Train a model that predicts matches between entities (for example, time series names to asset names). This is also known as fuzzy joining. If there are no trueMatches (labeled data), you train a static (unsupervised) model, otherwise a machine learned (supervised) model is trained.
Securityoidc-token or oauth2-client-credentials or oauth2-open-industrial-data or oauth2-auth-code
Request
Request Body schema: application/json
sources
required
Array of objects (Sources) [ 0 .. 2000000 ] items
List of custom source object to match from, for example, time series. String key -> value. Only string values are considered in the matching. Both id and externalId fields are optional, only mandatory if the item is to be referenced in trueMatches.
targets
required
Array of objects (Targets) [ 1 .. 2000000 ] items
List of custom target object to match to, for example, assets. String key -> value. Only string values are considered in the matching. Both id and externalId fields are optional, only mandatory if the item is to be referenced in trueMatches.
Array of objects or objects or objects or objects (TrueMatches) [ 1 .. 2000000 ] items
A list of confirmed source/target matches, which will be used to train the model. If omitted, an unsupervised
model is trained.
externalId
string (CogniteExternalId) <= 255 characters
The external ID provided by the client. Must be unique for the resource type.
name
string (ModelName) <= 256 characters
User defined name.
description
string (ModelDescription) <= 500 characters
User defined description.
featureType
string
Default: "simple"
Each feature type defines one combination of features that will be created and used in the entity matcher model. All features are based on matching tokens. Tokens are defined at the top of the Entity matching section.
The options are:
Simple: Calculates the cosine-distance similarity score for each of the pairs of fields defined in matchFields. This is the fastest option.
Insensitive: Similar to Simple, but ignores lowercase/uppercase differences.
Bigram: Similar to simple, but adds similarity score based on matching bigrams of the tokens.
FrequencyWeightedBigram: Similar to bigram, but give higher weights to less commonly occurring tokens.
BigramExtraTokenizers: Similar to bigram, but able to learn that leading zeros, spaces, and uppercase/lowercase differences should be ignored in matching.
BigramCombo: Calculates all of the above options, relying on the model to determine the appropriate features to use.
Hence, this option is only appropriate if there are labeled data/trueMatches. This is the slowest option.
List of pairs of fields from the target and source items, used to calculate features. All source and target items should have all the source and target fields specified here.
classifier
string (Classifier)
The classifier used in the model. Only relevant if there are trueMatches/labeled data and a supervised model is fitted.
If True, replaces missing fields in sources or targets entities, for fields set in set in matchFields, with empty strings. Else, returns an error if there are missing data.