SQL - Decaying Time Since Event Then Starting Over At the Next Event - sql

Question

There are many similar questions and answers already posted but I could not find one with these differences. 1) The count of NULLs starts over, and 2) there is a math function applied to the replaced value.
An event either takes place or not (NULL or 1), by date by customer. Can assume that a customer has one and only one row for every date.
I want to replace the NULLs with a decay function based on number of consecutive NULLs (time from event). A customer can have the event every day, skip a day, skip multiple days. But once the event takes place, the decay starts over. Currently my decay is divide by 2 but that is for example.
DT
CUSTOMER
EVENT
DESIRED
2022-01-01
a
1
1
2022-01-02
a
1
1
2022-01-03
a
1
1
2022-01-04
a
1
1
2022-01-05
a
1
1
2022-01-01
b
1
1
2022-01-02
b
0.5
2022-01-03
b
0.25
2022-01-04
b
1
1
2022-01-05
b
0.5
I can produce the desired result, but it is very unwieldy. Looking if there is a better way. This will need to be extended for multiple event columns.
create or replace temporary table the_data (
dt date,
customer char(10),
event int,
desired float)
;
insert into the_data values ('2022-01-01', 'a', 1, 1);
insert into the_data values ('2022-01-02', 'a', 1, 1);
insert into the_data values ('2022-01-03', 'a', 1, 1);
insert into the_data values ('2022-01-04', 'a', 1, 1);
insert into the_data values ('2022-01-05', 'a', 1, 1);
insert into the_data values ('2022-01-01', 'b', 1, 1);
insert into the_data values ('2022-01-02', 'b', NULL, 0.5);
insert into the_data values ('2022-01-03', 'b', NULL, 0.25);
insert into the_data values ('2022-01-04', 'b', 1, 1);
insert into the_data values ('2022-01-05', 'b', NULL, 0.5);
with
base as (
select * from the_data
),
find_nan as (
select *, case when event is null then 1 else 0 end as event_is_nan from base
),
find_nan_diff as (
select *, event_is_nan - coalesce(lag(event_is_nan) over (partition by customer order by dt), 0) as event_is_nan_diff from find_nan
),
find_nan_group as (
select *, sum(case when event_is_nan_diff = -1 then 1 else 0 end) over (partition by customer order by dt) as nan_group from find_nan_diff
),
consec_nans as (
select *, sum(event_is_nan) over (partition by customer, nan_group order by dt) as n_consec_nans from find_nan_group
),
decay as (
select *, case when n_consec_nans > 0 then 0.5 / n_consec_nans else 1 end as decay_factor from consec_nans
),
ffill as (
select *, first_value(event) over (partition by customer order by dt) as ffill_value from decay
),
final as (
select *, ffill_value * decay_factor as the_answer from ffill
)
select * from final
order by customer, dt
;
Thanks

Answer 1

The query could be simplified by using CONDITIONAL_CHANGE_EVENT to generate subgrp helper column:
WITH cte AS (
SELECT *, CONDITIONAL_CHANGE_EVENT(event IS NULL) OVER(PARTITION BY CUSTOMER
ORDER BY DT) AS subgrp
FROM the_data
)
SELECT *, COALESCE(EVENT, 0.5 / ROW_NUMBER() OVER(PARTITION BY CUSTOMER, SUBGRP
ORDER BY DT)) AS computed_decay
FROM cte
ORDER BY CUSTOMER, DT;
Output:
EDIT:
Without using CONDITIONAL_CHANGE_EVENT:
WITH cte AS (
SELECT *,
CASE WHEN
event = LAG(event,1, event) OVER(PARTITION BY customer ORDER BY dt)
OR (event IS NULL AND LAG(event) OVER(PARTITION BY customer ORDER BY dt) IS NULL)
THEN 0 ELSE 1 END AS l
FROM the_data
), cte2 AS (
SELECT *, SUM(l) OVER(PARTITION BY customer ORDER BY dt) AS SUBGRP
FROM cte
)
SELECT *, COALESCE(EVENT, 0.5 / ROW_NUMBER() OVER(PARTITION BY CUSTOMER, SUBGRP
ORDER BY DT)) AS computed_decay
FROM cte2
ORDER BY CUSTOMER, DT;
db<>fiddle demo