aggregations¶
This module contains all possible aggregations to be used with
Multirel
,
FastProp
,
Mapping
.
Refer to the feature learning section in the user guide for details about how these aggregations are used in the context of feature learning.
Functions

Returns a new subclass of tuple with named fields. 
Attributes
Average value of a given numerical column. 

Number of rows in a given column. 

Counts the number of values strictly greater than the mean. 

Counts the number of values strictly smaller than the mean. 

Count function with distinct clause. 

COUNT DISTINCT divided by COUNT. 

Counts minus counts distinct. 

Exponentially weighted moving average with an halflife of 1 second. 

Exponentially weighted moving average with an halflife of 1 minute. 

Exponentially weighted moving average with an halflife of 1 hour. 

Exponentially weighted moving average with an halflife of 1 day. 

Exponentially weighted moving average with an halflife of 7 days. 

Exponentially weighted moving average with an halflife of 30 days. 

Exponentially weighted moving average with an halflife of 90 days. 

Exponentially weighted moving average with an halflife of 365 days. 

First value of a given column, when ordered by the time stamp. 

The kurtosis of a given column. 

Last value of a given column, when ordered by the time stamp. 

Largest value of a given column. 

Median of a given column 

Smallest value of a given column. 

Most frequent value of a given column. 

The number of times we observe the maximum value. 

The number of times we observe the minimum value. 

The 1%quantile. 

The 5%quantile. 

The 10%quantile. 

The 25%quantile. 

The 75%quantile. 

The 90%quantile. 

The 95%quantile. 

The 99%quantile. 

Skewness of a given column. 

Standard deviation of a given column. 

Total sum of a given numerical column. 

The time difference between the first time we see the maximum value and the time stamp in the population table. 

The time difference between the first time we see the minimum value and the time stamp in the population table. 

The time difference between the last time we see the maximum value and the time stamp in the population table. 

The time difference between the last time we see the minimum value and the time stamp in the population table. 

Extracts a linear trend from a variable over time and extrapolates this trend to the current time stamp. 

Statistical variance of a given numerical column. 

VAR divided by MEAN. 

Set of default aggregations for 

Set of default aggregations for 

Set of default aggregations for 