Changes to ma_umn from doc-exports#1


This is an automatically created Pull Request for changes to ma_umn in opentelekomcloud-docs/doc-exports#1.
Please do not edit it manually, since update to the original PR will overwrite local changes.
Original patch file, as well as complete rst archive,  can be found in the artifacts of the opentelekomcloud-docs/doc-exports#1

Reviewed-by: kucerakk <kucerakk@gmail.com>

2022-05-09 12:59:46 +00:00

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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('/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 <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

6.0 KiB Raw Blame History

XGBoost

Training and Saving a Model

Inference Code

6.0 KiB

Raw Blame History