learning_curve_play2.py

# In this exercise we'll examine a learner which has high bias, and is incapable of
# learning the patterns in the data.
# Use the learning curve function from sklearn.learning_curve to plot learning curves
# of both training and testing error.

from sklearn.linear_model import LinearRegression
from sklearn.learning_curve import learning_curve
import matplotlib.pyplot as plt
from sklearn.metrics import explained_variance_score, make_scorer
from sklearn.cross_validation import KFold
import numpy as np

# Set the learning curve parameters; you'll need this for learning_curves
size = 1000
cv_sets = KFold(size,shuffle=True)
scorer = make_scorer(explained_variance_score)

# Create a series of data that forces a learner to have high bias
# Note for this quiz you do not need to create training and testing sets
X = np.reshape(np.random.normal(scale=2,size=size),(-1,1))
y = np.array([[1 - 2*x[0] +x[0]**2] for x in X])


def plot_curve():
    reg = LinearRegression()
    reg.fit(X,y)
    print "Regressor score: {:.4f}".format(reg.score(X,y))
    
    # TODO: Use learning_curve imported above to create learning curves from X and y.
    # You will need to use 'cv_sets' and 'scorer' as parameters in the function.
    
    train_sizes, train_scores, test_scores = learning_curve(reg, X, y, cv=cv_sets, scoring=scorer)

    # TODO: Plot the learning curves for both the training scores and testing scores.
    #       Use plt.plot() twice -- one for each score. Be sure to give them labels!
    # NOTE: Using plt.plot(train_scores) will get you 6 lines when we are looking to 
    # plot just 2(mean scores for training and testing). 
    # You can use np.mean(train_scores, axis =1) to get mean train_scores values. 
    # Similarly you can get the mean for the test_scores.
    train_scores_mean = np.mean(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    plt.plot(train_scores_mean, label='training_scores_mean')
    plt.plot(test_scores_mean, label='testing_scores_mean')
    
    plt.plot_date(X, y, label="data")
    
    # Plot aesthetics
    plt.ylim(-0.1, 1.1)
    plt.ylabel("Curve Score")
    plt.xlabel("Training Points")
    plt.legend(bbox_to_anchor=(1.1, 1.1))
    plt.show()

plot_curve()