valid_set_y_org=numpy.loadtxt(filename,delimiter='\t',dtype=object)
prev,valid_set_y_org=cl.change_class_labels(valid_set_y_org)
# test set
filename=data_dir + "GM12878_200bp_Data_3Cl_l2normalized_TestSet.txt";
test_set_x_org=numpy.loadtxt(filename,delimiter='\t',dtype='float32')
filename=data_dir + "GM12878_200bp_Classes_3Cl_l2normalized_TestSet.txt";
test_set_y_org=numpy.loadtxt(filename,delimiter='\t',dtype=object)
prev,test_set_y_org=cl.change_class_labels(test_set_y_org)
filename=data_dir + "GM12878_Features_Unique.txt";
features=numpy.loadtxt(filename,delimiter='\t',dtype=object)
rng=numpy.random.RandomState(1000)
# train
classifier_trained,training_time=logistic_sgd.train_model(learning_rate=0.1, n_epochs=1000,
train_set_x_org=train_set_x_org,train_set_y_org=train_set_y_org,
valid_set_x_org=valid_set_x_org,valid_set_y_org=valid_set_y_org,
batch_size=200)
# test
test_set_y_pred,test_set_y_pred_prob,test_time=logistic_sgd.test_model(classifier_trained,test_set_x_org)
print test_set_y_pred
print test_set_y_pred_prob
print test_time
# evaluate classification performance
perf,conf_mat=cl.perform(test_set_y_org,test_set_y_pred,numpy.unique(train_set_y_org))
print perf
print conf_mat
gc_collect()
filename = dir_save + prefix + "_given_image_sample_class_" + method + "_V1_2D.txt"
test_subset_y_2d = numpy.reshape(numpy.argmax(XM_view[1][:,
0:num_sampled_points],
axis=0),
newshape=(num_col,
num_sampled_points / num_col))
numpy.savetxt(filename, test_subset_y_2d, fmt="%s", delimiter="\t")
filename = dir_save + prefix + "_given_image_sample_class_" + method + "_V1_prob.txt"
numpy.savetxt(filename, XM_view[1].transpose(), fmt="%0.4f", delimiter="\t")
filename = dir_save + prefix + "_given_image_sample_class_" + method + "_V1_prob_rank.txt"
XM_view1_sort = numpy.argsort(XM_view[1].transpose(), axis=1)
XM_view1_sort = XM_view1_sort[:, ::-1]
numpy.savetxt(filename, XM_view1_sort, fmt="%s", delimiter="\t")
# calculate performance
perf, conf_mat = cl.perform(test_set_y,
test_subset_y_1d,
unique_classes=z_unique)
# save performance
cl.save_perform(path=dir_save,
filename=prefix + "_mean_performances_" + method + ".txt",
create_new_file=True,
perf=perf,
std=None,
auroc=None,
auroc_std=None,
auprc=None,
auprc_std=None,
conf_mat=conf_mat,
classes_unique=z_unique,
pretraining_time=pretrain_time,
training_time=None,
