diff --git a/metagpt/tools/libs/data_preprocess.py b/metagpt/tools/libs/data_preprocess.py index 9c571ad6b..66f579f66 100644 --- a/metagpt/tools/libs/data_preprocess.py +++ b/metagpt/tools/libs/data_preprocess.py @@ -99,7 +99,7 @@ class DataPreprocessTool(MLProcess): @register_tool(tool_type=TOOL_TYPE) -class FillMissingValue(MLProcess): +class FillMissingValue(DataPreprocessTool): """ Completing missing values with simple strategies. """ diff --git a/tests/data/rsp_cache.json b/tests/data/rsp_cache.json index 2257878e2..f92fb42c0 100644 --- a/tests/data/rsp_cache.json +++ b/tests/data/rsp_cache.json @@ -389,5 +389,11 @@ }, "[{\"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)" } } \ No newline at end of file