TimeWindow
Summary​
Configuration for specifying a TimeWindow that is applied in an Aggregation within a Batch or Stream Feature View.Â
This class describes the attributes of a time window to aggregate over which includes the size of the time window and the offset of the window's end time from a given reference point(end of aggregation window, spine timestamp, or the current time).
Description​
Tecton aggregations are applied over a specified time window using the
time_window
parameter. Use the TimeWindow
class to create an aggregation
over a fixed window size as shown in the example below:
from tecton import batch_feature_view, Aggregate
from tecton.types import Field, Float32
@batch_feature_view(
sources=[transactions],
mode="spark_sql",
entities=[user],
aggregation_interval=timedelta(days=1),
timestamp_field="timestamp",
features=[Aggregate(input_column=Field("amount", Float32), function="mean", time_window=timedelta(days=7))],
)
def user_average_transaction_amount(transactions):
return f"""
SELECT user_id, timestamp, amt
FROM {transactions}
"""
If you directly pass a datetime.timedelta
object to the time_window
parameter, as in time_window=datetime.timedelta(days=7)
, it will be inferred
as time_window=TimeWindow(window_size=datetime.timedelta(days=7))
The end time of this window will be the most recent aggregation interval
relative to the online request time or offline events
dataframe timestamp.
Offset Time Windows​
The end time of the time window can be adjusted via an offset
parameter in the
TimeWindow
class as shown in the example below. In this example, the window
will be from -10 days to -3 days:
from tecton import batch_feature_view, Aggregate, TimeWindow
from datetime import timedelta
from tecton.types import Field, Float32
@batch_feature_view(
sources=[transactions],
mode="spark_sql",
entities=[user],
aggregation_interval=timedelta(days=1),
timestamp_field="timestamp",
features=[
Aggregate(
input_column=Field("amount", Float32),
function="mean",
time_window=TimeWindow(window_size=timedelta(days=7), offset=timedelta(days=-3)),
)
],
)
def user_average_transaction_amount(transactions):
return f"""
SELECT user_id, timestamp, amt
FROM {transactions}
"""
The offset parameter must always be negative.
Example​
Consider the following example mock data:
user_id | timestamp | value | |
---|---|---|---|
0 | user_1 | 2022-05-14 00:00:00 | 1 |
1 | user_1 | 2022-05-15 00:00:00 | 3 |
2 | user_1 | 2022-05-16 00:00:00 | 6 |
3 | user_1 | 2022-05-17 00:00:00 | 11 |
4 | user_1 | 2022-05-18 00:00:00 | 23 |
A Feature View can have aggregations with and without an offset.
from tecton import Entity, batch_feature_view, Aggregate, TimeWindow
from tecton.types import Field, String
user_entity = Entity(name="user", join_keys=[Field("user_id", String)])
@batch_feature_view(
mode="spark_sql",
sources=[ds],
entities=[user_entity],
aggregation_interval=timedelta(days=1),
timestamp_field="timestamp",
offline=True,
online=True,
feature_start_time=datetime(2022, 5, 1),
features=[
Aggregate(input_column=Field("value", Float32), function="sum", time_window=TimeWindow(window_size=timedelta(days=2))),
Aggregate(
input_column=Field("value", Float32),
function="sum",
time_window=TimeWindow(window_size=timedelta(days=2), offset=timedelta(days=-2)),
),
],
)
def user_transaction_sums(input_table):
return f"""
SELECT user_id, timestamp, value
FROM {input_table}
"""
During Offline Retrieval, when you pass in an events
dataframe to join
against, the aggregation will be computed over the time window with an offset
relative to the timestamps in the input events
dataframe. We give examples of
how the aggregation is computed for different timestamps in the events
dataframe below.
import pandas as pd
import datetime
training_events = pd.DataFrame(
{
"user_id": ["user_1", "user_1", "user_1", "user_1"],
"timestamp": [datetime(2022, 5, 15), datetime(2022, 5, 18), datetime(2022, 5, 19), datetime(2022, 5, 20)],
}
)
df = user_transaction_sums.get_features_for_events(training_events).to_pandas()
display(df)
user_id | timestamp | user_transaction_sums__value_sum_2d_1d | user_transaction_sums__value_sum_2d_1d_offset_2d | |
---|---|---|---|---|
0 | user_1 | 2022-05-15 00:00:00 | 1 | None |
1 | user_1 | 2022-05-18 00:00:00 | 17 | 4 |
2 | user_1 | 2022-05-19 00:00:00 | 34 | 9 |
3 | user_1 | 2022-05-20 00:00:00 | 23 | 17 |
Attributes​
The attributes are the same as the __init__
method parameters. See below.
Methods​
__init__(...)​
Parameters
window_size
(timedelta
) - The size of the window, expressed as a positivedatetime.timedelta
Default:None
offset
(timedelta
) - The relative end time of the window, expressed as a negativedatetime.timedelta
Default:0:00:00