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skills/regression-analysis-tool/assets/README.md

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+# Assets
+
+Bundled resources for regression-analysis-tool skill
+
+- [ ] example_dataset.csv: Example CSV dataset for demonstrating regression analysis.
+- [ ] model_visualization_template.py: Python script template for visualizing the regression model and its performance.

skills/regression-analysis-tool/assets/example_dataset.csv

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+# Example dataset for regression analysis with the regression-analysis-tool plugin.
+# This dataset contains independent variables (X1, X2, X3) and a dependent variable (Y).
+# Use this dataset as a starting point for running the /run-regression command.
+# You can replace this data with your own dataset. Ensure your data is clean and properly formatted.
+# Columns should be comma-separated.  The first row should be the header row.
+# If you need to include text values, enclose them in double quotes.
+# This example contains 10 rows, but your dataset can contain more.
+
+X1,X2,X3,Y
+10,20,30,100
+15,25,35,120
+20,30,40,140
+25,35,45,160
+30,40,50,180
+35,45,55,200
+40,50,60,220
+45,55,65,240
+50,60,70,260
+55,65,75,280
+#Add your own data below.
+#<X1_VALUE>,<X2_VALUE>,<X3_VALUE>,<Y_VALUE>

skills/regression-analysis-tool/assets/model_visualization_template.py

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+#!/usr/bin/env python3
+
+"""
+This script provides a template for visualizing regression model results.
+
+It includes functionalities for plotting predicted vs. actual values,
+residual plots, and other visualizations to assess model performance.
+"""
+
+import argparse
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from sklearn.metrics import mean_squared_error, r2_score
+from typing import Optional
+
+
+def plot_predicted_vs_actual(
+    y_true: np.ndarray, y_pred: np.ndarray, title: str = "Predicted vs. Actual"
+) -> None:
+    """
+    Plots predicted values against actual values.
+
+    Args:
+        y_true (np.ndarray): Array of actual values.
+        y_pred (np.ndarray): Array of predicted values.
+        title (str, optional): Title of the plot. Defaults to "Predicted vs. Actual".
+    """
+    try:
+        plt.figure(figsize=(8, 6))
+        plt.scatter(y_true, y_pred, alpha=0.5)
+        plt.xlabel("Actual Values")
+        plt.ylabel("Predicted Values")
+        plt.title(title)
+        plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], "k--", lw=2)
+        plt.show()
+    except Exception as e:
+        print(f"Error plotting predicted vs. actual: {e}")
+
+
+def plot_residuals(
+    y_true: np.ndarray, y_pred: np.ndarray, title: str = "Residual Plot"
+) -> None:
+    """
+    Plots the residuals (errors) of the regression model.
+
+    Args:
+        y_true (np.ndarray): Array of actual values.
+        y_pred (np.ndarray): Array of predicted values.
+        title (str, optional): Title of the plot. Defaults to "Residual Plot".
+    """
+    try:
+        residuals = y_true - y_pred
+        plt.figure(figsize=(8, 6))
+        plt.scatter(y_pred, residuals, alpha=0.5)
+        plt.xlabel("Predicted Values")
+        plt.ylabel("Residuals")
+        plt.title(title)
+        plt.axhline(y=0, color="k", linestyle="--")  # Add a horizontal line at y=0
+        plt.show()
+    except Exception as e:
+        print(f"Error plotting residuals: {e}")
+
+
+def visualize_regression_results(
+    y_true: np.ndarray, y_pred: np.ndarray, model_name: str = "Regression Model"
+) -> None:
+    """
+    Visualizes the regression results, including predicted vs. actual and residual plots.
+
+    Args:
+        y_true (np.ndarray): Array of actual values.
+        y_pred (np.ndarray): Array of predicted values.
+        model_name (str, optional): Name of the regression model. Defaults to "Regression Model".
+    """
+    try:
+        plot_predicted_vs_actual(
+            y_true, y_pred, title=f"{model_name}: Predicted vs. Actual"
+        )
+        plot_residuals(y_true, y_pred, title=f"{model_name}: Residual Plot")
+
+        # Calculate and print metrics
+        mse = mean_squared_error(y_true, y_pred)
+        r2 = r2_score(y_true, y_pred)
+        print(f"{model_name} - Mean Squared Error: {mse:.4f}")
+        print(f"{model_name} - R-squared: {r2:.4f}")
+
+    except Exception as e:
+        print(f"Error visualizing regression results: {e}")
+
+
+def main(
+    actual_values_file: str, predicted_values_file: str, model_name: str = "Regression Model"
+) -> None:
+    """
+    Main function to load data and visualize regression results.
+
+    Args:
+        actual_values_file (str): Path to the CSV file containing actual values.
+        predicted_values_file (str): Path to the CSV file containing predicted values.
+        model_name (str, optional): Name of the regression model. Defaults to "Regression Model".
+    """
+    try:
+        # Load data from CSV files
+        actual_df = pd.read_csv(actual_values_file)
+        predicted_df = pd.read_csv(predicted_values_file)
+
+        # Assuming the CSVs have a single column with the values
+        y_true = actual_df.iloc[:, 0].values
+        y_pred = predicted_df.iloc[:, 0].values
+
+        visualize_regression_results(y_true, y_pred, model_name=model_name)
+
+    except FileNotFoundError:
+        print("Error: One or both of the input files were not found.")
+    except pd.errors.EmptyDataError:
+        print("Error: One or both of the input files are empty.")
+    except KeyError:
+        print("Error: The specified column does not exist in the input file.")
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Visualize regression model results from CSV files."
+    )
+    parser.add_argument(
+        "--actual",
+        type=str,
+        required=True,
+        help="Path to the CSV file containing actual values.",
+    )
+    parser.add_argument(
+        "--predicted",
+        type=str,
+        required=True,
+        help="Path to the CSV file containing predicted values.",
+    )
+    parser.add_argument(
+        "--model_name",
+        type=str,
+        default="Regression Model",
+        help="Name of the regression model (optional).",
+    )
+
+    args = parser.parse_args()
+
+    # Create dummy data files for example usage
+    example_actual_data = pd.DataFrame({"actual": np.random.rand(100)})
+    example_predicted_data = pd.DataFrame({"predicted": example_actual_data["actual"] + np.random.normal(0, 0.1, 100)})
+    example_actual_data.to_csv("actual_values.csv", index=False)
+    example_predicted_data.to_csv("predicted_values.csv", index=False)
+
+    # Example usage with command-line arguments
+    main(args.actual, args.predicted, args.model_name)
+    # Clean up dummy data files
+    import os
+    os.remove("actual_values.csv")
+    os.remove("predicted_values.csv")