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)
