggestion\": {{\n", - " : ,\n", - " : ,\n", - "}}\n", - "}}\n", - "\n", - "You should write down your thought process in \"reasoning\". If #Instruction asks for an answer, write it down in \"answer\". If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", - "\n", - "If no changes or answer are needed, just output TERMINATE.\n", - "\n", - "Now you see problem instance:\n", - "\n", - "================================\n", - "\n", - "#Instruction\n", - "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", - "\n", - "#Code\n", - "eval7 = eval(lst=list13, __code=__code1)\n", - "eq6 = eq(x=eval7, y=list14)\n", - "\n", - "#Documentation\n", - "[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).\n", - "[eq] This is an eq operator of x and y. .\n", - "\n", - "#Variables\n", - "(code) __code1:def strange_sort_list(lst):\n", - " '''\n", - " Given list of integers, return list in strange order.\n", - " Strange sorting, is when you start with the minimum value,\n", - " then maximum of the remaining integers, then minimum and so on.\n", - "\n", - " Examples:\n", - " strange_sort_list([1, 2, 3, 4]) == [1, 4, 2, 3]\n", - " strange_sort_list([5, 5, 5, 5]) == [5, 5, 5, 5]\n", - " strange_sort_list([]) == []\n", - " '''\n", - " if not lst:\n", - " return lst\n", - " sorted_lst = sorted(lst)\n", - " result = []\n", - " while sorted_lst:\n", - " result.append(sorted_lst.pop(0))\n", - " if sorted_lst:\n", - " result.append(sorted_lst.pop())\n", - " return result\n", - "\n", - "#Constraints\n", - "(code) __code1: The code should start with:\n", - "def strange_sort_list(lst):\n", - "\n", - "#Inputs\n", - "(list) list13=[1, 2, 3, 4]\n", - "(list) list14=[1, 4, 2, 3]\n", - "\n", - "#Others\n", - "(list) eval7=[1, 4, 2, 3]\n", - "\n", - "#Outputs\n", - "(bool) eq6=True\n", - "\n", - "#Feedback:\n", - "test case passed!\n", - "\n", - "================================\n", - "\n", - "\n", - "Your response:\n", - "\n", - "LLM response:\n", - " {\n", - " \"reasoning\": \"The code currently does what is expected according to the provided documentation and feedback. The function strange_sort_list, encapsulated in __code1, correctly sorts the list in a manner specified by its description. The evaluation and comparison operations are successful and result in 'True', which indicates that the transformed list meets the expected output. Since the feedback is positive, indicating that the 'test case passed!', there is no need to make any changes to the variables or code.\",\n", - " \"answer\": \"No changes are necessary as the current setup satisfied the requirements and passed the test case.\"\n", + "\"reasoning\": \"The problem here is that the intended functionality of the 'strange_sort_list' function within the '__code0' variable does not match its current implementation. The function is supposed to sort the list in a strange order, starting with the minimum value, then the maximum of the remaining integers, and so on, alternating between the minimum and maximum. However, the current implementation only sorts the list in ascending order and returns it that way. The output 'eval1' shows the result of this incorrect sorting, [1, 2, 3, 4], which matches the initial input 'list2' but does not match the expected 'list3' which is [1, 4, 2, 3]. This discrepancy causes 'eq1' to be False since the output of the 'strange_sort_list' function does not equal the expected output 'list3'. To correct this, the 'strange_sort_list' function needs to be rewritten to implement the correct sorting logic as described in the instruction.\",\n", + "\"answer\": \"\",\n", + "\"suggestion\": {\n", + " \"__code0\": \"def strange_sort_list(lst):\\n '''\\n Given list of integers, return list in strange order.\\n Strange sorting, is when you start with the minimum value,\\n then maximum of the remaining integers, then minimum and so on.\\n\\n Examples:\\n strange_sort_list([1, 2, 3, 4]) == [1, 4, 2, 3]\\n strange_sort_list([5, 5, 5, 5]) == [5, 5, 5, 5]\\n strange_sort_list([]) == []\\n '''\\n result = []\\n while lst:\\n result.append(min(lst))\\n lst.remove(min(lst))\\n if lst:\\n result.append(max(lst))\\n lst.remove(max(lst))\\n return result\"\n", "}\n", - "Cannot extract suggestion from LLM's response:\n", - "{\n", - " \"reasoning\": \"The code currently does what is expected according to the provided documentation and feedback. The function strange_sort_list, encapsulated in __code1, correctly sorts the list in a manner specified by its description. The evaluation and comparison operations are successful and result in 'True', which indicates that the transformed list meets the expected output. Since the feedback is positive, indicating that the 'test case passed!', there is no need to make any changes to the variables or code.\",\n", - " \"answer\": \"No changes are necessary as the current setup satisfied the requirements and passed the test case.\"\n", "}\n" ] } ], "source": [ "import autogen\n", - "from opto.optimizers import FunctionOptimizer\n", + "from opto.optimizers import OptoPrime\n", "\n", "test_ground_truth = [1, 4, 2, 3]\n", "test_input = [1, 2, 3, 4]\n", "\n", "epoch = 2\n", "\n", - "optimizer = FunctionOptimizer(strange_sort_list.parameters(), \n", + "optimizer = OptoPrime(strange_sort_list.parameters(), \n", " config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", "\n", "for _ in range(epoch):\n", @@ -535,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "id": "b036523a-d440-49e4-abb9-91970ffc68ba", "metadata": {}, "outputs": [ @@ -554,14 +452,13 @@ " strange_sort_list([5, 5, 5, 5]) == [5, 5, 5, 5]\n", " strange_sort_list([]) == []\n", " '''\n", - " if not lst:\n", - " return lst\n", - " sorted_lst = sorted(lst)\n", " result = []\n", - " while sorted_lst:\n", - " result.append(sorted_lst.pop(0))\n", - " if sorted_lst:\n", - " result.append(sorted_lst.pop())\n", + " while lst:\n", + " result.append(min(lst))\n", + " lst.remove(min(lst))\n", + " if lst:\n", + " result.append(max(lst))\n", + " lst.remove(max(lst))\n", " return result\n" ] } @@ -582,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 8, "id": "723e1b08-3031-4158-947f-862c596f09d2", "metadata": {}, "outputs": [ @@ -590,7 +487,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MessageNode: (eval:9, dtype=, data=[2, 5, 3, 5])\n" + "MessageNode: (eval:3, dtype=, data=[2, 5, 3, 5])\n" ] } ], @@ -612,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 9, "id": "0da4d986-1f78-4a77-b9ec-ade9edc2191b", "metadata": {}, "outputs": [], @@ -633,9 +530,9 @@ ], "metadata": { "kernelspec": { - "display_name": "verbal-gym", + "display_name": "newtrace", "language": "python", - "name": "verbal-gym" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -647,7 +544,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.18" + "version": "3.9.19" } }, "nbformat": 4, diff --git a/docs/tutorials/quick_start_2.ipynb b/docs/tutorials/quick_start_2.ipynb deleted file mode 100644 index 33ade380..00000000 --- a/docs/tutorials/quick_start_2.ipynb +++ /dev/null @@ -1,45 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# 🚀 Next 5 Minutes" - ], - "metadata": { - "collapsed": false - }, - "id": "6efc2727e308855d" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "fb6d7b470a57d80a" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/BasicsTutorial.ipynb b/notebooks/BasicsTutorial.ipynb deleted file mode 100644 index ff8dee7f..00000000 --- a/notebooks/BasicsTutorial.ipynb +++ /dev/null @@ -1,1081 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Basics: Node and MessageNode\n", - "\n", - "`trace` is a comptuational grpah framework for tracing and optimizing codes. Its core data structure is the \"node\" container of python objects. To create a node, use `node` method, which creates a `Node` object. To access, the content of a node, use the `data` attribute." