Fiddler Query Language

Overview

Custom Metrics and Segments are defined using the Fiddler Query Language (FQL), a flexible set of constants, operators, and functions which can accommodate a large variety of metrics.

Definitions

Term	Definition
Row-level function	A function which executes row-wise for a set of data. Returns a value for each row.
Aggregate function	A function which executes across rows. Returns a single value for a given set of rows.

Term

Definition

Row-level function

A function which executes row-wise for a set of data. Returns a value for each row.

Aggregate function

A function which executes across rows. Returns a single value for a given set of rows.

FQL Rules

Column names can be referenced by name either with double quotes ("my_column") or with no quotes (my_column).
Single quotes (') are used to represent string values.

Data Types

FQL distinguishes between three data types:

Data type Supported values Examples Supported Model Schema Data Types

Data type	Supported values	Examples	Supported Model Schema Data Types
Number	Any numeric value (integers and floats are both included)	`10` `2.34`	`Data.Type.INTEGER` `DataType.FLOAT`
Boolean	Only `true` and `false`	`true` `false`	`DataType.BOOLEAN`
String	Any value wrapped in single quotes (`'`)	`'This is a string.'` `'200.0'`	`DataType.CATEGORY` `DataType.STRING`

Number

Any numeric value (integers and floats are both included)

10 2.34

Data.Type.INTEGER DataType.FLOAT

Boolean

Only true and false

true false

DataType.BOOLEAN

String

Any value wrapped in single quotes (')

'This is a string.' '200.0'

DataType.CATEGORY DataType.STRING

Constants

Symbol Description

Symbol	Description
`true`	Boolean constant for true expressions
`false`	Boolean constant for false expressions

true

Boolean constant for true expressions

false

Boolean constant for false expressions

Operators

Symbol Description Syntax Returns Examples

Symbol	Description	Syntax	Returns	Examples
`^`	Exponentiation	`Number ^ Number`	`Number`	`2.5 ^ 4` `(column1 - column2)^2`
`-`	Unary negation	`-Number`	`Number`	`-column1`
`*`	Multiplication	`Number * Number`	`Number`	`2 * 10` `2 * column1` `column1 * column2` `sum(column1) * 10`
`/`	Division	`Number / Number`	`Number`	`2 / 10` `2 / column1` `column1 / column2` `sum(column1) / 10`
`%`	Modulo	`Number % Number`	`Number`	`2 % 10` `2 % column1` `column1 % column2` `sum(column1) % 10`
`+`	Addition	`Number + Number`	`Number`	`2 + 2` `2 + column1` `column1 + column2` `average(column1) + 2`
`-`	Subtraction	`Number - Number`	`Number`	`2 - 2` `2 - column1` `column1 - column2` `average(column1) - 2`
`<`	Less than	`Number < Number`	`Boolean`	`10 < 20` `column1 < 10` `column1 < column2` `average(column2) < 5`
`<=`	Less than or equal to	`Number <= Number`	`Boolean`	`10 <= 20` `column1 <= 10` `column1 <= column2` `average(column2) <= 5`
`>`	Greater than	`Number > Number`	`Boolean`	`10 > 20` `column1 > 10` `column1 > column2` `average(column2) > 5`
`>=`	Greater than or equal to	`Number >= Number`	`Boolean`	`10 >= 20` `column1 >= 10` `column1 >= column2` `average(column2) >= 5`
`==`	Equals	`Number == Number`	`Boolean`	`10 == 20` `column1 == 10` `column1 == column2` `average(column2) == 5`
`!=`	Does not equal	`Number != Number`	`Boolean`	`10 != 20` `column1 != 10` `column1 != column2` `average(column2) != 5`
`not`	Logical NOT	`not Boolean`	`Boolean`	`not true` `not column1`
`and`	Logical AND	`Boolean and Boolean`	`Boolean`	`true and false` `column1 and column2`
`or`	Logical OR	`Boolean or Boolean`	`Boolean`	`true or false` `column1 or column2`

