def q6():
""" Bagging - sample with replacement """
spamData = hw3.pandas_to_data(hw3.load_and_normalize_spambase())
y, X = hw4.split_truth_from_data(spamData)
bagged = bag.Bagging(max_rounds=100, sample_size=1000, learner=lambda: DecisionTreeClassifier(max_depth=3))
bagged.fit(X, y)
kf_fold = hw4.partition_folds(spamData, .4)
test_y, test_X = hw4.split_truth_from_data(kf_fold[0])
test_pred = bagged.predict(test_X)
test_y = bagged._check_y(test_y)
test_pred = bagged._check_y(test_pred)
test_error = float(sum([0 if py == ty else 1 for py, ty in zip(test_pred, test_y)]))/len(test_y)
print 'Final testing error: {}'.format(test_error)
def q2():
"""Boosting on UCI datasets"""
crx = dl.data_q3_crx()
#crx = dl.data_q3_vote()
num_points = len(crx)
for i in xrange(5, 85, 5):
percent = float(i)/100
all_folds = hw4.partition_folds(crx, percent)
kf_train = all_folds[0]
kf_test = all_folds[1]
y, X = hw4.split_truth_from_data(kf_train)
y_test, X_test = hw4.split_truth_from_data(kf_test)
adaboost = run_adaboost(X, y, X_test, y_test, 'q2_crx')
yt_pred = adaboost.predict(X_test)
yt_pred = adaboost._check_y(yt_pred)
y_test = adaboost._check_y(y_test)
round_err = float(np.sum([1 if yt!=yp else 0 for yt, yp in zip(yt_pred, y_test)]))/len(y_test)
last_round = adaboost.local_errors.keys()[-1]
#print 'Error at {}%: Train: {} Test: {}'.format(percent, adaboost.adaboost_error[last_round], round_err)
print 'Error at {}%: Test: {}'.format(percent, round_err)