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "node of int 1\n", - "string\n", - "[1, 2, 3]\n", - "{'a': 1, 'b': 2}\n", - "<__main__.Foo object at 0x7f9cd27827c0>\n" - ] - } - ], - "source": [ - "from opto.trace import node\n", - "\n", - "x = node(1) # node of int\n", - "print(\"node of int\", x.data)\n", - "x = node(\"string\") # node of str\n", - "print(x.data)\n", - "x = node([1, 2, 3]) # node of list\n", - "print(x.data)\n", - "x = node({\"a\": 1, \"b\": 2}) # node of dict\n", - "print(x.data)\n", - "\n", - "\n", - "class Foo:\n", - " def __init__(self, x):\n", - " self.x = x\n", - " self.secret = \"secret\"\n", - "\n", - " def print(self, val):\n", - " print(val)\n", - "\n", - "\n", - "x = node(Foo(\"foo\")) # node of a class instance\n", - "print(x.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When a computation is performed using the contents of nodes, the result is also a node. This allows for the creation of a computation graph. The computation graph is a directed acyclic graph where the edges indicate the data dependencies.\n", - "\n", - "Nodes that are defined manually can be marked as trainable by setting their `trainable` attribute to True; such nodes are a subclass of Node called `ParameterNode`.\n", - "Nodes that are created automatically as a result of computations are a different subclass of Node called `MessageNode`.\n", - "\n", - "Nodes can be copied. This can be done in two ways with `clone` or `detach`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "<__main__.Foo object at 0x7f9c0c7260d0>\n" - ] - } - ], - "source": [ - "# clone returns a MessageNode whose parent is the original node\n", - "x_clone = x.clone()\n", - "assert x in x_clone.parents\n", - "assert x_clone.data != x.data\n", - "assert x_clone.data.x == x.data.x\n", - "print(x_clone.data)\n", - "# detach returns a new Node which is not connected to the original node\n", - "x_detach = x.detach()\n", - "assert len(x_detach.parents) == 0\n", - "assert x_detach.data != x.data\n", - "assert x_detach.data.x == x.data.x" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`trace` overloads python's magic methods that gives return value explicitly (such as `__add__`), except logical operations such as `__bool__` and setters. (The comparison magic methods compares the level of the nodes in the global graph, rather than comparing the data.) \n", - "\n", - "When nodes are used with these magic methods, the output would be a `MessageNode`, which is a subclass of `Node` that has the inputs of the method as the parents. The attribute `description` of a `MessageNode` documents the method's function." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MessageNode: (divide:0, dtype=, data=0.3333333333333333)\n", - "MessageNode: (divide:0, dtype=, data=0.3333333333333333)\n", - "parents: ['node_x:0', 'node_y:0']\n", - "\n", - "\n", - "MessageNode: (getitem:0, dtype=, data=1)\n", - "parents: ['dict_node:0', 'str:1']\n", - "len(dict_node) = MessageNode: (len:0, dtype=, data=2)\n", - "\n", - "\n", - "Node: (str:3, dtype=, data=hello world)\n", - "MessageNode: (getattr:1, dtype=, data=secret)\n", - "parents: ['Foo:1', 'str:4']\n" - ] - } - ], - "source": [ - "def print_node(node):\n", - " print(node)\n", - " print(f\"parents: {[p.name for p in node.parents]}\")\n", - "\n", - "\n", - "# Basic arithmetic operations\n", - "x = node(1, name=\"node_x\")\n", - "y = node(3, name=\"node_y\")\n", - "z = x / y\n", - "z2 = x / 3 # the int 3 would be converted to a node automatically\n", - "print(z)\n", - "print_node(z)\n", - "print(\"\\n\")\n", - "\n", - "# Index a node\n", - "dict_node = node({\"a\": 1, \"b\": 2}, name=\"dict_node\")\n", - "a = dict_node[\"a\"]\n", - "print_node(a)\n", - "print(\"len(dict_node) =\", dict_node.len())\n", - "\n", - "print(\"\\n\")\n", - "\n", - "# Getting class attribute and calling class method\n", - "x = node(Foo(\"foo\"))\n", - "x.call(\"print\", \"hello world\")\n", - "print_node(x.getattr(\"secret\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nodes can not be used in logical operations like and, or, not. This is an explicit design choice so as to ensure that logical operations in python code is explicitly traced." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - } - ], - "source": [ - "x = node(True)\n", - "try:\n", - " if x:\n", - " print(\"True\")\n", - "except Exception as e:\n", - " print(e)\n", - " print(\"Use if x.data instead of if x\")\n", - "\n", - "\n", - "x = node([1, 2, 3])\n", - "try:\n", - " 1 in x\n", - "except Exception as e:\n", - " print(e)\n", - " print(\"Use 1 in x.data instead of 1 in x\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nodes can be used to encapsulate any python object, including functions. Here're a few examples" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output: MessageNode: (call:1, dtype=, data=2)\n", - "parents [('function:0', ), ('int:3', 1)]\n", - "\n", - "\n", - "\n", - "The attribute of the wrapped object cannot be directly accessed. Instead use getattr() or call()\n", - "foo_node: MessageNode: (getattr:2, dtype=, data=1)\n", - "parents [('Foo:2', <__main__.Foo object at 0x7f9c0c746be0>), ('str:5', 'node')]\n", - "non_node: MessageNode: (getattr:3, dtype=, data=2)\n", - "parents [('Foo:2', <__main__.Foo object at 0x7f9c0c746be0>), ('str:6', 'non_node')]\n", - "output: MessageNode: (call:2, dtype=, data=4)\n", - "parents [('getattr:4', >)]\n", - "output: MessageNode: (call:4, dtype=, data=4)\n", - "parents [('getattr:6', >)]\n" - ] - } - ], - "source": [ - "def fun(x):\n", - " return x + 1\n", - "\n", - "\n", - "fun_node = node(fun)\n", - "y = fun_node(node(1))\n", - "print(f\"output: {y}\\nparents {[(p.name, p.data) for p in y.parents]}\")\n", - "print(\"\\n\\n\")\n", - "\n", - "\n", - "class Foo:\n", - "\n", - " def __init__(self):\n", - " self.node = node(1)\n", - " self.non_node = 2\n", - "\n", - " def trace_fun(self):\n", - " return self.node * 2\n", - "\n", - " def non_trace_fun(self):\n", - " return self.non_node * 2\n", - "\n", - "\n", - "foo = node(Foo())\n", - "\n", - "try:\n", - " foo.node\n", - " foo.trace_fun()\n", - "except AttributeError:\n", - " print(\"The attribute of the wrapped object cannot be directly accessed. Instead use getattr() or call()\")\n", - "\n", - "\n", - "attr = foo.getattr(\"node\")\n", - "print(f\"foo_node: {attr}\\nparents {[(p.name, p.data) for p in attr.parents]}\")\n", - "\n", - "\n", - "attr = foo.getattr(\"non_node\")\n", - "print(f\"non_node: {attr}\\nparents {[(p.name, p.data) for p in attr.parents]}\")\n", - "\n", - "\n", - "fun = foo.getattr(\"non_trace_fun\")\n", - "y = fun()\n", - "print(f\"output: {y}\\nparents {[(p.name, p.data) for p in y.parents]}\")\n", - "\n", - "try:\n", - " fun = foo.getattr(\"trace_fun\")\n", - " y = fun()\n", - "except AssertionError as e:\n", - " print(e)\n", - "\n", - "y = foo.call(\"non_trace_fun\")\n", - "print(f\"output: {y}\\nparents {[(p.name, p.data) for p in y.parents]}\")\n", - "\n", - "try:\n", - " y = foo.call(\"trace_fun\")\n", - "except AssertionError as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Writing Custom Node Operators\n", - "In addition to magical methods, we can use `bundle` to write custom methods that are traceable. When decorating a method with `bundle`, it needs a description of the method. It has a format of `[method_name] description`. `bundle` will automatically add all nodes whose `data` attribute is used within the function as the parents of the output `MessageNode`.