def cross_validation_test():
glogger.setLoggingLevel(glogger.nothing)
filename = "/home/gibson/jonask/Dropbox/Ann-Survival-Phd/Two_thirds_of_SA_1889_dataset.txt"
#try:
# columns = input("Which columns to include? (Do NOT forget trailing comma if only one column is used, e.g. '3,'\nAvailable columns are: 2, -4, -3, -2, -1. Just press ENTER for all columns.\n")
#except SyntaxError:
columns = (2, -4, -3, -2, -1)
print('\nIncluding columns: ' + str(columns))
P, T = parse_file(filename, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True)
#remove tail censored
P, T = copy_without_tailcensored(P, T)
#try:
# comsize = input("Number of networks to cross-validate [10]: ")
#except SyntaxError:
comsize = 10
print('Number of networks to cross-validate: ' + str(comsize))
#try:
# netsize = input('Number of hidden nodes [3]: ')
#except SyntaxError as e:
if len(sys.argv) < 2:
netsize = 3
else:
netsize = sys.argv[1]
print("Number of hidden nodes: " + str(netsize))
#try:
# pop_size = input('Population size [50]: ')
#except SyntaxError as e:
pop_size = 50
print("Population size: " + str(pop_size))
#try:
# mutation_rate = input('Please input a mutation rate (0.25): ')
#except SyntaxError as e:
mutation_rate = 0.25
print("Mutation rate: " + str(mutation_rate))
#try:
# epochs = input("Number of generations (200): ")
#except SyntaxError as e:
epochs = 200
print("Epochs: " + str(epochs))
com = build_feedforward_committee(comsize, len(P[0]), netsize, 1, output_function = 'linear')
#1 is the column in the target array which holds teh binary censoring information
test_errors, vald_errors = train_committee(com, train_evolutionary, P, T, 1, epochs, error_function = c_index_error, population_size = pop_size, mutation_chance = mutation_rate)
print('\nTest Errors, Validation Errors:')
for terr, verr in zip(test_errors.values(), vald_errors.values()):
print(str(terr) + ", " + str(verr))
print('\nTest average, Validation average:')
print(str(sum(test_errors.values()) / len(test_errors.values())) + ', ' + str(sum(vald_errors.values()) / len(vald_errors.values())))
def cpu_multi(pop_size, P, num_of_hidden):
# number of input covariates
num_of_inputs = len(P[0])
# Tanh to keep implementation details easy in opencl
com = build_feedforward_committee(8, num_of_inputs, num_of_hidden, 1, output_function="tanh")
benchmark(com.sim)(P)
test_inputs, tst_t = parse_file(filename)
test = (inputs, targets)
validation = ([], [])
# Train!
# net = build_feedforward(8, 8, 1)
epochs = 10
# best = benchmark(train_evolutionary)(net, test, validation, 10, random_range = 1)
# best = benchmark(traingd_block)(net, test, validation, epochs, block_size = 10, stop_error_value = 0)
com = build_feedforward_committee(size=10, input_number=8, hidden_number=8, output_number=1)
print "Training evolutionary..."
benchmark(train_committee)(com, train_evolutionary, inputs, targets, epochs, random_range=1)
Y = com.sim(inputs)
area, best_cut = get_rocarea_and_best_cut(Y, targets)
[num_correct_first, num_correct_second, total_performance, num_first, num_second, missed] = stat(
Y, targets, cut=best_cut
)
print (
"Total number of data: " + str(len(targets)) + " (" + str(num_second) + " ones and " + str(num_first) + " zeros)"
)
print ("Number of misses: " + str(missed) + " (" + str(total_performance) + "% performance)")
print ("Specificity: " + str(num_correct_first) + "% (Success for class 0)")
print ("Sensitivity: " + str(num_correct_second) + "% (Success for class 1)")
def cross_validation_test():
filename = "/home/gibson/jonask/Dropbox/Ann-Survival-Phd/Two_thirds_of_SA_1889_dataset.txt"
#try:
# columns = input("Which columns to include? (Do NOT forget trailing comma if only one column is used, e.g. '3,'\nAvailable columns are: 2, -4, -3, -2, -1. Just press ENTER for all columns.\n")
#except SyntaxError:
if len(sys.argv) < 3:
columns = (2, -4, -3, -2, -1)
else:
columns = [int(col) for col in sys.argv[2:]]
print('\nIncluding columns: ' + str(columns))
P, T = parse_file(filename, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True)
#remove tail censored
#print('\nRemoving tail censored...')
#P, T = copy_without_censored(P, T)
print("\nData set:")
print("Number of patients with events: " + str(T[:, 1].sum()))
print("Number of censored patients: " + str((1 - T[:, 1]).sum()))
#try:
# comsize = input("Number of networks to cross-validate [10]: ")
#except SyntaxError:
comsize = 5
print('\nNumber of networks to cross-validate: ' + str(comsize))
times_to_cross = 3
print('\nNumber of times to repeat cross-validation: ' + str(times_to_cross))
#try:
# netsize = input('Number of hidden nodes [3]: ')
#except SyntaxError as e:
if len(sys.argv) < 2:
netsize = 1
else:
netsize = int(sys.argv[1])
print("Number of hidden nodes: " + str(netsize))
#try:
# pop_size = input('Population size [50]: ')
#except SyntaxError as e:
pop_size = 100
print("Population size: " + str(pop_size))
#try:
# mutation_rate = input('Please input a mutation rate (0.25): ')
#except SyntaxError as e:
mutation_rate = 0.05
print("Mutation rate: " + str(mutation_rate))
#try:
# epochs = input("Number of generations (200): ")
#except SyntaxError as e:
epochs = 400
print("Epochs: " + str(epochs))
for _ in xrange(times_to_cross):
com = build_feedforward_committee(comsize, len(P[0]), netsize, 1, output_function = 'linear')
#1 is the column in the target array which holds the binary censoring information
test_errors, vald_errors, data_sets = train_committee(com, train_evolutionary, P, T, 1, epochs, error_function = c_index_error, population_size = pop_size, mutation_chance = mutation_rate)
print('\nTest Errors, Validation Errors:')
for terr, verr in zip(test_errors.values(), vald_errors.values()):
print(str(terr) + ", " + str(verr))