learning_curve.py

""" Exploring learning curves for classification of handwritten digits """

import matplotlib.pyplot as plt
import numpy
from sklearn.datasets import *
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegression


data = load_digits()
print data.DESCR

num_trials = 100
train_percentages = range(5,95,5)
test_accuracies = []
for i in train_percentages:
	avg_test_accuracy = 0
	for j in range(0, num_trials):
		X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, train_size=i / 100.0)
		model = LogisticRegression(C=10**-3)
		model.fit(X_train, y_train)
		avg_test_accuracy += model.score(X_test, y_test)
	avg_test_accuracy /= num_trials
	print i
	print "Test accuracy %f"%avg_test_accuracy
	test_accuracies.append(avg_test_accuracy)

# train a model with training percentages between 5 and 90 (see train_percentages) and evaluate
# the resultant accuracy.
# You should repeat each training percentage num_trials times to smooth out variability
# for consistency with the previous example use model = LogisticRegression(C=10**-10) for your learner

# TODO: your code here
fig = plt.figure()
plt.plot(train_percentages, test_accuracies)
plt.xlabel('Percentage of Data Used for Training')
plt.ylabel('Accuracy on Test Set')
plt.show()