Classification and regression

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This page covers algorithms for Classification and Regression. It also includes sections discussing specific classes of algorithms, such as linear methods, trees, and ensembles.

Table of Contents

Classification
- Logistic regression
  - Binomial logistic regression
in the earlier example
- Multinomial logistic regression
- Decision tree classifier
Fit on whole dataset to include all labels in index.
We specify maxCategories so features with > 4 distinct values are treated as continuous.
- Random forest classifier
Fit on whole dataset to include all labels in index.
Set maxCategories so features with > 4 distinct values are treated as continuous.
- Gradient-boosted tree classifier
Fit on whole dataset to include all labels in index.
Set maxCategories so features with > 4 distinct values are treated as continuous.
- Multilayer perceptron classifier
input layer of size 4 (features), two intermediate of size 5 and 4
and output of size 3 (classes)
Regression
We specify maxCategories so features with > 4 distinct values are treated as continuous.
- Random forest regression
Set maxCategories so features with > 4 distinct values are treated as continuous.
- Gradient-boosted tree regression
Set maxCategories so features with > 4 distinct values are treated as continuous.
- Survival regression
- Isotonic regression
Linear methods
Decision trees
- Inputs and Outputs
  - Input Columns
  - Output Columns
Tree Ensembles
- Random Forests
  - Inputs and Outputs
    - Input Columns
    - Output Columns (Predictions)
- Gradient-Boosted Trees (GBTs)
  - Inputs and Outputs
    - Input Columns
    - Output Columns (Predictions)

Classification

Logistic regression

Logistic regression is a popular method to predict a categorical response. It is a special case of Generalized Linear models that predicts the probability of the outcomes. In spark.ml logistic regression can be used to predict a binary outcome by using binomial logistic regression, or it can be used to predict a multiclass outcome by using multinomial logistic regression. Use the family parameter to select between these two algorithms, or leave it unset and Spark will infer the correct variant.

Multinomial logistic regression can be used for binary classification by setting the family param to “multinomial”. It will produce two sets of coefficients and two intercepts.

When fitting LogisticRegressionModel without intercept on dataset with constant nonzero column, Spark MLlib outputs zero coefficients for constant nonzero columns. This behavior is the same as R glmnet but different from LIBSVM.

Binomial logistic regression

For more background and more details about the implementation of binomial logistic regression, refer to the documentation of logistic regression in spark.mllib.

Examples

The following example shows how to train binomial and multinomial logistic regression models for binary classification with elastic net regularization. elasticNetParam corresponds to $\alpha$ and regParam corresponds to $\lambda$.

More details on parameters can be found in the Scala API documentation.

import org.apache.spark.ml.classification.LogisticRegression

// Load training data val training = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

val lr = new LogisticRegression() .setMaxIter(10) .setRegParam(0.3) .setElasticNetParam(0.8)

// Fit the model val lrModel = lr.fit(training)

// Print the coefficients and intercept for logistic regression println(s“Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}”)

// We can also use the multinomial family for binary classification val mlr = new LogisticRegression() .setMaxIter(10) .setRegParam(0.3) .setElasticNetParam(0.8) .setFamily(“multinomial”)

val mlrModel = mlr.fit(training)

// Print the coefficients and intercepts for logistic regression with multinomial family println(s“Multinomial coefficients: ${mlrModel.coefficientMatrix}”) println(s“Multinomial intercepts: ${mlrModel.interceptVector}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionWithElasticNetExample.scala" in the Spark repo.

More details on parameters can be found in the Java API documentation.

import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.classification.LogisticRegressionModel; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load training data Dataset<Row> training = spark.read().format(“libsvm”) .load(“data/mllib/sample_libsvm_data.txt”);

LogisticRegression lr = new LogisticRegression() .setMaxIter(10) .setRegParam(0.3) .setElasticNetParam(0.8);

// Fit the model LogisticRegressionModel lrModel = lr.fit(training);

// Print the coefficients and intercept for logistic regression System.out.println(“Coefficients: “ + lrModel.coefficients() + ” Intercept: “ + lrModel.intercept());

// We can also use the multinomial family for binary classification LogisticRegression mlr = new LogisticRegression() .setMaxIter(10) .setRegParam(0.3) .setElasticNetParam(0.8) .setFamily(“multinomial”);

// Fit the model LogisticRegressionModel mlrModel = mlr.fit(training);

// Print the coefficients and intercepts for logistic regression with multinomial family System.out.println(“Multinomial coefficients: “ + lrModel.coefficientMatrix() + “\nMultinomial intercepts: “ + mlrModel.interceptVector());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java" in the Spark repo.

More details on parameters can be found in the Python API documentation.

from pyspark.ml.classification import LogisticRegression

# Load training data training = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)

# Fit the model lrModel = lr.fit(training)

# Print the coefficients and intercept for logistic regression print(“Coefficients: “ + str(lrModel.coefficients)) print(“Intercept: “ + str(lrModel.intercept))

# We can also use the multinomial family for binary classification mlr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8, family=“multinomial”)

# Fit the model mlrModel = mlr.fit(training)

# Print the coefficients and intercepts for logistic regression with multinomial family print(“Multinomial coefficients: “ + str(mlrModel.coefficientMatrix)) print(“Multinomial intercepts: “ + str(mlrModel.interceptVector))

Find full example code at "examples/src/main/python/ml/logistic_regression_with_elastic_net.py" in the Spark repo.

More details on parameters can be found in the R API documentation.

# Load training data df <- read.df(“data/mllib/sample_libsvm_data.txt”, source = “libsvm”) training <- df test <- df

</span># Fit an binomial logistic regression model with spark.logit model <- spark.logit(training, label ~ features, maxIter = 10, regParam = 0.3, elasticNetParam = 0.8)

</span># Model summary summary(model)

</span># Prediction predictions <- predict(model, test) head(predictions)

</span><div>Find full example code at “examples/src/main/r/ml/logit.R” in the Spark repo.</div>

The spark.ml implementation of logistic regression also supports extracting a summary of the model over the training set. Note that the predictions and metrics which are stored as DataFrame in LogisticRegressionSummary are annotated @transient and hence only available on the driver.

LogisticRegressionTrainingSummary provides a summary for a LogisticRegressionModel. In the case of binary classification, certain additional metrics are available, e.g. ROC curve. The binary summary can be accessed via the binarySummary method. See BinaryLogisticRegressionTrainingSummary.

Continuing the earlier example:

import org.apache.spark.ml.classification.LogisticRegression

// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier // example val trainingSummary = lrModel.binarySummary

// Obtain the objective per iteration. val objectiveHistory = trainingSummary.objectiveHistory println(“objectiveHistory:”) objectiveHistory.foreach(loss => println(loss))

// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC. val roc = trainingSummary.roc roc.show() println(s“areaUnderROC: ${trainingSummary.areaUnderROC}”)

// Set the model threshold to maximize F-Measure val fMeasure = trainingSummary.fMeasureByThreshold val maxFMeasure = fMeasure.select(max(“F-Measure”)).head().getDouble(0) val bestThreshold = fMeasure.where($“F-Measure” === maxFMeasure) .select(“threshold”).head().getDouble(0) lrModel.setThreshold(bestThreshold)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionSummaryExample.scala" in the Spark repo.

