Models in Vertica

@reli : we dont support it yet. if you can convert the pkl model to PMML format and load it into vertica. For more information, please visit the below link

https://www.vertica.com/docs/10.1.x/HTML/Content/Authoring/AnalyzingData/MachineLearning/UsingExternalModels/UsingPMML/ImportingPredictingWithPMMLModels.htm

Hi @reli,

The solution can be done by creating a UDF to do it. You have to be sure that the library used by your pickle are added in the dependencies when using the create library statement.

It can be something like:

import vertica_sdk

class my_model(vertica_sdk.ScalarFunction):

    def setup(self, server_interface, col_types):
                # unpickling the model and saving it as an attribute
        self.model = unpickle('...')

    def processBlock(self, server_interface, arg_reader, res_writer):
        while(True):
                        # scoring the row
            result = self.model([arg_reader.getFloat(0) ... arg_reader.getFloat(n)])
                        # writing the result
            res_writer.setFloat(result)
            res_writer.next()
            if not arg_reader.next():
                break

    def destroy(self, server_interface, col_types):
        pass

class my_model_factory(vertica_sdk.ScalarFunctionFactory):

    def createScalarFunction(self, srv):
        return my_model()

    def getPrototype(self, server_interface, arg_types, return_type):
        arg_types.addFloat()
                ...
        return_type.addFloat()

    def getReturnType(self, server_interface, arg_types, return_type):
        return_type.addFloat()

You can then install the function:

CREATE OR REPLACE LIBRARY my_model AS '/home/dbadmin/my_model.py' DEPENDS '/path/to/dependencies' LANGUAGE 'Python';
CREATE OR REPLACE FUNCTION my_model AS NAME 'my_model' LIBRARY python_math;

Hope it will help!

Badr

thank you all !!!