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| """Generating deployment files.""" | |
| import shutil | |
| from pathlib import Path | |
| import pandas as pd | |
| from concrete.ml.sklearn import LogisticRegression as ConcreteLogisticRegression | |
| from concrete.ml.deployment import FHEModelDev | |
| # Data files location | |
| TRAINING_FILE_NAME = "./data/Training_preprocessed.csv" | |
| TESTING_FILE_NAME = "./data/Testing_preprocessed.csv" | |
| # Load data | |
| df_train = pd.read_csv(TRAINING_FILE_NAME) | |
| df_test = pd.read_csv(TESTING_FILE_NAME) | |
| # Split the data into X_train, y_train, X_test_, y_test sets | |
| TARGET_COLUMN = ["prognosis_encoded", "prognosis"] | |
| y_train = df_train[TARGET_COLUMN[0]].values.flatten() | |
| y_test = df_test[TARGET_COLUMN[0]].values.flatten() | |
| X_train = df_train.drop(TARGET_COLUMN, axis=1) | |
| X_test = df_test.drop(TARGET_COLUMN, axis=1) | |
| # Concrete ML model | |
| # Models parameters | |
| optimal_param = {"C": 0.9, "n_bits": 13, "solver": "sag", "multi_class": "auto"} | |
| clf = ConcreteLogisticRegression(**optimal_param) | |
| # Fit the model | |
| clf.fit(X_train, y_train) | |
| # Compile the model | |
| fhe_circuit = clf.compile(X_train) | |
| fhe_circuit.client.keygen(force=False) | |
| path_to_model = Path("./deployment_files/").resolve() | |
| if path_to_model.exists(): | |
| shutil.rmtree(path_to_model) | |
| dev = FHEModelDev(path_to_model, clf) | |
| dev.save(via_mlir=True) |