def score(self, X, y):
"""Returns the proportion of X correctly classified by the leave-one-
out classification
Parameters:
-----------
X : array-like
Training data, shape = [n_features, n_samples]
y : array-like
Target values, shape = [n_samples]
Returns:
--------
score : float
The proportion of X correctly classified
"""
return 1 - nca_cost.cost(self.metric, X, y)[0] / np.size(X, 1)
def _survival_nca_cost(self, data, aliveStatus):
"""Gets cumulative cost and gradient for all time points"""
# initialize cum_f and cum_gradf
cum_f = 0
cum_gradf = np.zeros(self.A.shape)
# no of time points
T = aliveStatus.shape[1]
#t = 20
for t in range(T):
print("epoch {}: t = {} of {}".format(self.epochs, t, T - 1))
# Get patients with known survival status at time t
Y = aliveStatus[:, t]
keep = Y >= 0
# proceed only if there's enough known patients
if np.sum(0 + keep) < self.N_SUBSET:
print("skipping current t ...")
continue
Y = Y[keep]
X = data[keep, :]
# keep a random subset of patients (for efficiency)
keep = np.random.randint(0, X.shape[0], self.N_SUBSET)
Y = Y[keep]
X = X[keep, :]
f, gradf = nca_cost.cost(self.A.T,
X.T,
Y,
SIGMA=self.SIGMA,
LAMBDA=self.LAMBDA)
cum_f += f
cum_gradf += gradf.T # sum of derivative is derivative of sum
return [cum_f, cum_gradf]
# -*- coding: utf-8 -*-
import nca_cost
import numpy as np
N = 300
aux = (np.concatenate([0.5*np.ones((N/2, 1)),
np.zeros((N/2, 1)), 1.1*np.ones((N/2, 1))], axis=1))
X = np.concatenate([np.random.rand(N/2, 3),
np.random.rand(N/2, 3) + aux])
y = np.concatenate([np.concatenate([np.ones((N/2, 1)), np.zeros((N/2, 1))]),
np.concatenate([np.zeros((N/2, 1)), np.ones((N/2, 1))])], axis = 1)
X = X.T
y = y[:, 0]
A = np.array([[1, 0, 0], [0, 1, 0]])
print nca_cost.cost(A, X, y)
print nca_cost.cost_g(A, X, y)
# -*- coding: utf-8 -*-
import nca_cost
import numpy as np
N = 300
aux = (np.concatenate([
0.5 * np.ones((N / 2, 1)),
np.zeros((N / 2, 1)), 1.1 * np.ones((N / 2, 1))
],
axis=1))
X = np.concatenate([np.random.rand(N / 2, 3), np.random.rand(N / 2, 3) + aux])
y = np.concatenate([
np.concatenate([np.ones(
(N / 2, 1)), np.zeros((N / 2, 1))]),
np.concatenate([np.zeros(
(N / 2, 1)), np.ones((N / 2, 1))])
],
axis=1)
X = X.T
y = y[:, 0]
A = np.array([[1, 0, 0], [0, 1, 0]])
print nca_cost.cost(A, X, y)
print nca_cost.cost_g(A, X, y)
