def execute_hmm(params): config_prefix, config_suffix, data_file_base, num_support, crossval_num = params.split( "___") config_file = config_prefix + data_file_base + "_%s" % crossval_num + config_suffix temp_dir = "temp_%s" % (crossval_num) time.sleep(1 * int(crossval_num)) utils.remove_and_make_dir(temp_dir) os.chdir(temp_dir) HMM_command = ["./../HMM_EM", "Train", "../" + config_file ] # need to concatenate since we are running binary results = subprocess.check_output(HMM_command) test_data = None for lead in range(1, 14): try: roc = run_inference_hmm.run_inference(data_file_base, num_support, "test", lead, plot_roc=False, crossval=True, crossval_num=crossval_num) test_data = utils.add_to_data(test_data, [lead, roc]) except: pass os.chdir("..") return np.atleast_2d(test_data)
def execute_hmm(params): config_prefix, config_suffix, data_file_base, num_support, crossval_num = params.split("___") config_file = config_prefix + data_file_base + "_%s" % crossval_num + config_suffix temp_dir = "temp_%s" % (crossval_num) time.sleep(1 * int(crossval_num)) utils.remove_and_make_dir(temp_dir) os.chdir(temp_dir) HMM_command = ["./../HMM_EM", "Train", "../" + config_file] # need to concatenate since we are running binary results = subprocess.check_output(HMM_command) test_data = None for lead in range(1, 14): try: roc = run_inference_hmm.run_inference( data_file_base, num_support, "test", lead, plot_roc=False, crossval=True, crossval_num=crossval_num ) test_data = utils.add_to_data(test_data, [lead, roc]) except: pass os.chdir("..") return np.atleast_2d(test_data)
def execute_hmm(params): data_file_base, num_support, lead = params.split("___") return run_inference_hmm.run_inference(data_file_base, int(num_support), "train", int(lead), plot_roc=False)