[DOCS] Changes level offset of anomaly detection pages (elastic#59911)

docs/build.gradle

@@ -26,7 +26,7 @@ apply plugin: 'elasticsearch.rest-resources'
 
 /* List of files that have snippets that will not work until platinum tests can occur ... */
 buildRestTests.expectedUnconvertedCandidates = [
-  'reference/ml/anomaly-detection/transforms.asciidoc',
+  'reference/ml/anomaly-detection/ml-configuring-transform.asciidoc',
   'reference/ml/anomaly-detection/apis/delete-calendar-event.asciidoc',
   'reference/ml/anomaly-detection/apis/get-bucket.asciidoc',
   'reference/ml/anomaly-detection/apis/get-category.asciidoc',

docs/reference/ml/anomaly-detection/functions/count.asciidoc → docs/reference/ml/anomaly-detection/functions/ml-count-functions.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-count-functions]]
-=== Count functions
+= Count functions
 
 Count functions detect anomalies when the number of events in a bucket is
 anomalous.
@@ -22,7 +22,7 @@ The {ml-features} include the following count functions:
 
 [float]
 [[ml-count]]
-===== Count, high_count, low_count
+== Count, high_count, low_count
 
 The `count` function detects anomalies when the number of events in a bucket is
 anomalous.
@@ -145,7 +145,7 @@ and the `summary_count_field_name` property. For more information, see
 
 [float]
 [[ml-nonzero-count]]
-===== Non_zero_count, high_non_zero_count, low_non_zero_count
+== Non_zero_count, high_non_zero_count, low_non_zero_count
 
 The `non_zero_count` function detects anomalies when the number of events in a
 bucket is anomalous, but it ignores cases where the bucket count is zero. Use
@@ -215,7 +215,7 @@ data is sparse, use the `count` functions, which are optimized for that scenario
 
 [float]
 [[ml-distinct-count]]
-===== Distinct_count, high_distinct_count, low_distinct_count
+== Distinct_count, high_distinct_count, low_distinct_count
 
 The `distinct_count` function detects anomalies where the number of distinct
 values in one field is unusual.

docs/reference/ml/anomaly-detection/functions.asciidoc → docs/reference/ml/anomaly-detection/functions/ml-functions.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-functions]]
-== Function reference
+= Function reference
 
 The {ml-features} include analysis functions that provide a wide variety of
 flexible ways to analyze data for anomalies.
@@ -41,17 +41,3 @@ These functions effectively ignore empty buckets.
 * <<ml-rare-functions>>
 * <<ml-sum-functions>>
 * <<ml-time-functions>>
-
-include::functions/count.asciidoc[]
-
-include::functions/geo.asciidoc[]
-
-include::functions/info.asciidoc[]
-
-include::functions/metric.asciidoc[]
-
-include::functions/rare.asciidoc[]
-
-include::functions/sum.asciidoc[]
-
-include::functions/time.asciidoc[]

docs/reference/ml/anomaly-detection/functions/geo.asciidoc → docs/reference/ml/anomaly-detection/functions/ml-geo-functions.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-geo-functions]]
-=== Geographic functions
+= Geographic functions
 
 The geographic functions detect anomalies in the geographic location of the
 input data.
@@ -13,7 +13,7 @@ geographic functions.
 
 [float]
 [[ml-lat-long]]
-==== Lat_long
+== Lat_long
 
 The `lat_long` function detects anomalies in the geographic location of the
 input data.

docs/reference/ml/anomaly-detection/functions/info.asciidoc → docs/reference/ml/anomaly-detection/functions/ml-info-functions.asciidoc

@@ -1,5 +1,5 @@
 [[ml-info-functions]]
-=== Information Content Functions
+= Information Content Functions
 
 The information content functions detect anomalies in the amount of information
 that is contained in strings within a bucket. These functions can be used as
@@ -12,7 +12,7 @@ The {ml-features} include the following information content functions:
 
 [float]
 [[ml-info-content]]
-==== Info_content, High_info_content, Low_info_content
+== Info_content, High_info_content, Low_info_content
 
 The `info_content` function detects anomalies in the amount of information that
 is contained in strings in a bucket.

docs/reference/ml/anomaly-detection/functions/metric.asciidoc → docs/reference/ml/anomaly-detection/functions/ml-metric-functions.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-metric-functions]]
-=== 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
@@ -20,7 +20,7 @@ function.
 
 [float]
 [[ml-metric-min]]
-==== Min
+== Min
 
 The `min` function detects anomalies in the arithmetic minimum of a value.
 The minimum value is calculated for each bucket.
@@ -55,7 +55,7 @@ entry mistakes. It models the minimum amount for each product over time.
 
 [float]
 [[ml-metric-max]]
-==== Max
+== Max
 
 The `max` function detects anomalies in the arithmetic maximum of a value.
 The maximum value is calculated for each bucket.
@@ -113,7 +113,7 @@ response times for each bucket.
 
 [float]
 [[ml-metric-median]]
-==== Median, high_median, low_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.
@@ -151,7 +151,7 @@ median `responsetime` is unusual compared to previous `responsetime` values.
 
 [float]
 [[ml-metric-mean]]
-==== Mean, high_mean, low_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.
@@ -221,7 +221,7 @@ values.
 
