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 train_single(): try: netsize = input('Number of hidden nodes? [3]: ') except SyntaxError as e: netsize = 3 try: pop_size = input('Population size? [50]: ') except SyntaxError as e: pop_size = 50 try: mutation_rate = input('Please input a mutation rate (0.25): ') except SyntaxError as e: mutation_rate = 0.25 SB22 = "/home/gibson/jonask/Dropbox/Ann-Survival-Phd/Two_thirds_of_SA_1889_dataset_SB22.txt" Benmargskohorten = "/home/gibson/jonask/Dropbox/Ann-Survival-Phd/Two_thirds_of_SA_1889_dataset_Benmargskohorten.txt" SB91b = "/home/gibson/jonask/Dropbox/Ann-Survival-Phd/Two_thirds_of_SA_1889_dataset_SB91b.txt" all_studies = "/home/gibson/jonask/Dropbox/Ann-Survival-Phd/Two_thirds_of_SA_1889_dataset.txt" #Real data print("Studies to choose from:") print("1: SB22") print("2: Benmargskohorten") print("3: SB91b") print("0: All combined (default)") try: study = input("Which study to train on? [0]: ") except SyntaxError as e: study = 0 if study == 1: filename = SB22 elif study == 2: filename = Benmargskohorten elif study == 3: filename = SB91b else: filename = all_studies 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 = [2, -4, -3, -2, -1], ignorerows = [0], normalize = True) P, T = parse_file(filename, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True) #Used for output comparison studies = {} studies[SB22] = parse_file(SB22, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True) studies[Benmargskohorten] = parse_file(Benmargskohorten, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True) studies[SB91b] = parse_file(SB91b, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True) studies[all_studies] = parse_file(all_studies, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True) #remove tail censored #P, T = copy_without_tailcensored(P, T) #Divide into validation sets #((tP, tT), (vP, vT)) = get_validation_set(P, T, validation_size = 0.25, binary_column = 1) TandV = get_cross_validation_sets(P, T, 2 , binary_column = 1) #Network part p = len(P[0]) #number of input covariates net = build_feedforward(p, netsize, 1, output_function = 'linear') #net = build_feedforward_multilayered(p, [7, 10], 1, output_function = 'linear') try: epochs = input("Number of generations (200): ") except SyntaxError as e: epochs = 200 for times, ((tP, tT), (vP, vT)) in zip(xrange(2), TandV): #train net = test(net, tP, tT, vP, vT, filename, epochs, population_size = pop_size, mutation_rate = mutation_rate) raw_input("Press enter to show plots...") glogger.show()
#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 #print('\nRemoving tail censored...') #P, T = copy_without_tailcensored(P, T) try: pieces = input('Number of crossvalidation pieces? [1]: ') except SyntaxError as e: pieces = 1 #Divide into validation sets TandV = get_cross_validation_sets(P, T, pieces , binary_column = 1) for set, ((tP, tT), (vP, vT)) in zip(range(pieces), TandV): print("\nCross validation set " + str(set)) print("Training") print("Number of patients with events: " + str(tT[:, 1].sum())) print("Number of censored patients: " + str((1 - tT[:, 1]).sum())) print("Validation") print("Number of patients with events: " + str(vT[:, 1].sum())) print("Number of censored patients: " + str((1 - vT[:, 1]).sum())) try: netsize = input('\nNumber of hidden nodes? [3]: ') except SyntaxError as e: netsize = 3
def train_single(): try: netsize = input('Number of hidden nodes? [1]: ') except SyntaxError as e: netsize = 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 SB22 = "/home/gibson/jonask/Dropbox/Ann-Survival-Phd/Two_thirds_of_SA_1889_dataset_SB22.txt" Benmargskohorten = "/home/gibson/jonask/Dropbox/Ann-Survival-Phd/Two_thirds_of_SA_1889_dataset_Benmargskohorten.txt" SB91b = "/home/gibson/jonask/Dropbox/Ann-Survival-Phd/Two_thirds_of_SA_1889_dataset_SB91b.txt" all_studies = "/home/gibson/jonask/Dropbox/Ann-Survival-Phd/Two_thirds_of_SA_1889_dataset.txt" #Real data print("Studies to choose from:") print("1: SB22") print("2: Benmargskohorten") print("3: SB91b") print("0: All combined (default)") try: study = input("Which study to train on? [0]: ") except SyntaxError as e: study = 0 if study == 1: filename = SB22 elif study == 2: filename = Benmargskohorten elif study == 3: filename = SB91b else: filename = all_studies 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 = [2, -4, -3, -2, -1], ignorerows = [0], normalize = True) P, T = parse_file(filename, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True) #Used for output comparison studies = {} studies[SB22] = parse_file(SB22, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True) studies[Benmargskohorten] = parse_file(Benmargskohorten, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True) studies[SB91b] = parse_file(SB91b, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True) studies[all_studies] = parse_file(all_studies, 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: pieces = input('Size of validation set? Input denominator (1 for no validation set). Default is 1/[1] parts: ') except: pieces = 1 TandV = get_cross_validation_sets(P, T, pieces , binary_column = 1) #Network part p = len(P[0]) #number of input covariates net = build_feedforward(p, netsize, 1, output_function = 'linear') #net = build_feedforward_multilayered(p, [7, 10], 1, output_function = 'linear') #Initial state #outputs = net.sim(tP) #orderscatter(outputs, tT, filename, 's') try: epochs = input("Number of generations (1): ") except SyntaxError as e: epochs = 1 for ((tP, tT), (vP, vT)) in TandV: #train net = test(net, tP, tT, vP, vT, filename, epochs, population_size = pop_size, mutation_rate = mutation_rate) if plt: outputs = net.sim(tP) threshold = kaplanmeier(time_array = tT[:, 0], event_array = tT[:, 1], output_array = outputs[:, 0]) if len(vP) > 0: outputs = net.sim(vP) kaplanmeier(time_array = vT[:, 0], event_array = vT[:, 1], output_array = outputs[:, 0], threshold = threshold) print("\nThreshold dividing the training set in two equal pieces: " + str(threshold)) raw_input("\nPress enter to show plots...") plt.show() try: answer = input("Do you wish to print network output? Enter filename, or 'no' / 'n'. ['n']: ") except (SyntaxError, NameError): answer = 'n' if os.path.exists(answer): print("File exists. Will add random number to front") answer = str(random.randint(0, 123456)) + answer if answer != 'n' and answer != 'no': print_output(answer, net, filename, targetcols = [4, 5], inputcols = columns, ignorerows = [0], normalize = True)