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unittest for tool convert

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yzlin 2024-01-19 22:32:43 +08:00
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tests/metagpt/tools/test_tool_convert.py Normal file
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import pandas as pd
from metagpt.tools.tool_convert import convert_code_to_tool_schema, docstring_to_schema
def test_docstring_to_schema():
docstring = """
Some test desc.
Args:
features (list): Columns to be processed.
strategy (str, optional): The imputation strategy, notice 'mean' and 'median' can only be
used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.
fill_value (int, optional): Fill_value is used to replace all occurrences of missing_values.
Defaults to None.
Returns:
pd.DataFrame: The transformed DataFrame.
"""
expected = {
"description": " Some test desc. ",
"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"],
},
"returns": [{"type": "pd.DataFrame", "description": "The transformed DataFrame."}],
}
schema = docstring_to_schema(docstring)
assert schema == expected
class DummyClass:
"""
Completing missing values with simple strategies.
"""
def __init__(self, features: list, strategy: str = "mean", fill_value=None):
"""
Initialize self.
Args:
features (list): Columns to be processed.
strategy (str, optional): The imputation strategy, notice 'mean' and 'median' can only
be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.
fill_value (int, optional): Fill_value is used to replace all occurrences of missing_values.
Defaults to None.
"""
pass
def fit(self, df: pd.DataFrame):
"""
Fit the FillMissingValue model.
Args:
df (pd.DataFrame): The input DataFrame.
"""
pass
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Transform the input DataFrame with the fitted model.
Args:
df (pd.DataFrame): The input DataFrame.
Returns:
pd.DataFrame: The transformed DataFrame.
"""
pass
def dummy_fn(df: pd.DataFrame) -> dict:
"""
Analyzes a DataFrame and categorizes its columns based on data types.
Args:
df (pd.DataFrame): The DataFrame to be analyzed.
Returns:
dict: A dictionary with four keys ('Category', 'Numeric', 'Datetime', 'Others').
Each key corresponds to a list of column names belonging to that category.
"""
pass
def test_convert_code_to_tool_schema_class():
expected = {
"DummyClass": {
"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"],
},
},
"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."}],
},
},
}
}
schema = convert_code_to_tool_schema(DummyClass)
assert schema == expected
def test_convert_code_to_tool_schema_function():
expected = {
"dummy_fn": {
"type": "function",
"description": "Analyzes a DataFrame and categorizes its columns based on data types. ",
"parameters": {
"properties": {"df": {"type": "pd.DataFrame", "description": "The DataFrame to be analyzed."}},
"required": ["df"],
},
}
}
schema = convert_code_to_tool_schema(dummy_fn)
assert schema == expected