Hi,
I'm working through the Pydaal API but can't figure out how to serialize and deserialize a trained (SVM) model; every example script trains and tests the model in one shot, but I need to serialize it for later to predict on new data as it becomes available. How can I do this through the Python API?
Thanks,
Matt
Hi Matt,
You can use the Serialization and Deserialization interfaces available in DAAL to serialize model results into buffer(or even save to disk) and de-serialize at a later point to reconstruct the trained model. Note that, while de-serializing, an empty object must be constructed with the same type of serialized object.
Below code snippet is an extended functionality to the existing SVM code. Also, I have attached the complete code for your reference
import numpy as np from daal.data_management import InputDataArchive, OutputDataArchive def Serialize(model): # Construct input data archive Object # Serialize model contents into data archive Object # Copy data archive contents to numpy array dataArch = InputDataArchive () model.serialize (dataArch) length = dataArch.getSizeOfArchive () buffer_array = np.zeros (length, dtype=np.ubyte) dataArch.copyArchiveToArray (buffer_array) return buffer_array if __name__ == "__main__": trainingResult = trainModel() buffer = Serialize(trainingResult) # you can save this serialized object (which is a numpy array)to your disk #np.save(path, buffer) #Deserialization starts here #buffer = np.load(path) #Load numpy array dataArch = OutputDataArchive (buffer) trainedModel = training.Result() # Construct an empty training result object trainedModel.deserialize (dataArch) # Deserialize into trainedModel testModel(trainedModel)
