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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>
156 lines
3.7 KiB
Markdown
156 lines
3.7 KiB
Markdown
---
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layout: default
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title: Execute algorithm
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parent: ML Commons API
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nav_order: 30
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---
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# Execute algorithm
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Some algorithms, such as [Localization]({{site.url}}{{site.baseurl}}/ml-commons-plugin/algorithms#localization), don't require trained models. You can run no-model-based algorithms using the `execute` API.
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## Path and HTTP methods
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```json
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POST _plugins/_ml/_execute/<algorithm_name>
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```
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#### Example request: Execute localization
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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.
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```json
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POST /_plugins/_ml/_execute/anomaly_localization
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{
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"index_name": "rca-index",
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"attribute_field_names": [
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"attribute"
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],
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"aggregations": [
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{
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"sum": {
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"sum": {
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"field": "value"
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}
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}
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}
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],
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"time_field_name": "timestamp",
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"start_time": 1620630000000,
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"end_time": 1621234800000,
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"min_time_interval": 86400000,
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"num_outputs": 10
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}
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```
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{% include copy-curl.html %}
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#### Example response
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```json
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{
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"results" : [
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{
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"name" : "sum",
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"result" : {
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"buckets" : [
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{
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"start_time" : 1620630000000,
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"end_time" : 1620716400000,
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"overall_aggregate_value" : 65.0
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},
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{
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"start_time" : 1620716400000,
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"end_time" : 1620802800000,
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"overall_aggregate_value" : 75.0,
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"entities" : [
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{
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"key" : [
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"attr0"
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],
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"contribution_value" : 1.0,
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"base_value" : 2.0,
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"new_value" : 3.0
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},
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{
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"key" : [
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"attr1"
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],
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"contribution_value" : 1.0,
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"base_value" : 3.0,
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"new_value" : 4.0
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},
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{
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"key" : [
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"attr2"
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],
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"contribution_value" : 1.0,
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"base_value" : 4.0,
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"new_value" : 5.0
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},
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{
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"key" : [
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"attr3"
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],
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"contribution_value" : 1.0,
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"base_value" : 5.0,
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"new_value" : 6.0
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},
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{
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"key" : [
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"attr4"
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],
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"contribution_value" : 1.0,
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"base_value" : 6.0,
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"new_value" : 7.0
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},
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{
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"key" : [
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"attr5"
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],
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"contribution_value" : 1.0,
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"base_value" : 7.0,
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"new_value" : 8.0
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},
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{
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"key" : [
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"attr6"
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],
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"contribution_value" : 1.0,
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"base_value" : 8.0,
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"new_value" : 9.0
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},
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{
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"key" : [
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"attr7"
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],
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"contribution_value" : 1.0,
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"base_value" : 9.0,
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"new_value" : 10.0
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},
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{
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"key" : [
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"attr8"
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],
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"contribution_value" : 1.0,
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"base_value" : 10.0,
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"new_value" : 11.0
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},
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{
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"key" : [
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"attr9"
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],
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"contribution_value" : 1.0,
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"base_value" : 11.0,
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"new_value" : 12.0
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}
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]
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},
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...
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]
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}
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}
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]
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}
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```
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