def setUp(self):
self.paillier_encrypt = PaillierEncrypt()
self.paillier_encrypt.generate_key()
self.gradient_operator = LogisticGradient()
self.taylor_operator = TaylorLogisticGradient()
self.X = np.array([[1, 2, 3, 4, 5], [3, 2, 4, 5, 1], [
2,
2,
3,
1,
1,
]]) / 10
self.X1 = np.c_[self.X, np.ones(3)]
self.Y = np.array([[1], [1], [-1]])
self.values = []
for idx, x in enumerate(self.X):
inst = Instance(inst_id=idx, features=x, label=self.Y[idx])
self.values.append((idx, inst))
self.values1 = []
for idx, x in enumerate(self.X1):
inst = Instance(inst_id=idx, features=x, label=self.Y[idx])
self.values1.append((idx, inst))
self.coef = np.array([2, 2.3, 3, 4, 2.1]) / 10
self.coef1 = np.append(self.coef, [1])
class TestHomoLRGradient(unittest.TestCase):
def setUp(self):
self.paillier_encrypt = PaillierEncrypt()
self.paillier_encrypt.generate_key()
self.gradient_operator = LogisticGradient()
self.taylor_operator = TaylorLogisticGradient()
self.X = np.array([[1, 2, 3, 4, 5], [3, 2, 4, 5, 1], [
2,
2,
3,
1,
1,
]]) / 10
self.X1 = np.c_[self.X, np.ones(3)]
self.Y = np.array([[1], [1], [-1]])
self.values = []
for idx, x in enumerate(self.X):
inst = Instance(inst_id=idx, features=x, label=self.Y[idx])
self.values.append((idx, inst))
self.values1 = []
for idx, x in enumerate(self.X1):
inst = Instance(inst_id=idx, features=x, label=self.Y[idx])
self.values1.append((idx, inst))
self.coef = np.array([2, 2.3, 3, 4, 2.1]) / 10
self.coef1 = np.append(self.coef, [1])
def test_gradient_length(self):
fit_intercept = False
grad = self.gradient_operator.compute_gradient(self.values, self.coef,
0, fit_intercept)
self.assertEqual(grad.shape[0], self.X.shape[1])
taylor_grad = self.taylor_operator.compute_gradient(
self.values, self.coef, 0, fit_intercept)
self.assertEqual(taylor_grad.shape[0], self.X.shape[1])
self.assertTrue(np.sum(grad - taylor_grad) < 0.0001)
fit_intercept = True
grad = self.gradient_operator.compute_gradient(self.values, self.coef,
0, fit_intercept)
self.assertEqual(grad.shape[0], self.X.shape[1] + 1)
taylor_grad = self.taylor_operator.compute_gradient(
self.values, self.coef, 0, fit_intercept)
self.assertEqual(taylor_grad.shape[0], self.X.shape[1] + 1)
self.assertTrue(np.sum(grad - taylor_grad) < 0.0001)
def __init__(self):
super(HomoLRGuest, self).__init__()
self.gradient_operator = LogisticGradient()
self.loss_history = []
self.role = consts.GUEST
self.aggregator = aggregator.Guest()
self.zcl_encrypt_operator = PaillierEncrypt()
def _init_model(self, params):
super()._init_model(params)
self.cipher.register_paillier_cipher(self.transfer_variable)
if params.encrypt_param.method in [consts.PAILLIER]:
self.use_encrypt = True
self.gradient_operator = TaylorLogisticGradient()
self.re_encrypt_batches = params.re_encrypt_batches
else:
self.use_encrypt = False
self.gradient_operator = LogisticGradient()
def __init__(self):
super(HomoLRGuest, self).__init__()
self.gradient_operator = LogisticGradient()
self.loss_history = []
self.role = consts.GUEST