80 lines
2.5 KiB
Plaintext
80 lines
2.5 KiB
Plaintext
[[ml-geo-functions]]
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=== Geographic Functions
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The geographic functions detect anomalies in the geographic location of the
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input data.
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The {xpackml} features include the following geographic function: `lat_long`.
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NOTE: You cannot create forecasts for jobs that contain geographic functions.
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[float]
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[[ml-lat-long]]
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==== Lat_long
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The `lat_long` function detects anomalies in the geographic location of the
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input data.
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This function supports the following properties:
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* `field_name` (required)
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* `by_field_name` (optional)
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* `over_field_name` (optional)
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* `partition_field_name` (optional)
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For more information about those properties,
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see {ref}/ml-job-resource.html#ml-detectorconfig[Detector Configuration Objects].
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.Example 1: Analyzing transactions with the lat_long function
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[source,js]
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--------------------------------------------------
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{
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"function" : "lat_long",
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"field_name" : "transactionCoordinates",
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"by_field_name" : "creditCardNumber"
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}
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--------------------------------------------------
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If you use this `lat_long` function in a detector in your job, it
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detects anomalies where the geographic location of a credit card transaction is
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unusual for a particular customer’s credit card. An anomaly might indicate fraud.
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IMPORTANT: The `field_name` that you supply must be a single string that contains
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two comma-separated numbers of the form `latitude,longitude`. The `latitude` and
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`longitude` must be in the range -180 to 180 and represent a point on the
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surface of the Earth.
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For example, JSON data might contain the following transaction coordinates:
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[source,js]
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--------------------------------------------------
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{
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"time": 1460464275,
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"transactionCoordinates": "40.7,-74.0",
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"creditCardNumber": "1234123412341234"
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}
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--------------------------------------------------
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In {es}, location data is likely to be stored in `geo_point` fields. For more
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information, see {ref}/geo-point.html[Geo-point datatype]. This data type is not
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supported natively in {xpackml} features. You can, however, use Painless scripts
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in `script_fields` in your {dfeed} to transform the data into an appropriate
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format. For example, the following Painless script transforms
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`"coords": {"lat" : 41.44, "lon":90.5}` into `"lat-lon": "41.44,90.5"`:
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[source,js]
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--------------------------------------------------
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{
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"script_fields": {
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"lat-lon": {
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"script": {
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"source": "doc['coords'].lat + ',' + doc['coords'].lon",
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"lang": "painless"
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}
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}
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}
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}
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--------------------------------------------------
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For more information, see <<ml-configuring-transform>>.
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