^

Exponentiation

Number ^ Number

Number

2.5 ^ 4 (column1 - column2)^2

-

Unary negation

-Number

Number

-column1

*

Multiplication

Number * Number

Number

2 * 10 2 * column1 column1 * column2 sum(column1) * 10

/

Division

Number / Number

Number

2 / 10 2 / column1 column1 / column2 sum(column1) / 10

%

Modulo

Number % Number

Number

2 % 10 2 % column1 column1 % column2 sum(column1) % 10

+

Addition

Number + Number

Number

2 + 2 2 + column1 column1 + column2 average(column1) + 2

-

Subtraction

Number - Number

Number

2 - 2 2 - column1 column1 - column2 average(column1) - 2

<

Less than

Number < Number

Boolean

10 < 20 column1 < 10 column1 < column2 average(column2) < 5

<=

Less than or equal to

Number <= Number

Boolean

10 <= 20 column1 <= 10 column1 <= column2 average(column2) <= 5

>

Greater than

Number > Number

Boolean

10 > 20 column1 > 10 column1 > column2 average(column2) > 5

>=

Greater than or equal to

Number >= Number

Boolean

10 >= 20 column1 >= 10 column1 >= column2 average(column2) >= 5

==

Equals

Number == Number

Boolean

10 == 20 column1 == 10 column1 == column2 average(column2) == 5

!=

Does not equal

Number != Number

Boolean

10 != 20 column1 != 10 column1 != column2 average(column2) != 5

not

Logical NOT

not Boolean

Boolean

not true not column1

and

Logical AND

Boolean and Boolean

Boolean

true and false column1 and column2

or

Logical OR

Boolean or Boolean

Boolean

true or false column1 or column2

Constant functions

Symbol Description Syntax Returns Examples

Symbol	Description	Syntax	Returns	Examples
`e()`	Base of the natural logarithm	`e()`	`Number`	`e() == 2.718281828459045`
`pi()`	The ratio of a circle's circumference to its diameter	`pi()`	`Number`	`pi() == 3.141592653589793`

e()

Base of the natural logarithm

e()

Number

e() == 2.718281828459045

pi()

The ratio of a circle's circumference to its diameter

pi()

Number

pi() == 3.141592653589793

Row-level functions

Row-level functions can be applied either to a single value or to a column/row expression (in which case they are mapped element-wise to each value in the column/row expression).

Symbol Description Syntax Returns Examples

Symbol	Description	Syntax	Returns	Examples
`if(condition, value1, value2)`	Evaluates `condition` and returns `value1` if true, otherwise returns `value2`. `value1` and `value2` must have the same type.	`if(Boolean, Any, Any)`	`Any`	`if(false, 'yes', 'no') == 'no'` `if(column1 == 1, 'yes', 'no')`
`length(x)`	Returns the length of string `x`.	`length(String)`	`Number`	`length('Hello world') == 11`
`to_string(x)`	Converts a value `x` to a string.	`to_string(Any)`	`String`	`to_string(42) == '42'` `to_string(true) == 'true'`
`startswith(str, prefix)`	Returns `true` if `str` starts with `prefix`.	`startswith(String, String)`	`Boolean`	`startswith('abcde', 'abc')`
`substring(str, offset, length)`	Returns a substring of `str` of length `length` from offset `offset`. The first character has an offset of 1.	`substring(String, Number, Number)`	`String`	`substring('abcde', 2, 3) == 'bcd'`
`match(str, regex)`	Returns `true` if `str` matches the pattern `regex` in re2 regular syntax.	`match(String, String)`	`Boolean`	`match('abcde', 'a.c.*e')`
`is_null(x)`	Returns `true` if `x` is null, otherwise returns `false`.	`is_null(Any)`	`Boolean`	`is_null('') == true` `is_null("column1")`
`is_not_null(x)`	Returns `true` if `x` is not null, otherwise returns `false`.	`is_null(Any)`	`Boolean`	`is_not_null('') == false` `is_not_null("column1")
`abs(x)`	Returns the absolute value of number `x`.	`abs(Number)`	`Number`	`abs(-3) == 3`
`exp(x)`	Returns `e^x`, where `e` is the base of the natural logarithm.	`exp(Number)`	`Number`	`exp(1) == 2.718281828459045`
`log(x)`	Returns the natural logarithm (base `e`) of number `x`.	`log(Number)`	`Number`	`log(e) == 1`
`log2(x)`	Returns the binary logarithm (base `2`) of number `x`.	`log2(Number)`	`Number`	`log2(16) == 4`
`log10(x)`	Returns the binary logarithm (base `10`) of number `x`.	`log10(Number)`	`Number`	`log10(1000) == 3`
`sqrt(x)`	Returns the positive square root of number `x`.	`sqrt(Number)`	`Number`	`sqrt(144) == 12`

if(condition, value1, value2)