Continuing the earlier example:

import org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary; import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.classification.LogisticRegressionModel; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession; import org.apache.spark.sql.functions;

// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier // example BinaryLogisticRegressionTrainingSummary trainingSummary = lrModel.binarySummary();

// Obtain the loss per iteration. double[] objectiveHistory = trainingSummary.objectiveHistory(); for (double lossPerIteration : objectiveHistory) { System.out.println(lossPerIteration); }

// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC. Dataset<Row> roc = trainingSummary.roc(); roc.show(); roc.select(“FPR”).show(); System.out.println(trainingSummary.areaUnderROC());

// Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with // this selected threshold. Dataset<Row> fMeasure = trainingSummary.fMeasureByThreshold(); double maxFMeasure = fMeasure.select(functions.max(“F-Measure”)).head().getDouble(0); double bestThreshold = fMeasure.where(fMeasure.col(“F-Measure”).equalTo(maxFMeasure)) .select(“threshold”).head().getDouble(0); lrModel.setThreshold(bestThreshold);

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java" in the Spark repo.

Continuing the earlier example:

from pyspark.ml.classification import LogisticRegression

# Extract the summary from the returned LogisticRegressionModel instance trained

in the earlier example

</span>trainingSummary = lrModel.summary

# Obtain the objective per iteration objectiveHistory = trainingSummary.objectiveHistory print(“objectiveHistory:”) for objective in objectiveHistory: print(objective)

# Obtain the receiver-operating characteristic as a dataframe and areaUnderROC. trainingSummary.roc.show() print(“areaUnderROC: “ + str(trainingSummary.areaUnderROC))

# Set the model threshold to maximize F-Measure fMeasure = trainingSummary.fMeasureByThreshold maxFMeasure = fMeasure.groupBy().max(‘F-Measure’).select(‘max(F-Measure)’).head() bestThreshold = fMeasure.where(fMeasure[‘F-Measure’] == maxFMeasure[‘max(F-Measure)’]) \ .select(‘threshold’).head()[‘threshold’] lr.setThreshold(bestThreshold)

Find full example code at "examples/src/main/python/ml/logistic_regression_summary_example.py" in the Spark repo.

Multinomial logistic regression

Multiclass classification is supported via multinomial logistic (softmax) regression. In multinomial logistic regression, the algorithm produces $K$ sets of coefficients, or a matrix of dimension $K \times J$ where $K$ is the number of outcome classes and $J$ is the number of features. If the algorithm is fit with an intercept term then a length $K$ vector of intercepts is available.

Multinomial coefficients are available as coefficientMatrix and intercepts are available as interceptVector.

coefficients and intercept methods on a logistic regression model trained with multinomial family are not supported. Use coefficientMatrix and interceptVector instead.

The conditional probabilities of the outcome classes $k \in {1, 2, …, K}$ are modeled using the softmax function.

\[ P(Y=k|\mathbf{X}, \boldsymbol{\beta}_k, \beta_{0k}) = \frac{e^{\boldsymbol{\beta}_k \cdot \mathbf{X} + \beta_{0k}}}{\sum_{k'=0}^{K-1} e^{\boldsymbol{\beta}_{k'} \cdot \mathbf{X} + \beta_{0k'}}} \]

We minimize the weighted negative log-likelihood, using a multinomial response model, with elastic-net penalty to control for overfitting.

\[ \min_{\beta, \beta_0} -\left[\sum_{i=1}^L w_i \cdot \log P(Y = y_i|\mathbf{x}_i)\right] + \lambda \left[\frac{1}{2}\left(1 - \alpha\right)||\boldsymbol{\beta}||_2^2 + \alpha ||\boldsymbol{\beta}||_1\right] \]

For a detailed derivation please see here.

Examples

The following example shows how to train a multiclass logistic regression model with elastic net regularization, as well as extract the multiclass training summary for evaluating the model.

import org.apache.spark.ml.classification.LogisticRegression

// Load training data val training = spark .read .format(“libsvm”) .load(“data/mllib/sample_multiclass_classification_data.txt”)

val lr = new LogisticRegression() .setMaxIter(10) .setRegParam(0.3) .setElasticNetParam(0.8)

// Fit the model val lrModel = lr.fit(training)

// Print the coefficients and intercept for multinomial logistic regression println(s“Coefficients: \n${lrModel.coefficientMatrix}”) println(s“Intercepts: \n${lrModel.interceptVector}”)

val trainingSummary = lrModel.summary

// Obtain the objective per iteration val objectiveHistory = trainingSummary.objectiveHistory println(“objectiveHistory:”) objectiveHistory.foreach(println)

// for multiclass, we can inspect metrics on a per-label basis println(“False positive rate by label:”) trainingSummary.falsePositiveRateByLabel.zipWithIndex.foreach { case (rate, label) => println(s“label $label: $rate”) }

println(“True positive rate by label:”) trainingSummary.truePositiveRateByLabel.zipWithIndex.foreach { case (rate, label) => println(s“label $label: $rate”) }

println(“Precision by label:”) trainingSummary.precisionByLabel.zipWithIndex.foreach { case (prec, label) => println(s“label $label: $prec”) }

println(“Recall by label:”) trainingSummary.recallByLabel.zipWithIndex.foreach { case (rec, label) => println(s“label $label: $rec”) }

println(“F-measure by label:”) trainingSummary.fMeasureByLabel.zipWithIndex.foreach { case (f, label) => println(s“label $label: $f”) }

val accuracy = trainingSummary.accuracy val falsePositiveRate = trainingSummary.weightedFalsePositiveRate val truePositiveRate = trainingSummary.weightedTruePositiveRate val fMeasure = trainingSummary.weightedFMeasure val precision = trainingSummary.weightedPrecision val recall = trainingSummary.weightedRecall println(s“Accuracy: $accuracy\nFPR: $falsePositiveRate\nTPR: $truePositiveRate\n” + s“F-measure: $fMeasure\nPrecision: $precision\nRecall: $recall”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/MulticlassLogisticRegressionWithElasticNetExample.scala" in the Spark repo.

import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.classification.LogisticRegressionModel; import org.apache.spark.ml.classification.LogisticRegressionTrainingSummary; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load training data Dataset<Row> training = spark.read().format(“libsvm”) .load(“data/mllib/sample_multiclass_classification_data.txt”);

LogisticRegression lr = new LogisticRegression() .setMaxIter(10) .setRegParam(0.3) .setElasticNetParam(0.8);

// Fit the model LogisticRegressionModel lrModel = lr.fit(training);

// Print the coefficients and intercept for multinomial logistic regression System.out.println(“Coefficients: \n” + lrModel.coefficientMatrix() + ” \nIntercept: “ + lrModel.interceptVector()); LogisticRegressionTrainingSummary trainingSummary = lrModel.summary();

// Obtain the loss per iteration. double[] objectiveHistory = trainingSummary.objectiveHistory(); for (double lossPerIteration : objectiveHistory) { System.out.println(lossPerIteration); }

// for multiclass, we can inspect metrics on a per-label basis System.out.println(“False positive rate by label:”); int i = 0; double[] fprLabel = trainingSummary.falsePositiveRateByLabel(); for (double fpr : fprLabel) { System.out.println(“label “ + i + ”: “ + fpr); i++; }

System.out.println(“True positive rate by label:”); i = 0; double[] tprLabel = trainingSummary.truePositiveRateByLabel(); for (double tpr : tprLabel) { System.out.println(“label “ + i + ”: “ + tpr); i++; }

System.out.println(“Precision by label:”); i = 0; double[] precLabel = trainingSummary.precisionByLabel(); for (double prec : precLabel) { System.out.println(“label “ + i + ”: “ + prec); i++; }

