Custom Metric Definition Language
In Aporia, custom metrics are defined using syntax that is similar to python's.
There are three building blocks which can be used in order to create a custom metric expression:
- Constants - a numeric value (e.g.
2,0.5, ..) - Functions - out of the builtin function collection you can find below (e.g.
sum,count, ...). All those functions return a numeric value. - Binary operation -
+,-,*,/,**. Operands can be both constants or function calls.
Before we dive into each of the supported function, there are two general concepts you should be familiar with regarding all functions - field expressions and data segment filters.
A field expression can be described in the following format:
<field_category>.<field_name>[<segment filter>]
Field category is one of the following:
features / raw_inputs / predictions / actuals. Note that you can only use categories which you defined in you schema while creating your model version. In addition, don't forget that predictions and actuals categories have the same field names.The segment filter is optional, for further information about the filters read the section below.
Data segment filters are boolean expressions, designed to reduce to a specific data segment the field on which we perform the function.
Each boolean condition in a segment filter is a comparison between a field and a constant value. For example:
[features.Driving_License == True] // will filter out records in which Driving_License != True
[raw_inputs.Age <= 35] // will only include records in which Age <= 35
Conditions can be combined using
and / or and all fields can be checked for missing values using is None / is not None.The following describe the supported combinations:
Type / Operation | == | != | < | > | >= | <= |
|---|---|---|---|---|---|---|
Boolean | True/False | True/False | ✖️ | ✖️ | ✖️ | ✖️ |
Categorical | numeric constants | numeric constants | ✖️ | ✖️ | ✖️ | ✖️ |
String | numeric constants | numeric constants | ✖️ | ✖️ | ✖️ | ✖️ |
Numeric | numeric constants | numeric constants | numeric constants | numeric constants | numeric constants | numeric constants |
The table cells indicates the type we can compare to.
Examples
// Average annual premium of those with a driving license
sum(features.Annual_Premium[features.Driving_License == True]) / prediction_count()
// Three time number of prediction of those who are under 35 years old and live in CA
prediction_count(raw_inputs.Age <= 35 and raw_inputs.Region_Code == 28) * 3
prediction_count(features.Age > 27) / (sum(features.Annual_Premium) + sum(features.Vintage))
Parameters
- prediction: prediction probability field
- label: label field
- threshold: numeric. Probability threshold according to which we decide the if a class is positive
- average: the average strategy (micro / macro / weighted)
- top_k: consider only top-k items.
- filter: the filter we want to apply on the records before calculating the metric
Parameters
- prediction: prediction probability field
- label: label field
- threshold: numeric. Probability threshold according to which we decide the if a class is positive
- average: the average strategy (micro / macro / weighted)
- top_k: consider only top-k items.
- filter: the filter we want to apply on the records before calculating the metric
Parameters
- prediction: prediction probability field
- label: label field
- threshold: numeric. Probability threshold according to which we decide the if a class is positive
- average: the average strategy (micro / macro / weighted)
- top_k: consider only top-k items.
- filter: the filter we want to apply on the records before calculating the metric
Parameters
- field: the field for which the metric will be computed. Can be of any category (
feature/raw_input/prediction/actual) - value: the value we want to count
- filter: the filter we want to apply on the records before calculating the metric
- keys: keys to filter in when field type is dict.
Parameters
- field: the field for which the metric will be computed. Can be of any category (
feature/raw_input/prediction/actual) - value: the value we want to count
- filter: the filter we want to apply on the records before calculating the metric
- keys: keys to filter in when field type is dict.
Last modified 4mo ago