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QwenPaw committed 2026-06-19 06:38:31 +00:00
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metagpt/tools/schemas/CatCount.yml
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type: class
description: Add value counts of a categorical column as new feature.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, col: ''str'')'
parameters: 'Args: col (str): Column for value counts.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/CatCross.yml
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type: class
description: Add pairwise crossed features and convert them to numerical features.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, cols: ''list'', max_cat_num: ''int'' = 100)'
parameters: 'Args: cols (list): Columns to be pairwise crossed, at least 2 columns.
max_cat_num (int, optional): Maximum unique categories per crossed feature.
Defaults to 100.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/FillMissingValue.yml
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type: class
description: Completing missing values with simple strategies.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, features: ''list'', strategy: "Literal[''mean'', ''median'',
''most_frequent'', ''constant'']" = ''mean'', fill_value=None)'
parameters: 'Args: features (list): Columns to be processed. strategy (Literal["mean",
"median", "most_frequent", "constant"], optional): The imputation strategy,
notice ''mean'' and ''median'' can only be used for numeric features. Defaults
to ''mean''. fill_value (int, optional): Fill_value is used to replace all occurrences
of missing_values. Defaults to None.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/GPTvGenerator.yml
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type: class
description: Class for generating webpage code from a given webpage screenshot. This
class provides methods to generate webpages including all code (HTML, CSS, and JavaScript)
based on an image. It utilizes a vision model to analyze the layout from an image
and generate webpage codes accordingly.
methods:
__init__:
type: function
description: Initialize GPTvGenerator class with default values from the configuration.
signature: (self)
parameters: ''
generate_webpages:
type: async_function
description: 'Asynchronously generate webpages including all code (HTML, CSS,
and JavaScript) in one go based on the image. '
signature: '(self, image_path: str) -> str'
parameters: 'Args: image_path (str): The path of the image file. Returns: str:
Generated webpages content.'
save_webpages:
type: function
description: 'Save webpages including all code (HTML, CSS, and JavaScript) at
once. '
signature: '(webpages: str, save_folder_name: str = ''example'') -> pathlib.Path'
parameters: 'Args: webpages (str): The generated webpages content. save_folder_name
(str, optional): The name of the folder to save the webpages. Defaults to ''example''.
Returns: Path: The path of the saved webpages.'
metagpt/tools/schemas/GeneralSelection.yml
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type: class
description: Drop all nan feats and feats with only one unique value.
methods:
__init__:
type: function
description: Initialize self. See help(type(self)) for accurate signature.
signature: '(self, label_col: ''str'')'
parameters: ''
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/GroupStat.yml
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type: class
description: Aggregate specified column in a DataFrame grouped by another column,
adding new features named '<agg_col>_<agg_func>_by_<group_col>'.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, group_col: ''str'', agg_col: ''str'', agg_funcs: ''list'')'
parameters: 'Args: group_col (str): Column used for grouping. agg_col (str): Column
on which aggregation is performed. agg_funcs (list): List of aggregation functions
to apply, such as [''mean'', ''std'']. Each function must be supported by pandas.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/KFoldTargetMeanEncoder.yml
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type: class
description: Add a new feature to the DataFrame by k-fold mean encoding of a categorical
column using the label column.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, col: ''str'', label: ''str'', n_splits: ''int'' = 5, random_state:
''int'' = 2021)'
parameters: 'Args: col (str): Column to be k-fold mean encoded. label (str): Predicted
label column. n_splits (int, optional): Number of splits for K-fold. Defaults
to 5. random_state (int, optional): Random seed. Defaults to 2021.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/LabelEncode.yml
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type: class
description: Apply label encoding to specified categorical columns in-place.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, features: ''list'')'
parameters: 'Args: features (list): Categorical columns to be label encoded.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/MaxAbsScale.yml
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type: class
description: Scale each feature by its maximum absolute value.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, features: ''list'')'
parameters: 'Args: features (list): Columns to be processed.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/MinMaxScale.yml
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type: class
description: Transform features by scaling each feature to a range, which is (0, 1).
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, features: ''list'')'
parameters: 'Args: features (list): Columns to be processed.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/OneHotEncode.yml
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type: class
description: Apply one-hot encoding to specified categorical columns, the original
columns will be dropped.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, features: ''list'')'
parameters: 'Args: features (list): Columns to be processed.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/OrdinalEncode.yml
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type: class
description: Encode categorical features as ordinal integers.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, features: ''list'')'
parameters: 'Args: features (list): Columns to be processed.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/PolynomialExpansion.yml
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type: class
description: Add polynomial and interaction features from selected numeric columns
to input DataFrame.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, cols: ''list'', label_col: ''str'', degree: ''int'' = 2)'
parameters: 'Args: cols (list): Columns for polynomial expansion. label_col (str):
Label column name. degree (int, optional): The degree of the polynomial features.
