Custom Metric Syntax
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.
Builtin Functions
Before we dive into each of the supported functions, let's take a look at a few examples of custom metric definitions.
// Average annual premium of those with a driving license
sum(column="annual_premium") / count()
// Mean predicted probability
mean(column="proba")
// Model revenue
5 * tp_count(column="will_buy_insurance") -2 * fp_count(column="will_buy_insurance")
// nDCG@4 per step
ndcg_at_k(column="p_views", k=4)
ndcg_at_k(column="p_add_to_cart", k=4)
ndcg_at_k(column="p_purchases", k=4)
// accuracy using custom threshold
accuracy(column="proba", type="numeric", threshold=0.2)
// R-squared - Expanding brackets to use available aggregations
rss = squared_error_sum(column="prediction")
tss = squared_sum(column="actual") - 2*mean(column="actual")*sum(column="actual") + column_count(column="actual")*(mean(column="actual")**2)
1 - rss/tssFilters within functions
Aporia supports these cases by passing another argument to functions called "filter".
For example:
// Ratio of the annual premium of people above 70 out of the total premium
sum(column="annual_premium", filter="age > 70") / sum(column="annual_premium")To allow you to set any of your segments upon these metrics as a whole as well, setting a filter within a metric will create behind the scenes, the intersection of the segment within the filter with all of your existing filters. These segments will be counted as any regular segment.
Supported functions
Numerical Measures
absolute_sum
Returns the sum of absolutes for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be numeric field of any group (
feature/raw_input/prediction/actual)
column_count
Returns the number of rows with non-null values for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be any field.
max
Returns the maximum value for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be numeric field of any group (
feature/raw_input/prediction/actual)
max_length
Returns the maximum length for the given column (items for arrays/embeddings, characters for text).
Parameters
column: the name of the field on which we want to apply the function. Can be text/array/numeric array/embedding field of any group (
feature/raw_input/prediction/actual)
median
Returns the median value for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be numeric field of any group (
feature/raw_input/prediction/actual)
mean
Returns the average value for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be numeric field of any group (
feature/raw_input/prediction/actual)
mean_length
Returns the average length for the given column (items for arrays/embeddings, characters for text).
Parameters
column: the name of the field on which we want to apply the function. Can be text/array/numeric array/embedding field of any group (
feature/raw_input/prediction/actual)
min
Returns the minimum value for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be numeric field of any group (
feature/raw_input/prediction/actual)
min_length
Returns the minimum length for the given column (items for arrays/embeddings, characters for text).
Parameters
column: the name of the field on which we want to apply the function. Can be text/array/numeric array/embedding field of any group (
feature/raw_input/prediction/actual)
missing_count
Returns the number of rows with null values for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be any field.
missing_ratio
Returns the percentage of rows with null values for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be any field.
sum
Returns the sum for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be numeric field of any group (
feature/raw_input/prediction/actual)
squared_sum
Returns the sum of squared values for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be numeric field of any group (
feature/raw_input/prediction/actual)
squared_deviation_sum
Returns the sum of squares for the given column.
For column x, with m mean of all x samples, equals to sum of (x-m)².
Parameters
column: the name of the field on which we want to apply the function. Can be numeric field of any group (
feature/raw_input/prediction/actual)
value_count
Returns the number of entries where the given column is equal to the given value.
For example, value_count(column="bool", value=True) will return count of entries where bool=TRUE.
Parameters
column: the name of the field on which we want to apply the function. Can be any boolean/categorical field.
value: The value of the field to look for.
variance
Returns the variance for the given column.
Parameters
column: the name of the field on which we want to apply the function. Can be numeric field of any group (
feature/raw_input/prediction/actual)
Regression Metrics
absolute_error_sum
Returns the sum of absolute errors for the given prediction.
For a prediction P and actual A, returns the sum of |P-A|.
Parameters
column: the name of the numeric prediction field on which we want to apply the function. Must have a numeric actual mapped to it.
mae
Calculates MAE for the given prediction.
Parameters
column: the name of the numeric prediction field on which we want to apply the function. Must have a numeric actual mapped to it.
mse
Calculates MSE for the given prediction.
Parameters
column: the name of the numeric prediction field on which we want to apply the function. Must have a numeric actual mapped to it.
rmse
Calculates RMSE for the given prediction.
Parameters
column: the name of the numeric prediction field on which we want to apply the function. Must have a numeric actual mapped to it.
squared_error_sum
Returns the sum of squared errors for the given prediction.
For a prediction P and actual A, returns the sum of (P-A)².
Parameters
column: the name of the numeric prediction field on which we want to apply the function. Must have a numeric actual mapped to it.
Binary Classification Metrics
accuracy
Calculates accuracy for the given prediction.
Parameters
column: the name of the numeric/boolean prediction field on which we want to apply the function. Must have a boolean actual mapped to it.
threshold: probability threshold according to which we decide if a class is positive. Required for numeric predictions.
method: will define the average strategy to use. Can be: "macro", "micro" or "weighted". Required for categorical predictions.
auc_roc
Calculates AUC ROC for the given prediction.
