basket plugin
The basket plugin finds frequent patterns of attributes in the data and returns the patterns that pass a frequency threshold in that data. A pattern represents a subset of the rows that have the same value across one or more columns. The basket plugin is based on the Apriori algorithm originally developed for basket analysis data mining.
Syntax
T | evaluate basket ([ Threshold, WeightColumn, MaxDimensions, CustomWildcard, CustomWildcard, … ])
Parameters
| Name | Type | Required | Description |
|---|---|---|---|
| Threshold | long | A double in the range of 0.015 to 1 that sets the minimal ratio of the rows to be considered frequent. Patterns with a smaller ratio won’t be returned. The default value is 0.05. To use the default value, input the tilde: ~.Example: `T | |
| WeightColumn | string | The column name to use to consider each row in the input according to the specified weight. Must be a name of a numeric type column, such as int, long, real. By default, each row has a weight of 1. To use the default value, input the tilde: ~. A common use of a weight column is to take into account sampling or bucketing/aggregation of the data that is already embedded into each row.Example: `T | |
| MaxDimensions | int | Sets the maximal number of uncorrelated dimensions per basket, limited by default, to minimize the query runtime. The default is 5. To use the default value, input the tilde: ~.Example: `T | |
| CustomWildcard | string | Sets the wildcard value for a specific type in the result table that will indicate that the current pattern doesn’t have a restriction on this column. The default is null except for string columns whose default value is an empty string. If the default is a good value in the data, a different wildcard value should be used, such as *. To use the default value, input the tilde: ~.Example: `T |
Returns
The basket plugin returns frequent patterns that pass a ratio threshold. The default threshold is 0.05.
Each pattern is represented by a row in the results. The first column is the segment ID. The next two columns are the count and percentage of rows, from the original query that match the pattern. The remaining columns relate to the original query, with either a specific value from the column or a wildcard value, which is by default null, meaning a variable value.
Example
StormEvents
| where monthofyear(StartTime) == 5
| extend Damage = iff(DamageCrops + DamageProperty > 0 , "YES" , "NO")
| project State, EventType, Damage, DamageCrops
| evaluate basket(0.2)
Output
| SegmentId | Count | Percent | State | EventType | Damage | DamageCrops | ||
|---|---|---|---|---|---|---|---|---|
| 0 | 4574 | 77.7 | NO | 0 | ||||
| 1 | 2278 | 38.7 | Hail | NO | 0 | |||
| 2 | 5675 | 96.4 | 0 | |||||
| 3 | 2371 | 40.3 | Hail | 0 | ||||
| 4 | 1279 | 21.7 | Thunderstorm Wind | 0 | ||||
| 5 | 2468 | 41.9 | Hail | |||||
| 6 | 1310 | 22.3 | YES | |||||
| 7 | 1291 | 21.9 | Thunderstorm Wind |
Example with custom wildcards
StormEvents
| where monthofyear(StartTime) == 5
| extend Damage = iff(DamageCrops + DamageProperty > 0 , "YES" , "NO")
| project State, EventType, Damage, DamageCrops
| evaluate basket(0.2, '~', '~', '*', int(-1))
Output
| SegmentId | Count | Percent | State | EventType | Damage | DamageCrops | ||
|---|---|---|---|---|---|---|---|---|
| 0 | 4574 | 77.7 | * | * | NO | 0 | ||
| 1 | 2278 | 38.7 | * | Hail | NO | 0 | ||
| 2 | 5675 | 96.4 | * | * | * | 0 | ||
| 3 | 2371 | 40.3 | * | Hail | * | 0 | ||
| 4 | 1279 | 21.7 | * | Thunderstorm Wind | * | 0 | ||
| 5 | 2468 | 41.9 | * | Hail | * | -1 | ||
| 6 | 1310 | 22.3 | * | * | YES | -1 | ||
| 7 | 1291 | 21.9 | * | Thunderstorm Wind | * | -1 |
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