def predict(dataset, fitmodel_url, m=1, save_results=True, show=False):
    """
    Function starts a job that makes predictions to input data with a given model

    Parameters
    ----------
    input - dataset object with input urls and other parameters
    fitmodel_url - model created in fit phase
    m - m estimate is used with discrete features
    save_results - save results to ddfs
    show - show info about job execution

    Returns
    -------
    Urls of predictions on ddfs
    """
    from disco.worker.pipeline.worker import Worker, Stage
    from disco.core import Job, result_iterator
    import numpy as np

    try:
        m = float(m)
    except ValueError:
        raise Exception("Parameter m should be numerical.")

    if "naivebayes_fitmodel" in fitmodel_url:
        # fit model is loaded from ddfs
        fit_model = dict((k, v) for k, v in result_iterator(fitmodel_url["naivebayes_fitmodel"]))
        if len(fit_model["y_labels"]) < 2:
            print "There is only one class in training data."
            return []
    else:
        raise Exception("Incorrect fit model.")

    if dataset.params["X_meta"].count("d") > 0:  # if there are discrete features in the model
        # code calculates logarithms to optimize predict phase as opposed to calculation by every mapped.
        np.seterr(divide='ignore')
        for iv in fit_model["iv"]:
            dist = [fit_model.pop((y,) + iv, 0) for y in fit_model["y_labels"]]
            fit_model[iv] = np.nan_to_num(
                np.log(np.true_divide(np.array(dist) + m * fit_model["prior"], np.sum(dist) + m))) - fit_model[
                                "prior_log"]
        del (fit_model["iv"])

    # define a job and set save of results to ddfs
    job = Job(worker=Worker(save_results=save_results))

    # job parallelizes execution of mappers
    job.pipeline = [
        ("split", Stage("map", input_chain=dataset.params["input_chain"], init=simple_init, process=map_predict))]

    job.params = dataset.params  # job parameters (dataset object)
    job.params["fit_model"] = fit_model
    # define name of a job and input data urls
    job.run(name="naivebayes_predict", input=dataset.params["data_tag"])
    results = job.wait(show=show)
    return results
Function starts a job that makes predictions to input data with a given model

    Parameters
    ----------
    input - dataset object with input urls and other parameters
    fitmodel_url - model created in fit phase
    m - m estimate is used with discrete features
    save_results - save results to ddfs
    show - show info about job execution

    Returns
    -------
    Urls of predictions on ddfs