A windowing policy of type trim trims the temporal interval of a trial, both from the start and from the end of a specified temporal amount (e.g., 3 seconds).
The trim windowing has the following structure:
| Filed | Type | Value restrictions | Is required | Default value | Description |
|---|---|---|---|---|---|
In case a windowing policy is not specified, the default windowing corresponding to trim[0s,0s] is considered.
The following fragment shows a windowing strategy of type "trim" where the time window is specified to start 10s after the beginning of the trial and to end immediately before the end of the trial:
A windowing policy of type stability discards temporal intervals in which a given metric is not stable, and selects, among the remaining intervals, the ones in which another target metric is maximized or minimized. Stability windowing can be sample-based or time-frame based.
The stability windowing has the following structure:
| Field | Type | Value restrictions | Is required | Default value | Description |
|---|
and for the comparison metric section
| Field | Type | Value restrictions | Is required | Default value | Description |
|---|
The following fragment is an example of stability windowing (time-frame based):
type
string
{trim}
TRUE
the type of windowing strategy
trim
array of strings
The length of the array should be two.
Valid values should have the form of a whole number followed by either "s", "m", or "h"
TRUE
How to trim the temporal interval of a trial to get the window. ["0s", "10m"] means trim 0 seconds from the start of the interval, 10 minutes from the end. ["0s", "1h"] means trim 0 seconds from the start, 1 hour from the end
task
string
The name of a task of the workflow associated with the study
FALSE
If the field is specified, the trim offset calculation for the window will be applied from the start time of the assigned task. Otherwise, it will be calculated from the start time of the trial.
| string | {stability} | TRUE |
| The type of windowing. |
| string | It should match the name of an existing metric monitored by AKAMAS | TRUE |
| The metric whose stability is going to be verified to exclude some temporal intervals over the duration of a trial. |
| set of key-value pairs |
| FALSE |
| A set of key-value pairs that represent filtering conditions for retrieving the value of the |
| string | Valid values are in the form where | FALSE | 0s | The temporal resolution at which Akamas aggregate data points to determine feasible windows. |
| integer string |
| TRUE | The width of temporal intervals over the duration trial which are checked for the stability of the |
| double |
| TRUE |
| The stability condition, i.e, the maximum amount of standard deviation among the value of the data point of the metric tolerated for a temporal interval of size |
| string | It should match the name of an existing metric monitored by Akamas | TRUE |
| The metric whose value is analyzed to include or exclude temporal intervals over the duration of a trial, when another reference metric is stable. |
| set of key-value pairs |
| FALSE |
| A set of key-value pairs that represent filtering conditions for retrieving the value of the |
| string | {min,max} | TRUE |
| If the value of the metric should be maximum or minimum to include or exclude temporal intervals over the duration of a trial when another reference metric is stable. |
The Windowing field in a study specifies the windowing policy to be adopted to score the experiments of an optimization study.
The two available windowing strategies have different structures:
Trim windowing: trim the temporal interval of a trial, both from the start and the end of a specified temporal amount - this is the default strategy
Stability windowing: discard temporal intervals in which a given metric is not stable and selects the temporal interval in which a metric is maximized or minimized.
In case the windowing strategy is not specified, the entire time window is considered.