System.out.println(“Recall by label:”); i = 0; double[] recLabel = trainingSummary.recallByLabel(); for (double rec : recLabel) { System.out.println(“label “ + i + ”: “ + rec); i++; }

System.out.println(“F-measure by label:”); i = 0; double[] fLabel = trainingSummary.fMeasureByLabel(); for (double f : fLabel) { System.out.println(“label “ + i + ”: “ + f); i++; }

double accuracy = trainingSummary.accuracy(); double falsePositiveRate = trainingSummary.weightedFalsePositiveRate(); double truePositiveRate = trainingSummary.weightedTruePositiveRate(); double fMeasure = trainingSummary.weightedFMeasure(); double precision = trainingSummary.weightedPrecision(); double recall = trainingSummary.weightedRecall(); System.out.println(“Accuracy: “ + accuracy); System.out.println(“FPR: “ + falsePositiveRate); System.out.println(“TPR: “ + truePositiveRate); System.out.println(“F-measure: “ + fMeasure); System.out.println(“Precision: “ + precision); System.out.println(“Recall: “ + recall);

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaMulticlassLogisticRegressionWithElasticNetExample.java" in the Spark repo.

from pyspark.ml.classification import LogisticRegression

# Load training data training = spark \ .read \ .format(“libsvm”) \ .load(“data/mllib/sample_multiclass_classification_data.txt”)

lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)

# Fit the model lrModel = lr.fit(training)

# Print the coefficients and intercept for multinomial logistic regression print(“Coefficients: \n” + str(lrModel.coefficientMatrix)) print(“Intercept: “ + str(lrModel.interceptVector))

trainingSummary = lrModel.summary

# Obtain the objective per iteration objectiveHistory = trainingSummary.objectiveHistory print(“objectiveHistory:”) for objective in objectiveHistory: print(objective)

# for multiclass, we can inspect metrics on a per-label basis print(“False positive rate by label:”) for i, rate in enumerate(trainingSummary.falsePositiveRateByLabel): print(“label %d: %s” % (i, rate))

print(“True positive rate by label:”) for i, rate in enumerate(trainingSummary.truePositiveRateByLabel): print(“label %d: %s” % (i, rate))

print(“Precision by label:”) for i, prec in enumerate(trainingSummary.precisionByLabel): print(“label %d: %s” % (i, prec))

print(“Recall by label:”) for i, rec in enumerate(trainingSummary.recallByLabel): print(“label %d: %s” % (i, rec))

print(“F-measure by label:”) for i, f in enumerate(trainingSummary.fMeasureByLabel()): print(“label %d: %s” % (i, f))

accuracy = trainingSummary.accuracy falsePositiveRate = trainingSummary.weightedFalsePositiveRate truePositiveRate = trainingSummary.weightedTruePositiveRate fMeasure = trainingSummary.weightedFMeasure() precision = trainingSummary.weightedPrecision recall = trainingSummary.weightedRecall print(“Accuracy: %s\nFPR: %s\nTPR: %s\nF-measure: %s\nPrecision: %s\nRecall: %s” % (accuracy, falsePositiveRate, truePositiveRate, fMeasure, precision, recall))

Find full example code at "examples/src/main/python/ml/multiclass_logistic_regression_with_elastic_net.py" in the Spark repo.

More details on parameters can be found in the R API documentation.

# Load training data df <- read.df(“data/mllib/sample_multiclass_classification_data.txt”, source = “libsvm”) training <- df test <- df

</span># Fit a multinomial logistic regression model with spark.logit model <- spark.logit(training, label ~ features, maxIter = 10, regParam = 0.3, elasticNetParam = 0.8)

</span># Model summary summary(model)

</span># Prediction predictions <- predict(model, test) head(predictions)

</span><div>Find full example code at “examples/src/main/r/ml/logit.R” in the Spark repo.</div>

Decision tree classifier

Decision trees are a popular family of classification and regression methods. More information about the spark.ml implementation can be found further in the section on decision trees.

Examples

The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the DataFrame which the Decision Tree algorithm can recognize.

More details on parameters can be found in the Scala API documentation.

import org.apache.spark.ml.Pipeline import org.apache.spark.ml.classification.DecisionTreeClassificationModel import org.apache.spark.ml.classification.DecisionTreeClassifier import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}

// Load the data stored in LIBSVM format as a DataFrame. val data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

// Index labels, adding metadata to the label column. // Fit on whole dataset to include all labels in index. val labelIndexer = new StringIndexer() .setInputCol(“label”) .setOutputCol(“indexedLabel”) .fit(data) // Automatically identify categorical features, and index them. val featureIndexer = new VectorIndexer() .setInputCol(“features”) .setOutputCol(“indexedFeatures”) .setMaxCategories(4) // features with > 4 distinct values are treated as continuous. .fit(data)

// Split the data into training and test sets (30% held out for testing). val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

// Train a DecisionTree model. val dt = new DecisionTreeClassifier() .setLabelCol(“indexedLabel”) .setFeaturesCol(“indexedFeatures”)

// Convert indexed labels back to original labels. val labelConverter = new IndexToString() .setInputCol(“prediction”) .setOutputCol(“predictedLabel”) .setLabels(labelIndexer.labelsArray(0))

// Chain indexers and tree in a Pipeline. val pipeline = new Pipeline() .setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))

// Train model. This also runs the indexers. val model = pipeline.fit(trainingData)

// Make predictions. val predictions = model.transform(testData)

// Select example rows to display. predictions.select(“predictedLabel”, “label”, “features”).show(5)

// Select (prediction, true label) and compute test error. val evaluator = new MulticlassClassificationEvaluator() .setLabelCol(“indexedLabel”) .setPredictionCol(“prediction”) .setMetricName(“accuracy”) val accuracy = evaluator.evaluate(predictions) println(s“Test Error = ${(1.0 - accuracy)}”)

val treeModel = model.stages(2).asInstanceOf[DecisionTreeClassificationModel] println(s“Learned classification tree model:\n ${treeModel.toDebugString}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala" in the Spark repo.

More details on parameters can be found in the Java API documentation.

import org.apache.spark.ml.Pipeline; import org.apache.spark.ml.PipelineModel; import org.apache.spark.ml.PipelineStage; import org.apache.spark.ml.classification.DecisionTreeClassifier; import org.apache.spark.ml.classification.DecisionTreeClassificationModel; import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; import org.apache.spark.ml.feature.*; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load the data stored in LIBSVM format as a DataFrame. Dataset<Row> data = spark .read() .format(“libsvm”) .load(“data/mllib/sample_libsvm_data.txt”);

// Automatically identify categorical features, and index them. VectorIndexerModel featureIndexer = new VectorIndexer() .setInputCol(“features”) .setOutputCol(“indexedFeatures”) .setMaxCategories(4) // features with > 4 distinct values are treated as continuous. .fit(data);

// Split the data into training and test sets (30% held out for testing). Dataset<Row>[] splits = data.randomSplit(new double[]{0.7, 0.3}); Dataset<Row> trainingData = splits[0]; Dataset<Row> testData = splits[1];

// Train a DecisionTree model. DecisionTreeClassifier dt = new DecisionTreeClassifier() .setLabelCol(“indexedLabel”) .setFeaturesCol(“indexedFeatures”);

// Convert indexed labels back to original labels. IndexToString labelConverter = new IndexToString() .setInputCol(“prediction”) .setOutputCol(“predictedLabel”) .setLabels(labelIndexer.labelsArray()[0]);

// Chain indexers and tree in a Pipeline. Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});

// Train model. This also runs the indexers. PipelineModel model = pipeline.fit(trainingData);

// Make predictions. Dataset<Row> predictions = model.transform(testData);

// Select example rows to display. predictions.select(“predictedLabel”, “label”, “features”).show(5);

// Select (prediction, true label) and compute test error. MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() .setLabelCol(“indexedLabel”) .setPredictionCol(“prediction”) .setMetricName(“accuracy”); double accuracy = evaluator.evaluate(predictions); System.out.println(“Test Error = “ + (1.0 - accuracy));

DecisionTreeClassificationModel treeModel = (DecisionTreeClassificationModel) (model.stages()[2]); System.out.println(“Learned classification tree model:\n” + treeModel.toDebugString());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java" in the Spark repo.

