diff --git a/_ml-commons-plugin/api.md b/_ml-commons-plugin/api.md index a472fdaa..ca20799e 100644 --- a/_ml-commons-plugin/api.md +++ b/_ml-commons-plugin/api.md @@ -91,8 +91,6 @@ For asynchronous responses, the API returns the task_id, which can be used to ge You can retrieve information on your model using the model_id. -### Request - ```json GET /_plugins/_ml/models/ ``` @@ -114,8 +112,6 @@ The API returns information on the model, the algorithm used, and the content fo You can retrieve information about a task using the task_id. -### Request - ```json GET /_plugins/_ml/tasks/ ``` @@ -140,7 +136,7 @@ The response includes information about the task. ## Predict -Should you trained a synchronous, ML commons can predict new data with your trained model either from indexed data or a data frame. +ML commons can predict new data with your trained model either from indexed data or a data frame. ```json POST /_plugins/_ml/_predict// @@ -149,7 +145,7 @@ POST /_plugins/_ml/_predict// ### Request ```json -POST /_plugins/_ml/_predict/kmeans/eQlomX8Br-2Nu7fWjUu3 +POST /_plugins/_ml/_predict/kmeans/ { "input_query": { "_source": ["petal_length_in_cm", "petal_width_in_cm"], @@ -227,9 +223,473 @@ POST /_plugins/_ml/_predict/kmeans/eQlomX8Br-2Nu7fWjUu3 ``` +## 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/ +``` + +### 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 + } + ] + } + ] + } + } + ] +} +``` ## Search model +Use this command to search models you're already created. + + ```json POST /_plugins/_ml/models/_search {query} @@ -248,7 +708,7 @@ POST /_plugins/_ml/models/_search } ``` -### Example 2: query models with algorithm "BATCh_RCF" +### Example 2: Query models with algorithm "BATCh_RCF" ```json POST /_plugins/_ml/models/_search @@ -319,6 +779,8 @@ POST /_plugins/_ml/models/_search ## Delete task +Delete a task based on the task_id. + ```json DELETE /_plugins/_ml/tasks/{task_id} ``` @@ -344,13 +806,15 @@ DELETE /_plugins/_ml/tasks/{task_id} ## Search task +Search tasks based on parameters indicated in the request body. + ```json GET /_plugins/_ml/tasks/_search {query body} ``` -### Example: search task which "function_name" is "KMEANS" +### Example: Search task which "function_name" is "KMEANS" ```json GET /_plugins/_ml/tasks/_search @@ -431,17 +895,36 @@ GET /_plugins/_ml/tasks/_search } ``` -Stats +## Stats + +Get statistics related to the number of tasks. + +To receive all stats, use: ```json GET /_plugins/_ml/stats +``` + +To receive stats for a specific node, use: + +```json GET /_plugins/_ml//stats/ +``` + +To receive starts for a specific node and return a specified stat, use: + +```json GET /_plugins/_ml//stats/ +``` + +To receive information on a specific stat from all nodes, use: + +```json GET /_plugins/_ml/stats/ ``` -### Example1: get all stats +### Example: Get all stats ```json GET /_plugins/_ml/stats