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encrypted_credit_scoring / development.py

romanbredehoft-zama

Rename to applicant and credit bureau

8e0d56d 5 months ago

raw history blame

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2.95 kB

	"""Train and compile the model."""

	import shutil
	import numpy
	import pandas
	import pickle

	from settings import (
	DEPLOYMENT_PATH,
	DATA_PATH,
	INPUT_SLICES,
	PRE_PROCESSOR_APPLICANT_PATH,
	PRE_PROCESSOR_BANK_PATH,
	PRE_PROCESSOR_CREDIT_BUREAU_PATH,
	APPLICANT_COLUMNS,
	BANK_COLUMNS,
	CREDIT_BUREAU_COLUMNS,
	)
	from utils.client_server_interface import MultiInputsFHEModelDev
	from utils.model import MultiInputDecisionTreeClassifier, MultiInputDecisionTreeRegressor
	from utils.pre_processing import get_pre_processors


	def get_multi_inputs(data):
	"""Get inputs for all three parties from the input data, using fixed slices.

	Args:
	data (numpy.ndarray): The input data to consider.

	Returns:
	(Tuple[numpy.ndarray]): The inputs for all three parties.
	"""
	return (
	data[:, INPUT_SLICES["applicant"]],
	data[:, INPUT_SLICES["bank"]],
	data[:, INPUT_SLICES["credit_bureau"]]
	)


	print("Load and pre-process the data")

	# Load the data
	data = pandas.read_csv(DATA_PATH, encoding="utf-8")

	# Define input and target data
	data_x = data.copy()
	data_y = data_x.pop("Target").copy().to_frame()

	# Get data from all parties
	data_applicant = data_x[APPLICANT_COLUMNS].copy()
	data_bank = data_x[BANK_COLUMNS].copy()
	data_credit_bureau = data_x[CREDIT_BUREAU_COLUMNS].copy()

	# Feature engineer the data
	pre_processor_applicant, pre_processor_bank, pre_processor_credit_bureau = get_pre_processors()

	preprocessed_data_applicant = pre_processor_applicant.fit_transform(data_applicant)
	preprocessed_data_bank = pre_processor_bank.fit_transform(data_bank)
	preprocessed_data_credit_bureau = pre_processor_credit_bureau.fit_transform(data_credit_bureau)

	preprocessed_data_x = numpy.concatenate((preprocessed_data_applicant, preprocessed_data_bank, preprocessed_data_credit_bureau), axis=1)


	print("\nTrain and compile the model")

	model = MultiInputDecisionTreeClassifier()

	model, sklearn_model = model.fit_benchmark(preprocessed_data_x, data_y)

	multi_inputs_train = get_multi_inputs(preprocessed_data_x)

	model.compile(*multi_inputs_train, inputs_encryption_status=["encrypted", "encrypted", "encrypted"])

	print("\nSave deployment files")

	# Delete the deployment folder and its content if it already exists
	if DEPLOYMENT_PATH.is_dir():
	shutil.rmtree(DEPLOYMENT_PATH)

	# Save files needed for deployment (and enable cross-platform deployment)
	fhe_model_dev = MultiInputsFHEModelDev(DEPLOYMENT_PATH, model)
	fhe_model_dev.save(via_mlir=True)

	# Save pre-processors
	with (
	PRE_PROCESSOR_APPLICANT_PATH.open('wb') as file_applicant,
	PRE_PROCESSOR_BANK_PATH.open('wb') as file_bank,
	PRE_PROCESSOR_CREDIT_BUREAU_PATH.open('wb') as file_credit_bureau,
	):
	pickle.dump(pre_processor_applicant, file_applicant)
	pickle.dump(pre_processor_bank, file_bank)
	pickle.dump(pre_processor_credit_bureau, file_credit_bureau)

	print("\nDone !")