Evaluates condition and returns value1 if true, otherwise returns value2. value1 and value2 must have the same type.

if(Boolean, Any, Any)

Any

if(false, 'yes', 'no') == 'no' if(column1 == 1, 'yes', 'no')

length(x)

Returns the length of string x.

length(String)

Number

length('Hello world') == 11

to_string(x)

Converts a value x to a string.

to_string(Any)

String

to_string(42) == '42' to_string(true) == 'true'

startswith(str, prefix)

Returns true if str starts with prefix.

startswith(String, String)

Boolean

startswith('abcde', 'abc')

substring(str, offset, length)

Returns a substring of str of length length from offset offset. The first character has an offset of 1.

substring(String, Number, Number)

String

substring('abcde', 2, 3) == 'bcd'

match(str, regex)

Returns true if str matches the pattern regex in re2 regular syntax.

match(String, String)

Boolean

match('abcde', 'a.c.*e')

is_null(x)

Returns true if x is null, otherwise returns false.

is_null(Any)

Boolean

is_null('') == true is_null("column1")

is_not_null(x)

Returns true if x is not null, otherwise returns false.

is_null(Any)

Boolean

is_not_null('') == false `is_not_null("column1")

abs(x)

Returns the absolute value of number x.

abs(Number)

Number

abs(-3) == 3

exp(x)

Returns e^x, where e is the base of the natural logarithm.

exp(Number)

Number

exp(1) == 2.718281828459045

log(x)

Returns the natural logarithm (base e) of number x.

log(Number)

Number

log(e) == 1

log2(x)

Returns the binary logarithm (base 2) of number x.

log2(Number)

Number

log2(16) == 4

log10(x)

Returns the binary logarithm (base 10) of number x.

log10(Number)

Number

log10(1000) == 3

sqrt(x)

Returns the positive square root of number x.

sqrt(Number)

Number

sqrt(144) == 12

Aggregate functions

Every Custom Metric must be wrapped in an aggregate function or be a combination of aggregate functions.

Symbol Description Syntax Returns Examples

Symbol	Description	Syntax	Returns	Examples
`sum(x)`	Returns the sum of a numeric column or row expression `x`.	`sum(Number)`	`Number`	`sum(column1 + column2)`
`average(x)`	Returns the arithmetic mean/average value of a numeric column or row expression `x`.	`average(Number)`	`Number`	`average(2 * column1)`
`count(x)`	Returns the number of non-null rows of a column or row expression `x`.	`count(Any)`	`Number`	`count(column1)`

sum(x)

Returns the sum of a numeric column or row expression x.

sum(Number)

Number

sum(column1 + column2)

average(x)

Returns the arithmetic mean/average value of a numeric column or row expression x.

average(Number)

Number

average(2 * column1)

count(x)

Returns the number of non-null rows of a column or row expression x.

count(Any)

Number

count(column1)

