diff --git a/examples/crawl_webpage.py b/examples/crawl_webpage.py index 35413d2ff..7dcbf7993 100644 --- a/examples/crawl_webpage.py +++ b/examples/crawl_webpage.py @@ -5,7 +5,7 @@ @File : crawl_webpage.py """ -from metagpt.roles.code_interpreter import CodeInterpreter +from metagpt.roles.ci.code_interpreter import CodeInterpreter async def main(): diff --git a/examples/imitate_webpage.py b/examples/imitate_webpage.py index b69101861..5075e1e39 100644 --- a/examples/imitate_webpage.py +++ b/examples/imitate_webpage.py @@ -5,7 +5,7 @@ @Author : mannaandpoem @File : imitate_webpage.py """ -from metagpt.roles.code_interpreter import CodeInterpreter +from metagpt.roles.ci.code_interpreter import CodeInterpreter async def main(): diff --git a/examples/sd_tool_usage.py b/examples/sd_tool_usage.py index 92f4cd5b0..b4642af23 100644 --- a/examples/sd_tool_usage.py +++ b/examples/sd_tool_usage.py @@ -4,7 +4,7 @@ # @Desc : import asyncio -from metagpt.roles.code_interpreter import CodeInterpreter +from metagpt.roles.ci.code_interpreter import CodeInterpreter async def main(requirement: str = ""): diff --git a/metagpt/actions/ci/ml_action.py b/metagpt/actions/ci/ml_action.py index 6fecae898..9640a7918 100644 --- a/metagpt/actions/ci/ml_action.py +++ b/metagpt/actions/ci/ml_action.py @@ -25,7 +25,6 @@ class WriteCodeWithToolsML(WriteCodeWithTools): tool_schemas, tool_type_usage_prompt = await self._prepare_tools(plan=plan) # ML-specific variables to be used in prompt - code_steps = plan.current_task.code_steps finished_tasks = plan.get_finished_tasks() code_context = [remove_comments(task.code) for task in finished_tasks] code_context = "\n\n".join(code_context) @@ -38,7 +37,6 @@ class WriteCodeWithToolsML(WriteCodeWithTools): current_task=plan.current_task.instruction, column_info=column_info, tool_type_usage_prompt=tool_type_usage_prompt, - code_steps=code_steps, tool_schemas=tool_schemas, ) @@ -49,7 +47,6 @@ class WriteCodeWithToolsML(WriteCodeWithTools): current_task=plan.current_task.instruction, column_info=column_info, tool_type_usage_prompt=tool_type_usage_prompt, - code_steps=code_steps, ) tool_config = create_func_call_config(CODE_GENERATOR_WITH_TOOLS) rsp = await self.llm.aask_code(prompt, **tool_config) diff --git a/metagpt/actions/ci/write_analysis_code.py b/metagpt/actions/ci/write_analysis_code.py index 4e4ea7953..38fe107fd 100644 --- a/metagpt/actions/ci/write_analysis_code.py +++ b/metagpt/actions/ci/write_analysis_code.py @@ -79,7 +79,6 @@ class WriteCodeWithTools(BaseWriteAnalysisCode): async def _recommend_tool( self, task: str, - code_steps: str, available_tools: dict, ) -> list: """ @@ -87,7 +86,6 @@ class WriteCodeWithTools(BaseWriteAnalysisCode): Args: task (str): the task to recommend tools for - code_steps (str): the code steps to generate the full code for the task available_tools (dict): the available tools description Returns: @@ -95,7 +93,6 @@ class WriteCodeWithTools(BaseWriteAnalysisCode): """ prompt = TOOL_RECOMMENDATION_PROMPT.format( current_task=task, - code_steps=code_steps, available_tools=available_tools, ) tool_config = create_func_call_config(SELECT_FUNCTION_TOOLS) @@ -132,8 +129,7 @@ class WriteCodeWithTools(BaseWriteAnalysisCode): available_tools = self._get_tools_by_type(tool_type) if available_tools: available_tools = {tool_name: tool.schemas["description"] for tool_name, tool in available_tools.items()} - code_steps = plan.current_task.code_steps - tool_schemas = await self._recommend_tool(plan.current_task.instruction, code_steps, available_tools) + tool_schemas = await self._recommend_tool(plan.current_task.instruction, available_tools) return tool_schemas, tool_type_usage_prompt diff --git a/metagpt/plan/__init__.py b/metagpt/plan/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/metagpt/prompts/ci/ml_action.py b/metagpt/prompts/ci/ml_action.py index 582b01146..46d419dfb 100644 --- a/metagpt/prompts/ci/ml_action.py +++ b/metagpt/prompts/ci/ml_action.py @@ -84,15 +84,11 @@ Latest data info after previous tasks: Write complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc. Specifically, {tool_type_usage_prompt} -# Code Steps: -Strictly follow steps below when you writing code if it's convenient. -{code_steps} - # Output Example: -when current task is "train a lightgbm model on training data", and their are two steps in 'Code Steps', the code be like: +when current task is "train a lightgbm model on training data", the code can be like: ```python # Step 1: check data type and convert to numeric -ojb_cols = train.select_dtypes(include='object').columns.tolist() +obj_cols = train.select_dtypes(include='object').columns.tolist() for col in obj_cols: encoder = LabelEncoder() @@ -107,7 +103,6 @@ model.fit(train, y_train) # Constraints: - Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed. -- The output code should contain all steps implemented in 'Code Steps'. """ ML_TOOL_USAGE_PROMPT = """ @@ -130,10 +125,6 @@ Latest data info after previous tasks: Write complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc. Specifically, {tool_type_usage_prompt} -# Code Steps: -Strictly follow steps below when you writing code if it's convenient. -{code_steps} - # Capabilities - You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class. - You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc.. @@ -143,7 +134,7 @@ Each Class tool is described in JSON format. When you call a tool, import the to {tool_schemas} # Output Example: -when current task is "do data preprocess, like fill missing value, handle outliers, etc.", and their are two steps in 'Code Steps', the code be like: +when current task is "do data preprocess, like fill missing value, handle outliers, etc.", the code can be like: ```python # Step 1: fill missing value # Tools used: ['FillMissingValue'] @@ -170,6 +161,4 @@ for col in num_cols: - Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed. - Always prioritize using pre-defined tools for the same functionality. - Always copy the DataFrame before processing it and use the copy to process. -- The output code should contain all steps implemented correctly in 'Code Steps'. """ -# - If 'Code Steps' contains step done in 'Done Tasks', such as reading data, don't repeat it. diff --git a/metagpt/prompts/ci/write_analysis_code.py b/metagpt/prompts/ci/write_analysis_code.py index 4c8a5081e..15d8b1443 100644 --- a/metagpt/prompts/ci/write_analysis_code.py +++ b/metagpt/prompts/ci/write_analysis_code.py @@ -30,8 +30,6 @@ TOOL_RECOMMENDATION_PROMPT = """ ## Task Recommend up to five tools from 'Available Tools' that can help solve the 'User Requirement'. -This is a detailed code steps for current task. You can refer to it when recommending tools. -{code_steps} ## Available Tools: {available_tools} diff --git a/metagpt/prompts/tool_types.py b/metagpt/prompts/tool_types.py index 381fb25ad..f27fbea99 100644 --- a/metagpt/prompts/tool_types.py +++ b/metagpt/prompts/tool_types.py @@ -14,10 +14,10 @@ The current task is about data preprocessing, please note the following: FEATURE_ENGINEERING_PROMPT = """ The current task is about feature engineering. when performing it, please adhere to the following principles: - Generate as diverse features as possible to improve the model's performance step-by-step. -- If potential impactful features are not included in 'Code Steps', add new steps to generate them. +- Use available feature engineering tools if they are potential impactful. - Avoid creating redundant or excessively numerous features in one step. - Exclude ID columns from feature generation and remove them. -- Each step do feature engineering to train, must do same for test separately at the same time. +- Each feature engineering operation performed on the train set must also applies to the test separately at the same time. - Avoid using the label column to create features, except for cat encoding. - Use the data from previous task result if exist, do not mock or reload data yourself. """ diff --git a/metagpt/roles/code_interpreter.py b/metagpt/roles/ci/code_interpreter.py similarity index 100% rename from metagpt/roles/code_interpreter.py rename to metagpt/roles/ci/code_interpreter.py diff --git a/metagpt/roles/ml_engineer.py b/metagpt/roles/ci/ml_engineer.py similarity index 97% rename from metagpt/roles/ml_engineer.py rename to metagpt/roles/ci/ml_engineer.py index c7702771d..6fa6fe7b2 100644 --- a/metagpt/roles/ml_engineer.py +++ b/metagpt/roles/ci/ml_engineer.py @@ -2,7 +2,7 @@ from metagpt.actions.ci.debug_code import DebugCode from metagpt.actions.ci.execute_nb_code import ExecuteNbCode from metagpt.actions.ci.ml_action import UpdateDataColumns, WriteCodeWithToolsML from metagpt.logs import logger -from metagpt.roles.code_interpreter import CodeInterpreter +from metagpt.roles.ci.code_interpreter import CodeInterpreter from metagpt.tools.tool_types import ToolTypes from metagpt.utils.common import any_to_str diff --git a/metagpt/roles/role.py b/metagpt/roles/role.py index bcfec708c..3938664ba 100644 --- a/metagpt/roles/role.py +++ b/metagpt/roles/role.py @@ -33,9 +33,9 @@ from metagpt.actions.add_requirement import UserRequirement from metagpt.context_mixin import ContextMixin from metagpt.logs import logger from metagpt.memory import Memory -from metagpt.plan.planner import Planner from metagpt.provider import HumanProvider from metagpt.schema import Message, MessageQueue, SerializationMixin +from metagpt.strategy.planner import Planner from metagpt.utils.common import any_to_name, any_to_str, role_raise_decorator from metagpt.utils.project_repo import ProjectRepo from metagpt.utils.repair_llm_raw_output import extract_state_value_from_output diff --git a/metagpt/schema.py b/metagpt/schema.py index 1b0be279c..15854f676 100644 --- a/metagpt/schema.py +++ b/metagpt/schema.py @@ -335,7 +335,6 @@ class Task(BaseModel): dependent_task_ids: list[str] = [] # Tasks prerequisite to this Task instruction: str = "" task_type: str = "" - code_steps: str = "" code: str = "" result: str = "" is_success: bool = False @@ -348,7 +347,6 @@ class Task(BaseModel): self.is_finished = False def update_task_result(self, task_result: TaskResult): - self.code_steps = task_result.code_steps self.code = task_result.code self.result = task_result.result self.is_success = task_result.is_success @@ -357,7 +355,6 @@ class Task(BaseModel): class TaskResult(BaseModel): """Result of taking a task, with result and is_success required to be filled""" - code_steps: str = "" code: str = "" result: str is_success: bool diff --git a/metagpt/plan/planner.py b/metagpt/strategy/planner.py similarity index 94% rename from metagpt/plan/planner.py rename to metagpt/strategy/planner.py index 1b3971b7d..bcb0bda9b 100644 --- a/metagpt/plan/planner.py +++ b/metagpt/strategy/planner.py @@ -124,11 +124,6 @@ class Planner(BaseModel): def get_useful_memories(self, task_exclude_field=None) -> list[Message]: """find useful memories only to reduce context length and improve performance""" - # TODO dataset description , code steps - if task_exclude_field is None: - # Shorten the context as we don't need code steps after we get the codes. - # This doesn't affect current_task below, which should hold the code steps - task_exclude_field = {"code_steps"} user_requirement = self.plan.goal context = self.plan.context tasks = [task.dict(exclude=task_exclude_field) for task in self.plan.tasks] diff --git a/tests/data/rsp_cache.json b/tests/data/rsp_cache.json index f92fb42c0..c5f2e9643 100644 --- a/tests/data/rsp_cache.json +++ b/tests/data/rsp_cache.json @@ -316,11 +316,17 @@ "\nRole: You are a senior development and qa engineer, your role is summarize the code running result.\nIf the running result does not include an error, you should explicitly approve the result.\nOn the other hand, if the running result indicates some error, you should point out which part, the development code or the test code, produces the error,\nand give specific instructions on fixing the errors. Here is the code info:\n\n## Development Code File Name\na.txt\n## Development Code\n```python\nresult = 'helloworld'\n```\n## Test File Name\n\n## Test Code\n```python\nNone\n```\n## Running Command\n\n## Running Output\nstandard output: \n```text\nhelloworld\n```\nstandard errors: \n```text\n\n```\n\nNow you should begin your analysis\n---\n## instruction:\nPlease summarize the cause of the errors and give correction instruction\n## File To Rewrite:\nDetermine the ONE file to rewrite in order to fix the error, for example, xyz.py, or test_xyz.py\n## Status:\nDetermine if all of the code works fine, if so write PASS, else FAIL,\nWRITE ONLY ONE WORD, PASS OR FAIL, IN THIS SECTION\n## Send To:\nPlease write NoOne if there are no errors, Engineer if the errors are due to problematic development codes, else QaEngineer,\nWRITE ONLY ONE WORD, NoOne OR Engineer OR QaEngineer, IN THIS SECTION.\n---\nYou should fill in necessary instruction, status, send to, and finally return all content between the --- segment line.\n": "---\n## instruction:\nNo errors detected. The development code runs successfully and outputs the expected result without any errors.\n## File To Rewrite:\nNone\n## Status:\nPASS\n## Send To:\nNoOne\n---", "\nRole: You are a senior development and qa engineer, your role is summarize the code running result.\nIf the running result does not include an error, you should explicitly approve the result.\nOn the other hand, if the running result indicates some error, you should point out which part, the development code or the test code, produces the error,\nand give specific instructions on fixing the errors. Here is the code info:\n\n## Development Code File Name\na.sh\n## Development Code\n```python\necho 'Hello World'\n```\n## Test File Name\n\n## Test Code\n```python\nNone\n```\n## Running Command\necho Hello World\n## Running Output\nstandard output: \n```text\nHello World\n\n```\nstandard errors: \n```text\n\n```\n\nNow you should begin your analysis\n---\n## instruction:\nPlease summarize the cause of the errors and give correction instruction\n## File To Rewrite:\nDetermine the ONE file to rewrite in order to fix the error, for example, xyz.py, or test_xyz.py\n## Status:\nDetermine if all of the code works fine, if so write PASS, else FAIL,\nWRITE ONLY ONE WORD, PASS OR FAIL, IN THIS SECTION\n## Send To:\nPlease write NoOne if there are no errors, Engineer if the errors are due to problematic development codes, else QaEngineer,\nWRITE ONLY ONE WORD, NoOne OR Engineer OR QaEngineer, IN THIS SECTION.\n---\nYou should fill in necessary instruction, status, send to, and finally return all content between the --- segment line.