[[ml-metric-functions]]
=== Metric Functions

The metric functions include functions such as mean, min and max. These values
are calculated for each bucket. Field values that cannot be converted to
double precision floating point numbers are ignored.

The {xpackml} features include the following metric functions:

* <<ml-metric-min,`min`>>
* <<ml-metric-max,`max`>>
* xref:ml-metric-median[`median`, `high_median`, `low_median`]
* xref:ml-metric-mean[`mean`, `high_mean`, `low_mean`]
* <<ml-metric-metric,`metric`>>
* xref:ml-metric-varp[`varp`, `high_varp`, `low_varp`]

[float]
[[ml-metric-min]]
==== Min

The `min` function detects anomalies in the arithmetic minimum of a value.
The minimum value is calculated for each bucket.

High- and low-sided functions are not applicable.

This function supports the following properties:

* `field_name` (required)
* `by_field_name` (optional)
* `over_field_name` (optional)
* `partition_field_name` (optional)

For more information about those properties,
see <<ml-detectorconfig,Detector Configuration Objects>>.

.Example 1: Analyzing minimum transactions with the min function
[source,js]
--------------------------------------------------
{
  "function" : "min",
  "field_name" : "amt",
  "by_field_name" : "product"
}
--------------------------------------------------

If you use this `min` function in a detector in your job, it detects where the
smallest transaction is lower than previously observed. You can use this
function to detect items for sale at unintentionally low prices due to data
entry mistakes. It models the minimum amount for each product over time.

[float]
[[ml-metric-max]]
==== Max

The `max` function detects anomalies in the arithmetic maximum of a value.
The maximum value is calculated for each bucket.

High- and low-sided functions are not applicable.

This function supports the following properties:

* `field_name` (required)
* `by_field_name` (optional)
* `over_field_name` (optional)
* `partition_field_name` (optional)

For more information about those properties,
see <<ml-detectorconfig,Detector Configuration Objects>>.

.Example 2: Analyzing maximum response times with the max function
[source,js]
--------------------------------------------------
{
  "function" : "max",
  "field_name" : "responsetime",
  "by_field_name" : "application"
}
--------------------------------------------------

If you use this `max` function in a detector in your job, it detects where the
longest `responsetime` is longer than previously observed. You can use this
function to detect applications that have `responsetime` values that are
unusually lengthy. It models the maximum `responsetime` for each application
over time and detects when the longest `responsetime` is unusually long compared
to previous applications.

.Example 3: Two detectors with max and high_mean functions
[source,js]
--------------------------------------------------
{
  "function" : "max",
  "field_name" : "responsetime",
  "by_field_name" : "application"
},
{
  "function" : "high_mean",
  "field_name" : "responsetime",
  "by_field_name" : "application"
}
--------------------------------------------------

The analysis in the previous example can be performed alongside `high_mean`
functions by application. By combining detectors and using the same influencer
this job can detect both unusually long individual response times and average
response times for each bucket.

[float]
[[ml-metric-median]]
==== Median, High_median, Low_median

The `median` function detects anomalies in the statistical median of a value.
The median value is calculated for each bucket.

If you want to monitor unusually high median values, use the `high_median`
function.

If you are just interested in unusually low median values, use the `low_median`
function.

These functions support the following properties:

* `field_name` (required)
* `by_field_name` (optional)
* `over_field_name` (optional)
* `partition_field_name` (optional)

For more information about those properties,
see <<ml-detectorconfig,Detector Configuration Objects>>.

.Example 4: Analyzing response times with the median function
[source,js]
--------------------------------------------------
{
  "function" : "median",
  "field_name" : "responsetime",
  "by_field_name" : "application"
}
--------------------------------------------------

If you use this `median` function in a detector in your job, it models the
median `responsetime` for each application over time. It detects when the median
`responsetime` is unusual compared to previous `responsetime` values.

[float]
[[ml-metric-mean]]
==== Mean, High_mean, Low_mean

The `mean` function detects anomalies in the arithmetic mean of a value.
The mean value is calculated for each bucket.

If you want to monitor unusually high average values, use the `high_mean`
function.

If you are just interested in unusually low average values, use the `low_mean`
function.