Built-in metric functions

Symbol Description Syntax Returns Examples

Symbol	Description	Syntax	Returns	Examples
`jsd(column, baseline)`	The Jensen-Shannon distance of column `column` with respect to baseline `baseline`.	`jsd(Any, String)`	`Number`	`jsd(column1, 'my_baseline')`
`psi(column, baseline)`	The population stability index of column `column` with respect to baseline `baseline`.	`psi(Any, String)`	`Number`	`psi(column1, 'my_baseline')`
`null_violation_count(column)`	Number of rows with null values in column `column`.	`null_violation_count(Any)`	`Number`	`null_violation_count(column1)`
`range_violation_count(column)`	Number of rows with out-of-range values in column `column`.	`range_violation_count(Any)`	`Number`	`range_violation_count(column1)`
`type_violation_count(column)`	Number of rows with invalid data types in column `column`.	`type_violation_count(Any)`	`Number`	`type_violation_count(column1)`
`any_violation_count(column)`	Number of rows with at least one Data Integrity violation in `column`.	`any_violation_count(Any)`	`Number`	`any_violation_count(column1)`
`traffic()`	Total row count. Includes null rows.	`traffic()`	`Number`	`traffic()`
`tp(class)`	True positive count. Available for binary classification and multiclass classification models. For multiclass, `class` is used to specify the positive class.	`tp(class=Optional[String])`	`Number`	`tp()` `tp(class='class1')`
`tn(class)`	True negative count. Available for binary classification and multiclass classification models. For multiclass, `class` is used to specify the positive class.	`tn(class=Optional[String])`	`Number`	`tn()` `tn(class='class1')`
`fp(class)`	False positive count. Available for binary classification and multiclass classification models. For multiclass, `class` is used to specify the positive class.	`fp(class=Optional[String])`	`Number`	`fp()` `fp(class='class1')`
`fn(class)`	False negative count. Available for binary classification and multiclass classification models. For multiclass, `class` is used to specify the positive class.	`fn(class=Optional[String])`	`Number`	`fn()` `fn(class='class1')`
`precision(target, threshold)`	Precision between target and output. Available for binary classification model tasks. If `target` is specified, it will be used in place of the default target column.	`precision(target=Optional[Any], threshold=Optional[Number])`	`Number`	`precision()` `precision(target=column1)` `precision(threshold=0.5)` `precision(target=column1, threshold=0.5)`
`recall(target, threshold)`	Recall between target and output. Available for binary classification model tasks. If `target` is specified, it will be used in place of the default target column.	`recall(target=Optional[Any], threshold=Optional[Number])`	`Number`	`recall()` `recall(target=column1)` `recall(threshold=0.5)` `recall(target=column1, threshold=0.5)`
`f1_score(target, threshold)`	F1 score between target and output. Available for binary classification model tasks. If `target` is specified, it will be used in place of the default target column.	`f1_score(target=Optional[Any], threshold=Optional[Number])`	`Number`	`f1_score()` `f1_score(target=column1)` `f1_score(threshold=0.5)` `f1_score(target=column1, threshold=0.5)`
`fpr(target, threshold)`	False positive rate between target and output. Available for binary classification model tasks. If `target` is specified, it will be used in place of the default target column.	`fpr(target=Optional[Any], threshold=Optional[Number])`	`Number`	`fpr()` `fpr(target=column1)` `fpr(threshold=0.5)` `fpr(target=column1, threshold=0.5)`
`auroc(target)`	Area under the ROC curve between target and output. Available for binary classification model tasks. If `target` is specified, it will be used in place of the default target column.	`auroc(target=Optional[Any])`	`Number`	`auroc()` `auroc(target=column1)`
`geometric_mean(target, threshold)`	Geometric mean score between target and output. Available for binary classification model tasks. If `target` is specified, it will be used in place of the default target column.	`geometric_mean(target=Optional[Any], threshold=Optional[Number])`	`Number`	`geometric_mean()` `geometric_mean(target=column1)` `geometric_mean(threshold=0.5)` `geometric_mean(target=column1, threshold=0.5)`
`expected_calibration_error(target)`	Expected calibration error between target and output. Available for binary classification model tasks. If `target` is specified, it will be used in place of the default target column.	`expected_calibration_error(target=Optional[Any])`	`Number`	`expected_calibration_error()` `expected_calibration_error(target=column1)`
`log_loss(target)`	Log loss (binary cross entropy) between target and output. Available for binary classification model tasks. If `target` is specified, it will be used in place of the default target column.	`log_loss(target=Optional[Any])`	`Number`	`log_loss()` `log_loss(target=column1)`
`calibrated_threshold(target)`	Optimal threshold value for a high TPR and a low FPR. Available for binary classification model tasks. If `target` is specified, it will be used in place of the default target column.	`calibrated_threshold(target=Optional[Any])`	`Number`	`calibrated_threshold()` `calibrated_threshold(target=column1)`
`accuracy(target, threshold)`	Accuracy score between target and outputs. Available for multiclass classification model tasks. If `target` is specified, it will be used in place of the default target column.	`accuracy(target=Optional[Any], threshold=Optional[Number])`	`Number`	`accuracy()` `accuracy(target=column1)` `accuracy(threshold=0.5)` `accuracy(target=column1, threshold=0.5)`
`log_loss(target)`	Log loss score between target and outputs. Available for multiclass classification model tasks. If `target` is specified, it will be used in place of the default target column.	`log_loss(target=Optional[Any])`	`Number`	`log_loss()` `log_loss(target=column1)`
`r2(target)`	R-squared score between target and output. Available for regression model tasks. If `target` is specified, it will be used in place of the default target column.	`r2(target=Optional[Any])`	`Number`	`r2()` `r2(target=column1)`
`mse(target)`	Mean squared error between target and output. Available for regression model tasks. If `target` is specified, it will be used in place of the default target column.	`mse(target=Optional[Any])`	`Number`	`mse()` `mse(target=column1)`
`mae(target)`	Mean absolute error between target and output. Available for regression model tasks. If `target` is specified, it will be used in place of the default target column.	`mae(target=Optional[Any])`	`Number`	`mae()` `mae(target=column1)`
`mape(target)`	Mean absolute percentage error between target and output. Available for regression model tasks. If `target` is specified, it will be used in place of the default target column.	`mape(target=Optional[Any])`	`Number`	`mape()` `mape(target=column1)`
`wmape(target)`	Weighted mean absolute percentage error between target and output. Available for regression model tasks. If `target` is specified, it will be used in place of the default target column.	`wmape(target=Optional[Any])`	`Number`	`wmape()` `wmape(target=column1)`
`map(target)`	Mean average precision score. Available for ranking model tasks. If `target` is specified, it will be used in place of the default target column.	`map(target=Optional[Any])`	`Number`	`map()` `map(target=column1)`
`ndcg_mean(target)`	Mean normalized discounted cumulative gain score. Available for ranking model tasks. If `target` is specified, it will be used in place of the default target column.	`ndcg_mean(target=Optional[Any])`	`Number`	`ndcg_mean()` `ndcg_mean(target=column1)`
`query_count(target)`	Count of ranking queries. Available for ranking model tasks. If `target` is specified, it will be used in place of the default target column.	`query_count(target=Optional[Any])`	`Number`	`query_count()` `query_count(target=column1)`

