XGBoost¶
Training and Saving a Model¶
import pandas as pd
import xgboost as xgb
from sklearn.model_selection import train_test_split
# Prepare training data and setting parameters
iris = pd.read_csv('/home/ma-user/work/iris.csv')
X = iris.drop(['variety'],axis=1)
y = iris[['variety']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234565)
params = {
'booster': 'gbtree',
'objective': 'multi:softmax',
'num_class': 3,
'gamma': 0.1,
'max_depth': 6,
'lambda': 2,
'subsample': 0.7,
'colsample_bytree': 0.7,
'min_child_weight': 3,
'silent': 1,
'eta': 0.1,
'seed': 1000,
'nthread': 4,
}
plst = params.items()
dtrain = xgb.DMatrix(X_train, y_train)
num_rounds = 500
model = xgb.train(plst, dtrain, num_rounds)
model.save_model('/tmp/xgboost.m')
Before training, download the iris.csv dataset, decompress it, and upload it to the /home/ma-user/work/ directory of the notebook instance. Download the iris.csv dataset from https://gist.github.com/netj/8836201.
After the model is saved, it must be uploaded to the OBS directory before being published. The config.json and customize_service.py files must be contained during publishing. For details about the definition method, see Model Package Specifications.
Inference Code¶
# coding:utf-8
import collections
import json
import xgboost as xgb
from model_service.python_model_service import XgSklServingBaseService
class user_Service(XgSklServingBaseService):
# request data preprocess
def _preprocess(self, data):
list_data = []
json_data = json.loads(data, object_pairs_hook=collections.OrderedDict)
for element in json_data["data"]["req_data"]:
array = []
for each in element:
array.append(element[each])
list_data.append(array)
return list_data
# predict
def _inference(self, data):
xg_model = xgb.Booster(model_file=self.model_path)
pre_data = xgb.DMatrix(data)
pre_result = xg_model.predict(pre_data)
pre_result = pre_result.tolist()
return pre_result
# predict result process
def _postprocess(self,data):
resp_data = []
for element in data:
resp_data.append({"predictresult": element})
return resp_data