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zeekay
bde2e2531b
Machine Learning with FHE - fork of Zama Concrete-ML - Built-in FHE-friendly ML models - scikit-learn compatible API - PyTorch model conversion - Quantization-aware training
54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
import pickle
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from pathlib import Path
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import federated_utils
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import numpy
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from concrete.ml.sklearn import LogisticRegression
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if __name__ == "__main__":
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# Load scikit-learn model
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path_to_model = Path("./model.pkl")
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if not path_to_model.exists() or not path_to_model.is_file():
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raise ValueError(
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"Couldn't find file 'model.pkl'.\n"
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"Please run 'run.sh' to train the model with federated learning."
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)
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with path_to_model.open("rb") as file:
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sklearn_model = pickle.load(file)
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# Compile model without data since the server doesn't have access to it
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# Indeed in this scenario the users have the data but the server doesn't.
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# We then have to compile the model using random input sampled with the same
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# low and high bounds as the real data, in this context [0, 255].
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number_of_compile_samples = 1000
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compile_set = numpy.random.randint(0, 255, (number_of_compile_samples, 784)).astype(float)
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sklearn_model.classes_ = sklearn_model.classes_.astype(int)
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model = LogisticRegression.from_sklearn_model(sklearn_model, compile_set)
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model.compile(compile_set)
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# Evaluate the model
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# Load MNIST dataset from https://www.openml.org/d/554
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(_, _), (X_test, y_test) = federated_utils.load_mnist()
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concrete_predictions = model.predict(X_test)
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sklearn_predictions = sklearn_model.predict(X_test)
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simulation_predictions = model.predict(X_test, fhe="simulate")
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print(
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(concrete_predictions == sklearn_predictions).sum() / len(y_test),
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"quantized vs scikit-learn",
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)
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print(
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(concrete_predictions == simulation_predictions).sum() / len(y_test),
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"quantized vs simulation",
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)
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print(
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(simulation_predictions == sklearn_predictions).sum() / len(y_test),
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"simulation vs scikit-learn",
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)
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print((concrete_predictions == y_test).sum() / len(y_test), "quantized accuracy")
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print((sklearn_predictions == y_test).sum() / len(y_test), "scikit-learn accuracy")
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print((simulation_predictions == y_test).sum() / len(y_test), "simulation accuracy")
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