import numpy as np
import profile
from kaggler.online_model import ClassificationTree
from tqdm import tqdm
N_OBS = 1000
N_FEATURE = 10
def test():
data = np.random.randint(0, 1000, size=(N_OBS, N_FEATURE))
y = np.random.randint(2, size=N_OBS)
train = data[0:N_OBS // 2]
ytrain = y[0:N_OBS // 2]
test = data[N_OBS // 2:]
ytest = y[N_OBS // 2:]
learner = ClassificationTree(number_of_features=N_FEATURE)
for t, x in enumerate(tqdm(train)):
learner.update(x, ytrain[t])
correct_num = 0
for t, x in enumerate(tqdm(test)):
y_pred = learner.predict(x)
if y_pred == ytest[t]:
correct_num += 1
print(correct_num)
if __name__ == '__main__':
profile.run("test()")
