def test(net, P, T, vP, vT, filename, epochs, mutation_rate = 0.05, population_size = 50):
logger.info("Running genetic test for: " + filename + ' ' + str(epochs))
print("Number of patients with events: " + str(T[:, 1].sum()))
print("Number of censored patients: " + str((1 - T[:, 1]).sum()))
outputs = net.sim(P)
c_index = get_C_index(T, outputs)
logger.info("C index = " + str(c_index))
try:
net = train_evolutionary(net, (P, T), (vP, vT), epochs, error_function = c_index_error, population_size = population_size, mutation_chance = mutation_rate)
outputs = net.sim(P)
c_index = get_C_index(T, outputs)
logger.info("C index = " + str(c_index))
#net = traingd(net, (P, T), (None, None), epochs * 2, learning_rate = 1, block_size = 0, error_module = cox_error)
except FloatingPointError:
print('Aaawww....')
outputs = net.sim(P)
c_index = get_C_index(T, outputs)
logger.info("C index test = " + str(c_index))
outputs = net.sim(vP)
c_index = get_C_index(vT, outputs)
logger.info("C index vald = " + str(c_index))
if plt:
plot_network_weights(net)
return net
def test(net, P, T, vP, vT, filename, epochs, mutation_rate = 0.05, population_size = 50):
logger.info("Running genetic test for: " + filename + ' ' + str(epochs))
print("\nTraining set:")
print("Number of patients with events: " + str(T[:, 1].sum()))
print("Number of censored patients: " + str((1 - T[:, 1]).sum()))
print("\nValidation set:")
if vP is not None and len(vP) > 0:
print("Number of patients with events: " + str(vT[:, 1].sum()))
print("Number of censored patients: " + str((1 - vT[:, 1]).sum()))
else:
print("Empty")
outputs = net.sim(P)
c_index = get_C_index(T, outputs)
logger.info("C index test = " + str(c_index))
try:
net = train_evolutionary(net, (P, T), (vP, vT), epochs, error_function = c_index_error, population_size = population_size, mutation_chance = mutation_rate)
outputs = net.sim(P)
except FloatingPointError:
print('Aaawww....')
outputs = net.sim(P)
c_index = get_C_index(T, outputs)
logger.info("C index test = " + str(c_index))
if vP is not None and len(vP) > 0:
outputs = net.sim(vP)
c_index = get_C_index(vT, outputs)
logger.info("C index vald = " + str(c_index))
return net
Created on Jun 7, 2011
@author: jonask
'''
from kalderstam.neural.error_functions.sum_squares import total_error
from kalderstam.neural.network import build_feedforward
from kalderstam.util.filehandling import parse_data
from kalderstam.neural.training.gradientdescent import traingd
from kalderstam.neural.training.davis_genetic import train_evolutionary
import numpy
xor_set = [[0, 0, 0],
[0, 1, 1],
[1, 0, 1],
[1, 1, 0]]
xor_set = numpy.array(xor_set)
P, T = parse_data(xor_set, targetcols = 2, inputcols = [0, 1], normalize = False)
net = build_feedforward(2, 4, 1)
print("Error before training: " + str(total_error(T, net.sim(P))))
net = traingd(net, (P, T), (None, None), epochs = 1000, learning_rate = 0.1, block_size = 0)
print("Error after training: " + str(total_error(T, net.sim(P))))
net = build_feedforward(2, 4, 1)
print("Error before genetic training: " + str(total_error(T, net.sim(P))))
net = train_evolutionary(net, (P, T), (None, None), epochs = 100, population_size = 100)
print("Error after genetic training: " + str(total_error(T, net.sim(P))))
