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. For details about how to upload a file to a notebook instance, see .
After the model is saved, it must be uploaded to the OBS directory before being published. The config.json configuration and the customize_service.py inference code must be included during the publishing. For details about how to compile config.json, see Specifications for Compiling the Model Configuration File. For details about inference code, see Inference Code.
Inference Code¶
Inference code must be inherited from the BaseService class. For details about the import statements of different types of parent model classes, see Table 1.
# 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