Latu.ML.Regression.FMRegressionModel (latu_ml v0.2.0)

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Attributes of a fitted FMRegressionModel.

Every accessor here is one name on the server's allowlist for org.apache.spark.ml.regression.FMRegressionModel — 5 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.PredictionModel, org.apache.spark.ml.regression.FMRegressionModel, org.apache.spark.ml.util.Identifiable.

Summary

Functions

class()

The JVM class these accessors belong to.

factors(holder)

Model factor term.

intercept(holder)

Model intercept.

linear(holder)

Model linear term.

num_features(holder)

Returns the number of features the model was trained on. If unknown, returns -1.

predict(holder, value)

Predict label for the given features.

Functions

class()

@spec class() :: String.t()

The JVM class these accessors belong to.

factors(holder)

@spec factors(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}

Model factor term.

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.

intercept(holder)

@spec intercept(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}

Model intercept.

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.

linear(holder)

@spec linear(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}

Model linear term.

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.

num_features(holder)

@spec num_features(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}

Returns the number of features the model was trained on. If unknown, returns -1.

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.

predict(holder, value)

@spec predict(Latu.ML.Model.t(), Nx.Tensor.t() | Latu.ML.SparseVector.t()) ::
  {:ok, term()} | {:error, Latu.Error.t()}

Predict label for the given features.

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.

Arguments