def q2():
models = ['Bernoulli', 'Gaussian', '4-bins', '9-bins']
spamData = hw3.pandas_to_data(hw3.load_and_normalize_spambase())
k = 10
k_folds = hw3.partition_folds(spamData, k)
for model_type in range(4):
print '\nModel: {}'.format(models[model_type])
train_acc_sum = 0
nb_models = []
for ki in range(k - 1):
alpha = .001 if model_type==0 else 0
nb_model = nb.NaiveBayes(model_type, alpha=alpha)
truth_rows, data_rows, data_mus, y_mu = hw3.get_data_and_mus(k_folds[ki])
nb_model.train(data_rows, truth_rows)
predict = nb_model.predict(data_rows)
print predict
accuracy = hw3.get_accuracy(predict, truth_rows)
train_acc_sum += accuracy
print_output(ki, accuracy)
nb_models.append(nb_model)
nb_combined = nb.NaiveBayes(model_type, alpha=.001)
if model_type < 2:
nb_combined.aggregate_model(nb_models)
else:
nb_combined.aggregate_model3(nb_models)
truth_rows, data_rows, data_mus, y_mu = hw3.get_data_and_mus(k_folds[k - 1])
test_predict = nb_combined.predict(data_rows)
test_accuracy = hw3.get_accuracy(test_predict, truth_rows)
print_test_output(test_accuracy, float(train_acc_sum)/(k-1))
#print len(k_folds[0])
truth_rows, data_rows, data_mus, y_mu = hw3.get_data_and_mus(spamData)
def TreeTest():
spamDat = spamData()
k = 10
all_folds = hw3.partition_folds(spamDat, k)
num_in_fold = []
err_in_fold = []
for i in range(len(all_folds) - 1):
spam = all_folds[i]
num_in_fold.append(len(spam))
truth, f_data = decTree.split_truth_from_data(spam)
tree = decTree.TreeOptimal(max_depth=2)
#tree = decTree.TreeRandom()
tree.fit(f_data, truth)
print 'Prediction...\n'
predict = tree.predict(f_data)
print predict
print truth
error = 1. - hw3.get_accuracy(predict, truth)
err_in_fold.append(error)
print 'Tree error is: {}'.format(error)
spam = all_folds[k -1]
truth, f_data = decTree.split_truth_from_data(spam)
tree = decTree.TreeOptimal(max_depth=2)
#tree = decTree.TreeRandom()
tree.fit(f_data, truth)
predict = tree.predict(f_data)
error = 1. - hw3.get_accuracy(predict, truth)
sum_training_err = 0
for i in range(len(num_in_fold)):
sum_training_err += err_in_fold[i]
#sum_training_err += float(err_in_fold)/num_in_fold
average_training_error = float(sum_training_err)/len(num_in_fold)
print 'Average training error: {}\nAverage testing error: {}'.format(average_training_error, error)
def q2_plots():
models = ['Bernoulli', 'Gaussian', '4-bins', '9-bins']
spamData = hw3.pandas_to_data(hw3.load_and_normalize_spambase())
k = 10
num_points = 50
k_folds = hw3.partition_folds(spamData, k)
for model_type in range(4):
roc = ROC.ROC()
print '\nModel: {}'.format(models[model_type])
train_acc_sum = 0
nb_models = []
for ki in [0]:
alpha = .001 if model_type==0 else 0
nb_model = nb.NaiveBayes(model_type, alpha=alpha)
truth_rows, data_rows, data_mus, y_mu = hw3.get_data_and_mus(k_folds[ki])
nb_model.train(data_rows, truth_rows)
for ti in range(num_points + 2):
theta = ti * 1./(num_points + 1)
predict = nb_model.predict(data_rows, theta)
print predict
accuracy = hw3.get_accuracy(predict, truth_rows)
train_acc_sum += accuracy
roc.add_tp_tn(predict, truth_rows, theta)
#print_plot_output(ki, accuracy, theta)
roc.plot_ROC('/Users/Admin/Dropbox/ML/MachineLearning_CS6140/CS6140_A_MacLeay/Homeworks/roc_{}.pdf'.format(model_type))
roc.print_info()
def GaussianNB(X, num_features=None):
model_type = 1
train_acc_sum = 0
test_acc_sum = 0
k = 10
nb_models = []
if num_features is not None:
y, X = utils.split_truth_from_data(X)
q4_slct = SelectKBest(k=num_features).fit(X, y)
X = q4_slct.transform(X)
X = utils.add_row(X, y)
k_folds = hw3u.partition_folds(X, k)
for ki in range(k):
grouped_fold = hw5u.group_fold(k_folds, ki)
alpha = .001 if model_type==0 else 0
mask_cols = check_cols(grouped_fold)
#nb_model = nb.NaiveBayes(model_type, alpha=alpha, ignore_cols=mask_cols)
nb_model = BernoulliNB()
print 'len of kfolds {}'.format(len(grouped_fold))
#truth_rows, data_rows, data_mus, y_mu = hw3u.get_data_and_mus(grouped_fold)
truth_rows, data_rows = utils.split_truth_from_data(grouped_fold)
print 'len of data {}'.format(len(data_rows))
#nb_model.train(data_rows, truth_rows)
nb_model.fit(data_rows, truth_rows)
predict = nb_model.predict(data_rows)
#print predict
accuracy = hw3u.get_accuracy(predict, truth_rows)
train_acc_sum += accuracy
print_output(ki, accuracy)
nb_models.append(nb_model)
truth_rows, data_rows = utils.split_truth_from_data(k_folds[ki])
test_predict = nb_model.predict(data_rows)
test_accuracy = hw3u.get_accuracy(test_predict, truth_rows)
test_acc_sum += test_accuracy
print_output(ki, test_accuracy, 'test')
print_test_output(float(train_acc_sum)/k, float(test_acc_sum)/k)
