This commit adds the model_to_dict and model_from_dict functions, using
all of the logic that previously lived in model_to_json and
model_from_json, and converting those functions to simply reuse the new
ones.
This is useful because sometimes the user may want to serialize the dict
in some other way (e.g. another JSON serialization library such as ujson
or orjson, or something entirely different).
* cmdstan variable extraction update
* add backwards compatibility
* Fix bug
* Upgrade stan version in requirements and in CI testing
Co-authored-by: Ben Letham <bletham@gmail.com>
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
* function code
* add tests for regressor coefficients utility
* add documentation for regressor_coefficients util function
* generate Rd docs
* add regressor_coefficients to R namespace
* minor formatting nit
* fix bugs
* added `flat_growth_init()` function
* added validation for 'flat'
* changed `fit.prophet()` to handle `growth='flat'`
* added `trend='flat'` capabilities to `sample_predictive_trend()` and `fit.prophet()`
* updated STAN code to handle flat trend
* [Syntax fix] Removed unnecessary bracket
* updated documentation
* undid formatting that was accidentally applied by autoformatter
* undid more formatting that was accidentally applied by autoformatter
* added tests
* typo in `sample_predictive_trend()`
* updated notebook with example in R
* updated documentation
* used regex to format the `name`
Removes all singlequotes (') and doublequotes (") in the `name` variable
and replaces all whitespace with an underscore. Now, `ggplot2::aes_string()` can handle a column
name like `New Year's Day`.
* Used backticks in the `name` variable instead of regular expressions
Essentially, I escaped the string `name` with backticks so that
`ggplot2::geom_line()` and `ggplot2::ggplot()` functions can use any
arbitrary input.
