def run(args):
model = None
# 获取训练和测试数据
data = get_data(args.model)[0]
test_data = get_data(args.model)[1]
# 创建模型结果的目录
if not os.path.exists('results'):
os.makedirs('results')
if len(os.listdir('results')) > 0:
shutil.rmtree('results')
os.makedirs('results')
# 初始化模型
if args.model == 'regression':
model = GradientBoostingRegressor(learning_rate=args.lr,
n_trees=args.trees,
max_depth=args.depth,
min_samples_split=args.count,
is_log=args.log,
is_plot=args.plot)
if args.model == 'binary_cf':
model = GradientBoostingBinaryClassifier(learning_rate=args.lr,
n_trees=args.trees,
max_depth=args.depth,
is_log=args.log,
is_plot=args.plot)
if args.model == 'multi_cf':
model = GradientBoostingMultiClassifier(learning_rate=args.lr,
n_trees=args.trees,
max_depth=args.depth,
is_log=args.log,
is_plot=args.plot)
# 训练模型
model.fit(data)
# 记录日志
logger.removeHandler(logger.handlers[-1])
logger.addHandler(
logging.FileHandler('results/result.log'.format(iter),
mode='w',
encoding='utf-8'))
logger.info(data)
# 模型预测
model.predict(test_data)
# 记录日志
logger.setLevel(logging.INFO)
if args.model == 'regression':
logger.info((test_data['predict_value']))
if args.model == 'binary_cf':
logger.info((test_data['predict_proba']))
logger.info((test_data['predict_label']))
if args.model == 'multi_cf':
logger.info((test_data['predict_label']))
pass
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
logger.addHandler(ch)
if __name__ == '__main__':
data = pd.DataFrame(data=[
[1, 5, 20, 1.1],
[2, 7, 30, 1.3],
[3, 21, 70, 1.7],
[4, 30, 60, 1.8],
],
columns=['id', 'age', 'weight', 'label'])
model = GradientBoostingRegressor(learning_rate=0.1,
n_trees=10,
max_depth=3,
min_samples_split=2,
is_log=False,
is_plot=True)
model.fit(data)
logger.removeHandler(logger.handlers[-1])
logger.addHandler(
logging.FileHandler('results/result.log'.format(iter),
mode='w',
encoding='utf-8'))
logger.info(data)
test_data = pd.DataFrame(data=[[5, 25, 65]],
columns=['id', 'age', 'weight'])
model.predict(test_data)
logger.setLevel(logging.INFO)
logger.info((test_data['predict_value']))