\n": "---\n## instruction:\nNo errors detected in the running result. The development code executed successfully, and the output matches the expected result. Since there is no test code provided, no test execution was performed. Therefore, no specific instructions are needed for correction.\n## File To Rewrite:\nNone\n## Status:\nPASS\n## Send To:\nNoOne\n---", "\nRole: You are a senior development and qa engineer, your role is summarize the code running result.\nIf the running result does not include an error, you should explicitly approve the result.\nOn the other hand, if the running result indicates some error, you should point out which part, the development code or the test code, produces the error,\nand give specific instructions on fixing the errors. Here is the code info:\n\n## Development Code File Name\na.py\n## Development Code\n```python\npython -c \"print(1/0)\"\n```\n## Test File Name\n\n## Test Code\n```python\nNone\n```\n## Running Command\npython -c print(1/0)\n## Running Output\nstandard output: \n```text\n\n```\nstandard errors: \n```text\nTraceback (most recent call last):\n File \"\", line 1, in \nZeroDivisionError: division by zero\n\n```\n\nNow you should begin your analysis\n---\n## instruction:\nPlease summarize the cause of the errors and give correction instruction\n## File To Rewrite:\nDetermine the ONE file to rewrite in order to fix the error, for example, xyz.py, or test_xyz.py\n## Status:\nDetermine if all of the code works fine, if so write PASS, else FAIL,\nWRITE ONLY ONE WORD, PASS OR FAIL, IN THIS SECTION\n## Send To:\nPlease write NoOne if there are no errors, Engineer if the errors are due to problematic development codes, else QaEngineer,\nWRITE ONLY ONE WORD, NoOne OR Engineer OR QaEngineer, IN THIS SECTION.\n---\nYou should fill in necessary instruction, status, send to, and finally return all content between the --- segment line.\n": "---\n## instruction:\nThe error is caused by attempting to divide by zero in the development code. To fix this error, you should modify the division operation to avoid division by zero. For example, you can add a condition to check if the denominator is zero before performing the division.\n\n## File To Rewrite:\na.py\n\n## Status:\nFAIL\n\n## Send To:\nEngineer\n---", - "[{\"role\": \"system\", \"content\": \"You are an AI Python assistant. You will be given your previous implementation code of a task, runtime error results, and a hint to change the implementation appropriately. Write your full implementation \"}, {\"role\": \"user\", \"content\": \"\\nHere is an example for you.\\n\\nExample 1:\\n[previous impl]:\\n```python\\ndef add(a: int, b: int) -> int:\\n \\\"\\\"\\\"\\n Given integers a and b, return the total value of a and b.\\n \\\"\\\"\\\"\\n return a - b\\n```\\n\\n[runtime Error]:\\nTested passed:\\n\\nTests failed:\\nassert add(1, 2) == 3 # output: -1\\nassert add(1, 2) == 4 # output: -1\\n\\n[reflection on previous impl]:\\nThe implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input.\\n\\n[improved impl]:\\n```python\\ndef add(a: int, b: int) -> int:\\n \\\"\\\"\\\"\\n Given integers a and b, return the total value of a and b.\\n \\\"\\\"\\\"\\n return a + b\\n```\\n\\n[context]\\nuser: Solve the problem in Python:\\ndef sort_array(arr):\\n \\\"\\\"\\\"\\n In this Kata, you have to sort an array of non-negative integers according to\\n number of ones in their binary representation in ascending order.\\n For similar number of ones, sort based on decimal value.\\n\\n It must be implemented like this:\\n >>> sort_array([1, 5, 2, 3, 4]) == [1, 2, 3, 4, 5]\\n >>> sort_array([-2, -3, -4, -5, -6]) == [-6, -5, -4, -3, -2]\\n >>> sort_array([1, 0, 2, 3, 4]) [0, 1, 2, 3, 4]\\n \\\"\\\"\\\"\\n\\n\\n[previous impl]\\n\\ndef sort_array(arr):\\n # Helper function to count the number of ones in the binary representation\\n def count_ones(n):\\n return bin(n).count('1')\\n \\n # Sort the array using a custom key function\\n # The key function returns a tuple (number of ones, value) for each element\\n # This ensures that if two elements have the same number of ones, they are sorted by their value\\n sorted_arr = sorted(arr, key=lambda x: (count_ones(x), x))\\n \\n return sorted_arr\\n```\\n\\n[runtime Error]\\nTested passed:\\n\\nTests failed:\\nassert sort_array([1, 5, 2, 3, 4]) == [1, 2, 3, 4, 5] # output: [1, 2, 4, 3, 5]\\n\\n\\nAnalysis the error step by step, provide me improve method and code. Remember to follow [context] requirement. Don't forget write code for steps behind the error step.\\n[reflection on previous impl]:\\nxxx\\n\"}]": { - "reflection": "The implementation failed the test case where the input array is [1, 5, 2, 3, 4]. The issue arises because the code does not handle negative numbers correctly. The binary representation of negative numbers in Python uses two's complement, which is not what we want for this sorting task. To fix this issue, we should modify the `count_ones` function to only work with non-negative numbers and ignore negative numbers. We should also ensure that negative numbers are sorted based on their decimal value, as mentioned in the context.", - "improved_impl": "def sort_array(arr):\n # Helper function to count the number of ones in the binary representation\n def count_ones(n):\n # Only count ones for non-negative integers\n return bin(n).count('1') if n >= 0 else 0\n \n # Separate the array into non-negative and negative numbers\n non_negatives = [x for x in arr if x >= 0]\n negatives = [x for x in arr if x < 0]\n \n # Sort non-negative numbers using a custom key function\n sorted_non_negatives = sorted(non_negatives, key=lambda x: (count_ones(x), x))\n \n # Sort negative numbers based on their decimal value\n sorted_negatives = sorted(negatives)\n \n # Combine the sorted non-negative and negative numbers\n sorted_arr = sorted_non_negatives + sorted_negatives\n \n return sorted_arr" + "[{\"role\": \"user\", \"content\": \"Interface definition:\\n```text\\nInterface Name: Element Tagging\\nInterface Path: /projects/{project_key}/node-tags\\nMethod: POST\\n\\nRequest parameters:\\nPath parameters:\\nproject_key\\n\\nBody parameters:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\nnodes\\tarray\\tYes\\t\\tNodes\\n\\tnode_key\\tstring\\tNo\\t\\tNode key\\n\\ttags\\tarray\\tNo\\t\\tOriginal node tag list\\n\\tnode_type\\tstring\\tNo\\t\\tNode type DATASET / RECIPE\\noperations\\tarray\\tYes\\t\\t\\n\\ttags\\tarray\\tNo\\t\\tOperation tag list\\n\\tmode\\tstring\\tNo\\t\\tOperation type ADD / DELETE\\n\\nReturn data:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\ncode\\tinteger\\tYes\\t\\tStatus code\\nmsg\\tstring\\tYes\\t\\tPrompt message\\ndata\\tobject\\tYes\\t\\tReturned data\\nlist\\tarray\\tNo\\t\\tNode list true / false\\nnode_type\\tstring\\tNo\\t\\tNode type DATASET / RECIPE\\nnode_key\\tstring\\tNo\\t\\tNode key\\n```\\n\\nUnit test：\\n```python\\n@pytest.mark.parametrize(\\n\\\"project_key, nodes, operations, expected_msg\\\",\\n[\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"success\\\"),\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"dataset_002\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"tag1\\\"], \\\"mode\\\": \\\"DELETE\\\"}], \\\"success\\\"),\\n(\\\"\\\", [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Missing the required parameter project_key\\\"),\\n(123, [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Incorrect parameter type\\\"),\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"a\\\"*201, \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Request parameter exceeds field boundary\\\")\\n]\\n)\\ndef test_node_tags(project_key, nodes, operations, expected_msg):\\n pass\\n\\n# The above is an interface definition and a unit test example.\\n# Next, please play the role of an expert test manager with 20 years of experience at Google. When I give the interface definition, \\n# reply to me with a unit test. There are several requirements:\\n# 1. Only output one `@pytest.mark.parametrize` and the corresponding test_ function (inside pass, do not implement).\\n# -- The function parameter contains expected_msg for result verification.\\n# 2. The generated test cases use shorter text or numbers and are as compact as possible.\\n# 3. If comments are needed, use Chinese.\\n\\n# If you understand, please wait for me to give the interface definition and just answer \\\"Understood\\\" to save tokens.\\n\"}, {\"role\": \"user\", \"content\": \"Refer to the test types: such as SQL injection, cross-site scripting (XSS), unauthorized access and privilege escalation, \\nauthentication and authorization, parameter verification, exception handling, file upload and download.\\nPlease output 10 test cases within one `@pytest.mark.parametrize` scope.\\n```text\\nAPI Name: 获取 model 详情(job专用-后续开放给sdk)\\nAPI Path: /v1/projects/{project_key}/jobs/{job_id}/models/{model_key}\\nMethod: GET\\n\\nRequest Parameters:\\nPath Parameters:\\nproject_key \\njob_id \\nmodel_key \\n\\nBody Parameters:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\nproject_key\\tstring\\tYes\\t\\t\\njob_id\\tstring\\tYes\\t\\t\\nmodel_key\\tstring\\tYes\\t\\t\\n\\nResponse Data:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\ncode\\tnumber\\tYes\\t\\t0成功，非0失败\\nmsg\\tstring\\tYes\\t\\t如果失败，这里有错误信息\\ndata\\tobject\\tYes\\t\\tdata信息\\n\\tproject_key\\tstring\\tNo\\t\\tproject key\\n\\tname\\tstring\\tNo\\t\\t用户可修改的name\\n\\tmodel\\tobject\\tNo\\t\\tmodel信息\\n\\t\\ttype\\tstring\\tNo\\t\\tdataset type\\n\\t\\tmanaged\\tboolean\\tNo\\t\\t为false时是第一类dataset，数据不可删除\\n\\t\\tname\\tstring\\tNo\\t\\t用户可修改的name\\n\\t\\tproject_key\\tstring\\tNo\\t\\tproject key\\n\\t\\tformat_type\\tstring\\tNo\\t\\t文件类型的dataset才有这项。“csv”\\n\\t\\tflow_options\\tobject\\tNo\\t\\t创建dataset时的高级设置\\n\\t\\t\\tvirtualizable\\tboolean\\tNo\\t\\t高级设置里的参数。缺省false\\n\\t\\t\\trebuild_behavior\\tstring\\tNo\\t\\t高级设置里的参数。缺省NORMAL\\n\\t\\t\\tcross_project_build_behavior\\tstring\\tNo\\t\\t高级设置里的参数。缺省DEFAULT\\n\\t\\tformat_params\\tobject\\tNo\\t\\t文件类型的dataset才有\\n\\t\\t\\tstyle\\tstring\\tNo\\t\\t\\n\\t\\t\\tcharset\\tstring\\tNo\\t\\t\\n\\t\\t\\tseparator\\tstring\\tNo\\t\\t\\n\\t\\t\\tquote_char\\tstring\\tNo\\t\\t\\n\\t\\t\\tescape_char\\tstring\\tNo\\t\\t\\n\\t\\t\\tdate_serialization_format\\tstring\\tNo\\t\\t\\n\\t\\t\\tarray_map_format\\tstring\\tNo\\t\\t\\n\\t\\t\\thive_separators\\tarray\\tNo\\t\\t\\n\\t\\t\\tskip_rows_before_header\\tnumber\\tNo\\t\\t\\n\\t\\t\\tparse_header_row\\tboolean\\tNo\\t\\t\\n\\t\\t\\tskip_rows_after_header\\tnumber\\tNo\\t\\t\\n\\t\\t\\tprobable_number_of_records\\tnumber\\tNo\\t\\t\\n\\t\\t\\tnormalize_booleans\\tboolean\\tNo\\t\\t\\n\\t\\t\\tnormalize_doubles\\tboolean\\tNo\\t\\t\\n\\t\\ttags\\tarray\\tNo\\t\\t标签tags\\n\\t\\tparams\\tobject\\tNo\\t\\t必有这项，但不同类型的dataset里面的key有差别\\n\\t\\t\\tconnection\\tstring\\tNo\\t\\tconnection id,到db查其他参数\\n\\t\\t\\tpath\\tstring\\tNo\\t\\t文件类connection才有这项\\n\\t\\t\\ttable\\tstring\\tNo\\t\\tdb表名，DB类connection才有这项\\n\\t\\t\\tmode\\tstring\\tNo\\t\\t存储类型，比如“table\\\",DB类connection才有这项\\n\\t\\t\\tbucket\\tstring\\tNo\\t\\tS3类型的connection才有这项\\n\\t\\t\\tkey_name\\tstring\\tNo\\t\\tredis才有，key name\\n\\t\\t\\tkey_type\\tstring\\tNo\\t\\tredis才有，key type\\n\\t\\t\\tcollection\\tstring\\tNo\\t\\t非关系型数据库才有，collection name\\n\\t\\t\\tindex\\tstring\\tNo\\t\\t索引类型的才有这项\\n\\t\\t\\tnot_ready_if_empty\\tboolean\\tNo\\t\\t数据非空才认为是data ready\\n\\t\\t\\tfiles_selection_rules\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tmode\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\texclude_rules\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\tinclude_rules\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\texplicit_files\\tarray\\tNo\\t\\t\\n\\t\\tschema\\tobject\\tNo\\t\\tcolumns信息在这里\\n\\t\\t\\tcolumns\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\tname\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\ttype\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\torigin_type\\tstring\\tNo\\t\\t\\n\\t\\t\\tuser_modified\\tboolean\\tNo\\t\\t\\n\\t\\tcustom_fields\\tobject\\tNo\\t\\t自定义fields\\n\\t\\tlast_build\\tobject\\tNo\\t\\t最后一次构建的信息\\n\\t\\t\\tproject_key\\tstring\\tNo\\t\\tproject key\\n\\t\\t\\tid\\tstring\\tNo\\t\\tactivity id\\n\\t\\t\\tjob_id\\tstring\\tNo\\t\\tjob id\\n\\t\\t\\tjob_project_key\\tstring\\tNo\\t\\t\\n\\t\\t\\tbuild_start_time\\tnumber\\tNo\\t\\t构建开始时间\\n\\t\\t\\tbuild_end_time\\tnumber\\tNo\\t\\t构建结束时间\\n\\t\\t\\tbuild_success\\tstring\\tNo\\t\\tsuccess或failed\\n\\t\\tobject_key\\tstring\\tNo\\t\\tdataset_key，后台用的id，用户不可见不可改\\n\\t\\tcache\\tobject\\tNo\\t\\t下载缓存数据链接\\n\\t\\t\\ts3_path\\tstring\\tNo\\t\\t\\n\\tstatus\\tobject\\tNo\\t\\t数据状态\\n\\t\\tsize\\tobject\\tNo\\t\\t数据大小信息\\n\\t\\t\\ttotal_value\\tnumber\\tNo\\t\\t占多少字节磁盘\\n\\t\\t\\tlast_computed\\tnumber\\tNo\\t\\t\\n\\t\\t\\tfirst_computed\\tnumber\\tNo\\t\\t\\n\\t\\t\\thas_data\\tboolean\\tNo\\t\\t是否有数据，这个影响前端的图标显示\\n\\t\\t\\tincomplete\\tboolean\\tNo\\t\\t\\n\\t\\trecords\\tobject\\tNo\\t\\t\\n\\t\\t\\ttotal_value\\tnumber\\tNo\\t\\t\\n\\t\\t\\tlast_computed\\tnumber\\tNo\\t\\t\\n\\t\\t\\tfirst_computed\\tnumber\\tNo\\t\\t\\n\\t\\t\\thas_data\\tboolean\\tNo\\t\\t是否有数据，这个影响前端的图标显示\\n\\t\\t\\tincomplete\\tboolean\\tNo\\t\\t\\n\\t\\tpartitions_last_compute\\tnumber\\tNo\\t\\t\\n\\t\\tpartitions\\tnumber\\tNo\\t\\t\\n\\tbuildable\\tboolean\\tNo\\t\\t有recipe时为true\\n\\theaders\\tarray\\tNo\\t\\t\\n\\t\\tdataset_schema\\tobject\\tNo\\t\\t\\n\\t\\t\\tname\\tstring\\tNo\\t字段名称\\t\\n\\t\\t\\ttype\\tstring\\tNo\\t字段类型\\t\\n\\t\\tnormal_rate\\tobject\\tNo\\t缺失值统计信息\\t\\n\\n```\"}]": { + "code": "import string\nimport random\n\ndef random_string(length=10):\n return ''.join(random.choice(string.ascii_lowercase) for i in range(length))" }, - "[{\"role\": \"user\", \"content\": \"\\n## User Requirement:\\n对数据集进行数据清洗\\n\\n## Task\\nRecommend up to five tools from 'Available Tools' that can help solve the 'User Requirement'. \\nThis is a detailed code steps for current task. You can refer to it when recommending tools.\\n\\n\\n## Available Tools:\\n{'FillMissingValue': 'Completing missing values with simple strategies.', 'MinMaxScale': 'Transform features by scaling each feature to a range, which is (0, 1).', 'StandardScale': 'Standardize features by removing the mean and scaling to unit variance.', 'MaxAbsScale': 'Scale each feature by its maximum absolute value.', 'RobustScale': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'OrdinalEncode': 'Encode categorical features as ordinal integers.', 'OneHotEncode': 'Apply one-hot encoding to specified categorical columns, the original columns will be dropped.', 'LabelEncode': 'Apply label encoding to specified categorical columns in-place.'