Python remove_and_make_dir Examples

Example #1

File: run_hmm_cross_val.py Project: colin2328/dcap_bnet

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)

Example #2

File: run_train_hmm.py Project: Sandy4321/mooc_data_science

def execute_hmm(config_file_iter):
	config_file, iter_number = config_file_iter.split("___")
	temp_dir = "temp_%s" % (iter_number)
	time.sleep(1 * int(iter_number))
	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)
	lines = results.split("\n")
	last_line = ""
	for line in lines:
		if "Log-likelihood" in line:
			last_line = line
	start_idx = last_line.find(" -")
	log_liklihood = float(last_line[start_idx:])
	os.chdir("..")
	return log_liklihood

Example #3

File: run_train_hmm.py Project: colin2328/dcap_bnet

def execute_hmm(config_file_iter):
    config_file, iter_number = config_file_iter.split("___")
    temp_dir = "temp_%s" % (iter_number)
    time.sleep(1 * int(iter_number))
    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)
    lines = results.split("\n")
    last_line = ""
    for line in lines:
        if "Log-likelihood" in line:
            last_line = line
    start_idx = last_line.find(" -")
    log_liklihood = float(last_line[start_idx:])
    os.chdir("..")
    return log_liklihood

Example #4

File: run_hmm_cross_val.py Project: Sandy4321/mooc_data_science

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)