phil8192

# 4 elem vector
w <- c(10, 20, 30, 40)

# 4*3 matrix
#      [,1] [,2] [,3]
# [1,]    1    2    3
# [2,]    4    5    6
# [3,]    7    8    9
# [4,]   10   11   12
x <- matrix(1:12, ncol=3, byrow=T)

phil8192

##' calculate summary statistics from a confusion matrix.
##'
##' @param cm a confusion matrix, where rows = predicted, cols = actual.
##' @param dp round decimal place (default 2).
##' @return a classification report, similar to sklean.
##' @examples
##' \dontrun{
##' # 3 classes.
##' cm <- matrix(c(4, 6, 3, 1, 2, 0, 1, 2, 6), ncol=3, byrow=T)
##' cr(cm)

phil8192

epochs = 10

times = []
for k in [None, 256, 512, 1024, 2048]:

    pub_key, pri_key = paillier.generate_paillier_keypair(n_length=k) \
            if k is not None else (None, None)

    b = B(B_x, pub_key)
    a = A(A_x, yy, b, pub_key)

phil8192

class C:
    def __init__(self, a, test_x, test_y, pri_key=None):
        self.a = a # regerence to Host A.
        self.test_x = test_x
        self.test_y = test_y
        self.features = test_x.shape[1]
        self.pri_key = pri_key

    def optimise(self, epochs, batch_size, eta, gamma):  

phil8192

class B:
    def __init__(self, x, pub_key=None):
        self.x = x # Host B's X.
        self.features = x.shape[1]
        self.pub_key = pub_key

    # Called by Host (A) with current model Theta and A's
    # (encypted) part of the gradient calculation.
    def gradients(self, theta, u):

phil8192

class A:
    def __init__(self, x, y, b, pub_key=None):
        self.x = x # A's vertical partition of X.
        self.y = y # A's training labels.
        self.b = b # reference to Host B.
        self.features = x.shape[1]
        self.pub_key = pub_key

    # Called by Coordinator with current model Theta for each mini-batch
    # returns (encrypted) gradients for Host A, Host B.

phil8192

def taylor_gradient(theta, x, y):
    return 1/x.shape[0] * np.dot(0.25 * np.dot(x, theta) - 0.5 * y, x)

phil8192

def taylor_loss(theta, x, y):
    wx = np.dot(x, theta)
    return 1/x.shape[0] * np.sum(np.log(2) - 0.5 * y * wx + 0.125 * wx**2)

phil8192

import phe as paillier

def encrypt(pub_key, x):
    """encrypt a vector with pub_key"""
    return np.array([pub_key.encrypt(v) for v in x.tolist()])

def decrypt(pri_key, x):
    """decypt a vector with pri_key"""
    return np.array([pri_key.decrypt(v) for v in x])

phil8192

class FedAvg(BaseEstimator, ClassifierMixin):

    def __init__(self,
                 n_runners=1,
                 sample_size=1,
                 rounds=1,
                 combine='weighted',
                 partition_params={
                     'scheme': 'uniform'
                 },