def train_model():
data_file = './data/feature_data.csv'
dateset = DataSet(data_file)
gbdt = GBDT(max_iter=80,
sample_rate=0.8,
learn_rate=0.1,
max_depth=7,
loss_type='regression')
gbdt.fit(dateset, set(list(dateset.get_instances_idset())[:1200]))
GBDT.save_model(gbdt, "./", "test")
predict = gbdt.predict(dateset.instances[1])
print "predict", predict, dateset.get_instance(1)['label']
print "#########################"
predict = gbdt.predict(dateset.instances[2])
print "predict", predict, dateset.get_instance(2)['label']
print "#########################"
predict = gbdt.predict(dateset.instances[3])
print "predict", predict, dateset.get_instance(3)['label']
print "#########################"
predict = gbdt.predict(dateset.instances[4])
print "predict", predict, dateset.get_instance(4)['label']
predict = gbdt.predict(dateset.instances[402])
print "predict", predict, dateset.get_instance(402)['label']
iter9 : train loss=0.035695
iter10 : train loss=0.030581
iter11 : train loss=0.027034
iter12 : train loss=0.024570
iter13 : train loss=0.019227
iter14 : train loss=0.015794
iter15 : train loss=0.013484
iter16 : train loss=0.010941
iter17 : train loss=0.009879
iter18 : train loss=0.008619
iter19 : train loss=0.007306
iter20 : train loss=0.005610
"""
from gbdt.data import DataSet
from gbdt.model import GBDT
if __name__ == '__main__':
data_file = './data/credit.data.csv'
dataset = DataSet(data_file)
gbdt = GBDT(max_iter=20,
sample_rate=0.8,
learn_rate=0.5,
max_depth=7,
loss_type='binary-classification')
print(dataset.get_instances_idset())
gbdt.fit(dataset, dataset.get_instances_idset())
for id in dataset.get_instances_idset():
instance = dataset.get_instance(id)
gbdt.predict(instance)
#gbdt.predict(dataset)
from gbdt.model import GBDT
from gbdt.data import DataSet
model = GBDT(tree_depth=3, learning_rate=0.01, max_iter=2000)
dataset = DataSet('data/ages.csv', 'Age')
model.fit(dataset)
x = {'LikesGardening': False, 'PlaysVideoGames': True, 'LikesHats': False}
print model.predict(x)