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I did as PyStanBackend. And now when we use the method fit of Prophet, we can do like in the documentation: https://facebook.github.io/prophet/docs/additional_topics.html#updating-fitted-models def stan_init(m): """Retrieve parameters from a trained model. Retrieve parameters from a trained model in the format used to initialize a new Stan model. Parameters ---------- m: A trained model of the Prophet class. Returns ------- A Dictionary containing retrieved parameters of m. """ res = {} for pname in ['k', 'm', 'sigma_obs']: res[pname] = m.params[pname][0][0] for pname in ['delta', 'beta']: res[pname] = m.params[pname][0] return res df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv') df1 = df.loc[df['ds'] < '2016-01-19', :] # All data except the last day m1 = Prophet().fit(df1) # A model fit to all data except the last day %timeit m2 = Prophet().fit(df) # Adding the last day, fitting from scratch %timeit m2 = Prophet().fit(df, init=stan_init(m1)) # Adding the last day, warm-starting from m1 Update models.py Update models.py Update models.py Update models.py Update models.py Update models.py Update models.py Test Test2 Test4 Test4 Test are fixed |
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| .. | ||
| tests | ||
| __init__.py | ||
| diagnostics.py | ||
| forecaster.py | ||
| hdays.py | ||
| make_holidays.py | ||
| models.py | ||
| plot.py | ||
| serialize.py | ||
| utilities.py | ||