More details on parameters can be found in the Python API documentation.

from pyspark.ml import Pipeline from pyspark.ml.classification import DecisionTreeClassifier from pyspark.ml.feature import StringIndexer, VectorIndexer from pyspark.ml.evaluation import MulticlassClassificationEvaluator

# Load the data stored in LIBSVM format as a DataFrame. data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

# Index labels, adding metadata to the label column.

Fit on whole dataset to include all labels in index.

</span>labelIndexer = StringIndexer(inputCol=“label”, outputCol=“indexedLabel”).fit(data) # Automatically identify categorical features, and index them.

We specify maxCategories so features with > 4 distinct values are treated as continuous.

</span>featureIndexer =\ VectorIndexer(inputCol=“features”, outputCol=“indexedFeatures”, maxCategories=4).fit(data)

# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a DecisionTree model. dt = DecisionTreeClassifier(labelCol=“indexedLabel”, featuresCol=“indexedFeatures”)

# Chain indexers and tree in a Pipeline pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])

# Train model. This also runs the indexers. model = pipeline.fit(trainingData)

# Make predictions. predictions = model.transform(testData)

# Select example rows to display. predictions.select(“prediction”, “indexedLabel”, “features”).show(5)

# Select (prediction, true label) and compute test error evaluator = MulticlassClassificationEvaluator( labelCol=“indexedLabel”, predictionCol=“prediction”, metricName=“accuracy”) accuracy = evaluator.evaluate(predictions) print(“Test Error = %g “ % (1.0 - accuracy))

treeModel = model.stages[2] # summary only print(treeModel)

Find full example code at "examples/src/main/python/ml/decision_tree_classification_example.py" in the Spark repo.

Refer to the R API docs for more details.

# Load training data df <- read.df(“data/mllib/sample_libsvm_data.txt”, source = “libsvm”) training <- df test <- df

</span># Fit a DecisionTree classification model with spark.decisionTree model <- spark.decisionTree(training, label ~ features, “classification”)

</span># Model summary summary(model)

</span># Prediction predictions <- predict(model, test) head(predictions)

</span><div>Find full example code at “examples/src/main/r/ml/decisionTree.R” in the Spark repo.</div>

Random forest classifier

Random forests are a popular family of classification and regression methods. More information about the spark.ml implementation can be found further in the section on random forests.

Examples

Refer to the Scala API docs for more details.

import org.apache.spark.ml.Pipeline import org.apache.spark.ml.classification.{RandomForestClassificationModel, RandomForestClassifier} import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}

// Load and parse the data file, converting it to a DataFrame. val data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

// Index labels, adding metadata to the label column. // Fit on whole dataset to include all labels in index. val labelIndexer = new StringIndexer() .setInputCol(“label”) .setOutputCol(“indexedLabel”) .fit(data) // Automatically identify categorical features, and index them. // Set maxCategories so features with > 4 distinct values are treated as continuous. val featureIndexer = new VectorIndexer() .setInputCol(“features”) .setOutputCol(“indexedFeatures”) .setMaxCategories(4) .fit(data)

// Split the data into training and test sets (30% held out for testing). val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

// Train a RandomForest model. val rf = new RandomForestClassifier() .setLabelCol(“indexedLabel”) .setFeaturesCol(“indexedFeatures”) .setNumTrees(10)

// Convert indexed labels back to original labels. val labelConverter = new IndexToString() .setInputCol(“prediction”) .setOutputCol(“predictedLabel”) .setLabels(labelIndexer.labelsArray(0))

// Chain indexers and forest in a Pipeline. val pipeline = new Pipeline() .setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))

// Train model. This also runs the indexers. val model = pipeline.fit(trainingData)

// Make predictions. val predictions = model.transform(testData)

// Select example rows to display. predictions.select(“predictedLabel”, “label”, “features”).show(5)

val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel] println(s“Learned classification forest model:\n ${rfModel.toDebugString}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala" in the Spark repo.

Refer to the Java API docs for more details.

import org.apache.spark.ml.Pipeline; import org.apache.spark.ml.PipelineModel; import org.apache.spark.ml.PipelineStage; import org.apache.spark.ml.classification.RandomForestClassificationModel; import org.apache.spark.ml.classification.RandomForestClassifier; import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; import org.apache.spark.ml.feature.*; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load and parse the data file, converting it to a DataFrame. Dataset<Row> data = spark.read().format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”);

// Index labels, adding metadata to the label column. // Fit on whole dataset to include all labels in index. StringIndexerModel labelIndexer = new StringIndexer() .setInputCol(“label”) .setOutputCol(“indexedLabel”) .fit(data); // Automatically identify categorical features, and index them. // Set maxCategories so features with > 4 distinct values are treated as continuous. VectorIndexerModel featureIndexer = new VectorIndexer() .setInputCol(“features”) .setOutputCol(“indexedFeatures”) .setMaxCategories(4) .fit(data);

// Split the data into training and test sets (30% held out for testing) Dataset<Row>[] splits = data.randomSplit(new double[] {0.7, 0.3}); Dataset<Row> trainingData = splits[0]; Dataset<Row> testData = splits[1];

// Train a RandomForest model. RandomForestClassifier rf = new RandomForestClassifier() .setLabelCol(“indexedLabel”) .setFeaturesCol(“indexedFeatures”);

// Chain indexers and forest in a Pipeline Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, labelConverter});

// Train model. This also runs the indexers. PipelineModel model = pipeline.fit(trainingData);

// Make predictions. Dataset<Row> predictions = model.transform(testData);

// Select example rows to display. predictions.select(“predictedLabel”, “label”, “features”).show(5);

// Select (prediction, true label) and compute test error MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() .setLabelCol(“indexedLabel”) .setPredictionCol(“prediction”) .setMetricName(“accuracy”); double accuracy = evaluator.evaluate(predictions); System.out.println(“Test Error = “ + (1.0 - accuracy));

RandomForestClassificationModel rfModel = (RandomForestClassificationModel)(model.stages()[2]); System.out.println(“Learned classification forest model:\n” + rfModel.toDebugString());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java" in the Spark repo.

Refer to the Python API docs for more details.

from pyspark.ml import Pipeline from pyspark.ml.classification import RandomForestClassifier from pyspark.ml.feature import IndexToString, StringIndexer, VectorIndexer from pyspark.ml.evaluation import MulticlassClassificationEvaluator

# Load and parse the data file, converting it to a DataFrame. data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

# Index labels, adding metadata to the label column.

Fit on whole dataset to include all labels in index.

