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---
layout: default
title: API
has_children: false
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nav_order: 99
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---
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# ML Commons API
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---
#### Table of contents
- TOC
{:toc}
---
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The Machine Learning (ML) commons API lets you create, train, and store machine learning algorithms both synchronously and asynchronously.
In order to train tasks through the API, three inputs are required.
- Algorithm name: Usually `FunctionaName` . This determines what algorithm the ML Engine runs.
- Model hyper parameters: Adjust these parameters to make the model train better. You can also implement `MLAgoParamas` to build custom parameters for each model.
- Input data: The data input that teaches the ML model. To input data, query against your index or use data frame.
## Train model
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Training can occur both synchronously and asynchronously.
### Request
The following examples use the kmeans algorithm to train index data.
**Train with kmeans synchronously**
```json
POST /_plugins/_ml/_train/kmeans
{
"parameters": {
"centroids": 3,
"iterations": 10,
"distance_type": "COSINE"
},
"input_query": {
"_source": ["petal_length_in_cm", "petal_width_in_cm"],
"size": 10000
},
"input_index": [
"iris_data"
]
}
```
**Train with kmeans asynchronously**
```json
POST /_plugins/_ml/_train/kmeans?async=true
{
"parameters": {
"centroids": 3,
"iterations": 10,
"distance_type": "COSINE"
},
"input_query": {
"_source": ["petal_length_in_cm", "petal_width_in_cm"],
"size": 10000
},
"input_index": [
"iris_data"
]
}
```
### Response
**Synchronously**
For synchronous responses, the API returns the model_id, which can be used to get info or modify the model.
```json
{
"model_id" : "lblVmX8BO5w8y8RaYYvN",
"status" : "COMPLETED"
}
```
**Asynchronously**
For asynchronous responses, the API returns the task_id, which can be used to get info or modify a task.
```json
{
"task_id" : "lrlamX8BO5w8y8Ra2otd",
"status" : "CREATED"
}
```
## Get model information
You can retrieve information on your model using the model_id.
```json
GET /_plugins/_ml/models/< model-id >
```
The API returns information on the model, the algorithm used, and the content found within the model.
```json
{
"name" : "KMEANS",
"algorithm" : "KMEANS",
"version" : 1,
"content" : ""
}
```
## Get task information
You can retrieve information about a task using the task_id.
```json
GET /_plugins/_ml/tasks/< task_id >
```
The response includes information about the task.
```json
{
"model_id" : "l7lamX8BO5w8y8Ra2oty",
"task_type" : "TRAINING",
"function_name" : "KMEANS",
"state" : "COMPLETED",
"input_type" : "SEARCH_QUERY",
"worker_node" : "54xOe0w8Qjyze00UuLDfdA",
"create_time" : 1647545342556,
"last_update_time" : 1647545342587,
"is_async" : true
}
```
## Predict
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ML commons can predict new data with your trained model either from indexed data or a data frame.
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```json
POST /_plugins/_ml/_predict/< algorithm_name > /< model_id >
```
### Request
```json
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POST /_plugins/_ml/_predict/kmeans/< model-id >
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{
"input_query": {
"_source": ["petal_length_in_cm", "petal_width_in_cm"],
"size": 10000
},
"input_index": [
"iris_data"
]
}
```
### Response
```json
{
"status" : "COMPLETED",
"prediction_result" : {
"column_metas" : [
{
"name" : "ClusterID",
"column_type" : "INTEGER"
}
],
"rows" : [
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
}
]
}
```
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## Train and Predict
Use to train and then immediately predict against the same training data set. Can only be used with synchronous models and the kmeans algorithm.
