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