use_header=True) print("Retrieving test data...") unNormedTestP, T = parse_file(testdata, inputcols=columns, targetcols=targets, normalize=False, separator=',', use_header=True) print("Normalizing test data...") P = normalizeArrayLike(unNormedTestP, normP) #Scatter training data model_output_file = test_model(model, trainingdata, targets[0], targets[1], ',', time_step_size=2, *columns) scatterplot_files(model_output_file, 0, 2, model_output_file, 1) #Scatter test data model_output_file = test_model_arrays(model, testdata, P, T, time_step_size=2) scatterplot_files(model_output_file, 0, 2, model_output_file, 1)
print("Model: {}".format(model)) #Define the data testdata = '/home/gibson/jonask/DataSets/breast_cancer_1/n4369_targetthird.csv' columns = ['age', 'log(1+lymfmet)', 'n_pos', 'tumsize', 'log(1+er_cyt)', 'log(1+pgr_cyt)', 'pgr_cyt_pos', 'er_cyt_pos', 'size_gt_20', 'er_cyt', 'pgr_cyt'] targets = ['time_10y', 'event_10y'] trainingdata = '/home/gibson/jonask/DataSets/breast_cancer_1/n4369_trainingtwothirds.csv' print("Retrieving training data...") # Normalize the test data as we normalized the training data normP, bah = parse_file(trainingdata, inputcols = columns, targetcols = targets, normalize = False, separator = ',', use_header = True) print("Retrieving test data...") unNormedTestP, T = parse_file(testdata, inputcols = columns, targetcols = targets, normalize = False, separator = ',', use_header = True) print("Normalizing test data...") P = normalizeArrayLike(unNormedTestP, normP) #Scatter training data model_output_file = test_model(model, trainingdata, targets[0], targets[1], ',', time_step_size = 2, *columns) scatterplot_files(model_output_file, 0, 2, model_output_file, 1) #Scatter test data model_output_file = test_model_arrays(model, testdata, P, T, time_step_size=2) scatterplot_files(model_output_file, 0, 2, model_output_file, 1)
if __name__ == "__main__": #Test the model on the test data! model = '/home/gibson/jonask/Dropbox/Ann-Survival-Phd/publication_data/ann/cens_10y/2_tanh_1328829716.pcom' testdata = '/home/gibson/jonask/DataSets/breast_cancer_1/n4369_targetthird.csv' columns = ['age', 'log(1+lymfmet)', 'n_pos', 'tumsize', 'log(1+er_cyt)', 'log(1+pgr_cyt)', 'pgr_cyt_pos', 'er_cyt_pos', 'size_gt_20', 'er_cyt', 'pgr_cyt'] #targets = ['time_10y', 'event_10y'] trainingdata = '/home/gibson/jonask/DataSets/breast_cancer_1/n4369_trainingtwothirds.csv' print("Retrieving training data...") # Normalize the test data as we normalized the training data normP, bah = parse_file(trainingdata, inputcols = columns, normalize = False, separator = ',', use_header = True) print("Retrieving test data...") unNormedTestP, uT = parse_file(testdata, inputcols = columns, normalize = False, separator = ',', use_header = True) print("Normalizing test data...") P = normalizeArrayLike(unNormedTestP, normP) print("Getting outputs for test data...") #Wihtout targets, we only get the outputs outputs = test_model_arrays(model, testdata, P, None) print("We have outputs! Length: {}".format(len(outputs))) #model_output_file = test_model(model, testdata, None, None, *columns) #scatterplot_files(model_output_file, 0, 2, model_output_file, 1)
] #targets = ['time_10y', 'event_10y'] trainingdata = '/home/gibson/jonask/DataSets/breast_cancer_1/n4369_trainingtwothirds.csv' print("Retrieving training data...") # Normalize the test data as we normalized the training data normP, bah = parse_file(trainingdata, inputcols=columns, normalize=False, separator=',', use_header=True) print("Retrieving test data...") unNormedTestP, uT = parse_file(testdata, inputcols=columns, normalize=False, separator=',', use_header=True) print("Normalizing test data...") P = normalizeArrayLike(unNormedTestP, normP) print("Getting outputs for test data...") #Wihtout targets, we only get the outputs outputs = test_model_arrays(model, testdata, P, None) print("We have outputs! Length: {}".format(len(outputs))) #model_output_file = test_model(model, testdata, None, None, *columns) #scatterplot_files(model_output_file, 0, 2, model_output_file, 1)