}\\n\\n## Tool Selection and Instructions:\\n- Select tools most relevant to completing the 'User Requirement'.\\n- If you believe that no tools are suitable, indicate with an empty list.\\n- Only list the names of the tools, not the full schema of each tool.\\n- Ensure selected tools are listed in 'Available Tools'.\\n\"}]": { + "[{\"role\": \"user\", \"content\": \"Interface definition:\\n```text\\nInterface Name: Element Tagging\\nInterface Path: /projects/{project_key}/node-tags\\nMethod: POST\\n\\nRequest parameters:\\nPath parameters:\\nproject_key\\n\\nBody parameters:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\nnodes\\tarray\\tYes\\t\\tNodes\\n\\tnode_key\\tstring\\tNo\\t\\tNode key\\n\\ttags\\tarray\\tNo\\t\\tOriginal node tag list\\n\\tnode_type\\tstring\\tNo\\t\\tNode type DATASET / RECIPE\\noperations\\tarray\\tYes\\t\\t\\n\\ttags\\tarray\\tNo\\t\\tOperation tag list\\n\\tmode\\tstring\\tNo\\t\\tOperation type ADD / DELETE\\n\\nReturn data:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\ncode\\tinteger\\tYes\\t\\tStatus code\\nmsg\\tstring\\tYes\\t\\tPrompt message\\ndata\\tobject\\tYes\\t\\tReturned data\\nlist\\tarray\\tNo\\t\\tNode list true / false\\nnode_type\\tstring\\tNo\\t\\tNode type DATASET / RECIPE\\nnode_key\\tstring\\tNo\\t\\tNode key\\n```\\n\\nUnit test：\\n```python\\n@pytest.mark.parametrize(\\n\\\"project_key, nodes, operations, expected_msg\\\",\\n[\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"success\\\"),\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"dataset_002\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"tag1\\\"], \\\"mode\\\": \\\"DELETE\\\"}], \\\"success\\\"),\\n(\\\"\\\", [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Missing the required parameter project_key\\\"),\\n(123, [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Incorrect parameter type\\\"),\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"a\\\"*201, \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Request parameter exceeds field boundary\\\")\\n]\\n)\\ndef test_node_tags(project_key, nodes, operations, expected_msg):\\n pass\\n\\n# The above is an interface definition and a unit test example.\\n# Next, please play the role of an expert test manager with 20 years of experience at Google. When I give the interface definition, \\n# reply to me with a unit test. There are several requirements:\\n# 1. Only output one `@pytest.mark.parametrize` and the corresponding test_ function (inside pass, do not implement).\\n# -- The function parameter contains expected_msg for result verification.\\n# 2. The generated test cases use shorter text or numbers and are as compact as possible.\\n# 3. If comments are needed, use Chinese.\\n\\n# If you understand, please wait for me to give the interface definition and just answer \\\"Understood\\\" to save tokens.\\n\"}, {\"role\": \"user\", \"content\": \"Refer to the test types: such as SQL injection, cross-site scripting (XSS), unauthorized access and privilege escalation, \\nauthentication and authorization, parameter verification, exception handling, file upload and download.\\nPlease output 10 test cases within one `@pytest.mark.parametrize` scope.\\n```text\\nAPI Name: 获取managed folder详情（job专用)\\nAPI Path: /v1/projects/{project_key}/jobs/{job_id}/folders/{folder_key}\\nMethod: GET\\n\\nRequest Parameters:\\nPath Parameters:\\nproject_key \\njob_id \\nfolder_key \\n\\nBody Parameters:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\nproject_key\\tstring\\tYes\\t\\t\\njob_id\\tstring\\tYes\\t\\t\\nfolder_key\\tstring\\tYes\\t\\t\\n\\nResponse Data:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\ncode\\tnumber\\tYes\\t\\t0成功，非0失败\\nmsg\\tstring\\tYes\\t\\t失败时这里有错误信息\\ndata\\tobject\\tYes\\t\\t\\n\\tproject_key\\tstring\\tNo\\t\\tproject key\\n\\tfolder\\tobject\\tNo\\t\\tfolder配置在这里\\n\\t\\tproject_key\\tstring\\tNo\\t\\tproject key\\n\\t\\tobject_key\\tstring\\tNo\\t\\tobject key\\n\\t\\tname\\tstring\\tNo\\t\\t用户可编辑的那个name\\n\\t\\ttype\\tstring\\tNo\\t\\tfolder类型，与connection有关\\n\\t\\tparams\\tobject\\tNo\\t\\t数据读写相关配置在这里\\n\\t\\t\\tconnection\\tstring\\tNo\\t\\tconnection id\\n\\t\\t\\tpath\\tstring\\tNo\\t\\t文件夹内容存放的相对路径\\n\\t\\t\\tnot_ready_if_empty\\tboolean\\tNo\\t\\treserved\\n\\t\\t\\tfiles_selection_rules\\tobject\\tNo\\t\\t文件过滤规则\\n\\t\\t\\t\\tmode\\tstring\\tNo\\t\\tALL\\n\\t\\t\\t\\texclude_rules\\tarray\\tNo\\t\\t排除规则\\n\\t\\t\\t\\tinclude_rules\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\texplicit_files\\tarray\\tNo\\t\\t\\n\\t\\tflow_options\\tobject\\tNo\\t\\tflow参数\\n\\t\\t\\tvirtualizable\\tboolean\\tNo\\t\\t\\n\\t\\t\\trebuild_behavior\\tstring\\tNo\\t\\t构建方式\\n\\t\\t\\tcross_project_build_behavior\\tstring\\tNo\\t\\t\\n\\t\\tmetrics\\tobject\\tNo\\t\\t\\n\\t\\t\\tprobes\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\ttype\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\tenabled\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\tcompute_on_build_mode\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\tmeta\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tname\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\t\\tlevel\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\tconfiguration\\tobject\\tNo\\t\\t\\n\\t\\t\\tengine_config\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tpad_runs_with_metrics\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\thive\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tactive\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\textra_conf\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\tbasic\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tdss\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tactive\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\tselection\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tuse_mem_table\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tfilter\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\t\\tdistinct\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\t\\tenabled\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tpartition_selection_method\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tlatest_partitions_n\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tordering\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\t\\tenabled\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\t\\trules\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tsampling_method\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tmax_records\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\ttarget_ratio\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\twithin_first_n\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tmax_read_uncompressed_bytes\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\tsql\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tactive\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\timpala\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tactive\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\tspark\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tactive\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\textra_conf\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\tpython\\tobject\\tNo\\t\\t\\n\\t\\t\\tdisplayed_state\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tpartition\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\tcolumns\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\tmetrics\\tarray\\tNo\\t\\t\\n\\t\\tchecks\\tobject\\tNo\\t\\t\\n\\t\\t\\trun_on_build\\tboolean\\tNo\\t\\t\\n\\t\\t\\tchecks\\tarray\\tNo\\t\\t\\n\\t\\t\\tdisplayed_state\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tpartition\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\tchecks\\tarray\\tNo\\t\\t\\n\\t\\tversion_tag\\tobject\\tNo\\t\\t配置版本信息\\n\\t\\t\\tversion_number\\tnumber\\tNo\\t\\t\\n\\t\\t\\tlast_modified_by\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tlogin\\tstring\\tNo\\t\\t\\n\\t\\t\\tlast_modified_on\\tnumber\\tNo\\t\\t修改时间unix time ms\\n\\t\\tcreation_tag\\tobject\\tNo\\t\\t配置创建时间\\n\\t\\t\\tversion_number\\tnumber\\tNo\\t\\t1\\n\\t\\t\\tlast_modified_by\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tlogin\\tstring\\tNo\\t\\t\\n\\t\\t\\tlast_modified_on\\tnumber\\tNo\\t\\t创建时间unix time ms\\n\\t\\ttags\\tarray\\tNo\\t\\t文件夹标签\\n\\t\\tcustom_fields\\tobject\\tNo\\t\\t\\n\\t\\tchecklists\\tobject\\tNo\\t\\t\\n\\t\\t\\tchecklists\\tarray\\tNo\\t\\t\\n\\n```\"}]": { + "code": "import string\nimport random\n\ndef random_string(length=10):\n return ''.join(random.choice(string.ascii_lowercase) for i in range(length))" + }, + "[{\"role\": \"system\", \"content\": \"You are an AI Python assistant. You will be given your previous implementation code of a task, runtime error results, and a hint to change the implementation appropriately. Write your full implementation \"}, {\"role\": \"user\", \"content\": \"\\nHere is an example for you.\\n\\nExample 1:\\n[previous impl]:\\n```python\\ndef add(a: int, b: int) -> int:\\n \\\"\\\"\\\"\\n Given integers a and b, return the total value of a and b.\\n \\\"\\\"\\\"\\n return a - b\\n```\\n\\n[runtime Error]:\\nTested passed:\\n\\nTests failed:\\nassert add(1, 2) == 3 # output: -1\\nassert add(1, 2) == 4 # output: -1\\n\\n[reflection on previous impl]:\\nThe implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input.\\n\\n[improved impl]:\\n```python\\ndef add(a: int, b: int) -> int:\\n \\\"\\\"\\\"\\n Given integers a and b, return the total value of a and b.\\n \\\"\\\"\\\"\\n return a + b\\n```\\n\\n[context]\\nuser: Solve the problem in Python:\\ndef sort_array(arr):\\n \\\"\\\"\\\"\\n In this Kata, you have to sort an array of non-negative integers according to\\n number of ones in their binary representation in ascending order.\\n For similar number of ones, sort based on decimal value.\\n\\n It must be implemented like this:\\n >>> sort_array([1, 5, 2, 3, 4]) == [1, 2, 3, 4, 5]\\n >>> sort_array([-2, -3, -4, -5, -6]) == [-6, -5, -4, -3, -2]\\n >>> sort_array([1, 0, 2, 3, 4]) [0, 1, 2, 3, 4]\\n \\\"\\\"\\\"\\n\\n\\n[previous impl]\\n\\ndef sort_array(arr):\\n # Helper function to count the number of ones in the binary representation\\n def count_ones(n):\\n return bin(n).count('1')\\n \\n # Sort the array using a custom key function\\n # The key function returns a tuple (number of ones, value) for each element\\n # This ensures that if two elements have the same number of ones, they are sorted by their value\\n sorted_arr = sorted(arr, key=lambda x: (count_ones(x), x))\\n \\n return sorted_arr\\n```\\n\\n[runtime Error]\\nTested passed:\\n\\nTests failed:\\nassert sort_array([1, 5, 2, 3, 4]) == [1, 2, 3, 4, 5] # output: [1, 2, 4, 3, 5]\\n\\n\\nAnalysis the error step by step, provide me improve method and code. Remember to follow [context] requirement. Don't forget write code for steps behind the error step.\\n[reflection on previous impl]:\\nxxx\\n\"}]": { + "reflection": "The implementation failed the test case where the input array is [1, 5, 2, 3, 4]. The issue arises because the code does not correctly sort the array based on the number of ones in their binary representation and then by their decimal value. The current implementation sorts the array primarily by the number of ones in the binary representation, but it does not handle the secondary sorting by decimal value correctly for negative numbers, as it does not account for the fact that the binary representation of negative numbers in Python is different due to two's complement representation. To fix this issue, we should modify the count_ones function to correctly handle negative numbers by taking the absolute value before converting to binary and counting ones. Additionally, we should ensure that the array is sorted by the absolute value when the number of ones is the same.", + "improved_impl": "def sort_array(arr):\n # Helper function to count the number of ones in the binary representation\n def count_ones(n):\n return bin(abs(n)).count('1')\n \n # Sort the array using a custom key function\n # The key function returns a tuple (number of ones, absolute value, original value) for each element\n # This ensures that if two elements have the same number of ones, they are sorted by their absolute value\n sorted_arr = sorted(arr, key=lambda x: (count_ones(x), abs(x), x))\n \n return sorted_arr" + }, + "[{\"role\": \"user\", \"content\": \"\\n## User Requirement:\\n对数据集进行数据清洗\\n\\n## Task\\nRecommend up to five tools from 'Available Tools' that can help solve the 'User Requirement'. \\n\\n## Available Tools:\\n{'FillMissingValue': 'Completing missing values with simple strategies.', 'MinMaxScale': 'Transform features by scaling each feature to a range, which is (0, 1).', 'StandardScale': 'Standardize features by removing the mean and scaling to unit variance.', 'MaxAbsScale': 'Scale each feature by its maximum absolute value.', 'RobustScale': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'OrdinalEncode': 'Encode categorical features as ordinal integers.', 'OneHotEncode': 'Apply one-hot encoding to specified categorical columns, the original columns will be dropped.', 'LabelEncode': 'Apply label encoding to specified categorical columns in-place.'}\\n\\n## Tool Selection and Instructions:\\n- Select tools most relevant to completing the 'User Requirement'.\\n- If you believe that no tools are suitable, indicate with an empty list.\\n- Only list the names of the tools, not the full schema of each tool.\\n- Ensure selected tools are listed in 'Available Tools'.\\n\"}]": { "recommend_tools": [ "FillMissingValue", "MinMaxScale", @@ -329,16 +335,16 @@ "RobustScale" ] }, - "[{\"role\": \"user\", \"content\": \"\\n# Background\\nAs a data scientist, you need to help user to achieve their goal [构造数据集并进行数据清洗] step-by-step in an continuous Jupyter notebook.\\n\\n## Done Tasks\\n```python\\n import pandas as pd\\n df = pd.DataFrame({\\n 'a': [1, 2, 3, 4, 5],\\n 'b': [1.1, 2.2, 3.3, 4.4, np.nan],\\n 'c': ['aa', 'bb', 'cc', 'dd', 'ee'],\\n 'd': [1, 2, 3, 4, 5]\\n })\\n```end\\n\\n## Current Task\\n对数据集进行数据清洗\\n\\n# Latest Data Info\\nLatest data info after previous tasks:\\n\\n\\n# Task\\nWrite complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Code Steps:\\nStrictly follow steps below when you writing code if it's convenient.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools:\\nEach Class tool is described in JSON format. When you call a tool, import the tool from its path first.