</span>labelIndexer = StringIndexer(inputCol=“label”, outputCol=“indexedLabel”).fit(data)

# Automatically identify categorical features, and index them.

Set maxCategories so features with > 4 distinct values are treated as continuous.

</span>featureIndexer =\ VectorIndexer(inputCol=“features”, outputCol=“indexedFeatures”, maxCategories=4).fit(data)

# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a RandomForest model. rf = RandomForestClassifier(labelCol=“indexedLabel”, featuresCol=“indexedFeatures”, numTrees=10)

# Convert indexed labels back to original labels. labelConverter = IndexToString(inputCol=“prediction”, outputCol=“predictedLabel”, labels=labelIndexer.labels)

# Chain indexers and forest in a Pipeline pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf, labelConverter])

# Train model. This also runs the indexers. model = pipeline.fit(trainingData)

# Make predictions. predictions = model.transform(testData)

# Select example rows to display. predictions.select(“predictedLabel”, “label”, “features”).show(5)

rfModel = model.stages[2] print(rfModel) # summary only

Find full example code at "examples/src/main/python/ml/random_forest_classifier_example.py" in the Spark repo.

Refer to the R API docs for more details.

# Load training data df <- read.df(“data/mllib/sample_libsvm_data.txt”, source = “libsvm”) training <- df test <- df

</span># Fit a random forest classification model with spark.randomForest model <- spark.randomForest(training, label ~ features, “classification”, numTrees = 10)

</span># Model summary summary(model)

</span># Prediction predictions <- predict(model, test) head(predictions)

</span><div>Find full example code at “examples/src/main/r/ml/randomForest.R” in the Spark repo.</div>

Gradient-boosted tree classifier

Gradient-boosted trees (GBTs) are a popular classification and regression method using ensembles of decision trees. More information about the spark.ml implementation can be found further in the section on GBTs.

Examples

Refer to the Scala API docs for more details.

import org.apache.spark.ml.Pipeline import org.apache.spark.ml.classification.{GBTClassificationModel, GBTClassifier} import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}

// Load and parse the data file, converting it to a DataFrame. val data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

// Index labels, adding metadata to the label column. // Fit on whole dataset to include all labels in index. val labelIndexer = new StringIndexer() .setInputCol(“label”) .setOutputCol(“indexedLabel”) .fit(data) // Automatically identify categorical features, and index them. // Set maxCategories so features with > 4 distinct values are treated as continuous. val featureIndexer = new VectorIndexer() .setInputCol(“features”) .setOutputCol(“indexedFeatures”) .setMaxCategories(4) .fit(data)

// Split the data into training and test sets (30% held out for testing). val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

// Train a GBT model. val gbt = new GBTClassifier() .setLabelCol(“indexedLabel”) .setFeaturesCol(“indexedFeatures”) .setMaxIter(10) .setFeatureSubsetStrategy(“auto”)

// Convert indexed labels back to original labels. val labelConverter = new IndexToString() .setInputCol(“prediction”) .setOutputCol(“predictedLabel”) .setLabels(labelIndexer.labelsArray(0))

// Chain indexers and GBT in a Pipeline. val pipeline = new Pipeline() .setStages(Array(labelIndexer, featureIndexer, gbt, labelConverter))

// Train model. This also runs the indexers. val model = pipeline.fit(trainingData)

// Make predictions. val predictions = model.transform(testData)

// Select example rows to display. predictions.select(“predictedLabel”, “label”, “features”).show(5)

val gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel] println(s“Learned classification GBT model:\n ${gbtModel.toDebugString}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala" in the Spark repo.

Refer to the Java API docs for more details.

import org.apache.spark.ml.Pipeline; import org.apache.spark.ml.PipelineModel; import org.apache.spark.ml.PipelineStage; import org.apache.spark.ml.classification.GBTClassificationModel; import org.apache.spark.ml.classification.GBTClassifier; import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; import org.apache.spark.ml.feature.*; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load and parse the data file, converting it to a DataFrame. Dataset<Row> data = spark .read() .format(“libsvm”) .load(“data/mllib/sample_libsvm_data.txt”);

// Train a GBT model. GBTClassifier gbt = new GBTClassifier() .setLabelCol(“indexedLabel”) .setFeaturesCol(“indexedFeatures”) .setMaxIter(10);

// Chain indexers and GBT in a Pipeline. Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[] {labelIndexer, featureIndexer, gbt, labelConverter});

// Train model. This also runs the indexers. PipelineModel model = pipeline.fit(trainingData);

// Make predictions. Dataset<Row> predictions = model.transform(testData);

// Select example rows to display. predictions.select(“predictedLabel”, “label”, “features”).show(5);

GBTClassificationModel gbtModel = (GBTClassificationModel)(model.stages()[2]); System.out.println(“Learned classification GBT model:\n” + gbtModel.toDebugString());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java" in the Spark repo.

Refer to the Python API docs for more details.

from pyspark.ml import Pipeline from pyspark.ml.classification import GBTClassifier from pyspark.ml.feature import StringIndexer, VectorIndexer from pyspark.ml.evaluation import MulticlassClassificationEvaluator

# Load and parse the data file, converting it to a DataFrame. data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

# Index labels, adding metadata to the label column.

Fit on whole dataset to include all labels in index.

</span>labelIndexer = StringIndexer(inputCol=“label”, outputCol=“indexedLabel”).fit(data) # Automatically identify categorical features, and index them.

Set maxCategories so features with > 4 distinct values are treated as continuous.

</span>featureIndexer =\ VectorIndexer(inputCol=“features”, outputCol=“indexedFeatures”, maxCategories=4).fit(data)

# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a GBT model. gbt = GBTClassifier(labelCol=“indexedLabel”, featuresCol=“indexedFeatures”, maxIter=10)

# Chain indexers and GBT in a Pipeline pipeline = Pipeline(stages=[labelIndexer, featureIndexer, gbt])

# Train model. This also runs the indexers. model = pipeline.fit(trainingData)

# Make predictions. predictions = model.transform(testData)

# Select example rows to display. predictions.select(“prediction”, “indexedLabel”, “features”).show(5)

gbtModel = model.stages[2] print(gbtModel) # summary only

Find full example code at "examples/src/main/python/ml/gradient_boosted_tree_classifier_example.py" in the Spark repo.

Refer to the R API docs for more details.

# Load training data df <- read.df(“data/mllib/sample_libsvm_data.txt”, source = “libsvm”) training <- df test <- df

</span># Fit a GBT classification model with spark.gbt model <- spark.gbt(training, label ~ features, “classification”, maxIter = 10)

</span># Model summary summary(model)

</span># Prediction predictions <- predict(model, test) head(predictions)

</span><div>Find full example code at “examples/src/main/r/ml/gbt.R” in the Spark repo.</div>

Multilayer perceptron classifier

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. All other nodes map inputs to outputs by a linear combination of the inputs with the node’s weights $\wv$ and bias $\bv$ and applying an activation function. This can be written in matrix form for MLPC with $K+1$ layers as follows: \[ \mathrm{y}(\x) = \mathrm{f_K}(...\mathrm{f_2}(\wv_2^T\mathrm{f_1}(\wv_1^T \x+b_1)+b_2)...+b_K) \] Nodes in intermediate layers use sigmoid (logistic) function: \[ \mathrm{f}(z_i) = \frac{1}{1 + e^{-z_i}} \] Nodes in the output layer use softmax function: \[ \mathrm{f}(z_i) = \frac{e^{z_i}}{\sum_{k=1}^N e^{z_k}} \] The number of nodes $N$ in the output layer corresponds to the number of classes.