\n", - "\n", - "Given a function `fun`, the decorated function `bundle(description)(fun)` by default will unpack all the inputs (it unpacks all node containers), send them to `fun`, and then creates a `MessageNode` to wrap the output of `fun` which has parents containing all the nodes used in this operation. \n", - "\n", - "Since all inputs are unpacked, they will be set as the parents. The user can override this behavior by setting `bundle(description, unpack_input=False)`, which would let `fun` see the original inputs.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MessageNode: (add_1:0, dtype=, data=2)\n", - "parents: ['node_x:1']\n", - "\n", - "\n", - "MessageNode: (add:1, dtype=, data=3)\n", - "parents: ['node_x:2', 'node_y:1']\n", - "\n", - "\n", - "MessageNode: (pass_through:0, dtype=, data=(1, 2))\n", - "\n", - "\n", - "(, )\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/chinganc/codes/Trace/opto/trace/bundle.py:163: UserWarning: Setting n_outputs>1 will be deprecated.\n", - " warnings.warn(\"Setting n_outputs>1 will be deprecated.\")\n" - ] - } - ], - "source": [ - "from opto.trace import bundle\n", - "\n", - "\n", - "@bundle(\"[add_1] Add 1 to input x\")\n", - "def foo(x):\n", - " return x + 1\n", - "\n", - "\n", - "x = node(1, name=\"node_x\")\n", - "z = foo(x)\n", - "print_node(z)\n", - "print(\"\\n\")\n", - "\n", - "\n", - "@bundle(\"[add] Add input x and input y\")\n", - "def foo(x, y):\n", - " return x + y\n", - "\n", - "\n", - "x = node(1, name=\"node_x\")\n", - "y = node(2, name=\"node_y\")\n", - "z = foo(x, y)\n", - "print_node(z)\n", - "print(\"\\n\")\n", - "\n", - "# The output is a node of a tuple of two nodes\n", - "\n", - "\n", - "@bundle(\"[pass_through] No operation, just return inputs\")\n", - "def foo(x, y):\n", - " return x, y\n", - "\n", - "\n", - "x = node(1, name=\"node_x\")\n", - "y = node(2, name=\"node_y\")\n", - "z = foo(x, y)\n", - "print(z)\n", - "from opto.trace.nodes import Node\n", - "\n", - "assert isinstance(z, Node)\n", - "assert isinstance(z.data, tuple)\n", - "assert len(z.data) == 2\n", - "print(\"\\n\")\n", - "\n", - "\n", - "# The output is a tuple of two nodes\n", - "@bundle(\"[pass_through] No operation, just return inputs\", n_outputs=2)\n", - "def foo(x, y):\n", - " return x, y\n", - "\n", - "\n", - "x = node(1, name=\"node_x\")\n", - "y = node(2, name=\"node_y\")\n", - "z = foo(x, y)\n", - "print(z)\n", - "assert isinstance(z, tuple)\n", - "assert len(z) == 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Describing Relationship between Inputs and Outputs and Nodes in the Graph\n", - "One can additionally provide `node_dict` to specify how each variable mentioned in `description` is related to the nodes in the graph. This relationship is stored in the `inputs` attribute of `MessageNode`. See examples \n", - "below." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'x': ('node_x:4', 1)}\n", - "{'input': ('node_x:4', 1)}\n", - "{'x': ('node_x:4', 1), 'custom_x': ('node_x:4', 1)}\n" - ] - } - ], - "source": [ - "# The default value of node_dict is None. In this case, the key of the inputs dict is the name of the input nodes.\n", - "@bundle(\"[add_1] Add 1 to input x\")\n", - "def foo(x):\n", - " return x + 1\n", - "\n", - "\n", - "z = foo(x)\n", - "print({k: (v.name, v.data) for k, v in z.inputs.items()})\n", - "\n", - "# When node_dict is set to 'auto', the key of the inputs dict is the name specified in the function signature.\n", - "\n", - "\n", - "@bundle(\"[add_1] Add 1 to input x\", node_dict=\"auto\")\n", - "def foo(input):\n", - " return input + 1\n", - "\n", - "\n", - "z = foo(x)\n", - "print({k: (v.name, v.data) for k, v in z.inputs.items()})\n", - "\n", - "# When node_dict is set to a dict, the key of the inputs dict is the name specified in the dict.\n", - "node_dict = {\"custom_x\": x}\n", - "\n", - "\n", - "@bundle(\"[add_1] Add 1 to input x\", node_dict=node_dict)\n", - "def foo(x):\n", - " return x + 1\n", - "\n", - "\n", - "z = foo(x)\n", - "print({k: (v.name, v.data) for k, v in z.inputs.items()})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using node_dict is useful when the function uses nodes that are not in the function signature.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not all nodes used in the operator are specified as inputs of the returned node. Missing ['node_y:4'] \n", - "{'x': ('node_x:5', 1), 'node_y': ('node_y:4', 2)}\n" - ] - } - ], - "source": [ - "# By default, the inputs dict only contains the nodes that are in the function signature. One can update the inputs dict by using node_dict.\n", - "x = node(1, name=\"node_x\")\n", - "y = node(2, name=\"node_y\")\n", - "\n", - "\n", - "@bundle(\"[add_1] Add input x to node_y.\", node_dict=\"auto\")\n", - "def foo(x):\n", - " return x + y.data\n", - "\n", - "\n", - "try:\n", - " z = foo(x)\n", - "except Exception as e:\n", - " # Since the function signature does not contain y, the function will raise an error.\n", - " print(e)\n", - "# We can use node_dict to add y to the inputs dict.\n", - "node_dict = {\"node_y\": y}\n", - "\n", - "\n", - "@bundle(\"[add_1] Add input x to node_y.\", node_dict=node_dict)\n", - "def foo(x):\n", - " return x + y.data\n", - "\n", - "\n", - "z = foo(x)\n", - "print({k: (v.name, v.data) for k, v in z.inputs.items()})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualize Graph\n", - "\n", - "The graph of nodes can be visualized by calling `backward` method of a node. (Later we will cover how `backward` also sends feedback across the graph). " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "add0\n", - "\n", - "add0\n", - "[add] This is an add operator of x and y.\n", - "3\n", - "\n", - "\n", - "\n", - "add2\n", - "\n", - "add2\n", - "[add] This is an add operator of x and y.\n", - "5\n", - "\n", - "\n", - "\n", - "add0->add2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "add1\n", - "\n", - "add1\n", - "[add] This is an add operator of x and y.\n", - "2\n", - "\n", - "\n", - "\n", - "add1->add2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node_x0\n", - "\n", - "node_x0\n", - "[Node] This is a node in a computational graph.\n", - "1\n", - "\n", - "\n", - "\n", - "node_x0->add0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node_x0->add1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node_y0\n", - "\n", - "node_y0\n", - "[Node] This is a node in a computational graph.\n", - "2\n", - "\n", - "\n", - "\n", - "node_y0->add0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "int0\n", - "\n", - "int0\n", - "[Node] This is a node in a computational graph.