\\n{'FillMissingValue': {'type': 'class', 'description': 'Completing missing values with simple strategies.', 'methods': {'__init__': {'description': 'Initialize self. ', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}, 'strategy': {'type': 'str', 'description': \\\"The imputation strategy, notice 'mean' and 'median' can only be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.\\\", 'default': \\\"'mean'\\\", 'enum': [\\\"'mean'\\\", \\\"'median'\\\", \\\"'most_frequent'\\\", \\\"'constant'\\\"]}, 'fill_value': {'type': 'int', 'description': 'Fill_value is used to replace all occurrences of missing_values. Defaults to None.', 'default': 'None'}}, 'required': ['features']}}, 'fit': {'description': 'Fit the FillMissingValue model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MinMaxScale': {'type': 'class', 'description': 'Transform features by scaling each feature to a range, which is (0, 1).', 'methods': {'__init__': {'description': 'Initialize self. ', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit the MinMaxScale model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'StandardScale': {'type': 'class', 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'methods': {'__init__': {'description': 'Initialize self. ', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit the StandardScale model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MaxAbsScale': {'type': 'class', 'description': 'Scale each feature by its maximum absolute value.', 'methods': {'__init__': {'description': 'Initialize self. ', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit the MaxAbsScale model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'RobustScale': {'type': 'class', 'description': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'methods': {'__init__': {'description': 'Initialize the RobustScale instance with feature names. ', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'List of feature names to be scaled.'}}, 'required': ['features']}}, 'fit': {'description': 'Compute the median and IQR for scaling. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'Dataframe containing the features.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Scale features using the previously computed median and IQR. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'Dataframe containing the features to be scaled.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'A new dataframe with scaled features.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}}\\n\\n# Output Example:\\nwhen current task is \\\"do data preprocess, like fill missing value, handle outliers, etc.\\\", and their are two steps in 'Code Steps', the code be like:\\n```python\\n# Step 1: fill missing value\\n# Tools used: ['FillMissingValue']\\nfrom metagpt.tools.libs.data_preprocess import FillMissingValue\\n\\ntrain_processed = train.copy()\\ntest_processed = test.copy()\\nnum_cols = train_processed.select_dtypes(include='number').columns.tolist()\\nif 'label' in num_cols:\\n num_cols.remove('label')\\nfill_missing_value = FillMissingValue(features=num_cols, strategy='mean')\\nfill_missing_value.fit(train_processed)\\ntrain_processed = fill_missing_value.transform(train_processed)\\ntest_processed = fill_missing_value.transform(test_processed)\\n\\n# Step 2: handle outliers\\nfor col in num_cols:\\n low, high = train_processed[col].quantile([0.01, 0.99])\\n train_processed[col] = train_processed[col].clip(low, high)\\n test_processed[col] = test_processed[col].clip(low, high)\\n```end\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n- Always copy the DataFrame before processing it and use the copy to process.\\n- The output code should contain all steps implemented correctly in 'Code Steps'.\\n\"}]": { - "code": "# Step 1: Fill missing values\nfrom metagpt.tools.libs.data_preprocess import FillMissingValue\n\n# Create a copy of the dataframe to avoid modifying the original one\ndf_processed = df.copy()\n\n# Identify numeric columns to fill missing values with mean\nnumeric_cols = df_processed.select_dtypes(include='number').columns.tolist()\nfill_missing_value_numeric = FillMissingValue(features=numeric_cols, strategy='mean')\ndf_processed = fill_missing_value_numeric.fit_transform(df_processed)\n\n# Step 2: Scale numeric features\nfrom metagpt.tools.libs.data_preprocess import MinMaxScale\n\n# Initialize the MinMaxScaler and scale the numeric columns\nminmax_scaler = MinMaxScale(features=numeric_cols)\ndf_processed = minmax_scaler.fit_transform(df_processed)\n\n# Step 3: Encode categorical features\n# For simplicity, we'll use label encoding for this example\nfrom sklearn.preprocessing import LabelEncoder\n\nlabel_encoder = LabelEncoder()\ndf_processed['c'] = label_encoder.fit_transform(df_processed['c'])\n\n# The dataset is now preprocessed and ready for further analysis or modeling." + "[{\"role\": \"user\", \"content\": \"\\n# Background\\nAs a data scientist, you need to help user to achieve their goal [构造数据集并进行数据清洗] step-by-step in an continuous Jupyter notebook.\\n\\n## Done Tasks\\n```python\\n import pandas as pd\\n df = pd.DataFrame({\\n 'a': [1, 2, 3, 4, 5],\\n 'b': [1.1, 2.2, 3.3, 4.4, np.nan],\\n 'c': ['aa', 'bb', 'cc', 'dd', 'ee'],\\n 'd': [1, 2, 3, 4, 5]\\n })\\n```end\\n\\n## Current Task\\n对数据集进行数据清洗\\n\\n# Latest Data Info\\nLatest data info after previous tasks:\\n\\n\\n# Task\\nWrite complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools:\\nEach Class tool is described in JSON format. When you call a tool, import the tool from its path first.\\n{'FillMissingValue': {'type': 'class', 'description': 'Completing missing values with simple strategies.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}, 'strategy': {'type': 'str', 'description': \\\"The imputation strategy, notice 'mean' and 'median' can only be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.\\\", 'default': \\\"'mean'\\\", 'enum': [\\\"'mean'\\\", \\\"'median'\\\", \\\"'most_frequent'\\\", \\\"'constant'\\\"]}, 'fill_value': {'type': 'int', 'description': 'Fill_value is used to replace all occurrences of missing_values. Defaults to None.', 'default': 'None'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MinMaxScale': {'type': 'class', 'description': 'Transform features by scaling each feature to a range, which is (0, 1).', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'StandardScale': {'type': 'class', 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MaxAbsScale': {'type': 'class', 'description': 'Scale each feature by its maximum absolute value.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'RobustScale': {'type': 'class', 'description': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}}\\n\\n# Output Example:\\nwhen current task is \\\"do data preprocess, like fill missing value, handle outliers, etc.\\\", the code can be like:\\n```python\\n# Step 1: fill missing value\\n# Tools used: ['FillMissingValue']\\nfrom metagpt.tools.libs.data_preprocess import FillMissingValue\\n\\ntrain_processed = train.copy()\\ntest_processed = test.copy()\\nnum_cols = train_processed.select_dtypes(include='number').columns.tolist()\\nif 'label' in num_cols:\\n num_cols.remove('label')\\nfill_missing_value = FillMissingValue(features=num_cols, strategy='mean')\\nfill_missing_value.fit(train_processed)\\ntrain_processed = fill_missing_value.transform(train_processed)\\ntest_processed = fill_missing_value.transform(test_processed)\\n\\n# Step 2: handle outliers\\nfor col in num_cols:\\n low, high = train_processed[col].quantile([0.01, 0.99])\\n train_processed[col] = train_processed[col].clip(low, high)\\n test_processed[col] = test_processed[col].clip(low, high)\\n```end\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n- Always copy the DataFrame before processing it and use the copy to process.\\n\"}]": { + "code": "# Step 1: Fill missing values for numerical columns\nfrom metagpt.tools.libs.data_preprocess import FillMissingValue\n\n# Create a copy of the DataFrame to avoid modifying the original one\ndf_processed = df.copy()\n\n# Identify numerical columns with missing values\nnum_cols_with_missing = df_processed.select_dtypes(include='number').columns[df_processed.isnull().any()].tolist()\n\n# Initialize the FillMissingValue tool for numerical columns\nfill_missing_num = FillMissingValue(features=num_cols_with_missing, strategy='mean')\nfill_missing_num.fit(df_processed)\ndf_processed = fill_missing_num.transform(df_processed)\n\n# Step 2: Encode categorical columns\n# For simplicity, we will replace categorical values with a numerical code\n# This is a simple form of encoding that can be useful for tree-based models\n# and doesn't increase dimensionality like one-hot encoding\ncat_cols = df_processed.select_dtypes(include='object').columns.tolist()\nfor col in cat_cols:\n df_processed[col] = df_processed[col].astype('category').cat.codes\n\n# Display the processed DataFrame\ndisplay(df_processed)" }, - "[{\"role\": \"user\", \"content\": \"\\n## User Requirement:\\nclean and preprocess the data\\n\\n## Task\\nRecommend up to five tools from 'Available Tools' that can help solve the 'User Requirement'. \\nThis is a detailed code steps for current task. You can refer to it when recommending tools.\\n\\n\\n## Available Tools:\\n{'FillMissingValue': 'Filling missing values', 'SplitBins': 'Bin continuous data into intervals and return the bin identifier encoded as an integer value'}\\n\\n## Tool Selection and Instructions:\\n- Select tools most relevant to completing the 'User Requirement'.\\n- If you believe that no tools are suitable, indicate with an empty list.\\n- Only list the names of the tools, not the full schema of each tool.\\n- Ensure selected tools are listed in 'Available Tools'.\\n\"}]": { + "[{\"role\": \"user\", \"content\": \"\\n## User Requirement:\\nclean and preprocess the data\\n\\n## Task\\nRecommend up to five tools from 'Available Tools' that can help solve the 'User Requirement'. \\n\\n## Available Tools:\\n{'FillMissingValue': 'Filling missing values', 'SplitBins': 'Bin continuous data into intervals and return the bin identifier encoded as an integer value'}\\n\\n## Tool Selection and Instructions:\\n- Select tools most relevant to completing the 'User Requirement'.\\n- If you believe that no tools are suitable, indicate with an empty list.\\n- Only list the names of the tools, not the full schema of each tool.\\n- Ensure selected tools are listed in 'Available Tools'.\\n\"}]": { "recommend_tools": [ "FillMissingValue" ] }, - "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\n构造数据集并进行数据清洗\\n## Context\\n\\n## Current Plan\\n[Task(task_id='1', dependent_task_ids=[], instruction='随机生成一个pandas DataFrame数据集', task_type='other', code_steps='', code=\\\"\\\\n import pandas as pd\\\\n df = pd.DataFrame({\\\\n 'a': [1, 2, 3, 4, 5],\\\\n 'b': [1.1, 2.2, 3.3, 4.4, np.nan],\\\\n 'c': ['aa', 'bb', 'cc', 'dd', 'ee'],\\\\n 'd': [1, 2, 3, 4, 5]\\\\n })\\\\n \\\", result='', is_success=False, is_finished=True), Task(task_id='2', dependent_task_ids=['1'], instruction='对数据集进行数据清洗', task_type='data_preprocess', code_steps='', code='', result='', is_success=False, is_finished=False)]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"对数据集进行数据清洗\\\",\\\"task_type\\\":\\\"data_preprocess\\\",\\\"code_steps\\\":\\\"\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{'FillMissingValue': {'type': 'class', 'description': 'Completing missing values with simple strategies.', 'methods': {'__init__': {'description': 'Initialize self. ', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}, 'strategy': {'type': 'str', 'description': \\\"The imputation strategy, notice 'mean' and 'median' can only be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.\\\", 'default': \\\"'mean'\\\", 'enum': [\\\"'mean'\\\", \\\"'median'\\\", \\\"'most_frequent'\\\", \\\"'constant'\\\"]}, 'fill_value': {'type': 'int', 'description': 'Fill_value is used to replace all occurrences of missing_values. Defaults to None.', 'default': 'None'}}, 'required': ['features']}}, 'fit': {'description': 'Fit the FillMissingValue model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MinMaxScale': {'type': 'class', 'description': 'Transform features by scaling each feature to a range, which is (0, 1).', 'methods': {'__init__': {'description': 'Initialize self. ', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit the MinMaxScale model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'StandardScale': {'type': 'class', 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'methods': {'__init__': {'description': 'Initialize self. ', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit the StandardScale model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MaxAbsScale': {'type': 'class', 'description': 'Scale each feature by its maximum absolute value.', 'methods': {'__init__': {'description': 'Initialize self. ', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit the MaxAbsScale model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'RobustScale': {'type': 'class', 'description': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'methods': {'__init__': {'description': 'Initialize the RobustScale instance with feature names. ', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'List of feature names to be scaled.'}}, 'required': ['features']}}, 'fit': {'description': 'Compute the median and IQR for scaling. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'Dataframe containing the features.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Scale features using the previously computed median and IQR. ', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'Dataframe containing the features to be scaled.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'A new dataframe with scaled features.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": { - "code": "from metagpt.tools.libs.data_preprocess import FillMissingValue\n\n# Fill missing values in column 'b' with the mean value\nfill_missing = FillMissingValue(features=['b'], strategy='mean')\ndf = fill_missing.fit_transform(df)\n\n# Check the DataFrame after filling missing values\ndf" + "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\n构造数据集并进行数据清洗\\n## Context\\n\\n## Current Plan\\n[Task(task_id='1', dependent_task_ids=[], instruction='随机生成一个pandas DataFrame数据集', task_type='other', code=\\\"\\\\n import pandas as pd\\\\n df = pd.DataFrame({\\\\n 'a': [1, 2, 3, 4, 5],\\\\n 'b': [1.1, 2.2, 3.3, 4.4, np.nan],\\\\n 'c': ['aa', 'bb', 'cc', 'dd', 'ee'],\\\\n 'd': [1, 2, 3, 4, 5]\\\\n })\\\\n \\\", result='', is_success=False, is_finished=True), Task(task_id='2', dependent_task_ids=['1'], instruction='对数据集进行数据清洗', task_type='data_preprocess', code='', result='', is_success=False, is_finished=False)]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"对数据集进行数据清洗\\\",\\\"task_type\\\":\\\"data_preprocess\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{'FillMissingValue': {'type': 'class', 'description': 'Completing missing values with simple strategies.