modelarts-umn/umn/source/examples_of_custom_scripts/xgboost.rst

original_name

modelarts_23_0177.html

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 <modelarts_23_0091>.

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