MLPC employs backpropagation for learning the model. We use the logistic loss function for optimization and L-BFGS as an optimization routine.

Examples

Refer to the Scala API docs for more details.

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)}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala" in the Spark repo.

Refer to the Java API docs for more details.

import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession; import org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel; import org.apache.spark.ml.classification.MultilayerPerceptronClassifier; import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;

// Load training data String path = “data/mllib/sample_multiclass_classification_data.txt”; Dataset<Row> dataFrame = spark.read().format(“libsvm”).load(path);

// Split the data into train and test Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L); Dataset<Row> train = splits[0]; Dataset<Row> 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) int[] layers = new int[] {4, 5, 4, 3};

// create the trainer and set its parameters MultilayerPerceptronClassifier trainer = new MultilayerPerceptronClassifier() .setLayers(layers) .setBlockSize(128) .setSeed(1234L) .setMaxIter(100);

// train the model MultilayerPerceptronClassificationModel model = trainer.fit(train);

// compute accuracy on the test set Dataset<Row> result = model.transform(test); Dataset<Row> predictionAndLabels = result.select(“prediction”, “label”); MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() .setMetricName(“accuracy”);

System.out.println(“Test set accuracy = “ + evaluator.evaluate(predictionAndLabels));

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java" in the Spark repo.

Refer to the Python API docs for more details.

from pyspark.ml.classification import MultilayerPerceptronClassifier from pyspark.ml.evaluation import MulticlassClassificationEvaluator

# Load training data data = spark.read.format(“libsvm”)\ .load(“data/mllib/sample_multiclass_classification_data.txt”)

# Split the data into train and test splits = data.randomSplit([0.6, 0.4], 1234) train = splits[0] 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)

</span>layers = [4, 5, 4, 3]

# create the trainer and set its parameters trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234)

# train the model model = trainer.fit(train)

# compute accuracy on the test set result = model.transform(test) predictionAndLabels = result.select(“prediction”, “label”) evaluator = MulticlassClassificationEvaluator(metricName=“accuracy”) print(“Test set accuracy = “ + str(evaluator.evaluate(predictionAndLabels)))

Find full example code at "examples/src/main/python/ml/multilayer_perceptron_classification.py" in the Spark repo.

Refer to the R API docs for more details.

# Load training data df <- read.df(“data/mllib/sample_multiclass_classification_data.txt”, source = “libsvm”) training <- df test <- df

</span># 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) layers = c(4, 5, 4, 3)

</span># Fit a multi-layer perceptron neural network model with spark.mlp model <- spark.mlp(training, label ~ features, maxIter = 100, layers = layers, blockSize = 128, seed = 1234)

</span># Model summary summary(model)

</span># Prediction predictions <- predict(model, test) head(predictions)

</span><div>Find full example code at “examples/src/main/r/ml/mlp.R” in the Spark repo.</div>

Linear Support Vector Machine

A support vector machine constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training-data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. LinearSVC in Spark ML supports binary classification with linear SVM. Internally, it optimizes the Hinge Loss using OWLQN optimizer.

Examples

Refer to the Scala API docs for more details.

import org.apache.spark.ml.classification.LinearSVC

// Load training data val training = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

val lsvc = new LinearSVC() .setMaxIter(10) .setRegParam(0.1)

// Fit the model val lsvcModel = lsvc.fit(training)

// Print the coefficients and intercept for linear svc println(s“Coefficients: ${lsvcModel.coefficients} Intercept: ${lsvcModel.intercept}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/LinearSVCExample.scala" in the Spark repo.

Refer to the Java API docs for more details.

import org.apache.spark.ml.classification.LinearSVC; import org.apache.spark.ml.classification.LinearSVCModel; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load training data Dataset<Row> training = spark.read().format(“libsvm”) .load(“data/mllib/sample_libsvm_data.txt”);

LinearSVC lsvc = new LinearSVC() .setMaxIter(10) .setRegParam(0.1);

// Fit the model LinearSVCModel lsvcModel = lsvc.fit(training);

// Print the coefficients and intercept for LinearSVC System.out.println(“Coefficients: “ + lsvcModel.coefficients() + ” Intercept: “ + lsvcModel.intercept());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaLinearSVCExample.java" in the Spark repo.

Refer to the Python API docs for more details.

from pyspark.ml.classification import LinearSVC

# Load training data training = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

lsvc = LinearSVC(maxIter=10, regParam=0.1)

# Fit the model lsvcModel = lsvc.fit(training)

# Print the coefficients and intercept for linear SVC print(“Coefficients: “ + str(lsvcModel.coefficients)) print(“Intercept: “ + str(lsvcModel.intercept))

Find full example code at "examples/src/main/python/ml/linearsvc.py" in the Spark repo.

Refer to the R API docs for more details.

# load training data t <- as.data.frame(Titanic) training <- createDataFrame(t)

</span># fit Linear SVM model model <- spark.svmLinear(training, Survived ~ ., regParam = 0.01, maxIter = 10)

</span># Model summary summary(model)

</span># Prediction prediction <- predict(model, training) showDF(prediction)

</span><div>Find full example code at “examples/src/main/r/ml/svmLinear.R” in the Spark repo.</div>

One-vs-Rest classifier (a.k.a. One-vs-All)

OneVsRest is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently. It is also known as “One-vs-All.”

OneVsRest is implemented as an Estimator. For the base classifier, it takes instances of Classifier and creates a binary classification problem for each of the k classes. The classifier for class i is trained to predict whether the label is i or not, distinguishing class i from all other classes.

Predictions are done by evaluating each binary classifier and the index of the most confident classifier is output as label.

Examples

The example below demonstrates how to load the Iris dataset, parse it as a DataFrame and perform multiclass classification using OneVsRest. The test error is calculated to measure the algorithm accuracy.

Refer to the Scala API docs for more details.

import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest} import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

// load data file. val inputData = spark.read.format(“libsvm”) .load(“data/mllib/sample_multiclass_classification_data.txt”)

// generate the train/test split. val Array(train, test) = inputData.randomSplit(Array(0.8, 0.2))

// instantiate the base classifier val classifier = new LogisticRegression() .setMaxIter(10) .setTol(1E-6) .setFitIntercept(true)

// instantiate the One Vs Rest Classifier. val ovr = new OneVsRest().setClassifier(classifier)

// train the multiclass model. val ovrModel = ovr.fit(train)

// score the model on test data. val predictions = ovrModel.transform(test)

// obtain evaluator. val evaluator = new MulticlassClassificationEvaluator() .setMetricName(“accuracy”)

// compute the classification error on test data. val accuracy = evaluator.evaluate(predictions) println(s“Test Error = ${1 - accuracy}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala" in the Spark repo.

Refer to the Java API docs for more details.

import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.classification.OneVsRest; import org.apache.spark.ml.classification.OneVsRestModel; import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row;

// load data file. Dataset<Row> inputData = spark.read().format(“libsvm”) .load(“data/mllib/sample_multiclass_classification_data.txt”);

// generate the train/test split. Dataset<Row>[] tmp = inputData.randomSplit(new double[]{0.8, 0.2}); Dataset<Row> train = tmp[0]; Dataset<Row> test = tmp[1];

// configure the base classifier. LogisticRegression classifier = new LogisticRegression() .setMaxIter(10) .setTol(1E-6) .setFitIntercept(true);

// instantiate the One Vs Rest Classifier. OneVsRest ovr = new OneVsRest().setClassifier(classifier);

// train the multiclass model. OneVsRestModel ovrModel = ovr.fit(train);

// score the model on test data. Dataset<Row> predictions = ovrModel.transform(test) .select(“prediction”, “label”);

// obtain evaluator. MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() .setMetricName(“accuracy”);

// compute the classification error on test data. double accuracy = evaluator.evaluate(predictions); System.out.println(“Test Error = “ + (1 - accuracy));

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java" in the Spark repo.