Defaults to 2.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/RobustScale.yml
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type: class
description: Apply the RobustScaler to scale features using statistics that are robust
to outliers.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, features: ''list'')'
parameters: 'Args: features (list): Columns to be processed.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/SDEngine.yml
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type: class
description: Generate image using stable diffusion model. This class provides methods
to interact with a stable diffusion service to generate images based on text inputs.
methods:
__init__:
type: function
description: 'Initialize the SDEngine instance with configuration. '
signature: (self, sd_url='')
parameters: 'Args: sd_url (str, optional): URL of the stable diffusion service.
Defaults to "".'
construct_payload:
type: function
description: 'Modify and set the API parameters for image generation. '
signature: (self, prompt, negtive_prompt='(easynegative:0.8),black, dark,Low resolution',
width=512, height=512, sd_model='galaxytimemachinesGTM_photoV20')
parameters: 'Args: prompt (str): Text input for image generation. negtive_prompt
(str, optional): Text input for negative prompts. Defaults to None. width (int,
optional): Width of the generated image in pixels. Defaults to 512. height (int,
optional): Height of the generated image in pixels. Defaults to 512. sd_model
(str, optional): The model to use for image generation. Defaults to "galaxytimemachinesGTM_photoV20".
Returns: dict: Updated parameters for the stable diffusion API.'
run_t2i:
type: async_function
description: 'Run the stable diffusion API for multiple prompts asynchronously. '
signature: '(self, payloads: ''list'')'
parameters: 'Args: payloads (list): list of payload, each payload is a dictionary
of input parameters for the stable diffusion API.'
save:
type: function
description: 'Save generated images to the output directory. '
signature: (self, imgs, save_name='')
parameters: 'Args: imgs (str): Generated images. save_name (str, optional): Output
image name. Default is empty.'
simple_run_t2i:
type: function
description: 'Run the stable diffusion API for multiple prompts, calling the stable
diffusion API to generate images. '
signature: '(self, payload: ''dict'', auto_save: ''bool'' = True)'
parameters: 'Args: payload (dict): Dictionary of input parameters for the stable
diffusion API. auto_save (bool, optional): Save generated images automatically.
Defaults to True. Returns: list: The generated images as a result of the API
call.'
metagpt/tools/schemas/SplitBins.yml
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type: class
description: Inplace binning of continuous data into intervals, returning integer-encoded
bin identifiers directly.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, cols: ''list'', strategy: ''str'' = ''quantile'')'
parameters: 'Args: cols (list): Columns to be binned inplace. strategy (str, optional):
Strategy used to define the widths of the bins. Enum: [''quantile'', ''uniform'',
''kmeans'']. Defaults to ''quantile''.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/StandardScale.yml
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type: class
description: Standardize features by removing the mean and scaling to unit variance.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, features: ''list'')'
parameters: 'Args: features (list): Columns to be processed.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/TargetMeanEncoder.yml
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type: class
description: Encode a categorical column by the mean of the label column, and adds
the result as a new feature.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, col: ''str'', label: ''str'')'
parameters: 'Args: col (str): Column to be mean encoded. label (str): Predicted
label column.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/VarianceBasedSelection.yml
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type: class
description: Select features based on variance and remove features with low variance.
methods:
__init__:
type: function
description: 'Initialize self. '
signature: '(self, label_col: ''str'', threshold: ''float'' = 0)'
parameters: 'Args: label_col (str): Label column name. threshold (float, optional):
Threshold for variance. Defaults to 0.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: '(self, df: ''pd.DataFrame'')'
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: '(self, df: ''pd.DataFrame'') -> ''pd.DataFrame'''
parameters: 'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
metagpt/tools/schemas/email_login_imap.yml
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type: function
description: 'Use imap_tools package to log in to your email (the email that supports
IMAP protocol) to verify and return the account object. '
signature: (email_address, email_password)
parameters: 'Args: email_address (str): Email address that needs to be logged in and
linked. email_password (str): Password for the email address that needs to be logged
in and linked. Returns: object: The imap_tools''s MailBox object returned after
successfully connecting to the mailbox through imap_tools, including various information
about this account (email, etc.), or None if login fails.'
metagpt/tools/schemas/scrape_web_playwright.yml
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type: async_function
description: 'Asynchronously Scrape and save the HTML structure and inner text content
of a web page using Playwright. '
signature: (url)
parameters: 'Args: url (str): The main URL to fetch inner text from. Returns: dict:
The inner text content and html structure of the web page, keys are ''inner_text'',
''html''.'