These functions support the following properties:

* `field_name` (required)
* `by_field_name` (optional)
* `over_field_name` (optional)
* `partition_field_name` (optional)

For more information about those properties,
see <<ml-detectorconfig,Detector Configuration Objects>>.

.Example 5: Analyzing response times with the mean function
[source,js]
--------------------------------------------------
{
  "function" : "mean",
  "field_name" : "responsetime",
  "by_field_name" : "application"
}
--------------------------------------------------

If you use this `mean` function in a detector in your job, it models the mean
`responsetime` for each application over time. It detects when the mean
`responsetime` is unusual compared to previous `responsetime` values.

.Example 6: Analyzing response times with the high_mean function
[source,js]
--------------------------------------------------
{
  "function" : "high_mean",
  "field_name" : "responsetime",
  "by_field_name" : "application"
}
--------------------------------------------------

If you use this `high_mean` function in a detector in your job, it models the
mean `responsetime` for each application over time. It detects when the mean
`responsetime` is unusually high compared to previous `responsetime` values.

.Example 7: Analyzing response times with the low_mean function
[source,js]
--------------------------------------------------
{
  "function" : "low_mean",
  "field_name" : "responsetime",
  "by_field_name" : "application"
}
--------------------------------------------------

If you use this `low_mean` function in a detector in your job, it models the
mean `responsetime` for each application over time. It detects when the mean
`responsetime` is unusually low compared to previous `responsetime` values.

[float]
[[ml-metric-metric]]
==== Metric

The `metric` function combines `min`, `max`, and `mean` functions. You can use
it as a shorthand for a combined analysis. If you do not specify a function in
a detector, this is the default function.
//TBD: Is that default behavior still true?

High- and low-sided functions are not applicable. You cannot use this function
when a `summary_count_field_name` is specified.

This function supports the following properties:

* `field_name` (required)
* `by_field_name` (optional)
* `over_field_name` (optional)
* `partition_field_name` (optional)

For more information about those properties,
see <<ml-detectorconfig,Detector Configuration Objects>>.

.Example 8: Analyzing response times with the metric function
[source,js]
--------------------------------------------------
{
  "function" : "metric",
  "field_name" : "responsetime",
  "by_field_name" : "application"
}
--------------------------------------------------

If you use this `metric` function in a detector in your job, it models the
mean, min, and max `responsetime` for each application over time. It detects
when the mean, min, or max `responsetime` is unusual compared to previous
`responsetime` values.

[float]
[[ml-metric-varp]]
==== Varp, High_varp, Low_varp

The `varp` function detects anomalies in the variance of a value which is a
measure of the variability and spread in the data.

If you want to monitor unusually high variance, use the `high_varp` function.

If you are just interested in unusually low variance, use the `low_varp` function.

These functions support the following properties:

* `field_name` (required)
* `by_field_name` (optional)
* `over_field_name` (optional)
* `partition_field_name` (optional)

For more information about those properties,
see <<ml-detectorconfig,Detector Configuration Objects>>.

.Example 9: Analyzing response times with the varp function
[source,js]
--------------------------------------------------
{
  "function" : "varp",
  "field_name" : "responsetime",
  "by_field_name" : "application"
}
--------------------------------------------------

If you use this `varp` function in a detector in your job, it models the
variance in values of `responsetime` for each application over time. It detects
when the variance in `responsetime` is unusual compared to past application
behavior.

.Example 10: Analyzing response times with the high_varp function
[source,js]
--------------------------------------------------
{
  "function" : "high_varp",
  "field_name" : "responsetime",
  "by_field_name" : "application"
}
--------------------------------------------------

If you use this `high_varp` function in a detector in your job, it models the
variance in values of `responsetime` for each application over time. It detects
when the variance in `responsetime` is unusual compared to past application
behavior.

.Example 11: Analyzing response times with the low_varp function
[source,js]
--------------------------------------------------
{
  "function" : "low_varp",
  "field_name" : "responsetime",
  "by_field_name" : "application"
}
--------------------------------------------------

If you use this `low_varp` function in a detector in your job, it models the
variance in values of `responsetime` for each application over time. It detects
when the variance in `responsetime` is unusual compared to past application
behavior.
