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

[role="xpack"]
[testenv="platinum"]
[[ml-configuring-categories]]
= 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
machine-written messages and application output that typically consist of
repeated elements. For example, it works well on logs that contain a finite set
of possible messages:

//Obtained from it_ops_new_app_logs.json
[source,js]
----------------------------------
{"@timestamp":1549596476000,
"message":"org.jdbi.v2.exceptions.UnableToExecuteStatementException: com.mysql.jdbc.exceptions.MySQLTimeoutException: Statement cancelled due to timeout or client request [statement:\"SELECT id, customer_id, name, force_disabled, enabled FROM customers\"]",
"type":"logs"}
----------------------------------
//NOTCONSOLE

Categorization is tuned to work best on data like log messages by taking token
order into account, including stop words, and not considering synonyms in its
analysis. Complete sentences in human communication or literary text (for
example email, wiki pages, prose, or other human-generated content) can be 
extremely diverse in structure. Since categorization is tuned for machine data, 
it gives poor results for human-generated data. It would create so many
categories that they couldn't be handled effectively. Categorization is _not_
natural language processing (NLP).

When you create a categorization {anomaly-job}, the {ml} model learns what
volume and pattern is normal for each category over time. You can then detect
anomalies and surface rare events or unusual types of messages by using
<<ml-count-functions,count>> or <<ml-rare-functions,rare>> functions.

In {kib}, there is a categorization wizard to help you create this type of 
{anomaly-job}. For example, the following job generates categories from the
contents of the `message` field and uses the count function to determine when
certain categories are occurring at anomalous rates:

[role="screenshot"]
image::images/ml-category-wizard.jpg["Creating a categorization job in Kibana"]

[%collapsible]
.API example
====
[source,console]
----------------------------------
PUT _ml/anomaly_detectors/it_ops_app_logs
{
  "description" : "IT ops application logs",
  "analysis_config" : {
    "categorization_field_name": "message",<1>
    "bucket_span":"30m",
    "detectors" :[{
      "function":"count",
      "by_field_name": "mlcategory"<2>
    }]
  },
  "data_description" : {
    "time_field":"@timestamp"
  }
}
----------------------------------
// TEST[skip:needs-licence]
<1> This field is used to derive categories.
<2> The categories are used in a detector by setting `by_field_name`,
`over_field_name`, or `partition_field_name` to the keyword `mlcategory`. If you
do not specify this keyword in one of those properties, the API request fails.
====


You can use the **Anomaly Explorer** in {kib} to view the analysis results: 

[role="screenshot"]
image::images/ml-category-anomalies.jpg["Categorization results in the Anomaly Explorer"]

For this type of job, the results contain extra information for each anomaly:
the name of the category (for example, `mlcategory 2`) and examples of the
messages in that category. You can use these details to investigate occurrences
of unusually high message counts.

If you use the advanced {anomaly-job} wizard in {kib} or the
{ref}/ml-put-job.html[create {anomaly-jobs} API], there are additional
configuration options. For example, the optional `categorization_examples_limit`
property specifies the maximum number of examples that are stored in memory and
in the results data store for each category. The default value is `4`. Note that
this setting does not affect the categorization; it just affects the list of
visible examples. If you increase this value, more examples are available, but
you must have more storage available. If you set this value to `0`, no examples
are stored.

Another advanced option is the `categorization_filters` property, which can
contain an array of regular expressions. If a categorization field value matches
the regular expression, the portion of the field that is matched is not taken
into consideration when defining categories. The categorization filters are
applied in the order they are listed in the job configuration, which enables you
to disregard multiple sections of the categorization field value. In this
example, you might create a filter like `[ "\\[statement:.*\\]"]` to remove the
SQL statement from the categorization algorithm.

[discrete]
[[ml-configuring-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
the default categorization analyzer, only English language log messages are
supported, as described in the <<ml-limitations>>. 

