diff --git a/docs/category.json b/docs/category.json index bcd6318a46..4fc9ac2ac5 100644 --- a/docs/category.json +++ b/docs/category.json @@ -7,6 +7,7 @@ "user/admin/settings.rst" ], "ppl_cli": [ + "user/ppl/cmd/ad.rst", "user/ppl/cmd/dedup.rst", "user/ppl/cmd/eval.rst", "user/ppl/cmd/fields.rst", diff --git a/docs/user/ppl/cmd/ad.rst b/docs/user/ppl/cmd/ad.rst new file mode 100644 index 0000000000..f73ff143a3 --- /dev/null +++ b/docs/user/ppl/cmd/ad.rst @@ -0,0 +1,61 @@ +============= +ad +============= + +.. rubric:: Table of contents + +.. contents:: + :local: + :depth: 2 + + +Description +============ +| The ``ad`` command applies Random Cut Forest (RCF) algorithm in ml-commons plugin on the search result returned by a PPL command. Based on the input, two types of RCF algorithms will be utilized: fixed in time RCF for processing time-series data, batch RCF for processing non-time-series data. + + +Fixed In Time RCF For Time-series Data Command Syntax +===================================================== +ad + +* shingle_size: optional. A shingle is a consecutive sequence of the most recent records. The default value is 8. +* time_decay: optional. It specifies how much of the recent past to consider when computing an anomaly score. The default value is 0.001. +* time_field: mandatory. It specifies the time filed for RCF to use as time-series data. + + +Batch RCF for Non-time-series Data Command Syntax +================================================= +ad + +* shingle_size: optional. A shingle is a consecutive sequence of the most recent records. The default value is 8. +* time_decay: optional. It specifies how much of the recent past to consider when computing an anomaly score. The default value is 0.001. + + +Example1: Detecting events in New York City from taxi ridership data with time-series data +========================================================================================== + +The example trains a RCF model and use the model to detect anomalies in the time-series ridership data. + +PPL query:: + + os> source=nyc_taxi | fields value, timestamp | AD time_field='timestamp' | where value=10844.0' + +----------+---------------+-------+---------------+ + | value | timestamp | score | anomaly_grade | + |----------+---------------+-------+---------------| + | 10844.0 | 1404172800000 | 0.0 | 0.0 | + +----------+---------------+-------+---------------+ + + +Example2: Detecting events in New York City from taxi ridership data with non-time-series data +============================================================================================== + +The example trains a RCF model and use the model to detect anomalies in the non-time-series ridership data. + +PPL query:: + + os> source=nyc_taxi | fields value | AD | where value=10844.0' + +----------+--------+-----------+ + | value | score | anomalous | + |----------+--------+-----------| + | 10844.0 | 0.0 | false | + +----------+--------+-----------+ diff --git a/docs/user/ppl/cmd/kmeans.rst b/docs/user/ppl/cmd/kmeans.rst new file mode 100644 index 0000000000..b4aa78d8e0 --- /dev/null +++ b/docs/user/ppl/cmd/kmeans.rst @@ -0,0 +1,38 @@ +============= +kmeans +============= + +.. rubric:: Table of contents + +.. contents:: + :local: + :depth: 2 + + +Description +============ +| The ``kmeans`` command applies kmeans algorithm in ml-commons plugin on the search result returned by a PPL command. + + +Syntax +====== +kmeans + +* cluster-number: mandatory. The number of clusters you want to group your data points into. + + +Example: Clustering of Iris Dataset +=================================== + +The example shows how to classify three Iris species (Iris setosa, Iris virginica and Iris versicolor) based on the combination of four features measured from each sample: the length and the width of the sepals and petals. + +PPL query:: + + os> source=iris_data | fields