When Prophet prepares a dataframe for fitting or predicting, it runs a sort on the ds column.
This changes the sort algorithm from the pandas default sort to mergesort, which is a stable sort.
This is important in situations where multiple regressors are present and so the fitted dataframe is not unique by date.
Users may do something like the following:
df = <some dataframe>
# Example df
# ds x y
# 2020-01-01 0 3.2
# 2020-01-01 1 4.1
# 2020-01-02 0 3.7
# 2020-01-02 1 4.3
m = Prophet()
m.add_regressor('x')
m.fit(df)
predict_df = m.predict(df)
df['prediction'] = predict_df['yhat']
In m.predict(df), there is a call to df.sort_values('ds'). As this is not a stable sort, sometimes the x column above can get switched around, even if df is sorted. A stable sort addresses this.
This partially addresses #2322 if you construct the input correctly.
When Prophet prepares a dataframe for fitting or predicting, it runs a sort on the
dscolumn.This changes the sort algorithm from the pandas default sort to mergesort, which is a stable sort.
This is important in situations where multiple regressors are present and so the fitted dataframe is not unique by date.
Users may do something like the following:
In
m.predict(df), there is a call todf.sort_values('ds'). As this is not a stable sort, sometimes thexcolumn above can get switched around, even ifdfis sorted. A stable sort addresses this.This partially addresses #2322 if you construct the input correctly.