### Example: Train and predict with Indexed data
```json
POST /_plugins/_ml/_train_predict/kmeans
{
"parameters": {
"centroids": 2,
"iterations": 10,
"distance_type": "COSINE"
},
"input_query": {
"query": {
"bool": {
"filter": [
{
"range": {
"k1": {
"gte": 0
}
}
}
]
}
},
"size": 10
},
"input_index": [
"test_data"
]
}
```
### Example: Train and predict with data directly
```json
POST /_plugins/_ml/_train_predict/kmeans
{
"parameters": {
"centroids": 2,
"iterations": 1,
"distance_type": "EUCLIDEAN"
},
"input_data": {
"column_metas": [
{
"name": "k1",
"column_type": "DOUBLE"
},
{
"name": "k2",
"column_type": "DOUBLE"
}
],
"rows": [
{
"values": [
{
"column_type": "DOUBLE",
"value": 1.00
},
{
"column_type": "DOUBLE",
"value": 2.00
}
]
},
{
"values": [
{
"column_type": "DOUBLE",
"value": 1.00
},
{
"column_type": "DOUBLE",
"value": 4.00
}
]
},
{
"values": [
{
"column_type": "DOUBLE",
"value": 1.00
},
{
"column_type": "DOUBLE",
"value": 0.00
}
]
},
{
"values": [
{
"column_type": "DOUBLE",
"value": 10.00
},
{
"column_type": "DOUBLE",
"value": 2.00
}
]
},
{
"values": [
{
"column_type": "DOUBLE",
"value": 10.00
},
{
"column_type": "DOUBLE",
"value": 4.00
}
]
},
{
"values": [
{
"column_type": "DOUBLE",
"value": 10.00
},
{
"column_type": "DOUBLE",
"value": 0.00
}
]
}
]
}
}
```
### Response
**Response for index data**
```json
{
"status" : "COMPLETED",
"prediction_result" : {
"column_metas" : [
{
"name" : "ClusterID",
"column_type" : "INTEGER"
}
],
"rows" : [
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
}
]
}
}
```
**Response for data input directly**
```json
{
"status" : "COMPLETED",
"prediction_result" : {
"column_metas" : [
{
"name" : "score",
"column_type" : "DOUBLE"
},
{
"name" : "anomaly_grade",
"column_type" : "DOUBLE"
},
{
"name" : "timestamp",
"column_type" : "LONG"
}
],
"rows" : [
{
"values" : [
{
"column_type" : "DOUBLE",
"value" : 0.0
},
{
"column_type" : "DOUBLE",
"value" : 0.0
},
{
"column_type" : "LONG",
"value" : 1404187200000
}
]
},
...
]
}
}
```
## Execute
Use the Execute API to run no-model-based algorithms. You do not need to train a model in order to receive results from your chosen algorithm.
```json
POST _plugins/_ml/_execute/< algorithm_name >
```
### Example: Execute sample calculator, supported "operation": max/min/sum
```json
POST _plugins/_ml/_execute/local_sample_calculator
{
"operation": "max",
"input_data": [1.0, 2.0, 3.0]
}
```
### Example: Execute anomaly localization
```json
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": 2
}
```
### Response
**Sample calculator response**
```json
{
"sample_result" : 3.0
}
```
**Sample anomaly response**
```json
{
"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
}
]
},
{
"start_time" : 1620802800000,
"end_time" : 1620889200000,
"overall_aggregate_value" : 85.0,
"entities" : [
{
"key" : [
"attr0"
],
"contribution_value" : 2.0,
"base_value" : 2.0,
"new_value" : 4.0
},
{
"key" : [
"attr1"
],
"contribution_value" : 2.0,
"base_value" : 3.0,
"new_value" : 5.0
}
]
},
{
"start_time" : 1620889200000,
"end_time" : 1620975600000,
"overall_aggregate_value" : 95.0,
"entities" : [
{
"key" : [
"attr0"
],
"contribution_value" : 3.0,
"base_value" : 2.0,
"new_value" : 5.0
},
{
"key" : [
"attr1"
],
"contribution_value" : 3.0,
"base_value" : 3.0,
"new_value" : 6.0
}
]
},
{
"start_time" : 1620975600000,
"end_time" : 1621062000000,
"overall_aggregate_value" : 105.0,
"entities" : [
{
"key" : [
"attr0"
],
"contribution_value" : 4.0,
"base_value" : 2.0,
"new_value" : 6.0
},
{
"key" : [
"attr1"
],
"contribution_value" : 4.0,
"base_value" : 3.0,
"new_value" : 7.0
}
]
},
{
"start_time" : 1621062000000,
"end_time" : 1621148400000,
"overall_aggregate_value" : 115.0,
"entities" : [
{
"key" : [
"attr0"
],
"contribution_value" : 5.0,
"base_value" : 2.0,
"new_value" : 7.0
},
{
"key" : [
"attr1"
],
"contribution_value" : 5.0,
"base_value" : 3.0,
"new_value" : 8.0
}
]
},
{
"start_time" : 1621148400000,
"end_time" : 1621234800000,
"overall_aggregate_value" : 125.0,
"entities" : [
{
"key" : [
"attr0"
],
"contribution_value" : 6.0,
"base_value" : 2.0,
"new_value" : 8.0
},
{
"key" : [
"attr1"
],
"contribution_value" : 6.0,
"base_value" : 3.0,
"new_value" : 9.0
}
]
}
]
}
}
]
}
```
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## Search model
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Use this command to search models you're already created.
