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ML examples: - credit_scoring: Credit risk prediction with HMEQ data - disease_prediction: Healthcare ML on encrypted records - cifar: Encrypted image classification - federated_learning: Distributed FHE training - hybrid_model: Mixed FHE/plaintext inference Deployment: - breast_cancer: Cancer detection API - sentiment_analysis: Encrypted NLP - server: FHE inference server Blockchain: - hardhat-template: Solidity + FHE - react-template: Full dApp with FHE
24 lines
707 B
Python
24 lines
707 B
Python
import numpy
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from sklearn.datasets import load_breast_cancer
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from concrete.ml.deployment import FHEModelClient, FHEModelDev, FHEModelServer
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from concrete.ml.sklearn import XGBClassifier
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if __name__ == "__main__":
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# First get some data and train a model.
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X, y = load_breast_cancer(return_X_y=True)
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assert isinstance(X, numpy.ndarray)
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assert isinstance(y, numpy.ndarray)
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# Split X into X_model_owner and X_client
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X_train = X[:-10]
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y_train = y[:-10]
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# Train the model and compile it
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model = XGBClassifier(n_bits=2, n_estimators=8, max_depth=3)
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model.fit(X_train, y_train)
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model.compile(X_train)
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dev = FHEModelDev("./dev", model)
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dev.save(via_mlir=True)
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