Shift¶
Shifts a data stream by returning past values.
This can be used to compute statistics over past data. For instance, if you're computing daily averages, then shifting by 7 will be equivalent to computing averages from a week ago.
Shifting values is useful when you're calculating an average over a target value. Indeed, in this case it's important to shift the values in order not to introduce leakage. The recommended way to do this is to feature_extraction.TargetAgg
, which already takes care of shifting the target values once.
Parameters¶
-
amount
Default →
1
Shift amount. The
get
method will return thet - amount
value, wheret
is the current moment. -
fill_value
Default →
None
This value will be returned by the
get
method if not enough values have been observed.
Attributes¶
- name
Examples¶
It is rare to have to use Shift
by itself. A more common usage is to compose it with
other statistics. This can be done via the |
operator.
from river import stats
stat = stats.Shift(1) | stats.Mean()
for i in range(5):
stat.update(i)
print(stat.get())
0.0
0.0
0.5
1.0
1.5
A common usecase for using Shift
is when computing statistics on shifted data. For
instance, say you have a dataset which records the amount of sales for a set of shops. You
might then have a shop
field and a sales
field. Let's say you want to look at the
average amount of sales per shop. You can do this by using a feature_extraction.Agg
. When
you call transform_one
, you're expecting it to return the average amount of sales,
without including today's sales. You can do this by prepending an instance of
stats.Mean
with an instance of stats.Shift
.
from river import feature_extraction
agg = feature_extraction.Agg(
on='sales',
how=stats.Shift(1) | stats.Mean(),
by='shop'
)
Let's define a little example dataset.
X = iter([
{'shop': 'Ikea', 'sales': 10},
{'shop': 'Ikea', 'sales': 15},
{'shop': 'Ikea', 'sales': 20}
])
Now let's call the learn_one
method to update our feature extractor.
x = next(X)
agg.learn_one(x)
At this point, the average defaults to the initial value of stats.Mean
, which is 0.
agg.transform_one(x)
{'sales_mean_of_shift_1_by_shop': 0.0}
We can now update our feature extractor with the next data point and check the output.
agg.learn_one(next(X))
agg.transform_one(x)
{'sales_mean_of_shift_1_by_shop': 10.0}
agg.learn_one(next(X))
agg.transform_one(x)
{'sales_mean_of_shift_1_by_shop': 12.5}
Methods¶
get
Return the current value of the statistic.
update
Update the called instance.
Parameters
- x — 'numbers.Number'