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3b6d7db | 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 | from agency_swarm.tools import BaseTool
from pydantic import Field, ConfigDict
from typing import Dict, List, Union, Optional
import numpy as np
class StatisticalModelingTool(BaseTool):
"""
A tool for performing statistical analysis and modeling using AI-powered analysis instead of pandas.
"""
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:
try:
# Extract features and target
X = np.array([self.data[col] for col in self.feature_columns]).T
y = np.array(self.data[self.target_column])
# Add constant term
X = np.column_stack([np.ones(len(X)), X])
# Simple linear regression implementation
if self.model_type.lower() == "linear":
# Calculate coefficients using normal equation
beta = np.linalg.inv(X.T @ X) @ X.T @ y
# Calculate predictions and metrics
y_pred = X @ beta
mse = np.mean((y - y_pred) ** 2)
r2 = 1 - np.sum((y - y_pred) ** 2) / np.sum((y - np.mean(y)) ** 2)
# Format results
results = (
f"Model Summary:\n"
f"-------------\n"
f"Model Type: {self.model_type}\n"
f"Mean Squared Error: {mse:.4f}\n"
f"R-squared: {r2:.4f}\n\n"
f"Coefficients:\n"
)
for i, col in enumerate(['intercept'] + self.feature_columns):
results += f"{col}: {beta[i]:.4f}\n"
return results
else:
return "Currently only linear regression is supported"
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()) |