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}, 'strategy': {'type': 'str', 'description': \\\"The imputation strategy, notice 'mean' and 'median' can only be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.\\\", 'default': \\\"'mean'\\\", 'enum': [\\\"'mean'\\\", \\\"'median'\\\", \\\"'most_frequent'\\\", \\\"'constant'\\\"]}, 'fill_value': {'type': 'int', 'description': 'Fill_value is used to replace all occurrences of missing_values. Defaults to None.', 'default': 'None'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MinMaxScale': {'type': 'class', 'description': 'Transform features by scaling each feature to a range, which is (0, 1).', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'StandardScale': {'type': 'class', 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MaxAbsScale': {'type': 'class', 'description': 'Scale each feature by its maximum absolute value.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'RobustScale': {'type': 'class', 'description': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": { + "code": "from metagpt.tools.libs.data_preprocess import FillMissingValue\n\n# Instantiate the FillMissingValue class for column 'b'\nfill_missing_b = FillMissingValue(features=['b'], strategy='mean')\n\n# Fit and transform the DataFrame\ndf = fill_missing_b.fit_transform(df)" }, "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n ## User Requirement\\n read a dataset test.csv and print its head\\n ## Current Plan\\n [\\n {\\n \\\"task_id\\\": \\\"1\\\",\\n \\\"dependent_task_ids\\\": [],\\n \\\"instruction\\\": \\\"import pandas and load the dataset from 'test.csv'.\\\",\\n \\\"task_type\\\": \\\"\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_finished\\\": false\\n },\\n {\\n \\\"task_id\\\": \\\"2\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"1\\\"\\n ],\\n \\\"instruction\\\": \\\"Print the head of the dataset to display the first few rows.\\\",\\n \\\"task_type\\\": \\\"\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_finished\\\": false\\n }\\n ]\\n ## Current Task\\n {\\\"task_id\\\": \\\"1\\\", \\\"dependent_task_ids\\\": [], \\\"instruction\\\": \\\"import pandas and load the dataset from 'test.csv'.\\\", \\\"task_type\\\": \\\"\\\", \\\"code\\\": \\\"\\\", \\\"result\\\": \\\"\\\", \\\"is_finished\\\": false}\\n \"}, {\"role\": \"assistant\", \"content\": \"import pandas as pd\\ndata = pd.read_excel('test.csv')\\ndata\"}, {\"role\": \"user\", \"content\": \"\\n Traceback (most recent call last):\\n File \\\"\\\", line 2, in \\n File \\\"/Users/gary/miniconda3/envs/py39_scratch/lib/python3.9/site-packages/pandas/io/excel/_base.py\\\", line 478, in read_excel\\n io = ExcelFile(io, storage_options=storage_options, engine=engine)\\n File \\\"/Users/gary/miniconda3/envs/py39_scratch/lib/python3.9/site-packages/pandas/io/excel/_base.py\\\", line 1500, in __init__\\n raise ValueError(\\n ValueError: Excel file format cannot be determined, you must specify an engine manually.\\n \"}]": { "language": "python", @@ -348,52 +354,39 @@ "language": "python", "code": "print(data.head())" }, - "\n # Context:\n user: run analysis on sklearn iris dataset\n # Task:\n Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to 5 tasks.\n If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes. Give the whole plan unless instructed to modify only one task of the plan.\n If you encounter errors on the current task, revise and output the current single task only.\n Output a list of jsons following the format:\n ```json\n [\n {\n \"task_id\": str = \"unique identifier for a task in plan, can be an ordinal\",\n \"dependent_task_ids\": list[str] = \"ids of tasks prerequisite to this task\",\n \"instruction\": \"what you should do in this task, one short phrase or sentence\",\n },\n ...\n ]\n ```\n ": "```json\n[\n {\n \"task_id\": \"1\",\n \"dependent_task_ids\": [],\n \"instruction\": \"Import the Iris dataset from scikit-learn.\"\n },\n {\n \"task_id\": \"2\",\n \"dependent_task_ids\": [\"1\"],\n \"instruction\": \"Perform exploratory data analysis to understand the dataset.\"\n },\n {\n \"task_id\": \"3\",\n \"dependent_task_ids\": [\"2\"],\n \"instruction\": \"Preprocess the data if necessary (e.g., scaling, encoding).\"\n },\n {\n \"task_id\": \"4\",\n \"dependent_task_ids\": [\"3\"],\n \"instruction\": \"Split the dataset into training and testing sets.\"\n },\n {\n \"task_id\": \"5\",\n \"dependent_task_ids\": [\"4\"],\n \"instruction\": \"Choose a suitable model and train it on the dataset.\"\n },\n {\n \"task_id\": \"6\",\n \"dependent_task_ids\": [\"5\"],\n \"instruction\": \"Evaluate the model's performance on the test set.\"\n },\n {\n \"task_id\": \"7\",\n \"dependent_task_ids\": [\"6\"],\n \"instruction\": \"Report the results of the analysis.\"\n }\n]\n```", - "[{\"role\": \"user\", \"content\": \"\\nPlease assign a task type to each task in the list below from the given categories:\\nTask 1: Import the Iris dataset from scikit-learn.\\nTask 2: Perform exploratory data analysis to understand the dataset.\\nTask 3: Preprocess the data if necessary (e.g., scaling, encoding).\\nTask 4: Split the dataset into training and testing sets.\\nTask 5: Choose a suitable model and train it on the dataset.\\nTask 6: Evaluate the model's performance on the test set.\\nTask 7: Report the results of the analysis.\\n\\n## All Task Type:\\n- **eda**: For performing exploratory data analysis\\n- **data_preprocess**: Only for changing value inplace.\\n- **feature_engineering**: Only for creating new columns for input data.\\n- **model_train**: Only for training model.\\n- **model_evaluate**: Only for evaluating model.\\n- **stable_diffusion**: Related to text2image, image2image using stable diffusion model.\\n- **image2webpage**: For converting image into webpage code.\\n- **web_scraping**: For scraping data from web pages.\\n- **other**: Any tools not in the defined categories\\n\"}]": { + "\n # Context:\n user: run analysis on sklearn iris dataset\n # Task:\n Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to 5 tasks.\n If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes. Give the whole plan unless instructed to modify only one task of the plan.\n If you encounter errors on the current task, revise and output the current single task only.\n Output a list of jsons following the format:\n ```json\n [\n {\n \"task_id\": str = \"unique identifier for a task in plan, can be an ordinal\",\n \"dependent_task_ids\": list[str] = \"ids of tasks prerequisite to this task\",\n \"instruction\": \"what you should do in this task, one short phrase or sentence\",\n },\n ...\n ]\n ```\n ": "```json\n[\n {\n \"task_id\": \"1\",\n \"dependent_task_ids\": [],\n \"instruction\": \"Import the Iris dataset from sklearn.datasets\"\n },\n {\n \"task_id\": \"2\",\n \"dependent_task_ids\": [\"1\"],\n \"instruction\": \"Perform exploratory data analysis to understand the dataset\"\n },\n {\n \"task_id\": \"3\",\n \"dependent_task_ids\": [\"2\"],\n \"instruction\": \"Preprocess the data to prepare it for modeling\"\n },\n {\n \"task_id\": \"4\",\n \"dependent_task_ids\": [\"3\"],\n \"instruction\": \"Split the dataset into training and testing sets\"\n },\n {\n \"task_id\": \"5\",\n \"dependent_task_ids\": [\"4\"],\n \"instruction\": \"Train a classifier using the training set and evaluate it using the test set\"\n }\n]\n```", + "[{\"role\": \"user\", \"content\": \"\\nPlease assign a task type to each task in the list below from the given categories:\\nTask 1: Import the Iris dataset from sklearn.datasets\\nTask 2: Perform exploratory data analysis to understand the dataset\\nTask 3: Preprocess the data to prepare it for modeling\\nTask 4: Split the dataset into training and testing sets\\nTask 5: Train a classifier using the training set and evaluate it using the test set\\n\\n## All Task Type:\\n- **eda**: For performing exploratory data analysis\\n- **data_preprocess**: Only for changing value inplace.\\n- **feature_engineering**: Only for creating new columns for input data.\\n- **model_train**: Only for training model.\\n- **model_evaluate**: Only for evaluating model.\\n- **stable_diffusion**: Related to text2image, image2image using stable diffusion model.\\n- **image2webpage**: For converting image into webpage code.\\n- **web_scraping**: For scraping data from web pages.\\n- **other**: Any tools not in the defined categories\\n\"}]": { "task_type": [ "other", "eda", "data_preprocess", "data_preprocess", "model_train", - "model_evaluate", - "other" + "model_evaluate" ] }, - "\n # Context:\n user: \n## User Requirement\nRun data analysis on sklearn Iris dataset, include a plot\n## Context\n\n## Current Plan\n[]\n## Current Task\n{}\n\n # Task:\n Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to 3 tasks.\n If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes. Give the whole plan unless instructed to modify only one task of the plan.\n If you encounter errors on the current task, revise and output the current single task only.\n Output a list of jsons following the format:\n ```json\n [\n {\n \"task_id\": str = \"unique identifier for a task in plan, can be an ordinal\",\n \"dependent_task_ids\": list[str] = \"ids of tasks prerequisite to this task\",\n \"instruction\": \"what you should do in this task, one short phrase or sentence\",\n },\n ...\n ]\n ```\n ": "```json\n[\n {\n \"task_id\": \"1\",\n \"dependent_task_ids\": [],\n \"instruction\": \"Load the sklearn Iris dataset.\"\n },\n {\n \"task_id\": \"2\",\n \"dependent_task_ids\": [\"1\"],\n \"instruction\": \"Perform exploratory data analysis on the Iris dataset.\"\n },\n {\n \"task_id\": \"3\",\n \"dependent_task_ids\": [\"2\"],\n \"instruction\": \"Create a plot visualizing the Iris dataset features.\"\n }\n]\n```", - "[{\"role\": \"user\", \"content\": \"\\nPlease assign a task type to each task in the list below from the given categories:\\nTask 1: Load the sklearn Iris dataset.\\nTask 2: Perform exploratory data analysis on the Iris dataset.\\nTask 3: Create a plot visualizing the Iris dataset features.\\n\\n## All Task Type:\\n- **eda**: For performing exploratory data analysis\\n- **data_preprocess**: Only for changing value inplace.\\n- **feature_engineering**: Only for creating new columns for input data.\\n- **model_train**: Only for training model.\\n- **model_evaluate**: Only for evaluating model.\\n- **stable_diffusion**: Related to text2image, image2image using stable diffusion model.\\n- **image2webpage**: For converting image into webpage code.\\n- **web_scraping**: For scraping data from web pages.\\n- **other**: Any tools not in the defined categories\\n\"}]": { + "\n # Context:\n user: \n## User Requirement\nRun data analysis on sklearn Iris dataset, include a plot\n## Context\n\n## Current Plan\n[]\n## Current Task\n{}\n\n # Task:\n Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to 3 tasks.\n If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes. Give the whole plan unless instructed to modify only one task of the plan.\n If you encounter errors on the current task, revise and output the current single task only.\n Output a list of jsons following the format:\n ```json\n [\n {\n \"task_id\": str = \"unique identifier for a task in plan, can be an ordinal\",\n \"dependent_task_ids\": list[str] = \"ids of tasks prerequisite to this task\",\n \"instruction\": \"what you should do in this task, one short phrase or sentence\",\n },\n ...\n ]\n ```\n ": "```json\n[\n {\n \"task_id\": \"1\",\n \"dependent_task_ids\": [],\n \"instruction\": \"Load the sklearn Iris dataset.\"\n },\n {\n \"task_id\": \"2\",\n \"dependent_task_ids\": [\"1\"],\n \"instruction\": \"Perform exploratory data analysis on the Iris dataset.\"\n },\n {\n \"task_id\": \"3\",\n \"dependent_task_ids\": [\"2\"],\n \"instruction\": \"Create a plot visualizing the Iris dataset.\"\n }\n]\n```", + "[{\"role\": \"user\", \"content\": \"\\nPlease assign a task type to each task in the list below from the given categories:\\nTask 1: Load the sklearn Iris dataset.\\nTask 2: Perform exploratory data analysis on the Iris dataset.\\nTask 3: Create a plot visualizing the Iris dataset.\\n\\n## All Task Type:\\n- **eda**: For performing exploratory data analysis\\n- **data_preprocess**: Only for changing value inplace.\\n- **feature_engineering**: Only for creating new columns for input data.\\n- **model_train**: Only for training model.\\n- **model_evaluate**: Only for evaluating model.\\n- **stable_diffusion**: Related to text2image, image2image using stable diffusion model.\\n- **image2webpage**: For converting image into webpage code.\\n- **web_scraping**: For scraping data from web pages.\\n- **other**: Any tools not in the defined categories\\n\"}]": { "task_type": [ "other", "eda", "other" ] }, - "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n {\\n \\\"task_id\\\": \\\"1\\\",\\n \\\"dependent_task_ids\\\": [],\\n \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n },\\n {\\n \\\"task_id\\\": \\\"2\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"1\\\"\\n ],\\n \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n \\\"task_type\\\": \\\"eda\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n },\\n {\\n \\\"task_id\\\": \\\"3\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"2\\\"\\n ],\\n \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset features.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"1\\\",\\\"dependent_task_ids\\\":[],\\\"instruction\\\":\\\"Load the sklearn Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code_steps\\\":\\\"\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": { + "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n {\\n \\\"task_id\\\": \\\"1\\\",\\n \\\"dependent_task_ids\\\": [],\\n \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n },\\n {\\n \\\"task_id\\\": \\\"2\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"1\\\"\\n ],\\n \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n \\\"task_type\\\": \\\"eda\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n },\\n {\\n \\\"task_id\\\": \\\"3\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"2\\\"\\n ],\\n \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"1\\\",\\\"dependent_task_ids\\\":[],\\\"instruction\\\":\\\"Load the sklearn Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": { "code": "from sklearn.datasets import load_iris\niris_data = load_iris()" }, - "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n {\\n \\\"task_id\\\": \\\"1\\\",\\n \\\"dependent_task_ids\\\": [],\\n \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"from sklearn.datasets import load_iris\\\\niris_data = load_iris()\\\",\\n \\\"result\\\": \\\"a successful run\\\",\\n \\\"is_success\\\": true,\\n \\\"is_finished\\\": true\\n },\\n {\\n \\\"task_id\\\": \\\"2\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"1\\\"\\n ],\\n \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n \\\"task_type\\\": \\\"eda\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n },\\n {\\n \\\"task_id\\\": \\\"3\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"2\\\"\\n ],\\n \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset features.