def main(data_filename,stat_filename,max_iter,sample_rate,learn_rate,max_depth,split_points):
dataset=DataSet(data_filename);
print "Model parameters configuration:[data_file=%s,stat_file=%s,max_iter=%d,sample_rate=%f,learn_rate=%f,max_depth=%d,split_points=%d]"%(data_filename,stat_filename,max_iter,sample_rate,learn_rate,max_depth,split_points);
dataset.describe();
stat_file=open(stat_filename,"w");
stat_file.write("iteration\taverage loss in train data\tprediction accuracy on test data\taverage loss in test data\n");
model=Model(max_iter,sample_rate,learn_rate,max_depth,split_points);
train_data=sample(dataset.get_instances_idset(),int(dataset.size()*2.0/3.0));
test_data=set(dataset.get_instances_idset())-set(train_data);
model.train(dataset,train_data,stat_file,test_data);
#model.test(dataset,test_data);
stat_file.close();
iter4 : train loss=0.123063
iter5 : train loss=0.087872
iter6 : train loss=0.065684
iter7 : train loss=0.049936
iter8 : train loss=0.041866
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 data import DataSet
from model import GBDT
if __name__ == '__main__':
data_file = './credit.data.csv'
dateset = DataSet(data_file)
gbdt = GBDT(max_iter=20,
sample_rate=0.9,
learn_rate=0.5,
max_depth=7,
loss_type='binary-classification')
gbdt.fit(dateset, dateset.get_instances_idset())
