I've been working recently on deploying a machine learning model as a web service. I used Azure Machine Learning Studio for creating my own Workspace ID and Authorization Token. Then, I trained LogisticRegressionCV model from sklearn.linear_model locally on my machine (using python 2.7.13) and with the usage of below code snippet I wanted to publish my model as web service:
from azureml import services
@services.publish('workspaceID','authorization_token')
@services.types(var_1= float, var_2= float)
@services.returns(int)
def predicting(var_1, var_2):
input = np.array([var_1, var_2].reshape(1,-1)
return model.predict_proba(input)[0][1]
where the input variable is a list with data to be scored and the model variable contains trained classifier. Then after defining the above function, I want to make a prediction on the sample input vector:
predicting.service(1.21, 1.34)
However, the following error occurs:
RuntimeError: Error 0085: The following error occurred during script
evaluation, please view the output log for more information:
And the most important message in log is:
AttributeError: 'module' object has no attribute 'LogisticRegressionCV'
The error is strange to me because when I was using normal sklearn.linear_model.LogisticRegression everything was fine. I was able to make predictions sending POST requests to created endpoint, so I guess sklearn worked correctly. After changing to LogisticRegressionCV it does not.
Therefore I wanted to update the sklearn on my workspace.
Do you have any ideas on how to do it? Or even more general question: how to install any python module on azure machine learning studio in a way to use predict functions of any model I developed locally?
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