\n", - "1\n", - "\n", - "\n", - "\n", - "int0->add1\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from opto.trace.nodes import GRAPH\n", - "\n", - "GRAPH.clear() # to remove all the nodes\n", - "x = node(1, name=\"node_x\")\n", - "y = node(2, name=\"node_y\")\n", - "a = x + y\n", - "b = x + 1\n", - "final = a + b\n", - "final.backward(visualize=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Node: (bool:0, dtype=, data=True) Node: (int:0, dtype=, data=1) Node: (int:1, dtype=, data=0)\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "int0\n", - "\n", - "int0\n", - "[Node] This is a node in a computational graph.\n", - "1\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "GRAPH.clear()\n", - "x = node(True)\n", - "one = node(1)\n", - "zero = node(0)\n", - "print(x, one, zero)\n", - "# Logical operations are not traceable\n", - "y = one if x.data else zero\n", - "y.backward(visualize=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "bool0\n", - "\n", - "bool0\n", - "[Node] This is a node in a computational graph.\n", - "True\n", - "\n", - "\n", - "\n", - "fun0\n", - "\n", - "fun0\n", - "[fun] Return one if input x is True, otherwise return zero\n", - "1\n", - "\n", - "\n", - "\n", - "bool0->fun0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "int0\n", - "\n", - "int0\n", - "[Node] This is a node in a computational graph.\n", - "1\n", - "\n", - "\n", - "\n", - "int0->fun0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "int1\n", - "\n", - "int1\n", - "[Node] This is a node in a computational graph.\n", - "0\n", - "\n", - "\n", - "\n", - "int1->fun0\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# This is traceable\n", - "node_dict = {\"one\": one, \"zero\": zero}\n", - "\n", - "\n", - "@bundle(\"[fun] Return one if input x is True, otherwise return zero\", node_dict=node_dict)\n", - "def fun(x):\n", - " return one.data if x else zero.data\n", - "\n", - "\n", - "y = fun(x)\n", - "y.backward(visualize=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Broadcasting\n", - "Using `apply_op`, we can broadcast node operators to a container of nodes. A container of nodes are either `list`, `tuple`, `dict`, or subclass of an abstract class `BaseModule`. `apply_op` recursively applies the operator to all nodes in the container. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x [1, 2, 1]\n", - "y [3, 4, 2]\n", - "Elements in z should be added, except for the last one. Value: [4, 6, 1]\n", - "1+3=4\n", - "0==0==0\n", - "x_plus_y.x should be added. Value: xy\n", - "x_plus_y.y should be added. Value: [2, 4]\n", - "x_plus_y.z should be not added, just 1. Value: 1\n" - ] - } - ], - "source": [ - "from opto.trace import apply_op, node, NodeContainer\n", - "from opto.trace import operators as ops\n", - "\n", - "import copy\n", - "\n", - "# Using list as a node container\n", - "x = [node(1), node(2), 1]\n", - "y = [node(3), node(4), 2]\n", - "z = copy.deepcopy(x)\n", - "z = apply_op(ops.add, z, x, y)\n", - "print(\"x\", [x[0].data, x[1].data, x[2]])\n", - "print(\"y\", [y[0].data, y[1].data, y[2]])\n", - "print(\"Elements in z should be added, except for the last one. Value: \", [z[0].data, z[1].data, z[2]])\n", - "\n", - "\n", - "# Using list as a node container\n", - "x = dict(a=node(1), b=0)\n", - "y = dict(a=node(3), b=0)\n", - "z = copy.deepcopy(x)\n", - "z = apply_op(ops.add, z, x, y)\n", - "print(f\"{x['a'].data}+{y['a'].data}={z['a'].data}\")\n", - "print(f\"{x['b']}=={y['b']}=={z['b']}\")\n", - "\n", - "# Using a custom class as a node container\n", - "\n", - "\n", - "class Foo(NodeContainer):\n", - " def __init__(self, x):\n", - " self.x = node(x)\n", - " self.y = [node(1), node(2)]\n", - " self.z = 1\n", - "\n", - "\n", - "x = Foo(\"x\")\n", - "y = Foo(\"y\")\n", - "x_plus_y = Foo(\"template\")\n", - "x_plus_y = apply_op(ops.add, x_plus_y, x, y)\n", - "print(\"x_plus_y.x should be added. Value: \", x_plus_y.x.data)\n", - "print(\"x_plus_y.y should be added. Value: \", [n.data for n in x_plus_y.y])\n", - "print(\"x_plus_y.z should be not added, just 1. Value: \", x_plus_y.z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Nodes and Python Data Structure\n", - "\n", - "We can create a `node` over Python data structure like dictionary, tuple, set, or list. We automatically handle the iteration and you can wrap a node around any data structure and use them like normal python objects." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MessageNode: (getitem:0, dtype=, data=arg2)\n", - "MessageNode: (getitem:1, dtype=, data=arg1)\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "to_list0\n", - "\n", - "to_list0\n", - "[to_list] This converts x to a list.\n", - "['arg2', 'arg1']\n", - "\n", - "\n", - "\n", - "getitem1\n", - "\n", - "getitem1\n", - "[getitem] This is a getitem operator of x based on index.\n", - "arg1\n", - "\n", - "\n", - "\n", - "to_list0->getitem1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "int15\n", - "\n", - "int15\n", - "[Node] This is a node in a computational graph.\n", - "1\n", - "\n", - "\n", - "\n", - "int15->getitem1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "set0\n", - "\n", - "set0\n", - "[Node] This is a node in a computational graph.\n", - "{'arg2', 'arg1'}\n", - "\n", - "\n", - "\n", - "set0->to_list0\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from opto.trace import node\n", - "\n", - "args = node({\"arg1\", \"arg2\"}, trainable=False)\n", - "for a in args:\n", - " print(a)\n", - "\n", - "a.backward(visualize=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MessageNode: (getitem:2, dtype=, data=arg1) MessageNode: (getitem:3, dtype=, data=1)\n", - "MessageNode: (getitem:4, dtype=, data=arg2) MessageNode: (getitem:5, dtype=, data=2)\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "list0\n", - "\n", - "list0\n", - "[Node] This is a node in a computational graph.\n", - "[1, 2]\n", - "\n", - "\n", - "\n", - "getitem5\n", - "\n", - "getitem5\n", - "[getitem] This is a getitem operator of x based on index.\n", - "2\n", - "\n", - "\n", - "\n", - "list0->getitem5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "int19\n", - "\n", - "int19\n", - "[Node] This is a node in a computational graph.\n", - "1\n", - "\n", - "\n", - "\n", - "int19->getitem5\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parms = node([1, 2], trainable=False)\n", - "args = node([\"arg1\", \"arg2\"], trainable=False)\n", - "\n", - "for a, p in zip(args, parms):\n", - " print(a, p)\n", - "\n", - "p.backward(visualize=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "autogen", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.19" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/OptimizationTutorial.ipynb b/notebooks/OptimizationTutorial.ipynb deleted file mode 100644 index f8c5a201..00000000 --- a/notebooks/OptimizationTutorial.ipynb +++ /dev/null @@ -1,951 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import autogen\n", - "from opto.trace import bundle, node\n", - "from opto.optimizers import FunctionOptimizer\n", - "from opto.trace.nodes import GRAPH\n", - "\n", - "\n", - "def blackbox(x):\n", - " return -x * 2\n", - "\n", - "\n", - "@bundle()\n", - "def bar(x):\n", - " \"This is a test function, which does negative scaling.\"\n", - " return blackbox(x)\n", - "\n", - "\n", - "def foo(x):\n", - " y = x + 1\n", - " return x * y\n", - "\n", - "\n", - "# foobar is a composition of custom function and built-in functions\n", - "\n", - "\n", - "def foobar(x):\n", - " return foo(bar(x))\n", - "\n", - "\n", - "def user(x):\n", - " if x < 50:\n", - " return \"The number needs to be larger.\"\n", - " else:\n", - " return \"Success.