model, summary = searcher.train(data, pr_auc=True, verbose=False) stop = time() print("time in minutes: {}".format((stop - start) / 60)) print(summary) ##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")
def main(): RBPs = [("data/pum2.train.positive.fasta", "data/pum2.train.negative.fasta", "data/pum2.test.positive.fasta", "data/pum2.test.negative.fasta", "PUM2"), ("data/qki.train.positive.fasta", "data/qki.train.negative.fasta", "data/qki.test.positive.fasta", "data/qki.test.negative.fasta", "QKI"), ("data/igf2bp123.train.positive.fasta", "data/igf2bp123.train.negative.fasta", "data/igf2bp123.test.positive.fasta", "data/igf2bp123.test.negative.fasta", "IGF2BP123"), ("data/srsf1.train.positive.fasta", "data/srsf1.train.negative.fasta", "data/srsf1.test.positive.fasta", "data/srsf1.test.negative.fasta", "SRSF1"), ("data/taf2n.train.positive.fasta", "data/taf2n.train.negative.fasta", "data/taf2n.test.positive.fasta", "data/taf2n.test.negative.fasta", "TAF2N"), ("data/nova.train.positive.fasta", "data/nova.train.negative.fasta", "data/nova.test.positive.fasta", "data/nova.test.negative.fasta", "NOVA")] for entry in RBPs: output_folder = entry[4] + "_pysster/" if not os.path.isdir(output_folder): os.makedirs(output_folder) start = time() # predict secondary structures utils.predict_structures(entry[0], entry[0]+".struct.gz", annotate=True) utils.predict_structures(entry[1], entry[1]+".struct.gz", annotate=True) utils.predict_structures(entry[2], entry[2]+".struct.gz", annotate=True) utils.predict_structures(entry[3], entry[3]+".struct.gz", annotate=True) # load data data = Data([entry[0]+".struct.gz", entry[1]+".struct.gz"], ("ACGU", "HIMS")) data.train_val_test_split(0.8, 0.1999) # we need to have at least one test sequence, even though we don't need it print(data.get_summary()) # training params = {"kernel_len": 8} model = Model(params, data) model.train(data) # load and predict test data data_test = Data([entry[2]+".struct.gz", entry[3]+".struct.gz"], ("ACGU", "HIMS")) predictions = model.predict(data_test, "all") stop = time() print("{}, time in seconds: {}".format(entry[4], stop-start)) # performance evaluation labels = data_test.get_labels("all") utils.plot_roc(labels, predictions, output_folder+"roc.pdf") utils.plot_prec_recall(labels, predictions, output_folder+"prec.pdf") print(utils.get_performance_report(labels, predictions)) # get motifs activations = model.get_max_activations(data_test, "all") logos, scores = [], [] for kernel in range(model.params["kernel_num"]): logo, score = model.visualize_kernel(activations, data_test, kernel, output_folder) logos.append(logo) scores.append(score) # sort motifs by importance score sorted_idx = [i[0] for i in sorted(enumerate(scores), key=lambda x:x[1])] with open(output_folder+"kernel_scores.txt", "wt") as handle: for x in sorted_idx: print("kernel {:>3}: {:.3f}".format(x, scores[x])) handle.write("kernel {:>3}: {:.3f}\n".format(x, scores[x])) # save model to drive utils.save_model(model, "{}model.pkl".format(output_folder))