Attributes of a fitted VectorIndexerModel.
Every accessor here is one name on the server's allowlist for
org.apache.spark.ml.feature.VectorIndexerModel — 2 of them — so tab completion is that
allowlist. The server refuses anything else with CONNECT_ML.ATTRIBUTE_NOT_ALLOWED; these
refuse a model of the wrong class before it gets that far, and name the module that would
have taken it.
Allowed by the server but not here: toString is Identifiable's, and tells you less than
the model's own uid. Latu.ML.attribute/2 and Latu.ML.attribute/3 will send any of these
names; it is what comes back that has nowhere to go.
The allowlist is inherited rather than per class: this one is the union of
org.apache.spark.ml.feature.VectorIndexerModel, org.apache.spark.ml.util.Identifiable.
Summary
Functions
Spark's categoryMapsDF. PySpark exposes no attribute under that name, so there is no
description of it to quote here.
The JVM class these accessors belong to.
Number of features, i.e., length of Vectors which this transforms.
Functions
@spec category_maps_df(Latu.ML.Model.t()) :: Latu.DataFrame.t()
Spark's categoryMapsDF. PySpark exposes no attribute under that name, so there is no
description of it to quote here.
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.
@spec class() :: String.t()
The JVM class these accessors belong to.
@spec num_features(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Number of features, i.e., length of Vectors which this transforms.
An action: it reaches the server. A Vector or Matrix comes back as an Nx.Tensor, or a
Latu.ML.SparseVector where densifying would be this package's decision rather than yours.