Akmal Soliev
03/11/2023, 1:57 PMDataFrames
to fit the model? From what I see it is transformed into ndarray that is later processed, however, couldn't find the function that transforms from DataFrame into np array. Wanted to add polars supportfede (nixtla) (they/them)
03/13/2023, 5:16 PMAkmal Soliev
03/14/2023, 10:08 AMfede (nixtla) (they/them)
03/14/2023, 8:54 PMgenerate_series
controlling if the conversion is required, something like uid_to_categorical=True
Akmal Soliev
03/15/2023, 11:17 AMbefore:
________________________________________________________
Executed in 3.93 secs fish external
usr time 4.68 secs 0.06 millis 4.68 secs
sys time 1.44 secs 1.03 millis 1.43 secs
after:
________________________________________________________
Executed in 833.23 millis fish external
usr time 2.23 secs 0.07 millis 2.23 secs
sys time 0.50 secs 1.20 millis 0.50 secs
Max (Nixtla)
03/17/2023, 6:41 PMAkmal Soliev
05/09/2023, 9:21 PMutil.py
to be modified, as I have added Polars test.
NOTE:
β’ Have not yet implemented engine into _StatsForecast
and/or StatsForecast
class for I/O to match.
β’ core.ipynb
has `util.py`'s generate_series
function so that code can actually run. Will be removed in future.
More info:
https://github.com/Nixtla/statsforecast/pull/448#issuecomment-1537431035fede (nixtla) (they/them)
05/10/2023, 7:58 PMMax (Nixtla)
05/10/2023, 8:09 PMAkmal Soliev
05/11/2023, 3:58 PMStatsForecast
should run without any issues.
TODO:
β’ Modify plot
staticmethod to work both with polars and pandas
β’ Implement I/O matching, at current moment there is variable for that self.engine
β¦ At current moment it is:
β¦ polars in and pandas out
β¦ pandas in and pandas outMax (Nixtla)
05/11/2023, 7:26 PMAkmal Soliev
05/12/2023, 11:01 PMgenerate_series
.
More information:
https://github.com/Nixtla/statsforecast/pull/448#issuecomment-1546411593
@fede (nixtla) (they/them) could I ask you to please check the PR and let me know if I've missed anything. From my end everything worked smoothly.groupby
doesn't have sort param in polars, hence, have to chain .sort('unique_id')
P.S. Latest PR is up with bug fixes_parse_ds_type
bug on main
where in int
datestamps are converted into datetime in certain cases, due to the check failure; specifically issubclass(df["ds"].dtype.type, np.integer)
, which can be checked much better with kind,
np.array().dtype.kind in ["i", "f"]
, where i
stands for int and f
stands for float.
Implemented this change in my PR: https://github.com/Nixtla/statsforecast/pull/448