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[ML] change MultilayerPerceptronClassifierModel to MultilayerPerceptronClassificationModel #8164

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[ML] change MultilayerPerceptronClassifierModel to MultilayerPerceptronClassificationModel #8164

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16 changes: 8 additions & 8 deletions mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -131,7 +131,7 @@ private object LabelConverter {
*/
@Experimental
class MultilayerPerceptronClassifier(override val uid: String)
extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassifierModel]
extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel]
with MultilayerPerceptronParams {

def this() = this(Identifiable.randomUID("mlpc"))
Expand All @@ -146,7 +146,7 @@ class MultilayerPerceptronClassifier(override val uid: String)
* @param dataset Training dataset
* @return Fitted model
*/
override protected def train(dataset: DataFrame): MultilayerPerceptronClassifierModel = {
override protected def train(dataset: DataFrame): MultilayerPerceptronClassificationModel = {
val myLayers = $(layers)
val labels = myLayers.last
val lpData = extractLabeledPoints(dataset)
Expand All @@ -156,25 +156,25 @@ class MultilayerPerceptronClassifier(override val uid: String)
FeedForwardTrainer.LBFGSOptimizer.setConvergenceTol($(tol)).setNumIterations($(maxIter))
FeedForwardTrainer.setStackSize($(blockSize))
val mlpModel = FeedForwardTrainer.train(data)
new MultilayerPerceptronClassifierModel(uid, myLayers, mlpModel.weights())
new MultilayerPerceptronClassificationModel(uid, myLayers, mlpModel.weights())
}
}

/**
* :: Experimental ::
* Classifier model based on the Multilayer Perceptron.
* Classification model based on the Multilayer Perceptron.
* Each layer has sigmoid activation function, output layer has softmax.
* @param uid uid
* @param layers array of layer sizes including input and output layers
* @param weights vector of initial weights for the model that consists of the weights of layers
* @return prediction model
*/
@Experimental
class MultilayerPerceptronClassifierModel private[ml] (
class MultilayerPerceptronClassificationModel private[ml] (
override val uid: String,
layers: Array[Int],
weights: Vector)
extends PredictionModel[Vector, MultilayerPerceptronClassifierModel]
extends PredictionModel[Vector, MultilayerPerceptronClassificationModel]
with Serializable {

private val mlpModel = FeedForwardTopology.multiLayerPerceptron(layers, true).getInstance(weights)
Expand All @@ -187,7 +187,7 @@ class MultilayerPerceptronClassifierModel private[ml] (
LabelConverter.decodeLabel(mlpModel.predict(features))
}

override def copy(extra: ParamMap): MultilayerPerceptronClassifierModel = {
copyValues(new MultilayerPerceptronClassifierModel(uid, layers, weights), extra)
override def copy(extra: ParamMap): MultilayerPerceptronClassificationModel = {
copyValues(new MultilayerPerceptronClassificationModel(uid, layers, weights), extra)
}
}