MultiLayer Perceptron

It supports creation of full connected neural network.

Type

ml-estimator

Class

fire.nodes.ml.NodeMultilayerPerceptron

Fields

Name	Title	Description
featuresCol	Features Column	Features column of type vectorUDT for model fitting
labelCol	Label Column	The label column for model fitting
predictionCol	Prediction Column	The prediction column created during model scoring.
splitRatio	Split Ratio	Split Ratio
layers	Layers - comma separated list of integers	The integer array specifying the number of activation units in each layer
maxIter	Max number of iterations	Number of iterations to train the Neural network
blockSize	Block Size	Block size
seed	Seed	The initial seed to initialise the neural network.
tol	Tol
solver	Solver	solver
stepSize	Step Size	Step size
confusionMatrix	Confusion Matrix
output_confusion_matrix_chart	Output Confusion Matrix Chart	whether to display confusion matrix chart.
cm_chart_title	Confusion Matrix Chart Title	Title name to display in Confusion Matrix Chart
cm_chart_description	Confusion Matrix Chart Description	Description to display in Confusion Matrix CHart
confusionMatrixTargetLegend	Confusion Matrix Target Legend	Legend name to display for Target in Confusion Matrix
confusionMatrixPredictedLabelLegend	Confusion Matrix PredictedLabel Legend	Legend name to display for Predicted Label in Confusion Matrix
confusionMatrixCountLegend	Confusion Matrix Count Legend	Legend name to display for Count in Confusion Matrix
Description	Confusion Matrix Description
confusionMatrixRowDescription	Confusion Matrix Outcome description	One can provide the business details of the outcome of the confusion matrix rows
ROC Curve	ROC Curve
output_roc_curve	Output ROC Curve	whether to display confusion matrix chart.
roc_title	ROC Curve Chart Title	Title name to display in ROC Curve Chart
roc_description	ROC Curve Chart Description	Add Description for ROC Curve Chart
xlabel	X Label	X label
ylabel	Y Label	Y Label

Details

Multilayer perceptron classifier (MLPC) is a classifier based on the feedforward artificial neural network.

MLPC consists of multiple layers of nodes. Each layer is fully connected to the next layer in the network. Nodes in the input layer represent the input data.

Examples

Below example is available at : https://spark.apache.org/docs/latest/ml-classification-regression.html#multilayer-perceptron-classifier

import org.apache.spark.ml.classification.MultilayerPerceptronClassifier

import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

// Load the data stored in LIBSVM format as a DataFrame.

val data = spark.read.format(“libsvm”)

.load(“data/mllib/sample_multiclass_classification_data.txt”)

// Split the data into train and test

val splits = data.randomSplit(Array(0.6, 0.4), seed = 1234L)

val train = splits(0)

val test = splits(1)

// specify layers for the neural network:

// input layer of size 4 (features), two intermediate of size 5 and 4

// and output of size 3 (classes)

val layers = Array[Int](4, 5, 4, 3)

// create the trainer and set its parameters

val trainer = new MultilayerPerceptronClassifier()

.setLayers(layers)

.setBlockSize(128)

.setSeed(1234L)

.setMaxIter(100)

// train the model

val model = trainer.fit(train)

// compute accuracy on the test set

val result = model.transform(test)

val predictionAndLabels = result.select(“prediction”, “label”)

val evaluator = new MulticlassClassificationEvaluator()

.setMetricName(“accuracy”)

println(s”Test set accuracy = ${evaluator.evaluate(predictionAndLabels)}”)