\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Backpropagation \n", - "\n", - "We apply `FunctionOptimizer` to change the input to the function `foobar` such that the simulated user is satisfied. To this end, we backpropagated the user's language feedback about the output, through the graph that connects the input to the output." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# LLM Optimization based Language Feedback\n", - "\n", - "Here we show a small example of how to apply `trace` to optimize python objects based on language feedback. Here we want to change the input to function `foobar` such that output is large enough. `foobar` is a function that is composed of `foo` based on built-in operators and `bar` which is a blackbox function, whose information is only given via the docstring. \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "bar0\n", - "\n", - "bar0\n", - "[bar] This is a test function, which does negative scaling..\n", - "2.0\n", - "\n", - "\n", - "\n", - "multiply0\n", - "\n", - "multiply0\n", - "[multiply] This is a multiply operator of x and y. .\n", - "6.0\n", - "\n", - "\n", - "\n", - "bar0->multiply0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "add0\n", - "\n", - "add0\n", - "[add] This is an add operator of x and y. .\n", - "3.0\n", - "\n", - "\n", - "\n", - "bar0->add0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "add0->multiply0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "int0\n", - "\n", - "int0\n", - "[Node] This is a node in a computational graph.\n", - "1\n", - "\n", - "\n", - "\n", - "int0->add0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "float0\n", - "\n", - "float0\n", - "[ParameterNode] This is a ParameterNode in a computational graph.\n", - "-1.0\n", - "\n", - "\n", - "\n", - "float0->bar0\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# One-step optimization example\n", - "x = node(-1.0, trainable=True)\n", - "optimizer = FunctionOptimizer([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", - "output = foobar(x)\n", - "feedback = user(output.data)\n", - "optimizer.zero_feedback()\n", - "optimizer.backward(output, feedback, visualize=True) # this is equivalent to the below line\n", - "# output.backward(feedback, propagator=optimizer.propagator, visualize=visualize)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The propagated feedback contains graph structure, data of the nodes in the graph, and the transformation used in the graph. They're presented in a python-like syntax." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Function Feedback\n", - "Graph:\n", - " 1: bar0 = bar(x=float0)\n", - " 2: add0 = add(x=bar0, y=int0)\n", - " 3: multiply0 = multiply(x=bar0, y=add0)\n", - "Roots:\n", - " int0: (1, None)\n", - " float0: (-1.0, None)\n", - "Others:\n", - " bar0: (2.0, None)\n", - " add0: (3.0, None)\n", - "Documentation:\n", - " bar: [bar] This is a test function, which does negative scaling..\n", - " add: [add] This is an add operator of x and y. .\n", - " multiply: [multiply] This is a multiply operator of x and y. .\n", - "Output:\n", - " multiply0: (6.0, None)\n", - "User Feedback:\n", - " The number needs to be larger.\n" - ] - } - ], - "source": [ - "from opto.optimizers.function_optimizer import node_to_function_feedback\n", - "\n", - "print(\"Function Feedback\")\n", - "for k, v in x.feedback.items():\n", - " v = v[0]\n", - " f_feedback = node_to_function_feedback(v)\n", - " print(\"Graph:\")\n", - " for kk, vv in f_feedback.graph:\n", - " print(f\" {kk}: {vv}\")\n", - " print(\"Roots:\")\n", - " for kk, vv in f_feedback.roots.items():\n", - " print(f\" {kk}: {vv}\")\n", - " print(\"Others:\")\n", - " for kk, vv in f_feedback.others.items():\n", - " print(f\" {kk}: {vv}\")\n", - " print(\"Documentation:\")\n", - " for kk, vv in f_feedback.documentation.items():\n", - " print(f\" {kk}: {vv}\")\n", - " print(\"Output:\")\n", - " for kk, vv in f_feedback.output.items():\n", - " print(f\" {kk}: {vv}\")\n", - " print(\"User Feedback:\")\n", - " print(f\" {f_feedback.user_feedback}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once the feedback is propagated, we can call the optimization to change the variable based on the feedback." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prompt\n", - " \n", - "You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.\n", - "\n", - "Specifically, a problem will be composed of the following parts:\n", - "- #Instruction: the instruction which describes the things you need to do or the question you should answer.\n", - "- #Code: the code defined in the problem.\n", - "- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.\n", - "- #Variables: the input variables that you can change.\n", - "- #Constraints: the constraints or descriptions of the variables in #Variables.\n", - "- #Inputs: the values of other inputs to the code, which are not changeable.\n", - "- #Others: the intermediate values created through the code execution.\n", - "- #Outputs: the result of the code output.\n", - "- #Feedback: the feedback about the code's execution result.\n", - "\n", - "In #Variables, #Inputs, #Outputs, and #Others, the format is:\n", - "\n", - " = \n", - "\n", - "If is (code), it means is the source code of a python code, which may include docstring and definitions.\n", - "\n", - "Output_format: Your output should be in the following json format, satisfying the json syntax:\n", - "\n", - "{{\n", - "\"reasoning\": ,\n", - "\"answer\": ,\n", - "\"suggestion\": {{\n", - " : ,\n", - " : ,\n", - "}}\n", - "}}\n", - "\n", - "You should write down your thought process in \"reasoning\". If #Instruction asks for an answer, write it down in \"answer\". If you need to suggest a change in the values of #Variables, write down the suggested values in \"suggestion\". Remember you can change only the values in #Variables, not others. When of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.\n", - "\n", - "If no changes or answer are needed, just output TERMINATE.\n", - "\n", - "Now you see problem instance:\n", - "\n", - "================================\n", - "\n", - "#Instruction\n", - "You need to change the of the variables in #Variables to improve the output in accordance to #Feedback.\n", - "\n", - "#Code\n", - "bar0 = bar(x=float0)\n", - "add0 = add(x=bar0, y=int0)\n", - "multiply0 = multiply(x=bar0, y=add0)\n", - "\n", - "#Documentation\n", - "[bar] This is a test function, which does negative scaling..\n", - "[add] This is an add operator of x and y. .\n", - "[multiply] This is a multiply operator of x and y. .\n", - "\n", - "#Variables\n", - "(float) float0=-1.0\n", - "\n", - "#Constraints\n", - "\n", - "\n", - "#Inputs\n", - "(int) int0=1\n", - "\n", - "#Others\n", - "(float) bar0=2.0\n", - "(float) add0=3.0\n", - "\n", - "#Outputs\n", - "(float) multiply0=6.0\n", - "\n", - "#Feedback:\n", - "The number needs to be larger.