learning_curve.py

#3. Report the learning curve 
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import learning_curve

def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1, train_sizes=np.linspace(.05, 1., 20), verbose=0, plot=True):
    # Compute learning curve
    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, verbose=verbose)
    
    # Compute mean and standard deviation of training and test scores
    train_scores_mean, train_scores_std = np.mean(train_scores, axis=1), np.std(train_scores, axis=1)
    test_scores_mean, test_scores_std = np.mean(test_scores, axis=1), np.std(test_scores, axis=1)
    
    if plot:
        # Create a new figure
        plt.figure()
        
        # Set the title of the plot
        plt.title(title)
        
        # Set the y-axis limits if provided
        if ylim is not None:
            plt.ylim(*ylim)
        
        # Set the labels for x and y axes
        plt.xlabel("Training examples")
        plt.ylabel("Score")
        
        # Invert the y-axis
        plt.gca().invert_yaxis()
        
        # Add grid lines to the plot
        plt.grid()
    
        # Fill the area between the training score curves with a light blue color
        plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="b")
        
        # Fill the area between the test score curves with a light red color
        plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="r")
        
        # Plot the training score curve as blue circles
        plt.plot(train_sizes, train_scores_mean, 'o-', color="b", label="Training score")
        
        # Plot the test score curve as red circles
        plt.plot(train_sizes, test_scores_mean, 'o-', color="r", label="Cross-validation score")
        
        # Add a legend to the plot
        plt.legend(loc="best")
        
        # Show the plot
        plt.gca().invert_yaxis()
        plt.show()
    
    # Compute the midpoint and difference between training and test scores
    midpoint = (train_scores_mean[-1] + train_scores_std[-1] + test_scores_mean[-1] - test_scores_std[-1]) / 2
    diff = train_scores_mean[-1] + train_scores_std[-1] - (test_scores_mean[-1] - test_scores_std[-1])
    
    # Return the midpoint and difference
    return midpoint, diff