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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>
4.5 KiB
4.5 KiB
| layout | title | parent | grand_parent | has_children | nav_order |
|---|---|---|---|---|---|
| default | Train and predict | Train and Predict APIs | ML Commons API | true | 10 |
Train and predict
Use to train and then immediately predict against the same training dataset. Can only be used with unsupervised learning models and the following algorithms:
BATCH_RCFFIT_RCFk-means
Example request: 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"
]
}
{% include copy-curl.html %}
Example request: 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
}
]
}
]
}
}
{% include copy-curl.html %}
Example 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
}
]
}
]
}
}