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"Perform exploratory data analysis on the Iris dataset.\\\",\\\"task_type\\\":\\\"eda\\\",\\\"code_steps\\\":\\\"\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": { - "code": "import pandas as pd\n\n# Convert the loaded Iris dataset to a DataFrame for easier manipulation\niris_df = pd.DataFrame(data=iris_data.data, columns=iris_data.feature_names)\niris_df['target'] = iris_data.target\n\n# Display basic information about the dataset\niris_df_info = iris_df.info()\n\n# Display statistical summary of the dataset\niris_df_description = iris_df.describe()\n\n# Show the first few rows of the dataset\ndf_head = iris_df.head()\n\n# Output the results\ndisplay(iris_df_info, iris_df_description, df_head)" + "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n {\\n \\\"task_id\\\": \\\"1\\\",\\n \\\"dependent_task_ids\\\": [],\\n \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"from sklearn.datasets import load_iris\\\\niris_data = load_iris()\\\",\\n \\\"result\\\": \\\"a successful run\\\",\\n \\\"is_success\\\": true,\\n \\\"is_finished\\\": true\\n },\\n {\\n \\\"task_id\\\": \\\"2\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"1\\\"\\n ],\\n \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n \\\"task_type\\\": \\\"eda\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n },\\n {\\n \\\"task_id\\\": \\\"3\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"2\\\"\\n ],\\n \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"Perform exploratory data analysis on the Iris dataset.\\\",\\\"task_type\\\":\\\"eda\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": { + "code": "import pandas as pd\nimport numpy as np\n\n# Convert the loaded Iris dataset to a DataFrame for easier manipulation\niris_df = pd.DataFrame(data=np.c_[iris_data['data'], iris_data['target']], columns=iris_data['feature_names'] + ['target'])\n\n# Show basic information about the dataset\ndataset_info = iris_df.info()\n\n# Show basic statistics of the dataset\ndataset_description = iris_df.describe()\n\n# Show the first few rows of the dataset\ndataset_head = iris_df.head()\n\n# Count the number of samples for each class\nclass_distribution = iris_df['target'].value_counts()\n\n# Output the results\ndataset_info, dataset_description, dataset_head, class_distribution" }, - "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n {\\n \\\"task_id\\\": \\\"1\\\",\\n \\\"dependent_task_ids\\\": [],\\n \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"from sklearn.datasets import load_iris\\\\niris_data = load_iris()\\\",\\n \\\"result\\\": \\\"a successful run\\\",\\n \\\"is_success\\\": true,\\n \\\"is_finished\\\": true\\n },\\n {\\n \\\"task_id\\\": \\\"2\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"1\\\"\\n ],\\n \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n \\\"task_type\\\": \\\"eda\\\",\\n \\\"code\\\": \\\"import pandas as pd\\\\n\\\\n# Convert the loaded Iris dataset to a DataFrame for easier manipulation\\\\niris_df = pd.DataFrame(data=iris_data.data, columns=iris_data.feature_names)\\\\niris_df['target'] = iris_data.target\\\\n\\\\n# Display basic information about the dataset\\\\niris_df_info = iris_df.info()\\\\n\\\\n# Display statistical summary of the dataset\\\\niris_df_description = iris_df.describe()\\\\n\\\\n# Show the first few rows of the dataset\\\\ndf_head = iris_df.head()\\\\n\\\\n# Output the results\\\\ndisplay(iris_df_info, iris_df_description, df_head)\\\",\\n \\\"result\\\": \\\"a successful run\\\",\\n \\\"is_success\\\": true,\\n \\\"is_finished\\\": true\\n },\\n {\\n \\\"task_id\\\": \\\"3\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"2\\\"\\n ],\\n \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset features.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"3\\\",\\\"dependent_task_ids\\\":[\\\"2\\\"],\\\"instruction\\\":\\\"Create a plot visualizing the Iris dataset features.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code_steps\\\":\\\"\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": { - "code": "import seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Pairplot to visualize the relationships between features\nsns.pairplot(iris_df, hue='target', diag_kind='kde')\n\n# Show the plot\nplt.show()" + "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n {\\n \\\"task_id\\\": \\\"1\\\",\\n \\\"dependent_task_ids\\\": [],\\n \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"from sklearn.datasets import load_iris\\\\niris_data = load_iris()\\\",\\n \\\"result\\\": \\\"a successful run\\\",\\n \\\"is_success\\\": true,\\n \\\"is_finished\\\": true\\n },\\n {\\n \\\"task_id\\\": \\\"2\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"1\\\"\\n ],\\n \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n \\\"task_type\\\": \\\"eda\\\",\\n \\\"code\\\": \\\"import pandas as pd\\\\nimport numpy as np\\\\n\\\\n# Convert the loaded Iris dataset to a DataFrame for easier manipulation\\\\niris_df = pd.DataFrame(data=np.c_[iris_data['data'], iris_data['target']], columns=iris_data['feature_names'] + ['target'])\\\\n\\\\n# Show basic information about the dataset\\\\ndataset_info = iris_df.info()\\\\n\\\\n# Show basic statistics of the dataset\\\\ndataset_description = iris_df.describe()\\\\n\\\\n# Show the first few rows of the dataset\\\\ndataset_head = iris_df.head()\\\\n\\\\n# Count the number of samples for each class\\\\nclass_distribution = iris_df['target'].value_counts()\\\\n\\\\n# Output the results\\\\ndataset_info, dataset_description, dataset_head, class_distribution\\\",\\n \\\"result\\\": \\\"a successful run\\\",\\n \\\"is_success\\\": true,\\n \\\"is_finished\\\": true\\n },\\n {\\n \\\"task_id\\\": \\\"3\\\",\\n \\\"dependent_task_ids\\\": [\\n \\\"2\\\"\\n ],\\n \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n \\\"task_type\\\": \\\"other\\\",\\n \\\"code\\\": \\\"\\\",\\n \\\"result\\\": \\\"\\\",\\n \\\"is_success\\\": false,\\n \\\"is_finished\\\": false\\n }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"3\\\",\\\"dependent_task_ids\\\":[\\\"2\\\"],\\\"instruction\\\":\\\"Create a plot visualizing the Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": { + "code": "import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Set the style of seaborn\nsns.set(style=\"whitegrid\")\n\n# Create a pairplot to visualize the relationships between features\ng = sns.pairplot(iris_df, hue='target', palette='husl', markers=['o', 's', 'D'])\n\n# Add titles and labels\ng.fig.suptitle('Iris Dataset Pairplot', y=1.02) # y=1.02 to make space for the title\nplt.show()" }, - "[{\"role\": \"user\", \"content\": \"\\n# Background\\nAs a data scientist, you need to help user to achieve their goal [This is a titanic passenger survival dataset, your goal is to predict passenger survival outcome. The target column is Survived. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report accuracy on the eval data. Train data path: 'tests/data/ml_datasets/titanic/split_train.csv', eval data path: 'tests/data/ml_datasets/titanic/split_eval.csv'.] step-by-step in an continuous Jupyter notebook.\\n\\n## Done Tasks\\n```python\\n\\n```end\\n\\n## Current Task\\nPerform exploratory data analysis on the train dataset to understand the features and target variable.\\n\\n# Latest Data Info\\nLatest data info after previous tasks:\\n\\n\\n# Task\\nWrite complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Code Steps:\\nStrictly follow steps below when you writing code if it's convenient.\\n\\n\\n# Output Example:\\nwhen current task is \\\"train a lightgbm model on training data\\\", and their are two steps in 'Code Steps', the code be like:\\n```python\\n# Step 1: check data type and convert to numeric\\nojb_cols = train.select_dtypes(include='object').columns.tolist()\\n\\nfor col in obj_cols:\\n encoder = LabelEncoder()\\n train[col] = encoder.fit_transform(train[col].unique().tolist() + ['unknown'])\\n test[col] = test[col].apply(lambda x: x if x in encoder.classes_ else 'unknown')\\n test[col] = encoder.transform(test[col])\\n\\n# Step 2: train lightgbm model\\nmodel = LGBMClassifier()\\nmodel.fit(train, y_train)\\n```end\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- The output code should contain all steps implemented in 'Code Steps'.\\n\"}]": { - "code": "# Step 1: Load the training dataset\nimport pandas as pd\ntrain_data = pd.read_csv('tests/data/ml_datasets/titanic/split_train.csv')\n\n# Step 2: Display the first few rows of the dataset\ndisplay(train_data.head())\n\n# Step 3: Get a summary of the dataset\ntrain_data.info()\n\n# Step 4: Describe the numerical features\ntrain_data.describe()\n\n# Step 5: Describe the categorical features\ntrain_data.describe(include=['O'])\n\n# Step 6: Check for missing values\nmissing_values = train_data.isnull().sum()\nmissing_values[missing_values > 0]\n\n# Step 7: Visualize the distribution of the target variable 'Survived'\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nsns.countplot(x='Survived', data=train_data)\nplt.title('Distribution of Survival')\nplt.show()\n\n# Step 8: Explore the relationship between features and the target variable\n# Visualize the survival rate by sex\nsns.barplot(x='Sex', y='Survived', data=train_data)\nplt.title('Survival Rate by Sex')\nplt.show()\n\n# Visualize the survival rate by class\nsns.barplot(x='Pclass', y='Survived', data=train_data)\nplt.title('Survival Rate by Class')\nplt.show()\n\n# Visualize the survival rate by embarkation port\nsns.barplot(x='Embarked', y='Survived', data=train_data)\nplt.title('Survival Rate by Embarkation Port')\nplt.show()" + "[{\"role\": \"user\", \"content\": \"\\n# Background\\nAs a data scientist, you need to help user to achieve their goal [This is a titanic passenger survival dataset, your goal is to predict passenger survival outcome. The target column is Survived. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report accuracy on the eval data. Train data path: 'tests/data/ml_datasets/titanic/split_train.csv', eval data path: 'tests/data/ml_datasets/titanic/split_eval.csv'.] step-by-step in an continuous Jupyter notebook.\\n\\n## Done Tasks\\n```python\\n\\n```end\\n\\n## Current Task\\nPerform exploratory data analysis on the train dataset to understand the features and target variable.\\n\\n# Latest Data Info\\nLatest data info after previous tasks:\\n\\n\\n# Task\\nWrite complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Output Example:\\nwhen current task is \\\"train a lightgbm model on training data\\\", the code can be like:\\n```python\\n# Step 1: check data type and convert to numeric\\nobj_cols = train.select_dtypes(include='object').columns.tolist()\\n\\nfor col in obj_cols:\\n encoder = LabelEncoder()\\n train[col] = encoder.fit_transform(train[col].unique().tolist() + ['unknown'])\\n test[col] = test[col].apply(lambda x: x if x in encoder.classes_ else 'unknown')\\n test[col] = encoder.transform(test[col])\\n\\n# Step 2: train lightgbm model\\nmodel = LGBMClassifier()\\nmodel.fit(train, y_train)\\n```end\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n\"}]": { + "code": "# Perform exploratory data analysis on the train dataset\ndf_train = pd.read_csv('tests/data/ml_datasets/titanic/split_train.csv')\n\n# Display the first few rows of the dataset\ndisplay(df_train.head())\n\n# Summary statistics for numerical features\ndisplay(df_train.describe())\n\n# Summary information about the dataset including the data types and number of non-null values\ndisplay(df_train.info())\n\n# Distribution of the target variable 'Survived'\nsurvival_counts = df_train['Survived'].value_counts()\nprint(\"Survival counts:\\n\", survival_counts)\n\n# Visualizations\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Distribution of the target variable\nsns.countplot(x='Survived', data=df_train)\nplt.title('Distribution of Survival')\nplt.show()\n\n# Correlation matrix heatmap to understand the relationship between features\nplt.figure(figsize=(10, 8))\nsns.heatmap(df_train.corr(), annot=True, fmt='.2f')\nplt.title('Correlation Matrix')\nplt.show()\n\n# Pairplot to visualize the pairwise relationships between features\nsns.pairplot(df_train, hue='Survived')\nplt.title('Pairplot of Features')\nplt.show()" }, "[{\"role\": \"system\", \"content\": \"You are an AI Python assistant. You will be given your previous implementation code of a task, runtime error results, and a hint to change the implementation appropriately. Write your full implementation \"}, {\"role\": \"user\", \"content\": \"\\nHere is an example for you.\\n\\nExample 1:\\n[previous impl]:\\n```python\\ndef add(a: int, b: int) -> int:\\n \\\"\\\"\\\"\\n Given integers a and b, return the total value of a and b.\\n \\\"\\\"\\\"\\n return a - b\\n```\\n\\n[runtime Error]:\\nTested passed:\\n\\nTests failed:\\nassert add(1, 2) == 3 # output: -1\\nassert add(1, 2) == 4 # output: -1\\n\\n[reflection on previous impl]:\\nThe implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input.\\n\\n[improved impl]:\\n```python\\ndef add(a: int, b: int) -> int:\\n \\\"\\\"\\\"\\n Given integers a and b, return the total value of a and b.\\n \\\"\\\"\\\"\\n return a + b\\n```\\n\\n[context]\\nSolve the problem in Python:\\ndef sort_array(arr):\\n \\\"\\\"\\\"\\n In this Kata, you have to sort an array of non-negative integers according to\\n number of ones in their binary representation in ascending order.\\n For similar number of ones, sort based on decimal value.