Parameters
column: the name of the numeric prediction field on which we want to apply the function. Must have a boolean actual mapped to it.
fn_count
Returns the number of False-Negative results.
Parameters
column: the name of the numeric/boolean prediction field on which we want to apply the function. Must have a boolean actual mapped to it.
threshold: probability threshold according to which we decide if a class is positive. Required for numeric predictions.
fp_count
Returns the number of False-Positive results.
Parameters
column: the name of the numeric/boolean prediction field on which we want to apply the function. Must have a boolean actual mapped to it.
threshold: probability threshold according to which we decide if a class is positive. Required for numeric predictions.
f1
Calculates f1-score for the given prediction.
Parameters
column: the name of the numeric/boolean prediction field on which we want to apply the function. Must have a boolean actual mapped to it.
threshold: probability threshold according to which we decide if a class is positive. Required for numeric predictions.
method: will define the average strategy to use. Can be: "macro", "micro" or "weighted". Required for categorical predictions.
precision
Calculates precision for the given prediction.
Parameters
column: the name of the numeric/boolean prediction field on which we want to apply the function. Must have a boolean actual mapped to it.
threshold: probability threshold according to which we decide if a class is positive. Required for numeric predictions.
method: will define the average strategy to use. Can be: "macro", "micro" or "weighted". Required for categorical predictions.
recall
Calculates recall for the given prediction.
Parameters
column: the name of the numeric/boolean prediction field on which we want to apply the function. Must have a boolean actual mapped to it.
threshold: probability threshold according to which we decide if a class is positive. Required for numeric predictions.
method: will define the average strategy to use. Can be: "macro", "micro" or "weighted". Required for categorical predictions.
tn_count
Returns the number of True-Negative results.
Parameters
column: the name of the numeric/boolean prediction field on which we want to apply the function. Must have a boolean actual mapped to it.
threshold: probability threshold according to which we decide if a class is positive. Required for numeric predictions.
tp_count
Returns the number of True-Positive results.
Parameters
column: the name of the numeric/boolean prediction field on which we want to apply the function. Must have a boolean actual mapped to it.
threshold: probability threshold according to which we decide if a class is positive. Required for numeric predictions.
Multiclass Classification Metrics
accuracy_per_class
Calculates accuracy for the given prediction per the specified category class.
Parameters
column: the name of the categorical prediction field on which we want to apply the function. Must have a categorical actual mapped to it.
class_name: the class on which we want to calculate the function.
fn_count_per_class
Returns the number of False-Negative results per the specified category class.
Parameters
column: the name of the categorical prediction field on which we want to apply the function. Must have a categorical actual mapped to it.
class_name: the class on which we want to calculate the function.
fp_count_per_class
Returns the number of False-Positive results per the specified category class.
Parameters
column: the name of the categorical prediction field on which we want to apply the function. Must have a categorical actual mapped to it.
class_name: the class on which we want to calculate the function.
f1_per_class
Calculates f1-score for the given prediction per the specified category class.
Parameters
column: the name of the categorical prediction field on which we want to apply the function. Must have a categorical actual mapped to it.
class_name: the class on which we want to calculate the function.
precision_per_class
Calculates precision for the given prediction per the specified category class.
Parameters
column: the name of the categorical prediction field on which we want to apply the function. Must have a categorical actual mapped to it.
class_name: the class on which we want to calculate the function.
recall_per_class
Calculates recall for the given prediction per the specified category class.
Parameters
column: the name of the categorical prediction field on which we want to apply the function. Must have a categorical actual mapped to it.
class_name: the class on which we want to calculate the function.
tn_count_per_class
Returns the number of True-Negative results per the specified category class.
Parameters
column: the name of the categorical prediction field on which we want to apply the function. Must have a categorical actual mapped to it.
class_name: the class on which we want to calculate the function.
tp_count_per_class
Returns the number of True-Positive results per the specified category class.
Parameters
column: the name of the categorical prediction field on which we want to apply the function. Must have a categorical actual mapped to it.
class_name: the class on which we want to calculate the function.
Ranking Metrics
accuracy_at_k
Calculates Accuracy for the given prediction on the top K items.
Parameters
k: numeric integer between 1 to 12. Only the top-k items will be considered.
map_at_k
Calculates MAP (Mean-Average-Precision) for the given prediction on the top K items.
Parameters
k: numeric integer between 1 to 12. Only the top-k items will be considered.
mrr_at_k
Calculates MRR (Mean-Reciprocal-Rank) for the given prediction on the top K items.
Parameters
k: numeric integer between 1 to 12. Only the top-k items will be considered.
ndcg_at_k
Calculates NDCG for the given prediction on the top K items.
Parameters
k: numeric integer between 1 to 12. Only the top-k items will be considered.
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