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* Refactor ML section - local and remote models Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added command to calculate checksum Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add ONNX format to register API Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add sparse encoding predict example Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add API section Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Refactor the API section Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Typo Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Implemented Vale comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add get connector API Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Reword heading Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Addressed tech review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --------- Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Co-authored-by: Melissa Vagi <vagimeli@amazon.com>
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| layout | title | parent | nav_order |
|---|---|---|---|
| default | Execute algorithm | ML Commons API | 30 |
Execute algorithm
Some algorithms, such as Localization, don't require trained models. You can run no-model-based algorithms using the execute API.
Path and HTTP methods
POST _plugins/_ml/_execute/<algorithm_name>
Example request: Execute localization
The following example uses the Localization algorithm to find subset-level information for aggregate data (for example, aggregated over time) that demonstrates the activity of interest, such as spikes, drops, changes, or anomalies.
POST /_plugins/_ml/_execute/anomaly_localization
{
"index_name": "rca-index",
"attribute_field_names": [
"attribute"
],
"aggregations": [
{
"sum": {
"sum": {
"field": "value"
}
}
}
],
"time_field_name": "timestamp",
"start_time": 1620630000000,
"end_time": 1621234800000,
"min_time_interval": 86400000,
"num_outputs": 10
}
{% include copy-curl.html %}
Example response
{
"results" : [
{
"name" : "sum",
"result" : {
"buckets" : [
{
"start_time" : 1620630000000,
"end_time" : 1620716400000,
"overall_aggregate_value" : 65.0
},
{
"start_time" : 1620716400000,
"end_time" : 1620802800000,
"overall_aggregate_value" : 75.0,
"entities" : [
{
"key" : [
"attr0"
],
"contribution_value" : 1.0,
"base_value" : 2.0,
"new_value" : 3.0
},
{
"key" : [
"attr1"
],
"contribution_value" : 1.0,
"base_value" : 3.0,
"new_value" : 4.0
},
{
"key" : [
"attr2"
],
"contribution_value" : 1.0,
"base_value" : 4.0,
"new_value" : 5.0
},
{
"key" : [
"attr3"
],
"contribution_value" : 1.0,
"base_value" : 5.0,
"new_value" : 6.0
},
{
"key" : [
"attr4"
],
"contribution_value" : 1.0,
"base_value" : 6.0,
"new_value" : 7.0
},
{
"key" : [
"attr5"
],
"contribution_value" : 1.0,
"base_value" : 7.0,
"new_value" : 8.0
},
{
"key" : [
"attr6"
],
"contribution_value" : 1.0,
"base_value" : 8.0,
"new_value" : 9.0
},
{
"key" : [
"attr7"
],
"contribution_value" : 1.0,
"base_value" : 9.0,
"new_value" : 10.0
},
{
"key" : [
"attr8"
],
"contribution_value" : 1.0,
"base_value" : 10.0,
"new_value" : 11.0
},
{
"key" : [
"attr9"
],
"contribution_value" : 1.0,
"base_value" : 11.0,
"new_value" : 12.0
}
]
},
...
]
}
}
]
}