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| from agency_swarm.tools import BaseTool | |
| from pydantic import Field | |
| import pandas as pd | |
| import numpy as np | |
| import statsmodels.api as sm | |
| from sklearn.cluster import KMeans | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.metrics import mean_squared_error | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from statsmodels.tsa.arima.model import ARIMA | |
| class StatisticalModelingTool(BaseTool): | |
| """ | |
| This tool uses statistical modeling libraries such as statsmodels and scikit-learn to analyze economic data. | |
| It applies techniques like regression analysis, time series forecasting, and clustering to extract insights. | |
| The tool generates reports and visualizations to communicate findings. | |
| """ | |
| data: pd.DataFrame = Field( | |
| ..., description="The economic data to be analyzed, provided as a pandas DataFrame." | |
| ) | |
| target_variable: str = Field( | |
| ..., description="The target variable for regression analysis." | |
| ) | |
| time_series_column: str = Field( | |
| ..., description="The column name representing the time series data." | |
| ) | |
| n_clusters: int = Field( | |
| 3, description="The number of clusters for clustering analysis." | |
| ) | |
| def run(self): | |
| """ | |
| Analyzes the provided economic data using statistical modeling techniques. | |
| Generates reports and visualizations to communicate findings. | |
| """ | |
| # Perform regression analysis | |
| regression_results = self._perform_regression_analysis() | |
| # Perform time series forecasting | |
| time_series_forecast = self._perform_time_series_forecasting() | |
| # Perform clustering analysis | |
| clustering_results = self._perform_clustering_analysis() | |
| # Generate visualizations | |
| self._generate_visualizations() | |
| return { | |
| "regression_results": regression_results, | |
| "time_series_forecast": time_series_forecast, | |
| "clustering_results": clustering_results | |
| } | |
| def _perform_regression_analysis(self): | |
| """ | |
| Performs regression analysis on the economic data. | |
| """ | |
| X = self.data.drop(columns=[self.target_variable]) | |
| y = self.data[self.target_variable] | |
| X = sm.add_constant(X) # Add a constant term for the intercept | |
| model = sm.OLS(y, X).fit() | |
| return model.summary().as_text() | |
| def _perform_time_series_forecasting(self): | |
| """ | |
| Performs time series forecasting using ARIMA. | |
| """ | |
| ts_data = self.data.set_index(self.time_series_column) | |
| model = ARIMA(ts_data[self.target_variable], order=(1, 1, 1)) | |
| model_fit = model.fit() | |
| forecast = model_fit.forecast(steps=5) | |
| return forecast | |
| def _perform_clustering_analysis(self): | |
| """ | |
| Performs clustering analysis on the economic data. | |
| """ | |
| X = self.data.drop(columns=[self.target_variable]) | |
| kmeans = KMeans(n_clusters=self.n_clusters, random_state=42) | |
| self.data['Cluster'] = kmeans.fit_predict(X) | |
| return self.data['Cluster'].value_counts() | |
| def _generate_visualizations(self): | |
| """ | |
| Generates visualizations for the analysis. | |
| """ | |
| # Regression plot | |
| sns.pairplot(self.data, kind='reg') | |
| plt.title('Regression Analysis') | |
| plt.show() | |
| # Time series plot | |
| plt.figure(figsize=(10, 6)) | |
| plt.plot(self.data[self.time_series_column], self.data[self.target_variable], label='Actual') | |
| plt.title('Time Series Data') | |
| plt.xlabel('Time') | |
| plt.ylabel(self.target_variable) | |
| plt.legend() | |
| plt.show() | |
| # Clustering plot | |
| sns.scatterplot(x=self.data.columns[0], y=self.data.columns[1], hue='Cluster', data=self.data) | |
| plt.title('Clustering Analysis') | |
| plt.show() |