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()
learning_rate=0.1, corruption_level=0.1, cost_measure="cross_entropy", rng=rng) # cost_measure can be either "cross_entropy" or "euclidean" # extract features from test set and validation set test_set_x_extr = dA.test_model(model_trained, test_set_x_org) valid_set_x_extr = dA.test_model(model_trained, valid_set_x_org) # classification # train classifier logistic_trained, training_time_logistic = logistic_sgd.train_model( learning_rate=0.1, n_epochs=1000, train_set_x_org=train_set_x_extr, train_set_y_org=train_set_y_org, valid_set_x_org=valid_set_x_extr, valid_set_y_org=valid_set_y_org, batch_size=200) # test classifier test_set_y_pred = logistic_sgd.test_model(logistic_trained, test_set_x_extr) # 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()
# extract features from test set and validation set test_set_x_extr = rbm.test_model(model_trained, test_set_x_org) valid_set_x_extr =rbm.test_model(model_trained, valid_set_x_org) # classification # train classifier learning_rate=0.1 n_epochs=100 batch_size=100 logistic_trained,training_time_logistic=logistic_sgd.train_model(learning_rate=learning_rate, n_epochs=n_epochs, train_set_x_org=train_set_x_extr, train_set_y_org=train_set_y_org, valid_set_x_org=valid_set_x_extr, valid_set_y_org=valid_set_y_org, batch_size=batch_size) # test classifier test_set_y_pred,test_set_y_pred_prob,test_time=logistic_sgd.test_model(logistic_trained,test_set_x_extr) # evaluate classification performance perf_i,conf_mat_i=cl.perform(test_set_y_org,test_set_y_pred,numpy.unique(train_set_y_org)) print perf_i print conf_mat_i if i==0: perf=perf_i conf_mat=conf_mat_i training_times=training_time test_times=test_time else: perf=numpy.vstack((perf,perf_i)) conf_mat=conf_mat+conf_mat_i training_times=training_times + training_time test_times=test_times + test_time
# train, and extract features from training set model_trained, train_set_x_extr, training_time = rbm.train_model(rng=rng,train_set_x_org=train_set_x_org, training_epochs=10, batch_size=100, n_hidden=93, learning_rate=0.1, persistent_chain_k=30) # extract features from test set and validation set test_set_x_extr = rbm.test_model(model_trained, test_set_x_org) valid_set_x_extr =rbm.test_model(model_trained, valid_set_x_org) # classification # train classifier logistic_trained,training_time_logistic=logistic_sgd.train_model(learning_rate=0.1, n_epochs=1000, train_set_x_org=train_set_x_extr, train_set_y_org=train_set_y_org, valid_set_x_org=valid_set_x_extr, valid_set_y_org=valid_set_y_org, batch_size=200) # test classifier test_set_y_pred=logistic_sgd.test_model(logistic_trained,test_set_x_extr) # 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 # sample from RBM samples_vis, samples_vis_mf=rbm.sample_model(rng, model_trained, test_set_x_org, n_chains=20,n_samples=10,sample_gap=1000) # collect garbage gc_collect()
# classification # train classifier learning_rate = 0.1 n_epochs = 100 batch_size = 100 logistic_trained, training_time_logistic = logistic_sgd.train_model( learning_rate=learning_rate, n_epochs=n_epochs, train_set_x_org=train_set_x_extr, train_set_y_org=train_set_y_org, valid_set_x_org=valid_set_x_extr, valid_set_y_org=valid_set_y_org, batch_size=batch_size) # test classifier test_set_y_pred, test_set_y_pred_prob, test_time = logistic_sgd.test_model( logistic_trained, test_set_x_extr) # evaluate classification performance perf_i, conf_mat_i = cl.perform(test_set_y_org, test_set_y_pred, numpy.unique(train_set_y_org)) print perf_i print conf_mat_i if i == 0: perf = perf_i conf_mat = conf_mat_i training_times = training_time test_times = test_time else: perf = numpy.vstack((perf, perf_i)) conf_mat = conf_mat + conf_mat_i training_times = training_times + training_time