lstmoptim = 'adadelta' lstmnepochs = 2 #20 lstmbatchsize = 64 # # Generate the dataset and run analysis for each parameter combination of interest # # tuples in format (#static, #dynamic) params = [(10000, 10), (1000, 10), (100, 10), (10, 10), (10, 100), (10, 1000), (10, 10000)] for p in params: n_static = p[0] n_dynamic = p[1] print "Running with parameters n_static = %d, n_dynamic = %d" % (n_static, n_dynamic) static_train, dynamic_train, static_val, dynamic_val, labels_train, labels_val = generate_lstm_wins(5000, n_static, n_dynamic, 70) # merge train and test static_all = np.concatenate((static_train, static_val), axis=0) dynamic_all = np.concatenate((dynamic_train, dynamic_val), axis=0) labels_all = np.concatenate((labels_train, labels_val), axis=0) nsamples = static_all.shape[0] # k-fold CV for enrichment # prepare where to store the ratios ratios_all_hmm = np.empty(len(labels_all)) ratios_all_lstm = np.empty(len(labels_all)) # split indices into folds
lstmnepochs = 2 #20 lstmbatchsize = 64 # # Generate the dataset and run analysis for each parameter combination of interest # # tuples in format (#static, #dynamic) params = [(10000, 10), (1000, 10), (100, 10), (10, 10), (10, 100), (10, 1000), (10, 10000)] for p in params: n_static = p[0] n_dynamic = p[1] print "Running with parameters n_static = %d, n_dynamic = %d" % (n_static, n_dynamic) static_train, dynamic_train, static_val, dynamic_val, labels_train, labels_val = generate_lstm_wins( 5000, n_static, n_dynamic, 70) # merge train and test static_all = np.concatenate((static_train, static_val), axis=0) dynamic_all = np.concatenate((dynamic_train, dynamic_val), axis=0) labels_all = np.concatenate((labels_train, labels_val), axis=0) nsamples = static_all.shape[0] # k-fold CV for enrichment # prepare where to store the ratios ratios_all_hmm = np.empty(len(labels_all)) ratios_all_lstm = np.empty(len(labels_all)) # split indices into folds predict_idx_list = np.array_split(range(nsamples), nfolds)
lstmsize = 256 lstmdropout = 0.0 lstmoptim = 'rmsprop' lstmnepochs = 20 lstmbatchsize = 32 # open file to store results f = open('../../Results/grid_lstm_wins.csv', 'a') # # Load data # # generate the dataset train_static, train_dynamic, test_static, test_dynamic, train_labels, test_labels = generate_lstm_wins(nsamples, nfeatures, nseqfeatures, seqlen) train_nsamples = train_static.shape[0] test_nsamples = test_static.shape[0] # split training into two halves train_half = train_nsamples / 2 trainA_static = train_static[:train_half] trainB_static = train_static[train_half:] trainA_dynamic = train_dynamic[:train_half] trainB_dynamic = train_dynamic[train_half:] trainA_labels = train_labels[:train_half] trainB_labels = train_labels[train_half:] # # Train enrichment models