If you use the categorization wizard in {kib}, you can see which categorization
analyzer it uses and highlighted examples of the tokens that it identifies. You
can also change the tokenization rules by customizing the way the categorization
field values are interpreted:

[role="screenshot"]
image::images/ml-category-analyzer.jpg["Editing the categorization analyzer in Kibana"]

The categorization analyzer can refer to a built-in {es} analyzer or a
combination of zero or more character filters, a tokenizer, and zero or more
token filters. In this example, adding a 
{ref}/analysis-pattern-replace-charfilter.html[`pattern_replace` character filter]
achieves exactly the same behavior as the `categorization_filters` job
configuration option described earlier. For more details about these properties,
see the
{ref}/ml-put-job.html#ml-put-job-request-body[`categorization_analyzer` API object].

If you use the default categorization analyzer in {kib} or omit the
`categorization_analyzer` property from the API, the following default values
are used:

[source,console]
--------------------------------------------------
POST _ml/anomaly_detectors/_validate
{
  "analysis_config" : {
    "categorization_analyzer" : {
      "tokenizer" : "ml_classic",
      "filter" : [
        { "type" : "stop", "stopwords": [
          "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday",
          "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun",
          "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December",
          "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec",
          "GMT", "UTC"
        ] }
      ]
    },
    "categorization_field_name": "message",
    "detectors" :[{
      "function":"count",
      "by_field_name": "mlcategory"
    }]
  },
  "data_description" : {
  }
}
--------------------------------------------------

If you specify any part of the `categorization_analyzer`, however, any omitted
sub-properties are _not_ set to default values.

The `ml_classic` tokenizer and the day and month stopword filter are more or 
less equivalent to the following analyzer, which is defined using only built-in 
{es} {ref}/analysis-tokenizers.html[tokenizers] and
{ref}/analysis-tokenfilters.html[token filters]:

[source,console]
----------------------------------
PUT _ml/anomaly_detectors/it_ops_new_logs3
{
  "description" : "IT Ops Application Logs",
  "analysis_config" : {
    "categorization_field_name": "message",
    "bucket_span":"30m",
    "detectors" :[{
      "function":"count",
      "by_field_name": "mlcategory",
      "detector_description": "Unusual message counts"
    }],
    "categorization_analyzer":{
      "tokenizer": {
        "type" : "simple_pattern_split",
        "pattern" : "[^-0-9A-Za-z_.]+" <1>
      },
      "filter": [
        { "type" : "pattern_replace", "pattern": "^[0-9].*" }, <2>
        { "type" : "pattern_replace", "pattern": "^[-0-9A-Fa-f.]+$" }, <3>
        { "type" : "pattern_replace", "pattern": "^[^0-9A-Za-z]+" }, <4>
        { "type" : "pattern_replace", "pattern": "[^0-9A-Za-z]+$" }, <5>
        { "type" : "stop", "stopwords": [
          "",
          "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday",
          "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun",
          "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December",
          "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec",
          "GMT", "UTC"
        ] }
      ]
    }
  },
  "analysis_limits":{
    "categorization_examples_limit": 5
  },
  "data_description" : {
    "time_field":"time",
    "time_format": "epoch_ms"
  }
}
----------------------------------
// TEST[skip:needs-licence]

<1> Tokens basically consist of hyphens, digits, letters, underscores and dots.
<2> By default, categorization ignores tokens that begin with a digit.
<3> By default, categorization also ignores tokens that are hexadecimal numbers.
<4> Underscores, hyphens, and dots are removed from the beginning of tokens.
<5> Underscores, hyphens, and dots are also removed from the end of tokens.

The key difference between the default `categorization_analyzer` and this 
example analyzer is that using the `ml_classic` tokenizer is several times 
faster. The difference in behavior is that this custom analyzer does not include 
accented letters in tokens whereas the `ml_classic` tokenizer does, although 
that could be fixed by using more complex regular expressions.

If you are categorizing non-English messages in a language where words are
separated by spaces, you might get better results if you change the day or month
words in the stop token filter to the appropriate words in your language. If you
are categorizing messages in a language where words are not separated by spaces,
you must use a different tokenizer as well in order to get sensible
categorization results.

It is important to be aware that analyzing for categorization of machine
generated log messages is a little different from tokenizing for search.
Features that work well for search, such as stemming, synonym substitution, and
lowercasing are likely to make the results of categorization worse. However, in
order for drill down from {ml} results to work correctly, the tokens that the
categorization analyzer produces must be similar to those produced by the search
analyzer. If they are sufficiently similar, when you search for the tokens that
the categorization analyzer produces then you find the original document that
the categorization field value came from.