\\n\\n It must be implemented like this:\\n >>> sort_array([1, 5, 2, 3, 4]) == [1, 2, 3, 4, 5]\\n >>> sort_array([-2, -3, -4, -5, -6]) == [-6, -5, -4, -3, -2]\\n >>> sort_array([1, 0, 2, 3, 4]) [0, 1, 2, 3, 4]\\n \\\"\\\"\\\"\\n\\n\\n[previous impl]\\n\\ndef sort_array(arr):\\n # Helper function to count the number of ones in the binary representation\\n def count_ones(n):\\n return bin(n).count('1')\\n \\n # Sort the array using a custom key function\\n # The key function returns a tuple (number of ones, value) for each element\\n # This ensures that if two elements have the same number of ones, they are sorted by their value\\n sorted_arr = sorted(arr, key=lambda x: (count_ones(x), x))\\n \\n return sorted_arr\\n```\\n\\n[runtime Error]\\n[user: Tested passed:\\n\\nTests failed:\\nassert sort_array([1, 5, 2, 3, 4]) == [1, 2, 3, 4, 5] # output: [1, 2, 4, 3, 5]\\n]\\n\\nAnalysis the error step by step, provide me improve method and code. Remember to follow [context] requirement. Don't forget write code for steps behind the error step.\\n[reflection on previous impl]:\\nxxx\\n\"}]": { - "reflection": "The implementation failed the test case where the input array is [1, 5, 2, 3, 4]. The issue arises because the code does not correctly sort the array based on the number of ones in their binary representation followed by their decimal value. The error in the output indicates that the numbers 3 and 4 are not in the expected positions. The binary representations of 3 and 4 are '11' and '100', respectively, which means 3 has more ones than 4 and should come before 4 in the sorted array. To fix this issue, we need to ensure that the sorting is stable so that when two numbers have the same number of ones, they retain their original order if their decimal values are the same. We can achieve this by using the 'stable' sorting algorithm provided by Python's sorted function. Additionally, we need to handle negative numbers correctly by taking the absolute value before counting the ones in their binary representation.", - "improved_impl": "def sort_array(arr):\n # Helper function to count the number of ones in the binary representation\n def count_ones(n):\n return bin(abs(n)).count('1')\n \n # Sort the array using a custom key function\n # The key function returns a tuple (number of ones, value) for each element\n # This ensures that if two elements have the same number of ones, they are sorted by their value\n # Using 'stable' sorting algorithm to maintain the original order when necessary\n sorted_arr = sorted(arr, key=lambda x: (count_ones(x), x), reverse=False)\n \n return sorted_arr" - }, - "[{\"role\": \"user\", \"content\": \"Interface definition:\\n```text\\nInterface Name: Element Tagging\\nInterface Path: /projects/{project_key}/node-tags\\nMethod: POST\\n\\nRequest parameters:\\nPath parameters:\\nproject_key\\n\\nBody parameters:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\nnodes\\tarray\\tYes\\t\\tNodes\\n\\tnode_key\\tstring\\tNo\\t\\tNode key\\n\\ttags\\tarray\\tNo\\t\\tOriginal node tag list\\n\\tnode_type\\tstring\\tNo\\t\\tNode type DATASET / RECIPE\\noperations\\tarray\\tYes\\t\\t\\n\\ttags\\tarray\\tNo\\t\\tOperation tag list\\n\\tmode\\tstring\\tNo\\t\\tOperation type ADD / DELETE\\n\\nReturn data:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\ncode\\tinteger\\tYes\\t\\tStatus code\\nmsg\\tstring\\tYes\\t\\tPrompt message\\ndata\\tobject\\tYes\\t\\tReturned data\\nlist\\tarray\\tNo\\t\\tNode list true / false\\nnode_type\\tstring\\tNo\\t\\tNode type DATASET / RECIPE\\nnode_key\\tstring\\tNo\\t\\tNode key\\n```\\n\\nUnit test：\\n```python\\n@pytest.mark.parametrize(\\n\\\"project_key, nodes, operations, expected_msg\\\",\\n[\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"success\\\"),\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"dataset_002\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"tag1\\\"], \\\"mode\\\": \\\"DELETE\\\"}], \\\"success\\\"),\\n(\\\"\\\", [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Missing the required parameter project_key\\\"),\\n(123, [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Incorrect parameter type\\\"),\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"a\\\"*201, \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Request parameter exceeds field boundary\\\")\\n]\\n)\\ndef test_node_tags(project_key, nodes, operations, expected_msg):\\n pass\\n\\n# The above is an interface definition and a unit test example.\\n# Next, please play the role of an expert test manager with 20 years of experience at Google. When I give the interface definition, \\n# reply to me with a unit test. There are several requirements:\\n# 1. Only output one `@pytest.mark.parametrize` and the corresponding test_ function (inside pass, do not implement).\\n# -- The function parameter contains expected_msg for result verification.\\n# 2. The generated test cases use shorter text or numbers and are as compact as possible.\\n# 3. If comments are needed, use Chinese.\\n\\n# If you understand, please wait for me to give the interface definition and just answer \\\"Understood\\\" to save tokens.\\n\"}, {\"role\": \"user\", \"content\": \"Refer to the test types: such as SQL injection, cross-site scripting (XSS), unauthorized access and privilege escalation, \\nauthentication and authorization, parameter verification, exception handling, file upload and download.\\nPlease output 10 test cases within one `@pytest.mark.parametrize` scope.\\n```text\\nAPI Name: 获取 model 详情(job专用-后续开放给sdk)\\nAPI Path: /v1/projects/{project_key}/jobs/{job_id}/models/{model_key}\\nMethod: GET\\n\\nRequest Parameters:\\nPath Parameters:\\nproject_key \\njob_id \\nmodel_key \\n\\nBody Parameters:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\nproject_key\\tstring\\tYes\\t\\t\\njob_id\\tstring\\tYes\\t\\t\\nmodel_key\\tstring\\tYes\\t\\t\\n\\nResponse Data:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\ncode\\tnumber\\tYes\\t\\t0成功，非0失败\\nmsg\\tstring\\tYes\\t\\t如果失败，这里有错误信息\\ndata\\tobject\\tYes\\t\\tdata信息\\n\\tproject_key\\tstring\\tNo\\t\\tproject key\\n\\tname\\tstring\\tNo\\t\\t用户可修改的name\\n\\tmodel\\tobject\\tNo\\t\\tmodel信息\\n\\t\\ttype\\tstring\\tNo\\t\\tdataset type\\n\\t\\tmanaged\\tboolean\\tNo\\t\\t为false时是第一类dataset，数据不可删除\\n\\t\\tname\\tstring\\tNo\\t\\t用户可修改的name\\n\\t\\tproject_key\\tstring\\tNo\\t\\tproject key\\n\\t\\tformat_type\\tstring\\tNo\\t\\t文件类型的dataset才有这项。“csv”\\n\\t\\tflow_options\\tobject\\tNo\\t\\t创建dataset时的高级设置\\n\\t\\t\\tvirtualizable\\tboolean\\tNo\\t\\t高级设置里的参数。缺省false\\n\\t\\t\\trebuild_behavior\\tstring\\tNo\\t\\t高级设置里的参数。缺省NORMAL\\n\\t\\t\\tcross_project_build_behavior\\tstring\\tNo\\t\\t高级设置里的参数。缺省DEFAULT\\n\\t\\tformat_params\\tobject\\tNo\\t\\t文件类型的dataset才有\\n\\t\\t\\tstyle\\tstring\\tNo\\t\\t\\n\\t\\t\\tcharset\\tstring\\tNo\\t\\t\\n\\t\\t\\tseparator\\tstring\\tNo\\t\\t\\n\\t\\t\\tquote_char\\tstring\\tNo\\t\\t\\n\\t\\t\\tescape_char\\tstring\\tNo\\t\\t\\n\\t\\t\\tdate_serialization_format\\tstring\\tNo\\t\\t\\n\\t\\t\\tarray_map_format\\tstring\\tNo\\t\\t\\n\\t\\t\\thive_separators\\tarray\\tNo\\t\\t\\n\\t\\t\\tskip_rows_before_header\\tnumber\\tNo\\t\\t\\n\\t\\t\\tparse_header_row\\tboolean\\tNo\\t\\t\\n\\t\\t\\tskip_rows_after_header\\tnumber\\tNo\\t\\t\\n\\t\\t\\tprobable_number_of_records\\tnumber\\tNo\\t\\t\\n\\t\\t\\tnormalize_booleans\\tboolean\\tNo\\t\\t\\n\\t\\t\\tnormalize_doubles\\tboolean\\tNo\\t\\t\\n\\t\\ttags\\tarray\\tNo\\t\\t标签tags\\n\\t\\tparams\\tobject\\tNo\\t\\t必有这项，但不同类型的dataset里面的key有差别\\n\\t\\t\\tconnection\\tstring\\tNo\\t\\tconnection id,到db查其他参数\\n\\t\\t\\tpath\\tstring\\tNo\\t\\t文件类connection才有这项\\n\\t\\t\\ttable\\tstring\\tNo\\t\\tdb表名，DB类connection才有这项\\n\\t\\t\\tmode\\tstring\\tNo\\t\\t存储类型，比如“table\\\",DB类connection才有这项\\n\\t\\t\\tbucket\\tstring\\tNo\\t\\tS3类型的connection才有这项\\n\\t\\t\\tkey_name\\tstring\\tNo\\t\\tredis才有，key name\\n\\t\\t\\tkey_type\\tstring\\tNo\\t\\tredis才有，key type\\n\\t\\t\\tcollection\\tstring\\tNo\\t\\t非关系型数据库才有，collection name\\n\\t\\t\\tindex\\tstring\\tNo\\t\\t索引类型的才有这项\\n\\t\\t\\tnot_ready_if_empty\\tboolean\\tNo\\t\\t数据非空才认为是data ready\\n\\t\\t\\tfiles_selection_rules\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tmode\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\texclude_rules\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\tinclude_rules\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\texplicit_files\\tarray\\tNo\\t\\t\\n\\t\\tschema\\tobject\\tNo\\t\\tcolumns信息在这里\\n\\t\\t\\tcolumns\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\tname\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\ttype\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\torigin_type\\tstring\\tNo\\t\\t\\n\\t\\t\\tuser_modified\\tboolean\\tNo\\t\\t\\n\\t\\tcustom_fields\\tobject\\tNo\\t\\t自定义fields\\n\\t\\tlast_build\\tobject\\tNo\\t\\t最后一次构建的信息\\n\\t\\t\\tproject_key\\tstring\\tNo\\t\\tproject key\\n\\t\\t\\tid\\tstring\\tNo\\t\\tactivity id\\n\\t\\t\\tjob_id\\tstring\\tNo\\t\\tjob id\\n\\t\\t\\tjob_project_key\\tstring\\tNo\\t\\t\\n\\t\\t\\tbuild_start_time\\tnumber\\tNo\\t\\t构建开始时间\\n\\t\\t\\tbuild_end_time\\tnumber\\tNo\\t\\t构建结束时间\\n\\t\\t\\tbuild_success\\tstring\\tNo\\t\\tsuccess或failed\\n\\t\\tobject_key\\tstring\\tNo\\t\\tdataset_key，后台用的id，用户不可见不可改\\n\\t\\tcache\\tobject\\tNo\\t\\t下载缓存数据链接\\n\\t\\t\\ts3_path\\tstring\\tNo\\t\\t\\n\\tstatus\\tobject\\tNo\\t\\t数据状态\\n\\t\\tsize\\tobject\\tNo\\t\\t数据大小信息\\n\\t\\t\\ttotal_value\\tnumber\\tNo\\t\\t占多少字节磁盘\\n\\t\\t\\tlast_computed\\tnumber\\tNo\\t\\t\\n\\t\\t\\tfirst_computed\\tnumber\\tNo\\t\\t\\n\\t\\t\\thas_data\\tboolean\\tNo\\t\\t是否有数据，这个影响前端的图标显示\\n\\t\\t\\tincomplete\\tboolean\\tNo\\t\\t\\n\\t\\trecords\\tobject\\tNo\\t\\t\\n\\t\\t\\ttotal_value\\tnumber\\tNo\\t\\t\\n\\t\\t\\tlast_computed\\tnumber\\tNo\\t\\t\\n\\t\\t\\tfirst_computed\\tnumber\\tNo\\t\\t\\n\\t\\t\\thas_data\\tboolean\\tNo\\t\\t是否有数据，这个影响前端的图标显示\\n\\t\\t\\tincomplete\\tboolean\\tNo\\t\\t\\n\\t\\tpartitions_last_compute\\tnumber\\tNo\\t\\t\\n\\t\\tpartitions\\tnumber\\tNo\\t\\t\\n\\tbuildable\\tboolean\\tNo\\t\\t有recipe时为true\\n\\theaders\\tarray\\tNo\\t\\t\\n\\t\\tdataset_schema\\tobject\\tNo\\t\\t\\n\\t\\t\\tname\\tstring\\tNo\\t字段名称\\t\\n\\t\\t\\ttype\\tstring\\tNo\\t字段类型\\t\\n\\t\\tnormal_rate\\tobject\\tNo\\t缺失值统计信息\\t\\n\\n```\"}]": { - "code": "import string\nimport random\n\ndef random_string(length=10):\n return ''.join(random.choice(string.ascii_lowercase) for i in range(length))" - }, - "[{\"role\": \"user\", \"content\": \"Interface definition:\\n```text\\nInterface Name: Element Tagging\\nInterface Path: /projects/{project_key}/node-tags\\nMethod: POST\\n\\nRequest parameters:\\nPath parameters:\\nproject_key\\n\\nBody parameters:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\nnodes\\tarray\\tYes\\t\\tNodes\\n\\tnode_key\\tstring\\tNo\\t\\tNode key\\n\\ttags\\tarray\\tNo\\t\\tOriginal node tag list\\n\\tnode_type\\tstring\\tNo\\t\\tNode type DATASET / RECIPE\\noperations\\tarray\\tYes\\t\\t\\n\\ttags\\tarray\\tNo\\t\\tOperation tag list\\n\\tmode\\tstring\\tNo\\t\\tOperation type ADD / DELETE\\n\\nReturn data:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\ncode\\tinteger\\tYes\\t\\tStatus code\\nmsg\\tstring\\tYes\\t\\tPrompt message\\ndata\\tobject\\tYes\\t\\tReturned data\\nlist\\tarray\\tNo\\t\\tNode list true / false\\nnode_type\\tstring\\tNo\\t\\tNode type DATASET / RECIPE\\nnode_key\\tstring\\tNo\\t\\tNode key\\n```\\n\\nUnit test：\\n```python\\n@pytest.mark.parametrize(\\n\\\"project_key, nodes, operations, expected_msg\\\",\\n[\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"success\\\"),\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"dataset_002\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"tag1\\\"], \\\"mode\\\": \\\"DELETE\\\"}], \\\"success\\\"),\\n(\\\"\\\", [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Missing the required parameter project_key\\\"),\\n(123, [{\\\"node_key\\\": \\\"dataset_001\\\", \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Incorrect parameter type\\\"),\\n(\\\"project_key\\\", [{\\\"node_key\\\": \\\"a\\\"*201, \\\"tags\\\": [\\\"tag1\\\", \\\"tag2\\\"], \\\"node_type\\\": \\\"DATASET\\\"}], [{\\\"tags\\\": [\\\"new_tag1\\\"], \\\"mode\\\": \\\"ADD\\\"}], \\\"Request parameter exceeds field boundary\\\")\\n]\\n)\\ndef test_node_tags(project_key, nodes, operations, expected_msg):\\n pass\\n\\n# The above is an interface definition and a unit test example.\\n# Next, please play the role of an expert test manager with 20 years of experience at Google. When I give the interface definition, \\n# reply to me with a unit test. There are several requirements:\\n# 1. Only output one `@pytest.mark.parametrize` and the corresponding test_ function (inside pass, do not implement).\\n# -- The function parameter contains expected_msg for result verification.\\n# 2. The generated test cases use shorter text or numbers and are as compact as possible.\\n# 3. If comments are needed, use Chinese.\\n\\n# If you understand, please wait for me to give the interface definition and just answer \\\"Understood\\\" to save tokens.\\n\"}, {\"role\": \"user\", \"content\": \"Refer to the test types: such as SQL injection, cross-site scripting (XSS), unauthorized access and privilege escalation, \\nauthentication and authorization, parameter verification, exception handling, file upload and download.\\nPlease output 10 test cases within one `@pytest.mark.parametrize` scope.