batman

iris_flowers/polynomial_regression.py

@@ -0,0 +1,85 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from sklearn import datasets, linear_model
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.pipeline import make_pipeline
+import os
+
+def load_petal_data():
+    """
+    Loads the Iris dataset and extracts petal dimensions.
+    """
+    iris = datasets.load_iris(as_frame=True)
+    df = iris.frame
+    X = df[['petal length (cm)']]
+    y = df['petal width (cm)']
+    return X, y
+
+def train_and_evaluate_poly_model(degree, X_train, y_train, X_test, y_test):
+    """
+    Trains a polynomial regression model of a given degree.
+    """
+    # Create a pipeline that first transforms features, then applies linear regression
+    model = make_pipeline(PolynomialFeatures(degree), linear_model.LinearRegression())
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    mse = mean_squared_error(y_test, y_pred)
+    r2 = r2_score(y_test, y_pred)
+
+    print(f"Degree {degree} Model:")
+    print(f"  MSE: {mse:.4f}")
+    print(f"  R^2: {r2:.4f}")
+
+    return model
+
+def visualize_polynomial_fits(models, X, y):
+    """
+    Plots the data and the regression curves for different degrees.
+    """
+    if not os.path.exists('plots'):
+        os.makedirs('plots')
+        
+    plt.figure(figsize=(10, 6))
+    plt.scatter(X, y, color='black', alpha=0.5, label='Actual Data')
+    
+    # Generate a smooth range of X values for plotting curves
+    X_plot = np.linspace(X.min(), X.max(), 100).reshape(-1, 1)
+    
+    colors = ['blue', 'green', 'red']
+    
+    for degree, model in models.items():
+        y_plot = model.predict(X_plot)
+        plt.plot(X_plot, y_plot, color=colors[degree-1], linewidth=2, label=f'Degree {degree}')
+        
+    plt.xlabel('Petal Length (cm)')
+    plt.ylabel('Petal Width (cm)')
+    plt.title('Polynomial Regression Comparison')
+    plt.legend()
+    plt.grid(True, alpha=0.3)
+    
+    output_path = 'plots/polynomial_regression.png'
+    plt.savefig(output_path)
+    plt.close()
+    print(f"Polynomial plot saved to {output_path}")
+
+def main():
+    print("Starting Polynomial Regression Analysis...")
+    
+    X, y = load_petal_data()
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    models = {}
+    degrees = [1, 2, 3]
+    
+    for degree in degrees:
+        models[degree] = train_and_evaluate_poly_model(degree, X_train, y_train, X_test, y_test)
+        
+    visualize_polynomial_fits(models, X, y)
+    print("Analysis Complete.")
+
+if __name__ == "__main__":
+    main()