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

XGBoost

Training and Saving a Model

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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('/data/iris.csv') X = iris.drop(['virginica'],axis=1) y = iris[['virginica']] 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')

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