sepal_length_in_cm, sepal_width_in_cm, petal_length_in_cm, petal_width_in_cm | kmeans 3 + +--------------------+-------------------+--------------------+-------------------+-----------+ + | sepal_length_in_cm | sepal_width_in_cm | petal_length_in_cm | petal_width_in_cm | ClusterID | + |--------------------+-------------------+--------------------+-------------------+-----------| + | 5.1 | 3.5 | 1.4 | 0.2 | 1 | + | 5.6 | 3.0 | 4.1 | 1.3 | 0 | + | 6.7 | 2.5 | 5.8 | 1.8 | 2 | + +--------------------+-------------------+--------------------+-------------------+-----------+ diff --git a/docs/user/ppl/index.rst b/docs/user/ppl/index.rst index 9de3acd7eb..015797895d 100644 --- a/docs/user/ppl/index.rst +++ b/docs/user/ppl/index.rst @@ -36,12 +36,16 @@ The query start with search command and then flowing a set of command delimited - `Syntax `_ + - `ad command `_ + - `dedup command `_ - `eval command `_ - `fields command `_ + - `kmeans command `_ + - `parse command `_ - `rename command `_ diff --git a/doctest/build.gradle b/doctest/build.gradle index 0c0cc56d7e..f310806147 100644 --- a/doctest/build.gradle +++ b/doctest/build.gradle @@ -3,6 +3,7 @@ * SPDX-License-Identifier: Apache-2.0 */ +import java.util.concurrent.Callable import org.opensearch.gradle.testclusters.RunTask plugins { @@ -49,7 +50,18 @@ clean.dependsOn(cleanBootstrap) testClusters { docTestCluster { - plugin ':plugin' + plugin(provider(new Callable(){ + @Override + RegularFile call() throws Exception { + return new RegularFile() { + @Override + File getAsFile() { + return fileTree("resources/ml-commons").getSingleFile() + } + } + } + })) + testDistribution = 'integ_test' } } diff --git a/doctest/resources/ml-commons/opensearch-ml-1.3.0.0-SNAPSHOT.zip b/doctest/resources/ml-commons/opensearch-ml-1.3.0.0-SNAPSHOT.zip new file mode 100644 index 0000000000..2d4ee43458 Binary files /dev/null and b/doctest/resources/ml-commons/opensearch-ml-1.3.0.0-SNAPSHOT.zip differ diff --git a/doctest/test_data/nyc_taxi.json b/doctest/test_data/nyc_taxi.json new file mode 100644 index 0000000000..368dc30076 --- /dev/null +++ b/doctest/test_data/nyc_taxi.json @@ -0,0 +1,1946 @@ +{ "index" : { "_index" : "nyc_taxi" } } +{"timestamp":"2014-07-01 00:00:00","value":10844,"anomaly_type":"EXPECTED"} +{ "index" : { "_index" : "nyc_taxi" } } +{"timestamp":"2014-07-01 00:30:00","value":8127,"anomaly_type":"EXPECTED"} +{ "index" : { "_index" : "nyc_taxi" } } +{"timestamp":"2014-07-01 01:00:00","value":6210,"anomaly_type":"EXPECTED"} +{ "index" : { "_index" : "nyc_taxi" } } 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b/doctest/test_docs.py index 301251d1c7..c6c07d1d0d 100644 --- a/doctest/test_docs.py +++ b/doctest/test_docs.py @@ -23,6 +23,7 @@ EMPLOYEES = "employees" PEOPLE = "people" ACCOUNT2 = "account2" +NYC_TAXI = "nyc_taxi" class DocTestConnection(OpenSearchConnection): @@ -86,6 +87,7 @@ def set_up_test_indices(test): load_file("accounts.json", index_name=ACCOUNTS) load_file("people.json", index_name=PEOPLE) load_file("account2.json", index_name=ACCOUNT2) + load_file("nyc_taxi.json", index_name=NYC_TAXI) def load_file(filename, index_name): @@ -114,7 +116,7 @@ def set_up(test): def tear_down(test): # drop leftover tables after each test - test_data_client.indices.delete(index=[ACCOUNTS, EMPLOYEES, PEOPLE, ACCOUNT2], ignore_unavailable=True) + test_data_client.indices.delete(index=[ACCOUNTS, EMPLOYEES, PEOPLE, ACCOUNT2, NYC_TAXI], ignore_unavailable=True) docsuite = partial(doctest.DocFileSuite, diff --git a/doctest/test_mapping/nyc_taxi.json b/doctest/test_mapping/nyc_taxi.json new file mode 100644 index 0000000000..d9932379ad --- /dev/null +++ b/doctest/test_mapping/nyc_taxi.json @@ -0,0 +1,16 @@ +{ + "mappings": { + "properties": { + "anomaly_type": { + "type": "keyword" + }, + "timestamp": { + "type": "date", + "format": "yyyy-MM-dd HH:mm:ss" + }, + "value": { + "type": "float" + } + } + } +} \ No newline at end of file