kolchfa-aws 826e6771ed
Refactor ML section - local and remote models (#5609)
* 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>
2023-11-17 15:59:27 -05:00

215 lines
4.5 KiB
Markdown

---
layout: default
title: Train and predict
parent: Train and Predict APIs
grand_parent: ML Commons API
has_children: true
nav_order: 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_RCF`
- `FIT_RCF`
- `k-means`
#### Example request: 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"
]
}
```
{% include copy-curl.html %}
#### Example request: 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
}
]
}
]
}
}
```
{% include copy-curl.html %}
#### Example 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" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
}
]
}
}
```