Refer to the Python API docs for more details.

from pyspark.ml.classification import LogisticRegression, OneVsRest from pyspark.ml.evaluation import MulticlassClassificationEvaluator

# load data file. inputData = spark.read.format(“libsvm”) \ .load(“data/mllib/sample_multiclass_classification_data.txt”)

# generate the train/test split. (train, test) = inputData.randomSplit([0.8, 0.2])

# instantiate the base classifier. lr = LogisticRegression(maxIter=10, tol=1E-6, fitIntercept=True)

# instantiate the One Vs Rest Classifier. ovr = OneVsRest(classifier=lr)

# train the multiclass model. ovrModel = ovr.fit(train)

# score the model on test data. predictions = ovrModel.transform(test)

# obtain evaluator. evaluator = MulticlassClassificationEvaluator(metricName=“accuracy”)

# compute the classification error on test data. accuracy = evaluator.evaluate(predictions) print(“Test Error = %g” % (1.0 - accuracy))

Find full example code at "examples/src/main/python/ml/one_vs_rest_example.py" in the Spark repo.

Naive Bayes

Naive Bayes classifiers are a family of simple probabilistic, multiclass classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between every pair of features.

Naive Bayes can be trained very efficiently. With a single pass over the training data, it computes the conditional probability distribution of each feature given each label. For prediction, it applies Bayes’ theorem to compute the conditional probability distribution of each label given an observation.

MLlib supports Multinomial naive Bayes, Complement naive Bayes, Bernoulli naive Bayes and Gaussian naive Bayes.

Input data: These Multinomial, Complement and Bernoulli models are typically used for document classification. Within that context, each observation is a document and each feature represents a term. A feature’s value is the frequency of the term (in Multinomial or Complement Naive Bayes) or a zero or one indicating whether the term was found in the document (in Bernoulli Naive Bayes). Feature values for Multinomial and Bernoulli models must be non-negative. The model type is selected with an optional parameter “multinomial”, “complement”, “bernoulli” or “gaussian”, with “multinomial” as the default. For document classification, the input feature vectors should usually be sparse vectors. Since the training data is only used once, it is not necessary to cache it.

Additive smoothing can be used by setting the parameter $\lambda$ (default to $1.0$).

Examples

Refer to the Scala API docs for more details.

import org.apache.spark.ml.classification.NaiveBayes 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_libsvm_data.txt”)

// Split the data into training and test sets (30% held out for testing) val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3), seed = 1234L)

// Train a NaiveBayes model. val model = new NaiveBayes() .fit(trainingData)

// Select example rows to display. val predictions = model.transform(testData) predictions.show()

// Select (prediction, true label) and compute test error val evaluator = new MulticlassClassificationEvaluator() .setLabelCol(“label”) .setPredictionCol(“prediction”) .setMetricName(“accuracy”) val accuracy = evaluator.evaluate(predictions) println(s“Test set accuracy = $accuracy”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala" in the Spark repo.

Refer to the Java API docs for more details.

import org.apache.spark.ml.classification.NaiveBayes; import org.apache.spark.ml.classification.NaiveBayesModel; import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load training data Dataset<Row> dataFrame = spark.read().format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”); // Split the data into train and test Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L); Dataset<Row> train = splits[0]; Dataset<Row> test = splits[1];

// create the trainer and set its parameters NaiveBayes nb = new NaiveBayes();

// train the model NaiveBayesModel model = nb.fit(train);

// Select example rows to display. Dataset<Row> predictions = model.transform(test); predictions.show();

// compute accuracy on the test set MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() .setLabelCol(“label”) .setPredictionCol(“prediction”) .setMetricName(“accuracy”); double accuracy = evaluator.evaluate(predictions); System.out.println(“Test set accuracy = “ + accuracy);

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java" in the Spark repo.

Refer to the Python API docs for more details.

from pyspark.ml.classification import NaiveBayes from pyspark.ml.evaluation import MulticlassClassificationEvaluator

# Load training data data = spark.read.format(“libsvm”) \ .load(“data/mllib/sample_libsvm_data.txt”)

# Split the data into train and test splits = data.randomSplit([0.6, 0.4], 1234) train = splits[0] test = splits[1]

# create the trainer and set its parameters nb = NaiveBayes(smoothing=1.0, modelType=“multinomial”)

# train the model model = nb.fit(train)

# select example rows to display. predictions = model.transform(test) predictions.show()

# compute accuracy on the test set evaluator = MulticlassClassificationEvaluator(labelCol=“label”, predictionCol=“prediction”, metricName=“accuracy”) accuracy = evaluator.evaluate(predictions) print(“Test set accuracy = “ + str(accuracy))

Find full example code at "examples/src/main/python/ml/naive_bayes_example.py" in the Spark repo.

Refer to the R API docs for more details.

# Fit a Bernoulli naive Bayes model with spark.naiveBayes titanic <- as.data.frame(Titanic) titanicDF <- createDataFrame(titanic[titanic$Freq > 0, -5]) nbDF <- titanicDF nbTestDF <- titanicDF nbModel <- spark.naiveBayes(nbDF, Survived ~ Class + Sex + Age)

</span># Model summary summary(nbModel)

</span># Prediction nbPredictions <- predict(nbModel, nbTestDF) head(nbPredictions)

</span><div>Find full example code at “examples/src/main/r/ml/naiveBayes.R” in the Spark repo.</div>

Regression

Linear regression

The interface for working with linear regression models and model summaries is similar to the logistic regression case.

When fitting LinearRegressionModel without intercept on dataset with constant nonzero column by “l-bfgs” solver, Spark MLlib outputs zero coefficients for constant nonzero columns. This behavior is the same as R glmnet but different from LIBSVM.

Examples

The following example demonstrates training an elastic net regularized linear regression model and extracting model summary statistics.

More details on parameters can be found in the Scala API documentation.

import org.apache.spark.ml.regression.LinearRegression

// Load training data val training = spark.read.format(“libsvm”) .load(“data/mllib/sample_linear_regression_data.txt”)

val lr = new LinearRegression() .setMaxIter(10) .setRegParam(0.3) .setElasticNetParam(0.8)

// Fit the model val lrModel = lr.fit(training)

// Print the coefficients and intercept for linear regression println(s“Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}”)

// Summarize the model over the training set and print out some metrics val trainingSummary = lrModel.summary println(s“numIterations: ${trainingSummary.totalIterations}”) println(s“objectiveHistory: [${trainingSummary.objectiveHistory.mkString(“,”)}]”) trainingSummary.residuals.show() println(s“RMSE: ${trainingSummary.rootMeanSquaredError}”) println(s“r2: ${trainingSummary.r2}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionWithElasticNetExample.scala" in the Spark repo.