\n", - "\n", - "================================\n", - "\n", - "\n", - "Your response:\n", - "\n", - "LLM response:\n", - " {\n", - "\"reasoning\": \"Given the feedback indicating that the output number needs to be larger, we have to analyze the operations performed by the code and how the variables affect the final output. The current operation flow is as follows: bar0 = bar(x=float0) performs a negative scaling on float0, which is currently -1.0, resulting in bar0 being 2.0. This indicates that the 'bar' function reverses the sign and possibly multiplies by a constant factor. The add0 operation adds bar0 and int0, resulting in 3.0. Finally, multiply0 multiplies bar0 and add0, resulting in 6.0. To increase the output of the multiplication, we need to increase the input values, since all operations (negative scaling, addition, multiplication) positively contribute to the output magnitude. Increasing float0 will increase bar0 directly (assuming a direct scaling relationship), and consequently, both add0 and multiply0 will increase. Currently, float0 is set to -1.0. Increasing this value to a larger negative number, based on the documentation, might not be beneficial since 'bar' does negative scaling, which suggests it converts negative to positive in a way that might be dependent on the magnitude. Thus, moving float0 in the positive direction seems more promising to increase the 'bar0' value further, thereby increasing 'multiply0'.\",\n", - "\"answer\": \"\",\n", - "\"suggestion\": {\n", - "\"float0\": \"-0.5\"\n", - "}\n", - "}\n", - "\n", - "After step\n", - "old variable -1.0\n", - "new variable -0.5\n" - ] - } - ], - "source": [ - "old_variable = x.data\n", - "optimizer.step(verbose=True)\n", - "\n", - "print(\"\\nAfter step\")\n", - "print(\"old variable\", old_variable)\n", - "print(\"new variable\", x.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example of Full Optimization Loop\n", - "\n", - "We can apply the steps above repetitively to create a training loop to optimize the variable according to the user. Notice because of the way `foobar` works, the optimizer actually needs to change the input to be lower in order to make the output to be larger (which is what the user suggests). \n", - "\n", - "This is a non-trivial problem, becasue the optimizer sees only\n", - "\n", - "```\n", - "output = blackbox(x) * (blackbox(x)+1)\n", - "```\n", - "\n", - "and the hint/docstring `\"This is a test function, which does scaling and negation.\"` about how `blackbox` works. The optimizer needs to figure out how to change the input based on this vague information.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "variable=-1.0, output=6.0, feedback=The number needs to be larger.\n", - "variable=-0.5, output=2.0, feedback=The number needs to be larger.\n", - "variable=-2.0, output=20.0, feedback=The number needs to be larger.\n", - "variable=-4.0, output=72.0, feedback=Success.\n", - "Cannot extract suggestion from LLM's response:\n", - "{\"reasoning\": \"Based on the provided information and the feedback indicating success, there is no need for any adjustments or changes to be made.\", \"answer\": \"\", \"suggestion\": {}}\n", - "History\n", - " 0: -1.0\n", - " 1: -0.5\n", - " 2: -2.0\n", - " 3: -4.0\n", - " 4: -4.0\n" - ] - } - ], - "source": [ - "# A small example of how to use the optimizer in a loop\n", - "GRAPH.clear()\n", - "x = node(-1.0, trainable=True)\n", - "optimizer = FunctionOptimizer([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", - "\n", - "history = [x.data]\n", - "feedback = \"\"\n", - "while feedback.lower() != \"Success.\".lower():\n", - " output = foobar(x)\n", - " feedback = user(output.data)\n", - " optimizer.zero_feedback()\n", - " optimizer.backward(output, feedback)\n", - " print(f\"variable={x.data}, output={output.data}, feedback={feedback}\") # logging\n", - " optimizer.step()\n", - " history.append(x.data) # logging\n", - "\n", - "print(\"History\")\n", - "for i, v in enumerate(history):\n", - " print(f\" {i}: {v}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Adding constraint. \n", - "\n", - "We can add constraint to parameter nodes to guide the search. In this small example, the constraint info helps save one optimization step." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "variable=-1.0, output=6.0, feedback=The number needs to be larger.\n", - "variable=5.0, output=90.0, feedback=Success.\n", - "Cannot extract suggestion from LLM's response:\n", - "{\n", - "\"reasoning\": \"Given the instructions and the feedback, there is no need to modify the inputs or the code as the feedback indicates success. The goal was to improve the output according to the Feedback section, but since the feedback already states success, it means the output is already considered correct or optimal with the current inputs. The problem does not specify any additional criteria for improvement or define a specific target output that needs to be achieved beyond what has been already accomplished.\",\n", - "\"answer\": \"No changes are needed\",\n", - "\"suggestion\": {}\n", - "}\n", - "History\n", - " 0: -1.0\n", - " 1: 5.0\n", - " 2: 5.0\n" - ] - } - ], - "source": [ - "# A small example of how to use the optimizer in a loop\n", - "GRAPH.clear()\n", - "x = node(-1.0, trainable=True, constraint=\"The value should be greater than 2.0\")\n", - "optimizer = FunctionOptimizer([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", - "\n", - "history = [x.data]\n", - "feedback = \"\"\n", - "while feedback.lower() != \"Success.\".lower():\n", - " output = foobar(x)\n", - " feedback = user(output.data)\n", - " optimizer.zero_feedback()\n", - " optimizer.backward(output, feedback)\n", - " print(f\"variable={x.data}, output={output.data}, feedback={feedback}\") # logging\n", - " optimizer.step()\n", - " history.append(x.data) # logging\n", - "\n", - "print(\"History\")\n", - "for i, v in enumerate(history):\n", - " print(f\" {i}: {v}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example of optimizing strings\n", - "\n", - "Below is a similar example, except the variable is written in text and is converted by a poor converter to numbers before inputting to `foo` and `bar`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "@bundle()\n", - "def convert_english_to_numbers(x):\n", - " \"\"\"This is a function that converts English to numbers. This function has limited ability.\"\"\"\n", - " # remove speical characters, like, \", &, etc.\n", - " x = x.replace('\"', \"\")\n", - " try: # Convert string to integer\n", - " return int(x)\n", - " except:\n", - " pass\n", - " # Convert intergers written in Engligsh in [-10, 10] to numbers\n", - " if x == \"negative ten\":\n", - " return -10\n", - " if x == \"negative nine\":\n", - " return -9\n", - " if x == \"negative eight\":\n", - " return -8\n", - " if x == \"negative seven\":\n", - " return -7\n", - " if x == \"negative six\":\n", - " return -6\n", - " if x == \"negative five\":\n", - " return -5\n", - " if x == \"negative four\":\n", - " return -4\n", - " if x == \"negative three\":\n", - " return -3\n", - " if x == \"negative two\":\n", - " return -2\n", - " if x == \"negative one\":\n", - " return -1\n", - " if x == \"zero\":\n", - " return 0\n", - " if x == \"one\":\n", - " return 1\n", - " if x == \"two\":\n", - " return 2\n", - " if x == \"three\":\n", - " return 3\n", - " if x == \"four\":\n", - " return 4\n", - " if x == \"five\":\n", - " return 5\n", - " if x == \"six\":\n", - " return 6\n", - " if x == \"seven\":\n", - " return 7\n", - " if x == \"eight\":\n", - " return 8\n", - " if x == \"nine\":\n", - " return 9\n", - " if x == \"ten\":\n", - " return 10\n", - " return \"FAIL\"\n", - "\n", - "\n", - "def user(x):\n", - " if x == \"FAIL\":\n", - " return \"The text cannot be converted to a number.\"\n", - " if x < 50:\n", - " return \"The number needs to be larger.\"\n", - " else:\n", - " return \"Success.