def test_utils_save_as_meme(self): logos = [Motif('ACGT', ['GATTACA']), Motif('ACGT', ['AAAA'])] utils.save_as_meme(logos, gettempdir() + "/test.meme") with open(self.folder + "/data/ref.meme", 'rt') as handle: ref = handle.read() with open(gettempdir() + "/test.meme", 'rt') as handle: comp = handle.read() self.assertTrue(ref == comp) remove(gettempdir() + "/test.meme")
##Perfomance evaluation predictions = model.predict(data, "test") predictions labels = data.get_labels("test") labels utils.plot_roc(labels, predictions, output_folder + "roc.png") utils.plot_prec_recall(labels, predictions, output_folder + "prec.png") print(utils.get_performance_report(labels, predictions)) Image(output_folder + "roc.png") Image(output_folder + "prec.png") activations = model.get_max_activations(data, "test") logos = model.visualize_all_kernels(activations, data, output_folder) Image(output_folder + "motif_kernel_13.png") Image(output_folder + "activations_kernel_13.png") Image(output_folder + "position_kernel_13.png") Image(output_folder + "data/alu.png") utils.save_as_meme([logo[0] for logo in logos], output_folder + "motifs_seq.meme") utils.save_as_meme([logo[1] for logo in logos], output_folder + "motifs_struct.meme") model.plot_clustering(activations, output_folder + "clustering.png") Image(output_folder + "clustering.png") utils.save_data(data, output_folder + "data.pkl") utils.save_model(model, output_folder + "model.pkl")