Scikit Learn¶
Training and Saving a Model¶
import json
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib
iris = pd.read_csv('/home/ma-user/work/iris.csv')
X = iris.drop(['variety'],axis=1)
y = iris[['variety']]
# Create a LogisticRegression instance and train model
logisticRegression = LogisticRegression(C=1000.0, random_state=0)
logisticRegression.fit(X,y)
# Save model to local path
joblib.dump(logisticRegression, '/tmp/sklearn.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.
Inference Code¶
# coding:utf-8
import collections
import json
from sklearn.externals import joblib
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):
sk_model = joblib.load(self.model_path)
pre_result = sk_model.predict(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