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

Copy Markdown View Source

Attributes of a fitted AFTSurvivalRegressionModel.

Every accessor here is one name on the server's allowlist for org.apache.spark.ml.regression.AFTSurvivalRegressionModel — 6 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.AFTSurvivalRegressionModel, org.apache.spark.ml.util.Identifiable.

Summary

Functions

class()

The JVM class these accessors belong to.

coefficients(holder)

Model coefficients.

intercept(holder)

Model intercept.

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.

predict_quantiles(holder, features)

Predicted Quantiles.

scale(holder)

Model scale parameter.

Functions

class()

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

The JVM class these accessors belong to.

coefficients(holder)

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

Model coefficients.

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.

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

predict_quantiles(holder, features)

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

Predicted Quantiles.

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

scale(holder)

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

Model scale parameter.

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.