Attributes of a fitted StringIndexerModel.
Every accessor here is one name on the server's allowlist for
org.apache.spark.ml.feature.StringIndexerModel — 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.
This model does not have to be fitted: from_labels/3, from_arrays_of_labels/3 builds one
on the server out of what you pass, through its own helper method. That is a cache entry
like a fit's, and yours to give back.
The allowlist is inherited rather than per class: this one is the union of
org.apache.spark.ml.feature.StringIndexerModel, org.apache.spark.ml.util.Identifiable.
Summary
Functions
The JVM class these accessors belong to.
Construct the model directly from an array of array of label strings, requires an active
SparkContext. The server builds it and registers it, so what comes back is a cache reference
like a fit's and is yours to Latu.ML.delete/1. The uid is generated here and sent with the
call. opts are params set on the model afterwards, checked against its own table. Built
by stringIndexerModelFromLabelsArray on the server's helper object, from
StringIndexerModel.from_arrays_of_labels.
Construct the model directly from an array of label strings, requires an active
SparkContext. The server builds it and registers it, so what comes back is a cache reference
like a fit's and is yours to Latu.ML.delete/1. The uid is generated here and sent with the
call. opts are params set on the model afterwards, checked against its own table. Built
by stringIndexerModelFromLabels on the server's helper object, from
StringIndexerModel.from_labels.
Ordered list of labels, corresponding to indices to be assigned.
Array of ordered list of labels, corresponding to indices to be assigned for each input column.
Functions
@spec class() :: String.t()
The JVM class these accessors belong to.
@spec from_arrays_of_labels(Latu.Session.t(), [[String.t() | atom()]], keyword()) :: {:ok, Latu.ML.Model.t()} | {:error, Latu.Error.t()}
Construct the model directly from an array of array of label strings, requires an active
SparkContext. The server builds it and registers it, so what comes back is a cache reference
like a fit's and is yours to Latu.ML.delete/1. The uid is generated here and sent with the
call. opts are params set on the model afterwards, checked against its own table. Built
by stringIndexerModelFromLabelsArray on the server's helper object, from
StringIndexerModel.from_arrays_of_labels.
@spec from_labels(Latu.Session.t(), [String.t() | atom()], keyword()) :: {:ok, Latu.ML.Model.t()} | {:error, Latu.Error.t()}
Construct the model directly from an array of label strings, requires an active
SparkContext. The server builds it and registers it, so what comes back is a cache reference
like a fit's and is yours to Latu.ML.delete/1. The uid is generated here and sent with the
call. opts are params set on the model afterwards, checked against its own table. Built
by stringIndexerModelFromLabels on the server's helper object, from
StringIndexerModel.from_labels.
@spec labels(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Ordered list of labels, corresponding to indices to be assigned.
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.
@spec labels_array(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Array of ordered list of labels, corresponding to indices to be assigned for each input column.
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.