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
* Added optional colour to plot_cross_validation_metric to facilitate the comparison of different model's performance on the one plot
* use color
Change from colour to color for consistency.
Co-authored-by: Ben Letham <bletham@gmail.com>
* Add DeepSource config
* Simplify if statement
* Refactor use of dict() with empty literal
* Use set comprehension syntax
* Refactor chained comparison
* Rewrite instances of list literal within set() as set literals
* Add test_slow to class definition
* Update performance_metrics() in diagnostics.py
Include smape as a valid metric.
Add support for monthly horizons.
* Update performance_metrics() docstring
* Update performance_metrics() docstring
* Include smape in test_diagnostics.py
* sMAPE code formatting improvement
* Check frequency of data
* If sub-daily, use plot_seasonality()
* Format xticks as weekdays for plot_seasonality()
* Display 8 xticks for weekly component
* Issue #1525: Using m.construct_holiday_dataframe() to make sure 'holidays' is a DataFrame.
* #1525 | issuecomment-639399524 | enhancement: have plotly graph the holiday component consistent with matplotlib.
Co-authored-by: Robin Teuwens <teuwens@electris.lu>
* update docstring with mdape in list for python perf metric func
* re-run python notebook cell to generate mdape in results table
* remove comment
Co-authored-by: Ben Letham <bletham@gmail.com>
To solve this [issue](https://github.com/facebook/prophet/issues/1437) by @chongdae
Korean lunar calendar which is different from Chinese lunar calendar is added python-holidays package recently.
This commit is about dropping the Korean and KR class in hdays.py file and hdays_part1(holidays) will work for Korean holidays.
```
import fbprophet.hdays as hdays_part2
import holidays as hdays_part1
``
* add implementation for constant trend
* force k and delta params to be 0s
* add tests and fix n_changepoints, changepoints_t to 0
* Add test for cv with constant trend
* Add docs and test for checking invalid input
* make changes to stan
* add transformed params block in stan and output flat trend vector
* correct syntax
* transformed params syntax
* Fix test and port changes to win stan file
* add test for flat trend function
* Add separate function for flat trend init
* fix test