\"\n", - "\n", - "\n", - "def foobar_text(x):\n", - " output = convert_english_to_numbers(x)\n", - " if output.data == \"FAIL\": # This is not traced\n", - " return output\n", - " else:\n", - " return foo(bar(output))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "variable=negative point one, output=FAIL, feedback=The text cannot be converted to a number.\n", - "variable=negative one tenth, output=FAIL, feedback=The text cannot be converted to a number.\n", - "variable=negative ten, output=420, feedback=Success.\n", - "Cannot extract suggestion from LLM's response:\n", - "{\n", - "\"reasoning\": \"According to the feedback, the process was a success. This means the functions worked as intended and the given inputs produced the correct output without needing any adjustments. Each step of the outlined code logically follows from the inputs and functions documented: str0='negative ten' is correctly converted to -10, bar0 correctly does some operation to produce 20 (though the exact nature of 'negative scaling' is not detailed, the positive result suggests it might be negating and scaling the number), add0 adds this result to int0 (1) to get 21, and multiply0 multiplies bar0 (20) by add0 (21) to get 420. Without more specific feedback indicating a problem or an incorrect output, there seems to be no need for modification.\",\n", - "\"answer\": \"\",\n", - "\"suggestion\": {}\n", - "}\n", - "History\n", - " 0: negative point one\n", - " 1: negative one tenth\n", - " 2: negative ten\n", - " 3: negative ten\n" - ] - } - ], - "source": [ - "GRAPH.clear()\n", - "x = node(\"negative point one\", trainable=True)\n", - "optimizer = FunctionOptimizer([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", - "\n", - "history = [x.data]\n", - "feedback = \"\"\n", - "while feedback.lower() != \"Success.\".lower():\n", - " output = foobar_text(x)\n", - " feedback = user(output.data)\n", - " optimizer.zero_feedback()\n", - " optimizer.backward(output, feedback)\n", - " print(f\"variable={x.data}, output={output.data}, feedback={feedback}\") # logging\n", - " optimizer.step()\n", - " history.append(x.data) # logging\n", - "\n", - "print(\"History\")\n", - "for i, v in enumerate(history):\n", - " print(f\" {i}: {v}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example of optimizing functions\n", - "\n", - "We can use `trace` to optimize python function directly. This can be achieved by setting `variable=True` when decorating a custom function by `@bundle`. This would create a `ParameterNode` in the operator, which can be accessed by the `parameter` attribute of the decorated function. It can be used like any other parameters and sent to the optimizer." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output=2, feedback=Try again. The output should be negative, variables=\n", - "\n", - "__code:0 def my_fun(x):\n", - " \"\"\"Test function\"\"\"\n", - " return x**2 + 1\n", - "output=-2, feedback=Success., variables=\n", - "\n", - "__code:0 def my_fun(x):\n", - " \"\"\"Test function\"\"\"\n", - " return x**2 - 3\n", - "Cannot extract suggestion from LLM's response:\n", - "{\n", - "\"reasoning\": \n", - "\"The code provided takes an integer input x and passes it to the function my_fun which calculates x^2 - 3. The result of evaluating the current input value of -1 is -2 as expected. The Feedback confirms that the result is a success, indicating that the current logic and inputs are correct and need no adjustments.\",\n", - "\"answer\": \"The function and the input are correct, resulting in the expected output.\",\n", - "\"suggestion\": {}\n", - "}\n" - ] - } - ], - "source": [ - "GRAPH.clear()\n", - "\n", - "\n", - "def user(output):\n", - " if output < 0:\n", - " return \"Success.\"\n", - " else:\n", - " return \"Try again. The output should be negative\"\n", - "\n", - "\n", - "# We make this function as a parameter that can be optimized.\n", - "\n", - "\n", - "@bundle(trainable=True)\n", - "def my_fun(x):\n", - " \"\"\"Test function\"\"\"\n", - " return x**2 + 1\n", - "\n", - "\n", - "x = node(-1, trainable=False)\n", - "optimizer = FunctionOptimizer([my_fun.parameter], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", - "\n", - "feedback = \"\"\n", - "while feedback != \"Success.\":\n", - " output = my_fun(x)\n", - " feedback = user(output.data)\n", - " optimizer.zero_feedback()\n", - " optimizer.backward(output, feedback)\n", - "\n", - " print(f\"output={output.data}, feedback={feedback}, variables=\\n\") # logging\n", - " for p in optimizer.parameters:\n", - " print(p.name, p.data)\n", - " optimizer.step(verbose=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example of hyper-parameter optimization for ML models\n", - "\n", - "We can use `trace` to optimize the hyper-parameters of a machine learning model using language feedbacks." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/chinganc/miniconda3/envs/trace/lib/python3.8/site-packages/sklearn/datasets/_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n", - " warn(\n" - ] - } - ], - "source": [ - "from sklearn.datasets import fetch_openml\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.utils import check_random_state\n", - "import numpy as np\n", - "\n", - "\n", - "train_samples = 10000\n", - "X, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n", - "\n", - "random_state = check_random_state(0)\n", - "permutation = random_state.permutation(X.shape[0])\n", - "X = X[permutation]\n", - "y = y[permutation]\n", - "X = X.reshape((X.shape[0], -1))\n", - "\n", - "X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=train_samples, test_size=20000)\n", - "\n", - "scaler = StandardScaler()\n", - "X_train = scaler.fit_transform(X_train)\n", - "X_validation = scaler.transform(X_validation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The language feedback consists of a text representation of the validation accuracy:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def scorer(classifier, guess, history):\n", - " score = classifier.score(X_validation, y_validation) * 100\n", - " sparsity = np.mean(classifier.coef_ == 0) * 100\n", - " return_feedback = f\"\\nScore is the accuracy of the classifier on the validation set, and should be maximized.\"\n", - " return_feedback += f\"\\nSparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.\"\n", - " return_feedback += f\"By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\"\n", - " return_feedback += f\"\\n\\nMost recent guess: \\nRegularization Parameter: {guess:.4f}, Score: {score:.2f}%, Sparsity: {sparsity:.2f}%\"\n", - " if len(history) > 0:\n", - " return_feedback += f\"\\n\\nHistory of guesses:\"\n", - " for item in history:\n", - " return_feedback += (\n", - " f\"\\nRegularization Parameter: {item[0]:.4f}, Score: {item[1]:.2f}%, Sparsity: {item[2]:.2f}%\"\n", - " )\n", - " return return_feedback, score, sparsity\n", - "\n", - "\n", - "@bundle(trainable=False)\n", - "def train_classifier(regularization_parameter):\n", - " \"\"\"regularization_parameter is a positive number that controls the sparsity of the classifier. Lower values will increase sparsity, and higher values will decrease sparsity.