def ModelLearning(X, y, max_depth=[1, 2, 3, 4], beta=0.5):
""" Calculates the performance of several models with varying sizes of training data.
The learning and testing scores for each model are then plotted. """
# Create 10 cross-validation sets for training and testing
cv = ShuffleSplit(X.shape[0], n_iter=10, test_size=0.2, random_state=0)
# Generate the training set sizes increasing by 50
train_sizes = np.rint(np.linspace(1, X.shape[0] * 0.8 - 1, 9)).astype(int)
# Create the figure window
fig = pl.figure(figsize=(10, 7))
# Create three different models based on max_depth
for k, depth in enumerate(max_depth):
# Create a Decision tree regressor at max_depth = depth
regressor = DecisionTreeClassifier(max_depth=depth)
# Calculate the training and testing scores
sizes, train_scores, test_scores = curves.learning_curve(
regressor,
X,
y,
cv=cv,
train_sizes=train_sizes,
scoring=make_scorer(fbeta_score, beta=beta))
# Find the mean and standard deviation for smoothing
train_std = np.std(train_scores, axis=1)
train_mean = np.mean(train_scores, axis=1)
test_std = np.std(test_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)
# Subplot the learning curve
ax = fig.add_subplot(2, 2, k + 1)
ax.plot(sizes, train_mean, 'o-', color='r', label='Training Score')
ax.plot(sizes, test_mean, 'o-', color='g', label='Testing Score')
# ax.fill_between(sizes, train_mean - train_std, \
# train_mean + train_std, alpha=0.15, color='r')
# ax.fill_between(sizes, test_mean - test_std, \
# test_mean + test_std, alpha=0.15, color='g')
# Labels
ax.set_title('max_depth = %s' % (depth))
ax.set_xlabel('Number of Training Points')
ax.set_ylabel('Score')
ax.set_xlim([0, X.shape[0] * 0.8])
ax.set_ylim([-0.05, 1.05])
# Visual aesthetics
ax.legend(bbox_to_anchor=(1.05, 2.05), loc='lower left', borderaxespad=0.)
fig.suptitle('Decision Tree Regressor Learning Performances',
fontsize=16,
y=1.03)
fig.show()
pl.show()
def ModelLearning(X, y):
""" Calculates the performance of several models with varying sizes of training data.
The learning and testing scores for each model are then plotted. """
# Create 10 cross-validation sets for training and testing
cv = ShuffleSplit(X.shape[0], n_iter = 10, test_size = 0.2, random_state = 0)
# Generate the training set sizes increasing by 50
train_sizes = np.rint(np.linspace(1, X.shape[0]*0.8 - 1, 9)).astype(int)
# Create the figure window
fig = pl.figure(figsize=(10,7))
# Create three different models based on max_depth
for k, depth in enumerate([1,3,6,10]):
# Create a Decision tree regressor at max_depth = depth
regressor = DecisionTreeRegressor(max_depth = depth)
# Calculate the training and testing scores
sizes, train_scores, test_scores = curves.learning_curve(regressor, X, y, \
cv = cv, train_sizes = train_sizes, scoring = 'r2')
# Find the mean and standard deviation for smoothing
train_std = np.std(train_scores, axis = 1)
train_mean = np.mean(train_scores, axis = 1)
test_std = np.std(test_scores, axis = 1)
test_mean = np.mean(test_scores, axis = 1)
# Subplot the learning curve
ax = fig.add_subplot(2, 2, k+1)
ax.plot(sizes, train_mean, 'o-', color = 'r', label = 'Training Score')
ax.plot(sizes, test_mean, 'o-', color = 'g', label = 'Testing Score')
ax.fill_between(sizes, train_mean - train_std, \
train_mean + train_std, alpha = 0.15, color = 'r')
ax.fill_between(sizes, test_mean - test_std, \
test_mean + test_std, alpha = 0.15, color = 'g')
# Labels
ax.set_title('max_depth = %s'%(depth))
ax.set_xlabel('Number of Training Points')
ax.set_ylabel('Score')
ax.set_xlim([0, X.shape[0]*0.8])
ax.set_ylim([-0.05, 1.05])
# Visual aesthetics
ax.legend(bbox_to_anchor=(1.05, 2.05), loc='lower left', borderaxespad = 0.)
fig.suptitle('Decision Tree Regressor Learning Performances', fontsize = 16, y = 1.03)
fig.tight_layout()
fig.show()