ML (Trainers)

linear_regression_training()¶

Demonstrates the linear_regression_training function. This is the core FHE circuit logic used by FHELinearRegressionTrainer to calculate sufficient statistics directly on encrypted data.

In [ ]:

from concrete import fhe
from concrete_fhe_toolkit.ml.trainers import linear_regression_training

def training_circuit(X_train, y_train):
    return linear_regression_training(X_train, y_train, 1, 2)

compiler = fhe.Compiler(training_circuit, {'X_train': 'encrypted', 'y_train': 'encrypted'})
inputset = [([[1, 2]], [0])]
circuit = compiler.compile(inputset)

expected = training_circuit(*inputset[0])
result = circuit.encrypt_run_decrypt(*inputset[0])
import numpy as np
np.testing.assert_array_equal(result, expected)
print('linear_regression_training compiled and executed successfully!')

from concrete import fhe from concrete_fhe_toolkit.ml.trainers import linear_regression_training def training_circuit(X_train, y_train): return linear_regression_training(X_train, y_train, 1, 2) compiler = fhe.Compiler(training_circuit, {'X_train': 'encrypted', 'y_train': 'encrypted'}) inputset = [([[1, 2]], [0])] circuit = compiler.compile(inputset) expected = training_circuit(*inputset[0]) result = circuit.encrypt_run_decrypt(*inputset[0]) import numpy as np np.testing.assert_array_equal(result, expected) print('linear_regression_training compiled and executed successfully!')

FHELinearRegressionTrainer¶

Demonstrates the FHELinearRegressionTrainer class. Runs fit_encrypted in simulation mode to train an encrypted model.

In [ ]:

from concrete_fhe_toolkit.ml.trainers import FHELinearRegressionTrainer
import numpy as np

trainer = FHELinearRegressionTrainer(simulate=True)
X_train = [[1], [3]]
y_train = [0, 1]
try:
    model = trainer.fit_encrypted(X_train, y_train)
    print('FHELinearRegressionTrainer executed successfully!')
except Exception as e:
    print(f'Execution failed: {e}')

from concrete_fhe_toolkit.ml.trainers import FHELinearRegressionTrainer import numpy as np trainer = FHELinearRegressionTrainer(simulate=True) X_train = [[1], [3]] y_train = [0, 1] try: model = trainer.fit_encrypted(X_train, y_train) print('FHELinearRegressionTrainer executed successfully!') except Exception as e: print(f'Execution failed: {e}')

---

FHEDecisionTreeTrainer¶

Demonstrates the FHEDecisionTreeTrainer class. Runs fit_encrypted in simulation mode to train an encrypted model.

In [ ]:

from concrete_fhe_toolkit.ml.trainers import FHEDecisionTreeTrainer
import numpy as np

trainer = FHEDecisionTreeTrainer(candidate_thresholds=[[2]], max_depth=1, num_classes=2, min_samples_leaf=1, simulate=True)
X_train = [[1], [3]]
y_train = [0, 1]
try:
    model = trainer.fit_encrypted(X_train, y_train)
    print('FHEDecisionTreeTrainer executed successfully!')
except Exception as e:
    print(f'Execution failed: {e}')

from concrete_fhe_toolkit.ml.trainers import FHEDecisionTreeTrainer import numpy as np trainer = FHEDecisionTreeTrainer(candidate_thresholds=[[2]], max_depth=1, num_classes=2, min_samples_leaf=1, simulate=True) X_train = [[1], [3]] y_train = [0, 1] try: model = trainer.fit_encrypted(X_train, y_train) print('FHEDecisionTreeTrainer executed successfully!') except Exception as e: print(f'Execution failed: {e}')

---

FHEKMeansTrainer¶

Demonstrates the FHEKMeansTrainer class. Runs fit_encrypted in simulation mode to train an encrypted model.

In [ ]:

from concrete_fhe_toolkit.ml.trainers import FHEKMeansTrainer
import numpy as np

trainer = FHEKMeansTrainer(initial_centroids=[[1, 1], [3, 3]], min_value=-2, max_value=3, n_iterations=1, simulate=True)
X_train = [[1, 1], [3, 3]]
try:
    model = trainer.fit_encrypted(X_train)
    print('FHEKMeansTrainer executed successfully!')
except Exception as e:
    print(f'Execution failed: {e}')

from concrete_fhe_toolkit.ml.trainers import FHEKMeansTrainer import numpy as np trainer = FHEKMeansTrainer(initial_centroids=[[1, 1], [3, 3]], min_value=-2, max_value=3, n_iterations=1, simulate=True) X_train = [[1, 1], [3, 3]] try: model = trainer.fit_encrypted(X_train) print('FHEKMeansTrainer executed successfully!') except Exception as e: print(f'Execution failed: {e}')

---