def q5():
""" ECOC for image analysis
1000 Set: train. Accuracy: 1.000
Set: test. Accuracy: 0.851
12,000 (20% of 60,000)
Set: train. Accuracy: 0.923
Set: test. Accuracy: 0.905
Process finished with exit code 0
http://colah.github.io/posts/2014-10-Visualizing-MNIST/
"""
path = os.path.join(os.getcwd(), 'data/HW5/haar')
limit = 60000
images, labels = load_mnist('training', path=path)
images /= 128.0
X = []
print 'processing images'
black = [hw5u.count_black(b) for b in images[:limit]]
#bdf = [pd.DataFrame(bd) for bd in black]
#with open('save_img_' + str(limit) + '.csv', 'w') as fimg:
# pd.concat(bdf, axis=1).to_csv(fimg)
print 'finished processing'
rects = hw5u.get_rect_coords(100)
#hw5u.show_rectangles(rects)
for i in range(len(black)):
row = []
for r in range(len(rects)):
h_diff, v_diff = hw5u.get_features(black[i], rects[r])
row.append(h_diff)
row.append(v_diff)
X.append(row)
save(X, labels)
# Each image is a row in table X.
# Features are
# rectangle_1_horizontal_difference, rectangle_1_vertical_difference, rectangle_2_ho...
data = utils.add_row(X, labels)
data_split = hw5u.split_test_and_train(data, .2)
data_test = data_split[0]
data_train = data_split[1]
y_train, X_train = utils.split_truth_from_data(data_train)
y_test, X_test = utils.split_truth_from_data(data_test)
cls = ec.ECOCClassifier(learner=lambda: adac.AdaboostOptimal(learner=lambda: DecisionTreeClassifier(max_depth=1), max_rounds=200), #LogisticRegression, # TODO: replace with AdaBoost
#cls = ec.ECOCClassifier(learner=LogisticRegression, # TODO: replace with AdaBoost
verbose=True,
encoding_type='exhaustive').fit(X_train, y_train)
for set_name, X, y in [('train', X_train, y_train),
('test', X_test, y_test)]:
print("Set: {}. Accuracy: {:.3f}".format(set_name, accuracy_score(y, cls.predict(X))))
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)