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 committee_test():
try:
netsize = input('Number of hidden nodes? [1]: ')
except SyntaxError as e:
netsize = 1
try:
comsize = input('Committee size? [1]: ')
except SyntaxError as e:
comsize = 1
try:
pop_size = input('Population size? [100]: ')
except SyntaxError as e:
pop_size = 100
try:
mutation_rate = input('Please input a mutation rate (0.05): ')
except SyntaxError as e:
mutation_rate = 0.05
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)
P, T = parse_file(filename, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True)
#remove tail censored
try:
cutoff = input('Cutoff for censored data? [9999 years]: ')
except SyntaxError as e:
cutoff = 9999
P, T = copy_without_censored(P, T, cutoff)
#Divide into validation sets
try:
test_size = float(input('Size of test set (not used in training)? Input in fractions. Default is [0.0]: '))
except:
test_size = 0.0
((TP, TT), (VP, VT)) = get_validation_set(P, T, validation_size = test_size, binary_column = 1)
print("Length of training set: " + str(len(TP)))
print("Length of test set: " + str(len(VP)))
try:
epochs = input("\nNumber of generations (1): ")
except SyntaxError as e:
epochs = 1
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)
com.set_training_sets([set[0][0] for set in data_sets]) #first 0 gives training sets, second 0 gives inputs.
print('\nTest C_indices, Validation C_indices:')
for terr, verr in zip(test_errors.values(), vald_errors.values()):
print(str(1 / terr) + ", " + str(1 / verr))
if plt:
outputs = numpy.array([[com.risk_eval(inputs)] for inputs in TP]) #Need double brackets for dimensions to be right for numpy
kaplanmeier(time_array = TT[:, 0], event_array = TT[:, 1], output_array = outputs[:, 0], threshold = 0.5)
train_c_index = get_C_index(TT, outputs)
print("\nC-index on the training set: " + str(train_c_index))
if len(VP) > 0:
outputs = numpy.array([[com.risk_eval(inputs)] for inputs in VP]) #Need double brackets for dimensions to be right for numpy
test_c_index = get_C_index(VT, outputs)
kaplanmeier(time_array = VT[:, 0], event_array = VT[:, 1], output_array = outputs[:, 0], threshold = 0.5)
print("C-index on the test set: " + str(test_c_index))
#raw_input("\nPress enter to show plots...")
plt.show()
try:
answer = input("\nDo you wish to print committee risk output? ['n']: ")
except (SyntaxError, NameError):
answer = 'n'
if answer != 'n' and answer != 'no':
inputs = read_data_file(filename)
P, T = parse_file(filename, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True)
outputs = [[com.risk_eval(patient)] for patient in P]
while len(inputs) > len(outputs):
outputs.insert(0, ["net_output"])
print("\n")
for rawline in zip(inputs, outputs):
line = ''
for col in rawline[0]:
line += str(col)
line += ','
for col in rawline[1]:
line += str(col)
print(line)
def com_cross():
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)
#Divide into validation sets
#test_size = 0.33
#print('Size of test set (not used in training): ' + str(test_size))
#((TP, TT), (VP, VT)) = get_validation_set(P, T, validation_size = test_size, binary_column = 1)
print("\nData set:")
print("Number of patients with events: " + str(T[:, 1].sum()))
print("Number of censored patients: " + str((1 - T[:, 1]).sum()))
#print("Length of training set: " + str(len(TP)))
#print("Length of test set: " + str(len(VP)))
#try:
# comsize = input("Number of networks to cross-validate [10]: ")
#except SyntaxError:
if len(sys.argv) < 2:
netsize = 1
else:
netsize = int(sys.argv[1])
print("\nNumber of hidden nodes: " + str(netsize))
comsize = 4
print('Number of members in each committee: ' + str(comsize))
comnum = 5
print('Number of committees to cross-validate: ' + str(comnum))
times_to_cross = 3
print('Number of times to repeat cross-validation: ' + str(times_to_cross))
#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 = 100
print("Epochs: " + str(epochs))
for _cross_time in xrange(times_to_cross):
data_sets = get_cross_validation_sets(P, T, comnum , binary_column = 1)
print('\nTest Errors, Validation Errors:')
for _com_num, (TS, VS) in zip(xrange(comnum), data_sets):
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, internal_sets = train_committee(com, train_evolutionary, TS[0], TS[1], 1, epochs, error_function = c_index_error, population_size = pop_size, mutation_chance = mutation_rate)
com.set_training_sets([set[0][0] for set in internal_sets]) #first 0 gives training sets, second 0 gives inputs.
outputs = numpy.array([[com.risk_eval(inputs)] for inputs in TS[0]]) #Need double brackets for dimensions to be right for numpy
train_c_index = get_C_index(TS[1], outputs)
outputs = numpy.array([[com.risk_eval(inputs)] for inputs in VS[0]]) #Need double brackets for dimensions to be right for numpy
val_c_index = get_C_index(VS[1], outputs)
print(str(1.0 / train_c_index) + ", " + str(1.0 / val_c_index))
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))