 [float]
 [[ml-metric-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
@@ -258,7 +258,7 @@ when the mean, min, or max `responsetime` is unusual compared to previous
 
 [float]
 [[ml-metric-varp]]
-==== Varp, high_varp, low_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.

docs/reference/ml/anomaly-detection/functions/rare.asciidoc → docs/reference/ml/anomaly-detection/functions/ml-rare-functions.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-rare-functions]]
-=== Rare functions
+= Rare functions
 
 The rare functions detect values that occur rarely in time or rarely for a
 population.
@@ -35,7 +35,7 @@ The {ml-features} include the following rare functions:
 
 [float]
 [[ml-rare]]
-==== Rare
+== Rare
 
 The `rare` function detects values that occur rarely in time or rarely for a
 population. It detects anomalies according to the number of distinct rare values.
@@ -93,7 +93,7 @@ is rare, even if it occurs for that client IP in every bucket.
 
 [float]
 [[ml-freq-rare]]
-==== Freq_rare
+== Freq_rare
 
 The `freq_rare` function detects values that occur rarely for a population.
 It detects anomalies according to the number of times (frequency) that rare

docs/reference/ml/anomaly-detection/functions/sum.asciidoc → docs/reference/ml/anomaly-detection/functions/ml-sum-functions.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-sum-functions]]
-=== Sum functions
+= Sum functions
 
 The sum functions detect anomalies when the sum of a field in a bucket is
 anomalous.
@@ -19,7 +19,7 @@ The {ml-features} include the following sum functions:
 
 [float]
 [[ml-sum]]
-==== Sum, high_sum, low_sum
+== Sum, high_sum, low_sum
 
 The `sum` function detects anomalies where the sum of a field in a bucket is
 anomalous.
@@ -75,7 +75,7 @@ to find users that are abusing internet privileges.
 
 [float]
 [[ml-nonnull-sum]]
-==== Non_null_sum, high_non_null_sum, low_non_null_sum
+== Non_null_sum, high_non_null_sum, low_non_null_sum
 
 The `non_null_sum` function is useful if your data is sparse. Buckets without
 values are ignored and buckets with a zero value are analyzed.

docs/reference/ml/anomaly-detection/functions/time.asciidoc → docs/reference/ml/anomaly-detection/functions/ml-time-functions.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-time-functions]]
-=== Time functions
+= Time functions
 
 The time functions detect events that happen at unusual times, either of the day
 or of the week. These functions can be used to find unusual patterns of behavior,
@@ -37,7 +37,7 @@ step change in behavior and the new times will be learned quickly.
 
 [float]
 [[ml-time-of-day]]
-==== Time_of_day
+== Time_of_day
 
 The `time_of_day` function detects when events occur that are outside normal
 usage patterns. For example, it detects unusual activity in the middle of the
@@ -73,7 +73,7 @@ its past behavior.
 
 [float]
 [[ml-time-of-week]]
-==== Time_of_week
+== Time_of_week
 
 The `time_of_week` function detects when events occur that are outside normal
 usage patterns. For example, it detects login events on the weekend.

docs/reference/ml/anomaly-detection/aggregations.asciidoc → docs/reference/ml/anomaly-detection/ml-configuring-aggregations.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-configuring-aggregation]]
-=== Aggregating data for faster performance
+= Aggregating data for faster performance
 
 By default, {dfeeds} fetch data from {es} using search and scroll requests.
 It can be significantly more efficient, however, to aggregate data in {es}
@@ -17,7 +17,7 @@ search and scroll behavior.
 
 [discrete]
 [[aggs-limits-dfeeds]]
-==== Requirements and limitations
+== Requirements and limitations
 
 There are some limitations to using aggregations in {dfeeds}. Your aggregation
 must include a `date_histogram` aggregation, which in turn must contain a `max`
@@ -48,7 +48,7 @@ functions, set the interval to the same value as the bucket span.
 
 [discrete]
 [[aggs-include-jobs]]
-==== Including aggregations in {anomaly-jobs}
+== Including aggregations in {anomaly-jobs}
 
 When you create or update an {anomaly-job}, you can include the names of
 aggregations, for example:
@@ -134,7 +134,7 @@ that match values in the job configuration are fed to the job.
 
 [discrete]
 [[aggs-dfeeds]]
-==== Nested aggregations in {dfeeds}
+== Nested aggregations in {dfeeds}
 
 {dfeeds-cap} support complex nested aggregations. This example uses the 
 `derivative` pipeline aggregation to find the first order derivative of the 
@@ -180,7 +180,7 @@ counter `system.network.out.bytes` for each value of the field `beat.name`.
 
 [discrete]
 [[aggs-single-dfeeds]]
-==== Single bucket aggregations in {dfeeds}
+== Single bucket aggregations in {dfeeds}
 
 {dfeeds-cap} not only supports multi-bucket aggregations, but also single bucket
 aggregations. The following shows two `filter` aggregations, each gathering the
@@ -232,7 +232,7 @@ number of unique entries for the `error` field.
 
 [discrete]
 [[aggs-define-dfeeds]]
-==== Defining aggregations in {dfeeds}
+== Defining aggregations in {dfeeds}
 
 When you define an aggregation in a {dfeed}, it must have the following form:
 

docs/reference/ml/anomaly-detection/categories.asciidoc → docs/reference/ml/anomaly-detection/ml-configuring-categories.asciidoc

@@ -1,7 +1,7 @@
 [role="xpack"]
 [testenv="platinum"]
 [[ml-configuring-categories]]
-=== Detecting anomalous categories of data
+= Detecting anomalous categories of data
 
 Categorization is a {ml} process that tokenizes a text field, clusters similar
 data together, and classifies it into categories. It works best on
@@ -100,7 +100,7 @@ SQL statement from the categorization algorithm.
 
 [discrete]
 [[ml-configuring-analyzer]]
-==== Customizing the categorization analyzer
+== Customizing the categorization analyzer
 
 Categorization uses English dictionary words to identify log message categories.
 By default, it also uses English tokenization rules. For this reason, if you use