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,