14 KiB
14 KiB
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ML Commons API
Table of contents
- TOC {:toc}
The Machine Learning (ML) commons API lets you train ML algorithms synchronously and asynchronously, make predictions with that trained model, and train and predict with the same data set.
In order to train tasks through the API, three inputs are required.
- Algorithm name: Must be one of a FunctionaName. This determines what algorithm the ML Engine runs. To add a new function, see How To Add a New Function.
- Model hyper parameters: Adjust these parameters to make the model train better.
- Input data: The data input that trains the ML model, or applies the ML models to predictions. You can input data in two ways, query against your index or use data frame.
Train model
Training can occur both synchronously and asynchronously.
Request
The following examples use the kmeans algorithm to train index data.
Train with kmeans synchronously
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
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 on the model or modify the model.
{
"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.
{
"task_id" : "lrlamX8BO5w8y8Ra2otd",
"status" : "CREATED"
}
Get model information
You can retrieve information on your model using the model_id.
GET /_plugins/_ml/models/<model-id>
The API returns information on the model, the algorithm used, and the content found within the model.
{
"name" : "KMEANS",
"algorithm" : "KMEANS",
"version" : 1,
"content" : ""
}
Get task information
You can retrieve information about a task using the task_id.
GET /_plugins/_ml/tasks/<task_id>
The response includes information about the task.
{
"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
ML commons can predict new data with your trained model either from indexed data or a data frame. The model_id is required to use the Predict API.
POST /_plugins/_ml/_predict/<algorithm_name>/<model_id>
Request
POST /_plugins/_ml/_predict/kmeans/<model-id>
{
"input_query": {
"_source": ["petal_length_in_cm", "petal_width_in_cm"],
"size": 10000
},
"input_index": [
"iris_data"
]
}
Response
{
"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
}
]
}
]
}
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 following algorithms:
- BATCH_RCF
- FIT_RCF
- kmeans
Example: Train and predict with Indexed data
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
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
{
"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" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
}
]
}
}
Search model
Use this command to search models you're already created.
POST /_plugins/_ml/models/_search
{query}
Example 1: Query all models
POST /_plugins/_ml/models/_search
{
"query": {
"match_all": {}
},
"size": 1000
}
Example 2: Query models with algorithm "FIT_RCF"
POST /_plugins/_ml/models/_search
{
"query": {
"term": {
"algorithm": {
"value": "FIT_RCF"
}
}
}
}
Response
{
"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"
}
}
]
}
}
Search task
Search tasks based on parameters indicated in the request body.
GET /_plugins/_ml/tasks/_search
{query body}
Example: Search task which "function_name" is "KMEANS"
GET /_plugins/_ml/tasks/_search
{
"query": {
"bool": {
"filter": [
{
"term": {
"function_name": "KMEANS"
}
}
]
}
}
}
{
"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"
}
}
]
}
}
Stats
Get statistics related to the number of tasks.
To receive all stats, use:
GET /_plugins/_ml/stats
To receive stats for a specific node, use:
GET /_plugins/_ml/<nodeId>/stats/
To receive starts for a specific node and return a specified stat, use:
GET /_plugins/_ml/<nodeId>/stats/<stat>
To receive information on a specific stat from all nodes, use:
GET /_plugins/_ml/stats/<stat>
Example: Get all stats
GET /_plugins/_ml/stats
Response
{
"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
}
}
Delete task
Delete a task based on the task_id.
DELETE /_plugins/_ml/tasks/{task_id}
The API returns the following:
{
"_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
}
Delete model
Deletes a model based on the model_id
DELETE /_plugins/_ml/models/<model_id>
The API returns the following:
{
"_index" : ".plugins-ml-model",
"_type" : "_doc",
"_id" : "MzcIJX8BA7mbufL6DOwl",
"_version" : 2,
"result" : "deleted",
"_shards" : {
"total" : 2,
"successful" : 2,
"failed" : 0
},
"_seq_no" : 27,
"_primary_term" : 18
}