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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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | 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() |