\"\"\"\n", - " classifier = LogisticRegression(C=regularization_parameter, penalty=\"l1\", solver=\"saga\", tol=0.1)\n", - " classifier.fit(X_train, y_train)\n", - " return classifier" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "variable=0.005, feedback=\n", - "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", - "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", - "\n", - "Most recent guess: \n", - "Regularization Parameter: 0.0050, Score: 83.67%, Sparsity: 80.70%\n", - "variable=0.01, feedback=\n", - "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", - "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", - "\n", - "Most recent guess: \n", - "Regularization Parameter: 0.0100, Score: 86.08%, Sparsity: 61.81%\n", - "\n", - "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.67%, Sparsity: 80.70%\n", - "variable=0.02, feedback=\n", - "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", - "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", - "\n", - "Most recent guess: \n", - "Regularization Parameter: 0.0200, Score: 87.20%, Sparsity: 48.80%\n", - "\n", - "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.67%, Sparsity: 80.70%\n", - "Regularization Parameter: 0.0100, Score: 86.08%, Sparsity: 61.81%\n", - "variable=0.03, feedback=\n", - "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", - "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", - "\n", - "Most recent guess: \n", - "Regularization Parameter: 0.0300, Score: 87.64%, Sparsity: 43.12%\n", - "\n", - "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.67%, Sparsity: 80.70%\n", - "Regularization Parameter: 0.0100, Score: 86.08%, Sparsity: 61.81%\n", - "Regularization Parameter: 0.0200, Score: 87.20%, Sparsity: 48.80%\n", - "variable=0.04, feedback=\n", - "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", - "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", - "\n", - "Most recent guess: \n", - "Regularization Parameter: 0.0400, Score: 87.56%, Sparsity: 35.59%\n", - "\n", - "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.67%, Sparsity: 80.70%\n", - "Regularization Parameter: 0.0100, Score: 86.08%, Sparsity: 61.81%\n", - "Regularization Parameter: 0.0200, Score: 87.20%, Sparsity: 48.80%\n", - "Regularization Parameter: 0.0300, Score: 87.64%, Sparsity: 43.12%\n", - "variable=0.035, feedback=\n", - "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", - "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", - "\n", - "Most recent guess: \n", - "Regularization Parameter: 0.0350, Score: 87.52%, Sparsity: 34.62%\n", - "\n", - "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.67%, Sparsity: 80.70%\n", - "Regularization Parameter: 0.0100, Score: 86.08%, Sparsity: 61.81%\n", - "Regularization Parameter: 0.0200, Score: 87.20%, Sparsity: 48.80%\n", - "Regularization Parameter: 0.0300, Score: 87.64%, Sparsity: 43.12%\n", - "Regularization Parameter: 0.0400, Score: 87.56%, Sparsity: 35.59%\n", - "variable=0.032, feedback=\n", - "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", - "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", - "\n", - "Most recent guess: \n", - "Regularization Parameter: 0.0320, Score: 87.78%, Sparsity: 45.03%\n", - "\n", - "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.67%, Sparsity: 80.70%\n", - "Regularization Parameter: 0.0100, Score: 86.08%, Sparsity: 61.81%\n", - "Regularization Parameter: 0.0200, Score: 87.20%, Sparsity: 48.80%\n", - "Regularization Parameter: 0.0300, Score: 87.64%, Sparsity: 43.12%\n", - "Regularization Parameter: 0.0400, Score: 87.56%, Sparsity: 35.59%\n", - "Regularization Parameter: 0.0350, Score: 87.52%, Sparsity: 34.62%\n", - "variable=0.033, feedback=\n", - "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", - "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", - "\n", - "Most recent guess: \n", - "Regularization Parameter: 0.0330, Score: 87.77%, Sparsity: 42.09%\n", - "\n", - "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.67%, Sparsity: 80.70%\n", - "Regularization Parameter: 0.0100, Score: 86.08%, Sparsity: 61.81%\n", - "Regularization Parameter: 0.0200, Score: 87.20%, Sparsity: 48.80%\n", - "Regularization Parameter: 0.0300, Score: 87.64%, Sparsity: 43.12%\n", - "Regularization Parameter: 0.0400, Score: 87.56%, Sparsity: 35.59%\n", - "Regularization Parameter: 0.0350, Score: 87.52%, Sparsity: 34.62%\n", - "Regularization Parameter: 0.0320, Score: 87.78%, Sparsity: 45.03%\n", - "variable=0.0325, feedback=\n", - "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", - "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", - "\n", - "Most recent guess: \n", - "Regularization Parameter: 0.0325, Score: 87.43%, Sparsity: 37.84%\n", - "\n", - "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.67%, Sparsity: 80.70%\n", - "Regularization Parameter: 0.0100, Score: 86.08%, Sparsity: 61.81%\n", - "Regularization Parameter: 0.0200, Score: 87.20%, Sparsity: 48.80%\n", - "Regularization Parameter: 0.0300, Score: 87.64%, Sparsity: 43.12%\n", - "Regularization Parameter: 0.0400, Score: 87.56%, Sparsity: 35.59%\n", - "Regularization Parameter: 0.0350, Score: 87.52%, Sparsity: 34.62%\n", - "Regularization Parameter: 0.0320, Score: 87.78%, Sparsity: 45.03%\n", - "Regularization Parameter: 0.0330, Score: 87.77%, Sparsity: 42.09%\n", - "variable=0.0315, feedback=\n", - "Score is the accuracy of the classifier on the validation set, and should be maximized.\n", - "Sparsity is the percentage of zero coefficients in the classifier. If the classifier is overfit, a higher sparsity will yield a better score. If the classifier is underfit however, a lower sparsity will yield a better score.By lowering the regularization parameter (must always be positive), the sparsity will increase. By increasing the regularization parameter, the sparsity will decrease.\n", - "\n", - "Most recent guess: \n", - "Regularization Parameter: 0.0315, Score: 87.81%, Sparsity: 44.06%\n", - "\n", - "History of guesses:\n", - "Regularization Parameter: 0.0050, Score: 83.67%, Sparsity: 80.70%\n", - "Regularization Parameter: 0.0100, Score: 86.08%, Sparsity: 61.81%\n", - "Regularization Parameter: 0.0200, Score: 87.20%, Sparsity: 48.80%\n", - "Regularization Parameter: 0.0300, Score: 87.64%, Sparsity: 43.12%\n", - "Regularization Parameter: 0.0400, Score: 87.56%, Sparsity: 35.59%\n", - "Regularization Parameter: 0.0350, Score: 87.52%, Sparsity: 34.62%\n", - "Regularization Parameter: 0.0320, Score: 87.78%, Sparsity: 45.03%\n", - "Regularization Parameter: 0.0330, Score: 87.77%, Sparsity: 42.09%\n", - "Regularization Parameter: 0.0325, Score: 87.43%, Sparsity: 37.84%\n", - "Best regularization parameter: 0.0315\n", - "Best score: 87.805\n" - ] - } - ], - "source": [ - "x = node(0.005, trainable=True)\n", - "optimizer = FunctionOptimizer([x], config_list=autogen.config_list_from_json(\"OAI_CONFIG_LIST\"))\n", - "\n", - "history = []\n", - "bestScore = None\n", - "bestRegularization = None\n", - "for i in range(10):\n", - " classifier = train_classifier(x)\n", - " fb, score, sparsity = scorer(classifier.data, x.data, history)\n", - " history.append((x.data, score, sparsity))\n", - " print(f\"variable={x.data}, feedback={fb}\") # logging\n", - " if bestScore is None or score > bestScore:\n", - " bestScore = score\n", - " bestRegularization = x.data\n", - "\n", - " optimizer.zero_feedback()\n", - " optimizer.backward(classifier, fb)\n", - " optimizer.step()\n", - "\n", - "print(\"Best regularization parameter:\", bestRegularization)\n", - "print(\"Best score:\", bestScore)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.19" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}