jsd(column, baseline)

The Jensen-Shannon distance of column column with respect to baseline baseline.

jsd(Any, String)

Number

jsd(column1, 'my_baseline')

psi(column, baseline)

The population stability index of column column with respect to baseline baseline.

psi(Any, String)

Number

psi(column1, 'my_baseline')

null_violation_count(column)

Number of rows with null values in column column.

null_violation_count(Any)

Number

null_violation_count(column1)

range_violation_count(column)

Number of rows with out-of-range values in column column.

range_violation_count(Any)

Number

range_violation_count(column1)

type_violation_count(column)

Number of rows with invalid data types in column column.

type_violation_count(Any)

Number

type_violation_count(column1)

any_violation_count(column)

Number of rows with at least one Data Integrity violation in column.

any_violation_count(Any)

Number

any_violation_count(column1)

traffic()

Total row count. Includes null rows.

traffic()

Number

traffic()

tp(class)

True positive count. Available for binary classification and multiclass classification models. For multiclass, class is used to specify the positive class.

tp(class=Optional[String])

Number

tp() tp(class='class1')

tn(class)

True negative count. Available for binary classification and multiclass classification models. For multiclass, class is used to specify the positive class.

tn(class=Optional[String])

Number

tn() tn(class='class1')

fp(class)

False positive count. Available for binary classification and multiclass classification models. For multiclass, class is used to specify the positive class.

fp(class=Optional[String])

Number

fp() fp(class='class1')

fn(class)

False negative count. Available for binary classification and multiclass classification models. For multiclass, class is used to specify the positive class.

fn(class=Optional[String])

Number

fn() fn(class='class1')

precision(target, threshold)

Precision between target and output. Available for binary classification model tasks. If target is specified, it will be used in place of the default target column.

precision(target=Optional[Any], threshold=Optional[Number])

Number

precision() precision(target=column1) precision(threshold=0.5) precision(target=column1, threshold=0.5)

recall(target, threshold)

Recall between target and output. Available for binary classification model tasks. If target is specified, it will be used in place of the default target column.

recall(target=Optional[Any], threshold=Optional[Number])

