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Reviewed-by: Jiang, Beibei <beibei.jiang@t-systems.com> Co-authored-by: proposalbot <proposalbot@otc-service.com> Co-committed-by: proposalbot <proposalbot@otc-service.com>
84 lines
2.6 KiB
ReStructuredText
84 lines
2.6 KiB
ReStructuredText
:original_name: modelarts_23_0177.html
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.. _modelarts_23_0177:
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XGBoost
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=======
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Training and Saving a Model
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---------------------------
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.. code-block::
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import pandas as pd
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import xgboost as xgb
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from sklearn.model_selection import train_test_split
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# Prepare training data and setting parameters
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iris = pd.read_csv('/home/ma-user/work/iris.csv')
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X = iris.drop(['variety'],axis=1)
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y = iris[['variety']]
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234565)
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params = {
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'booster': 'gbtree',
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'objective': 'multi:softmax',
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'num_class': 3,
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'gamma': 0.1,
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'max_depth': 6,
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'lambda': 2,
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'subsample': 0.7,
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'colsample_bytree': 0.7,
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'min_child_weight': 3,
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'silent': 1,
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'eta': 0.1,
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'seed': 1000,
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'nthread': 4,
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}
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plst = params.items()
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dtrain = xgb.DMatrix(X_train, y_train)
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num_rounds = 500
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model = xgb.train(plst, dtrain, num_rounds)
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model.save_model('/tmp/xgboost.m')
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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.
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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 :ref:`Model Package Specifications <modelarts_23_0091>`.
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Inference Code
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--------------
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.. code-block::
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# coding:utf-8
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import collections
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import json
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import xgboost as xgb
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from model_service.python_model_service import XgSklServingBaseService
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class user_Service(XgSklServingBaseService):
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# request data preprocess
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def _preprocess(self, data):
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list_data = []
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json_data = json.loads(data, object_pairs_hook=collections.OrderedDict)
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for element in json_data["data"]["req_data"]:
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array = []
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for each in element:
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array.append(element[each])
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list_data.append(array)
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return list_data
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# predict
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def _inference(self, data):
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xg_model = xgb.Booster(model_file=self.model_path)
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pre_data = xgb.DMatrix(data)
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pre_result = xg_model.predict(pre_data)
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pre_result = pre_result.tolist()
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return pre_result
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# predict result process
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def _postprocess(self,data):
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resp_data = []
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for element in data:
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resp_data.append({"predictresult": element})
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return resp_data
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