Attributes of a fitted ALSModel.
Every accessor here is one name on the server's allowlist for
org.apache.spark.ml.recommendation.ALSModel — 7 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.recommendation.ALSModel, org.apache.spark.ml.util.Identifiable.
Summary
Functions
The JVM class these accessors belong to.
a DataFrame that stores item factors in two columns: id and features.
rank of the matrix factorization model.
Returns top numUsers users recommended for each item, for all items.
Returns top numItems items recommended for each user, for all users.
Returns top numUsers users recommended for each item id in the input data set. Note that
if there are duplicate ids in the input dataset, only one set of recommendations per unique
id will be returned.
Returns top numItems items recommended for each user id in the input data set. Note that
if there are duplicate ids in the input dataset, only one set of recommendations per unique
id will be returned.
a DataFrame that stores user factors in two columns: id and features.
Functions
@spec class() :: String.t()
The JVM class these accessors belong to.
@spec item_factors(Latu.ML.Model.t()) :: Latu.DataFrame.t()
a DataFrame that stores item factors in two columns: id and features.
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.
@spec rank(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
rank of the matrix factorization model.
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 recommend_for_all_items(Latu.ML.Model.t(), integer()) :: Latu.DataFrame.t()
Returns top numUsers users recommended for each item, for all items.
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.
Arguments
num_users— an integer
@spec recommend_for_all_users(Latu.ML.Model.t(), integer()) :: Latu.DataFrame.t()
Returns top numItems items recommended for each user, for all users.
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.
Arguments
num_items— an integer
@spec recommend_for_item_subset(Latu.ML.Model.t(), Latu.DataFrame.t(), integer()) :: Latu.DataFrame.t()
Returns top numUsers users recommended for each item id in the input data set. Note that
if there are duplicate ids in the input dataset, only one set of recommendations per unique
id will be returned.
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.
Arguments
dataset— aLatu.DataFrame, sent as a relation rather than a literalnum_users— an integer
@spec recommend_for_user_subset(Latu.ML.Model.t(), Latu.DataFrame.t(), integer()) :: Latu.DataFrame.t()
Returns top numItems items recommended for each user id in the input data set. Note that
if there are duplicate ids in the input dataset, only one set of recommendations per unique
id will be returned.
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.
Arguments
dataset— aLatu.DataFrame, sent as a relation rather than a literalnum_items— an integer
@spec user_factors(Latu.ML.Model.t()) :: Latu.DataFrame.t()
a DataFrame that stores user factors in two columns: id and features.
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.