Latu.ML.Classification.MultilayerPerceptronClassificationModel (latu_ml v0.2.0)

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Attributes of a fitted MultilayerPerceptronClassificationModel.

Every accessor here is one name on the server's allowlist for org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel — 8 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; evaluate answers with a training summary, which has no struct yet. 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.classification.ClassificationModel, org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel, org.apache.spark.ml.classification.ProbabilisticClassificationModel, org.apache.spark.ml.util.HasTrainingSummary, org.apache.spark.ml.util.Identifiable.

Summary

Functions

class()

The JVM class these accessors belong to.

has_summary(holder)

Indicates whether a training summary exists for this model instance.

num_classes(holder)

Number of classes (values which the label can take).

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_probability(holder, value)

Predict the probability of each class given the features.

predict_raw(holder, value)

Raw prediction for each possible label.

summary(model)

Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set. An exception is thrown if trainingSummary is None.

weights(holder)

the weights of layers.

Functions

class()

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

The JVM class these accessors belong to.

has_summary(holder)

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

Indicates whether a training summary exists for this model instance.

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_classes(holder)

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

Number of classes (values which the label can take).

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_probability(holder, value)

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

Predict the probability of each class given the 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_raw(holder, value)

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

Raw prediction for each possible label.

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

summary(model)

@spec summary(Latu.ML.Model.t()) :: Latu.ML.Summary.t()

Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set. An exception is thrown if trainingSummary is None.

A lazy builder: Spark reaches a summary through the model that owns it, so the reference is composed here and nothing is sent. Latu.ML.summary/1 is the same thing without the class check.

weights(holder)

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

the weights of layers.

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.