Attributes of a fitted FPGrowthModel.
Every accessor here is one name on the server's allowlist for
org.apache.spark.ml.fpm.FPGrowthModel — 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.fpm.FPGrowthModel, org.apache.spark.ml.util.Identifiable.
Summary
Functions
DataFrame with four columns: antecedent - Array of the same type as the input column.
consequent - Array of the same type as the input column. confidence - Confidence for
the rule (DoubleType). lift - Lift for the rule (DoubleType).
The JVM class these accessors belong to.
DataFrame with two columns: items - Itemset of the same type as the input column.
freq - Frequency of the itemset (LongType).
Functions
@spec association_rules(Latu.ML.Model.t()) :: Latu.DataFrame.t()
DataFrame with four columns: antecedent - Array of the same type as the input column.
consequent - Array of the same type as the input column. confidence - Confidence for
the rule (DoubleType). lift - Lift for the rule (DoubleType).
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 freq_itemsets(Latu.ML.Model.t()) :: Latu.DataFrame.t()
DataFrame with two columns: items - Itemset of the same type as the input column.
freq - Frequency of the itemset (LongType).
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.