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```json
POST /_plugins/_ml/models/_search
{query}
```
### Example 1: Query all models
```json
POST /_plugins/_ml/models/_search
{
"query": {
"match_all": {}
},
"size": 1000
}
```
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### Example 2: Query models with algorithm "BATCh_RCF"
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```json
POST /_plugins/_ml/models/_search
{
"query": {
"term": {
"algorithm": {
"value": "BATCH_RCF"
}
}
}
}
```
### Response
```json
{
"took" : 8,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 2.4159138,
"hits" : [
{
"_index" : ".plugins-ml-model",
"_type" : "_doc",
"_id" : "-QkKJX8BvytMh9aUeuLD",
"_version" : 1,
"_seq_no" : 12,
"_primary_term" : 15,
"_score" : 2.4159138,
"_source" : {
"name" : "FIT_RCF",
"version" : 1,
"content" : "xxx",
"algorithm" : "FIT_RCF"
}
},
{
"_index" : ".plugins-ml-model",
"_type" : "_doc",
"_id" : "OxkvHn8BNJ65KnIpck8x",
"_version" : 1,
"_seq_no" : 2,
"_primary_term" : 8,
"_score" : 2.4159138,
"_source" : {
"name" : "FIT_RCF",
"version" : 1,
"content" : "xxx",
"algorithm" : "FIT_RCF"
}
}
]
}
}
```
## Delete task
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Delete a task based on the task_id.
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```json
DELETE /_plugins/_ml/tasks/{task_id}
```
### Response
```json
{
"_index" : ".plugins-ml-task",
"_type" : "_doc",
"_id" : "xQRYLX8BydmmU1x6nuD3",
"_version" : 4,
"result" : "deleted",
"_shards" : {
"total" : 2,
"successful" : 2,
"failed" : 0
},
"_seq_no" : 42,
"_primary_term" : 7
}
```
## Search task
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Search tasks based on parameters indicated in the request body.
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```json
GET /_plugins/_ml/tasks/_search
{query body}
```
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### Example: Search task which "function_name" is "KMEANS"
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```json
GET /_plugins/_ml/tasks/_search
{
"query": {
"bool": {
"filter": [
{
"term": {
"function_name": "KMEANS"
}
}
]
}
}
}
```
```json
{
"took" : 12,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 0.0,
"hits" : [
{
"_index" : ".plugins-ml-task",
"_type" : "_doc",
"_id" : "_wnLJ38BvytMh9aUi-Ia",
"_version" : 4,
"_seq_no" : 29,
"_primary_term" : 4,
"_score" : 0.0,
"_source" : {
"last_update_time" : 1645640125267,
"create_time" : 1645640125209,
"is_async" : true,
"function_name" : "KMEANS",
"input_type" : "SEARCH_QUERY",
"worker_node" : "jjqFrlW7QWmni1tRnb_7Dg",
"state" : "COMPLETED",
"model_id" : "AAnLJ38BvytMh9aUi-M2",
"task_type" : "TRAINING"
}
},
{
"_index" : ".plugins-ml-task",
"_type" : "_doc",
"_id" : "wwRRLX8BydmmU1x6I-AI",
"_version" : 3,
"_seq_no" : 38,
"_primary_term" : 7,
"_score" : 0.0,
"_source" : {
"last_update_time" : 1645732766656,
"create_time" : 1645732766472,
"is_async" : true,
"function_name" : "KMEANS",
"input_type" : "SEARCH_QUERY",
"worker_node" : "A_IiqoloTDK01uZvCjREaA",
"state" : "COMPLETED",
"model_id" : "xARRLX8BydmmU1x6I-CG",
"task_type" : "TRAINING"
}
}
]
}
}
```
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## Stats
Get statistics related to the number of tasks.
To receive all stats, use:
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```json
GET /_plugins/_ml/stats
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```
To receive stats for a specific node, use:
```json
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GET /_plugins/_ml/< nodeId > /stats/
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```
To receive starts for a specific node and return a specified stat, use:
```json
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GET /_plugins/_ml/< nodeId > /stats/< stat >
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```
To receive information on a specific stat from all nodes, use:
```json
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GET /_plugins/_ml/stats/< stat >
```
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### Example: Get all stats
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```json
GET /_plugins/_ml/stats
```
### Response
```json
{
"zbduvgCCSOeu6cfbQhTpnQ" : {
"ml_executing_task_count" : 0
},
"54xOe0w8Qjyze00UuLDfdA" : {
"ml_executing_task_count" : 0
},
"UJiykI7bTKiCpR-rqLYHyw" : {
"ml_executing_task_count" : 0
},
"zj2_NgIbTP-StNlGZJlxdg" : {
"ml_executing_task_count" : 0
},
"jjqFrlW7QWmni1tRnb_7Dg" : {
"ml_executing_task_count" : 0
},
"3pSSjl5PSVqzv5-hBdFqyA" : {
"ml_executing_task_count" : 0
},
"A_IiqoloTDK01uZvCjREaA" : {
"ml_executing_task_count" : 0
}
}
```
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