forked from docs/modelarts
2d9d363ec9
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>
84 lines
6.0 KiB
ReStructuredText
84 lines
6.0 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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+-----------------------------------+---------------------------------------------------------------------------------------------------+
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| :: | :: |
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| 1 | import pandas as pd |
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| 2 | import xgboost as xgb |
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| 3 | from sklearn.model_selection import train_test_split |
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| 4 | |
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| 5 | # Prepare training data and setting parameters |
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| 6 | iris = pd.read_csv('/data/iris.csv') |
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| 7 | X = iris.drop(['virginica'],axis=1) |
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| 8 | y = iris[['virginica']] |
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| 9 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234565) |
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| 10 | params = { |
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| 11 | 'booster': 'gbtree', |
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| 12 | 'objective': 'multi:softmax', |
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| 13 | 'num_class': 3, |
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| 14 | 'gamma': 0.1, |
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| 15 | 'max_depth': 6, |
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| 16 | 'lambda': 2, |
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| 17 | 'subsample': 0.7, |
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| 18 | 'colsample_bytree': 0.7, |
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| 19 | 'min_child_weight': 3, |
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| 20 | 'silent': 1, |
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| 21 | 'eta': 0.1, |
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| 22 | 'seed': 1000, |
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| 23 | 'nthread': 4, |
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| 24 | } |
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| 25 | plst = params.items() |
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| 26 | dtrain = xgb.DMatrix(X_train, y_train) |
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| 27 | num_rounds = 500 |
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| 28 | model = xgb.train(plst, dtrain, num_rounds) |
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| 29 | model.save_model('/tmp/xgboost.m') |
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+-----------------------------------+---------------------------------------------------------------------------------------------------+
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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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