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06ba83e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | from agency_swarm.tools import BaseTool
from pydantic import Field, ConfigDict
import pandas as pd
from typing import Any, Dict, List, Optional
import statsmodels.api as sm
import numpy as np
class StatisticalModelingTool(BaseTool):
"""
A tool for performing statistical analysis and modeling on data.
"""
model_config = ConfigDict(arbitrary_types_allowed=True)
data: Dict[str, List[float]] = Field(
...,
description="Data dictionary with column names as keys and numeric lists as values"
)
target_column: str = Field(
...,
description="Name of the target variable column"
)
feature_columns: List[str] = Field(
...,
description="List of feature column names to use in the model"
)
model_type: str = Field(
"linear",
description="Type of statistical model to use (linear, logistic)"
)
def run(self) -> str:
"""
Run statistical analysis and modeling on the provided data.
"""
try:
# Convert dictionary to DataFrame
df = pd.DataFrame(self.data)
# Prepare features and target
X = df[self.feature_columns]
y = df[self.target_column]
# Add constant for statsmodels
X = sm.add_constant(X)
# Fit model based on type
if self.model_type.lower() == "linear":
model = sm.OLS(y, X).fit()
elif self.model_type.lower() == "logistic":
model = sm.Logit(y, X).fit()
else:
return f"Unsupported model type: {self.model_type}"
# Generate summary statistics
summary = model.summary()
# Calculate additional metrics
predictions = model.predict(X)
mse = np.mean((y - predictions) ** 2)
r2 = model.rsquared if hasattr(model, 'rsquared') else None
# Format results
results = (
f"Model Summary:\n"
f"-------------\n"
f"Model Type: {self.model_type}\n"
f"Mean Squared Error: {mse:.4f}\n"
)
if r2 is not None:
results += f"R-squared: {r2:.4f}\n"
results += f"\nDetailed Summary:\n{str(summary)}"
return results
except Exception as e:
return f"Error in statistical modeling: {str(e)}"
if __name__ == "__main__":
# Test data
test_data = {
"x": [1, 2, 3, 4, 5],
"y": [2.1, 3.8, 5.2, 6.9, 8.3]
}
tool = StatisticalModelingTool(
data=test_data,
target_column="y",
feature_columns=["x"],
model_type="linear"
)
print(tool.run()) |