Number

recall() recall(target=column1) recall(threshold=0.5) recall(target=column1, threshold=0.5)

f1_score(target, threshold)

F1 score between target and output. Available for binary classification model tasks. If target is specified, it will be used in place of the default target column.

f1_score(target=Optional[Any], threshold=Optional[Number])

Number

f1_score() f1_score(target=column1) f1_score(threshold=0.5) f1_score(target=column1, threshold=0.5)

fpr(target, threshold)

False positive rate between target and output. Available for binary classification model tasks. If target is specified, it will be used in place of the default target column.

fpr(target=Optional[Any], threshold=Optional[Number])

Number

fpr() fpr(target=column1) fpr(threshold=0.5) fpr(target=column1, threshold=0.5)

auroc(target)

Area under the ROC curve between target and output. Available for binary classification model tasks. If target is specified, it will be used in place of the default target column.

auroc(target=Optional[Any])

Number

auroc() auroc(target=column1)

geometric_mean(target, threshold)

Geometric mean score between target and output. Available for binary classification model tasks. If target is specified, it will be used in place of the default target column.

geometric_mean(target=Optional[Any], threshold=Optional[Number])

Number

geometric_mean() geometric_mean(target=column1) geometric_mean(threshold=0.5) geometric_mean(target=column1, threshold=0.5)

expected_calibration_error(target)

Expected calibration error between target and output. Available for binary classification model tasks. If target is specified, it will be used in place of the default target column.

expected_calibration_error(target=Optional[Any])

Number

expected_calibration_error() expected_calibration_error(target=column1)

log_loss(target)

Log loss (binary cross entropy) between target and output. Available for binary classification model tasks. If target is specified, it will be used in place of the default target column.

log_loss(target=Optional[Any])

Number

log_loss() log_loss(target=column1)

calibrated_threshold(target)

Optimal threshold value for a high TPR and a low FPR. Available for binary classification model tasks. If target is specified, it will be used in place of the default target column.

calibrated_threshold(target=Optional[Any])

Number

calibrated_threshold() calibrated_threshold(target=column1)

accuracy(target, threshold)

Accuracy score between target and outputs. Available for multiclass classification model tasks. If target is specified, it will be used in place of the default target column.

accuracy(target=Optional[Any], threshold=Optional[Number])

Number

accuracy() accuracy(target=column1) accuracy(threshold=0.5) accuracy(target=column1, threshold=0.5)

log_loss(target)

Log loss score between target and outputs. Available for multiclass classification model tasks. If target is specified, it will be used in place of the default target column.

log_loss(target=Optional[Any])

Number

log_loss() log_loss(target=column1)

r2(target)

R-squared score between target and output. Available for regression model tasks. If target is specified, it will be used in place of the default target column.

r2(target=Optional[Any])

Number

r2() r2(target=column1)

mse(target)

Mean squared error between target and output. Available for regression model tasks. If target is specified, it will be used in place of the default target column.

mse(target=Optional[Any])

Number

mse() mse(target=column1)

mae(target)

Mean absolute error between target and output. Available for regression model tasks. If target is specified, it will be used in place of the default target column.

mae(target=Optional[Any])

Number

mae() mae(target=column1)

mape(target)

Mean absolute percentage error between target and output. Available for regression model tasks. If target is specified, it will be used in place of the default target column.

mape(target=Optional[Any])

Number

mape() mape(target=column1)

wmape(target)

Weighted mean absolute percentage error between target and output. Available for regression model tasks. If target is specified, it will be used in place of the default target column.

wmape(target=Optional[Any])

Number

wmape() wmape(target=column1)

map(target)

Mean average precision score. Available for ranking model tasks. If target is specified, it will be used in place of the default target column.

map(target=Optional[Any])

Number

map() map(target=column1)

ndcg_mean(target)

Mean normalized discounted cumulative gain score. Available for ranking model tasks. If target is specified, it will be used in place of the default target column.

ndcg_mean(target=Optional[Any])

Number

ndcg_mean() ndcg_mean(target=column1)

query_count(target)

Count of ranking queries. Available for ranking model tasks. If target is specified, it will be used in place of the default target column.

query_count(target=Optional[Any])

Number

query_count() query_count(target=column1)

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