run_prophet.py

from fbprophet import Prophet
import pandas as pd
import os

# https://stackoverflow.com/questions/2125702/how-to-suppress-console-output-in-python
# https://medium.com/spikelab/forecasting-multiples-time-series-using-prophet-in-parallel-2515abd1a245
# https://facebook.github.io/prophet/docs/quick_start.html#python-api


class suppress_stdout_stderr(object):
    '''
    # https://github.com/facebook/prophet/issues/223
    A context manager for doing a "deep suppression" of stdout and stderr in
    Python, i.e. will suppress all print, even if the print originates in a
    compiled C/Fortran sub-function.
       This will not suppress raised exceptions, since exceptions are printed
    to stderr just before a script exits, and after the context manager has
    exited (at least, I think that is why it lets exceptions through).

    '''
    def __init__(self):
        # Open a pair of null files
        self.null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)]
        # Save the actual stdout (1) and stderr (2) file descriptors.
        self.save_fds = [os.dup(1), os.dup(2)]

    def __enter__(self):
        # Assign the null pointers to stdout and stderr.
        os.dup2(self.null_fds[0], 1)
        os.dup2(self.null_fds[1], 2)

    def __exit__(self, *_):
        # Re-assign the real stdout/stderr back to (1) and (2)
        os.dup2(self.save_fds[0], 1)
        os.dup2(self.save_fds[1], 2)
        # Close the null files
        for fd in self.null_fds + self.save_fds:
            os.close(fd)

def run_prophet(chunked_data, n_prediction_units=1, prediction_frequency='1min'):
    # install gotcha: https://github.com/facebook/prophet/issues/775#issuecomment-449139135
    ds_col = 'ds'
    results = []

    for x, y in chunked_data:

        m = Prophet(uncertainty_samples=False)

        with suppress_stdout_stderr():
            m.fit(x)

        future = m.make_future_dataframe(periods=n_prediction_units
                                         , freq=prediction_frequency
                                         , include_history=False)

        yhat = m.predict(future)[[ds_col, 'yhat']]

        result = pd.merge(y, yhat, left_on=ds_col, right_on=ds_col).drop(columns='day')

        results.append(result)

    return results