romanbredehoft-zama's picture
Compile deployment files to python 3.10 and improve app display
e5b8aae
"""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)