\\n```text\\nAPI Name: 获取managed folder详情（job专用)\\nAPI Path: /v1/projects/{project_key}/jobs/{job_id}/folders/{folder_key}\\nMethod: GET\\n\\nRequest Parameters:\\nPath Parameters:\\nproject_key \\njob_id \\nfolder_key \\n\\nBody Parameters:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\nproject_key\\tstring\\tYes\\t\\t\\njob_id\\tstring\\tYes\\t\\t\\nfolder_key\\tstring\\tYes\\t\\t\\n\\nResponse Data:\\nName\\tType\\tRequired\\tDefault Value\\tRemarks\\ncode\\tnumber\\tYes\\t\\t0成功，非0失败\\nmsg\\tstring\\tYes\\t\\t失败时这里有错误信息\\ndata\\tobject\\tYes\\t\\t\\n\\tproject_key\\tstring\\tNo\\t\\tproject key\\n\\tfolder\\tobject\\tNo\\t\\tfolder配置在这里\\n\\t\\tproject_key\\tstring\\tNo\\t\\tproject key\\n\\t\\tobject_key\\tstring\\tNo\\t\\tobject key\\n\\t\\tname\\tstring\\tNo\\t\\t用户可编辑的那个name\\n\\t\\ttype\\tstring\\tNo\\t\\tfolder类型，与connection有关\\n\\t\\tparams\\tobject\\tNo\\t\\t数据读写相关配置在这里\\n\\t\\t\\tconnection\\tstring\\tNo\\t\\tconnection id\\n\\t\\t\\tpath\\tstring\\tNo\\t\\t文件夹内容存放的相对路径\\n\\t\\t\\tnot_ready_if_empty\\tboolean\\tNo\\t\\treserved\\n\\t\\t\\tfiles_selection_rules\\tobject\\tNo\\t\\t文件过滤规则\\n\\t\\t\\t\\tmode\\tstring\\tNo\\t\\tALL\\n\\t\\t\\t\\texclude_rules\\tarray\\tNo\\t\\t排除规则\\n\\t\\t\\t\\tinclude_rules\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\texplicit_files\\tarray\\tNo\\t\\t\\n\\t\\tflow_options\\tobject\\tNo\\t\\tflow参数\\n\\t\\t\\tvirtualizable\\tboolean\\tNo\\t\\t\\n\\t\\t\\trebuild_behavior\\tstring\\tNo\\t\\t构建方式\\n\\t\\t\\tcross_project_build_behavior\\tstring\\tNo\\t\\t\\n\\t\\tmetrics\\tobject\\tNo\\t\\t\\n\\t\\t\\tprobes\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\ttype\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\tenabled\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\tcompute_on_build_mode\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\tmeta\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tname\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\t\\tlevel\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\tconfiguration\\tobject\\tNo\\t\\t\\n\\t\\t\\tengine_config\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tpad_runs_with_metrics\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\thive\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tactive\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\textra_conf\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\tbasic\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tdss\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tactive\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\tselection\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tuse_mem_table\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tfilter\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\t\\tdistinct\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\t\\tenabled\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tpartition_selection_method\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tlatest_partitions_n\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tordering\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\t\\tenabled\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\t\\trules\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tsampling_method\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tmax_records\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\ttarget_ratio\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\twithin_first_n\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\t\\t\\tmax_read_uncompressed_bytes\\tnumber\\tNo\\t\\t\\n\\t\\t\\t\\tsql\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tactive\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\timpala\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tactive\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\tspark\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\t\\tactive\\tboolean\\tNo\\t\\t\\n\\t\\t\\t\\t\\textra_conf\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\tpython\\tobject\\tNo\\t\\t\\n\\t\\t\\tdisplayed_state\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tpartition\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\tcolumns\\tarray\\tNo\\t\\t\\n\\t\\t\\t\\tmetrics\\tarray\\tNo\\t\\t\\n\\t\\tchecks\\tobject\\tNo\\t\\t\\n\\t\\t\\trun_on_build\\tboolean\\tNo\\t\\t\\n\\t\\t\\tchecks\\tarray\\tNo\\t\\t\\n\\t\\t\\tdisplayed_state\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tpartition\\tstring\\tNo\\t\\t\\n\\t\\t\\t\\tchecks\\tarray\\tNo\\t\\t\\n\\t\\tversion_tag\\tobject\\tNo\\t\\t配置版本信息\\n\\t\\t\\tversion_number\\tnumber\\tNo\\t\\t\\n\\t\\t\\tlast_modified_by\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tlogin\\tstring\\tNo\\t\\t\\n\\t\\t\\tlast_modified_on\\tnumber\\tNo\\t\\t修改时间unix time ms\\n\\t\\tcreation_tag\\tobject\\tNo\\t\\t配置创建时间\\n\\t\\t\\tversion_number\\tnumber\\tNo\\t\\t1\\n\\t\\t\\tlast_modified_by\\tobject\\tNo\\t\\t\\n\\t\\t\\t\\tlogin\\tstring\\tNo\\t\\t\\n\\t\\t\\tlast_modified_on\\tnumber\\tNo\\t\\t创建时间unix time ms\\n\\t\\ttags\\tarray\\tNo\\t\\t文件夹标签\\n\\t\\tcustom_fields\\tobject\\tNo\\t\\t\\n\\t\\tchecklists\\tobject\\tNo\\t\\t\\n\\t\\t\\tchecklists\\tarray\\tNo\\t\\t\\n\\n```\"}]": { - "code": "import string\nimport random\n\ndef random_string(length=10):\n return ''.join(random.choice(string.ascii_lowercase) for i in range(length))" - }, - "[{\"role\": \"user\", \"content\": \"\\n# Background\\nAs a data scientist, you need to help user to achieve their goal [构造数据集并进行数据清洗] step-by-step in an continuous Jupyter notebook.\\n\\n## Done Tasks\\n```python\\n import pandas as pd\\n df = pd.DataFrame({\\n 'a': [1, 2, 3, 4, 5],\\n 'b': [1.1, 2.2, 3.3, 4.4, np.nan],\\n 'c': ['aa', 'bb', 'cc', 'dd', 'ee'],\\n 'd': [1, 2, 3, 4, 5]\\n })\\n```end\\n\\n## Current Task\\n对数据集进行数据清洗\\n\\n# Latest Data Info\\nLatest data info after previous tasks:\\n\\n\\n# Task\\nWrite complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Code Steps:\\nStrictly follow steps below when you writing code if it's convenient.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools:\\nEach Class tool is described in JSON format. When you call a tool, import the tool from its path first.\\n{'FillMissingValue': {'type': 'class', 'description': 'Completing missing values with simple strategies.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}, 'strategy': {'type': 'str', 'description': \\\"The imputation strategy, notice 'mean' and 'median' can only be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.\\\", 'default': \\\"'mean'\\\", 'enum': [\\\"'mean'\\\", \\\"'median'\\\", \\\"'most_frequent'\\\", \\\"'constant'\\\"]}, 'fill_value': {'type': 'int', 'description': 'Fill_value is used to replace all occurrences of missing_values. Defaults to None.', 'default': 'None'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MinMaxScale': {'type': 'class', 'description': 'Transform features by scaling each feature to a range, which is (0, 1).', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'StandardScale': {'type': 'class', 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MaxAbsScale': {'type': 'class', 'description': 'Scale each feature by its maximum absolute value.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'RobustScale': {'type': 'class', 'description': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}}\\n\\n# Output Example:\\nwhen current task is \\\"do data preprocess, like fill missing value, handle outliers, etc.\\\", and their are two steps in 'Code Steps', the code be like:\\n```python\\n# Step 1: fill missing value\\n# Tools used: ['FillMissingValue']\\nfrom metagpt.tools.libs.data_preprocess import FillMissingValue\\n\\ntrain_processed = train.copy()\\ntest_processed = test.copy()\\nnum_cols = train_processed.select_dtypes(include='number').columns.tolist()\\nif 'label' in num_cols:\\n num_cols.remove('label')\\nfill_missing_value = FillMissingValue(features=num_cols, strategy='mean')\\nfill_missing_value.fit(train_processed)\\ntrain_processed = fill_missing_value.transform(train_processed)\\ntest_processed = fill_missing_value.transform(test_processed)\\n\\n# Step 2: handle outliers\\nfor col in num_cols:\\n low, high = train_processed[col].quantile([0.01, 0.99])\\n train_processed[col] = train_processed[col].clip(low, high)\\n test_processed[col] = test_processed[col].clip(low, high)\\n```end\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n- Always copy the DataFrame before processing it and use the copy to process.\\n- The output code should contain all steps implemented correctly in 'Code Steps'.\\n\"}]": { - "code": "# Data preprocessing\n\n# Step 1: Fill missing values\nfrom metagpt.tools.libs.data_preprocess import FillMissingValue\n\n# Create a copy of the dataframe to avoid modifying the original one\ndf_processed = df.copy()\n\n# Identify numeric columns to fill missing values\nnumeric_cols = df_processed.select_dtypes(include='number').columns.tolist()\n\n# Initialize the FillMissingValue tool for numeric columns\nfill_missing_value = FillMissingValue(features=numeric_cols, strategy='mean')\n\n# Fit and transform the dataframe\ndf_processed = fill_missing_value.fit_transform(df_processed)\n\n# Step 2: Scale numeric columns\nfrom metagpt.tools.libs.data_preprocess import MinMaxScale\n\n# Initialize the MinMaxScale tool for numeric columns\nminmax_scale = MinMaxScale(features=numeric_cols)\n\n# Fit and transform the dataframe\ndf_processed = minmax_scale.fit_transform(df_processed)\n\n# Display the first few rows of the processed dataframe\ndf_processed.head()" - }, - "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\n构造数据集并进行数据清洗\\n## Context\\n\\n## Current Plan\\n[Task(task_id='1', dependent_task_ids=[], instruction='随机生成一个pandas DataFrame数据集', task_type='other', code_steps='', code=\\\"\\\\n import pandas as pd\\\\n df = pd.DataFrame({\\\\n 'a': [1, 2, 3, 4, 5],\\\\n 'b': [1.1, 2.2, 3.3, 4.4, np.nan],\\\\n 'c': ['aa', 'bb', 'cc', 'dd', 'ee'],\\\\n 'd': [1, 2, 3, 4, 5]\\\\n })\\\\n \\\", result='', is_success=False, is_finished=True), Task(task_id='2', dependent_task_ids=['1'], instruction='对数据集进行数据清洗', task_type='data_preprocess', code_steps='', code='', result='', is_success=False, is_finished=False)]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"对数据集进行数据清洗\\\",\\\"task_type\\\":\\\"data_preprocess\\\",\\\"code_steps\\\":\\\"\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{'FillMissingValue': {'type': 'class', 'description': 'Completing missing values with simple strategies.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}, 'strategy': {'type': 'str', 'description': \\\"The imputation strategy, notice 'mean' and 'median' can only be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.\\\", 'default': \\\"'mean'\\\", 'enum': [\\\"'mean'\\\", \\\"'median'\\\", \\\"'most_frequent'\\\", \\\"'constant'\\\"]}, 'fill_value': {'type': 'int', 'description': 'Fill_value is used to replace all occurrences of missing_values. Defaults to None.', 'default': 'None'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MinMaxScale': {'type': 'class', 'description': 'Transform features by scaling each feature to a range, which is (0, 1).', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'StandardScale': {'type': 'class', 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MaxAbsScale': {'type': 'class', 'description': 'Scale each feature by its maximum absolute value.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'RobustScale': {'type': 'class', 'description': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": { - "code": "from metagpt.tools.libs.data_preprocess import FillMissingValue\n\n# Instantiate the FillMissingValue class for column 'b'\nfill_missing_b = FillMissingValue(features=['b'], strategy='mean')\n\n# Fit and transform the DataFrame for column 'b'\ndf = fill_missing_b.fit_transform(df)" + "reflection": "The implementation failed the test case where the input array is [1, 5, 2, 3, 4]. The issue arises because the code does not handle the sorting of elements with the same number of ones in their binary representation correctly. According to the context, for similar number of ones, the elements should be sorted based on their decimal value in ascending order. However, the output [1, 2, 4, 3, 5] shows that 3 and 4 are not in the correct order, indicating that the sorting by decimal value is not functioning as intended. To fix this issue, we should ensure that the sorting is stable, so that when two elements have the same number of ones, they maintain their relative order if their decimal values are already in ascending order.", + "improved_impl": "def sort_array(arr):\n # Helper function to count the number of ones in the binary representation\n def count_ones(n):\n return bin(n).count('1')\n \n # Sort the array using a custom key function\n # The key function returns a tuple (number of ones, value) for each element\n # This ensures that if two elements have the same number of ones, they are sorted by their value\n # We use a stable sorting algorithm to maintain the relative order of elements with the same number of ones\n sorted_arr = sorted(arr, key=lambda x: (count_ones(x), x), reverse=True)\n sorted_arr = sorted(sorted_arr, key=count_ones)\n \n return sorted_arr\n" } } \ No newline at end of file diff --git a/tests/metagpt/actions/ci/test_write_analysis_code.py b/tests/metagpt/actions/ci/test_write_analysis_code.py index 72071fa35..dbd83fe40 100644 --- a/tests/metagpt/actions/ci/test_write_analysis_code.py +++ b/tests/metagpt/actions/ci/test_write_analysis_code.py @@ -8,8 +8,8 @@ from metagpt.actions.ci.write_analysis_code import ( WriteCodeWithTools, ) from metagpt.logs import logger -from metagpt.plan.planner import STRUCTURAL_CONTEXT from metagpt.schema import Message, Plan, Task +from metagpt.strategy.planner import STRUCTURAL_CONTEXT @pytest.mark.skip @@ -37,13 +37,12 @@ async def test_write_code_by_list_plan(): @pytest.mark.asyncio async def test_tool_recommendation(): task = "clean and preprocess the data" - code_steps = "" available_tools = { "FillMissingValue": "Filling missing values", "SplitBins": "Bin continuous data into intervals and return the bin identifier encoded as an integer value", } write_code = WriteCodeWithTools() - tools = await write_code._recommend_tool(task, code_steps, available_tools) + tools = await write_code._recommend_tool(task, available_tools) assert len(tools) == 1 assert "FillMissingValue" in tools diff --git a/tests/metagpt/roles/test_code_interpreter.py b/tests/metagpt/roles/ci/test_code_interpreter.py similarity index 90% rename from tests/metagpt/roles/test_code_interpreter.py rename to tests/metagpt/roles/ci/test_code_interpreter.py index 2d71fcbb0..f23292965 100644 --- a/tests/metagpt/roles/test_code_interpreter.py +++ b/tests/metagpt/roles/ci/test_code_interpreter.py @@ -1,7 +1,7 @@ import pytest from metagpt.logs import logger -from metagpt.roles.code_interpreter import CodeInterpreter +from metagpt.roles.ci.code_interpreter import CodeInterpreter @pytest.mark.asyncio diff --git a/tests/metagpt/roles/test_ml_engineer.py b/tests/metagpt/roles/ci/test_ml_engineer.py similarity index 96% rename from tests/metagpt/roles/test_ml_engineer.py rename to tests/metagpt/roles/ci/test_ml_engineer.py index 2728c6411..144201f85 100644 --- a/tests/metagpt/roles/test_ml_engineer.py +++ b/tests/metagpt/roles/ci/test_ml_engineer.py @@ -2,7 +2,7 @@ import pytest from metagpt.actions.ci.execute_nb_code import ExecuteNbCode from metagpt.logs import logger -from metagpt.roles.ml_engineer import MLEngineer +from metagpt.roles.ci.ml_engineer import MLEngineer from metagpt.schema import Message, Plan, Task from metagpt.tools.tool_types import ToolTypes from tests.metagpt.actions.ci.test_debug_code import CODE, DebugContext, ErrorStr @@ -22,7 +22,6 @@ MockPlan = Plan( dependent_task_ids=[], instruction="Perform exploratory data analysis on the train dataset to understand the features and target variable.", task_type="eda", - code_steps="", code="", result="", is_success=False, @@ -35,7 +34,6 @@ MockPlan = Plan( dependent_task_ids=[], instruction="Perform exploratory data analysis on the train dataset to understand the features and target variable.", task_type="eda", - code_steps="", code="", result="", is_success=False,