More details on parameters can be found in the Java API documentation.

import org.apache.spark.ml.regression.LinearRegression; import org.apache.spark.ml.regression.LinearRegressionModel; import org.apache.spark.ml.regression.LinearRegressionTrainingSummary; import org.apache.spark.ml.linalg.Vectors; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load training data. Dataset<Row> training = spark.read().format(“libsvm”) .load(“data/mllib/sample_linear_regression_data.txt”);

LinearRegression lr = new LinearRegression() .setMaxIter(10) .setRegParam(0.3) .setElasticNetParam(0.8);

// Fit the model. LinearRegressionModel lrModel = lr.fit(training);

// Print the coefficients and intercept for linear regression. System.out.println(“Coefficients: “ + lrModel.coefficients() + ” Intercept: “ + lrModel.intercept());

// Summarize the model over the training set and print out some metrics. LinearRegressionTrainingSummary trainingSummary = lrModel.summary(); System.out.println(“numIterations: “ + trainingSummary.totalIterations()); System.out.println(“objectiveHistory: “ + Vectors.dense(trainingSummary.objectiveHistory())); trainingSummary.residuals().show(); System.out.println(“RMSE: “ + trainingSummary.rootMeanSquaredError()); System.out.println(“r2: “ + trainingSummary.r2());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java" in the Spark repo.

More details on parameters can be found in the Python API documentation.

from pyspark.ml.regression import LinearRegression

# Load training data training = spark.read.format(“libsvm”)\ .load(“data/mllib/sample_linear_regression_data.txt”)

lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)

# Fit the model lrModel = lr.fit(training)

# Print the coefficients and intercept for linear regression print(“Coefficients: %s” % str(lrModel.coefficients)) print(“Intercept: %s” % str(lrModel.intercept))

# Summarize the model over the training set and print out some metrics trainingSummary = lrModel.summary print(“numIterations: %d” % trainingSummary.totalIterations) print(“objectiveHistory: %s” % str(trainingSummary.objectiveHistory)) trainingSummary.residuals.show() print(“RMSE: %f” % trainingSummary.rootMeanSquaredError) print(“r2: %f” % trainingSummary.r2)

Find full example code at "examples/src/main/python/ml/linear_regression_with_elastic_net.py" in the Spark repo.

Generalized linear regression

Contrasted with linear regression where the output is assumed to follow a Gaussian distribution, generalized linear models (GLMs) are specifications of linear models where the response variable $Y_i$ follows some distribution from the exponential family of distributions. Spark’s GeneralizedLinearRegression interface allows for flexible specification of GLMs which can be used for various types of prediction problems including linear regression, Poisson regression, logistic regression, and others. Currently in spark.ml, only a subset of the exponential family distributions are supported and they are listed below.

NOTE: Spark currently only supports up to 4096 features through its GeneralizedLinearRegression interface, and will throw an exception if this constraint is exceeded. See the advanced section for more details. Still, for linear and logistic regression, models with an increased number of features can be trained using the LinearRegression and LogisticRegression estimators.

GLMs require exponential family distributions that can be written in their “canonical” or “natural” form, aka natural exponential family distributions. The form of a natural exponential family distribution is given as:

$f_Y(y|\theta, \tau) = h(y, \tau)\exp{\left( \frac{\theta \cdot y - A(\theta)}{d(\tau)} \right)}$

where $\theta$ is the parameter of interest and $\tau$ is a dispersion parameter. In a GLM the response variable $Y_i$ is assumed to be drawn from a natural exponential family distribution:

$Y_i \sim f\left(\cdot|\theta_i, \tau \right)$

where the parameter of interest $\theta_i$ is related to the expected value of the response variable $\mu_i$ by

$\mu_i = A'(\theta_i)$

Here, $A’(\theta_i)$ is defined by the form of the distribution selected. GLMs also allow specification of a link function, which defines the relationship between the expected value of the response variable $\mu_i$ and the so called linear predictor $\eta_i$:

$g(\mu_i) = \eta_i = \vec{x_i}^T \cdot \vec{\beta}$

Often, the link function is chosen such that $A’ = g^{-1}$, which yields a simplified relationship between the parameter of interest $\theta$ and the linear predictor $\eta$. In this case, the link function $g(\mu)$ is said to be the “canonical” link function.

$\theta_i = A'^{-1}(\mu_i) = g(g^{-1}(\eta_i)) = \eta_i$

A GLM finds the regression coefficients $\vec{\beta}$ which maximize the likelihood function.

$\max_{\vec{\beta}} \mathcal{L}(\vec{\theta}|\vec{y},X) = \prod_{i=1}^{N} h(y_i, \tau) \exp{\left(\frac{y_i\theta_i - A(\theta_i)}{d(\tau)}\right)}$

where the parameter of interest $\theta_i$ is related to the regression coefficients $\vec{\beta}$ by

$\theta_i = A'^{-1}(g^{-1}(\vec{x_i} \cdot \vec{\beta}))$

Spark’s generalized linear regression interface also provides summary statistics for diagnosing the fit of GLM models, including residuals, p-values, deviances, the Akaike information criterion, and others.

See here for a more comprehensive review of GLMs and their applications.

Available families

Family	Response Type	Supported Links
Gaussian	Continuous	Identity*, Log, Inverse
Binomial	Binary	Logit*, Probit, CLogLog
Poisson	Count	Log*, Identity, Sqrt
Gamma	Continuous	Inverse*, Idenity, Log
Tweedie	Zero-inflated continuous	Power link function
* Canonical Link

Examples

The following example demonstrates training a GLM with a Gaussian response and identity link function and extracting model summary statistics.

Refer to the Scala API docs for more details.

import org.apache.spark.ml.regression.GeneralizedLinearRegression

// Load training data val dataset = spark.read.format(“libsvm”) .load(“data/mllib/sample_linear_regression_data.txt”)

val glr = new GeneralizedLinearRegression() .setFamily(“gaussian”) .setLink(“identity”) .setMaxIter(10) .setRegParam(0.3)

// Fit the model val model = glr.fit(dataset)

// Print the coefficients and intercept for generalized linear regression model println(s“Coefficients: ${model.coefficients}”) println(s“Intercept: ${model.intercept}”)

// Summarize the model over the training set and print out some metrics val summary = model.summary println(s“Coefficient Standard Errors: ${summary.coefficientStandardErrors.mkString(“,”)}”) println(s“T Values: ${summary.tValues.mkString(“,”)}”) println(s“P Values: ${summary.pValues.mkString(“,”)}”) println(s“Dispersion: ${summary.dispersion}”) println(s“Null Deviance: ${summary.nullDeviance}”) println(s“Residual Degree Of Freedom Null: ${summary.residualDegreeOfFreedomNull}”) println(s“Deviance: ${summary.deviance}”) println(s“Residual Degree Of Freedom: ${summary.residualDegreeOfFreedom}”) println(s“AIC: ${summary.aic}”) println(“Deviance Residuals: “) summary.residuals().show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/GeneralizedLinearRegressionExample.scala" in the Spark repo.

Refer to the Java API docs for more details.

import java.util.Arrays;

import org.apache.spark.ml.regression.GeneralizedLinearRegression; import org.apache.spark.ml.regression.GeneralizedLinearRegressionModel; import org.apache.spark.ml.regression.GeneralizedLinearRegressionTrainingSummary; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row;

// Load training data Dataset<Row> dataset = spark.read().format(“libsvm”) .load(“data/mllib/sample_linear_regression_data.txt”);

GeneralizedLinearRegression glr = new GeneralizedLinearRegression() .setFamily(“gaussian”) .setLink(“identity”) .setMaxIter(10) .setRegParam(0.3);

// Fit the model GeneralizedLinearRegressionModel model = glr.fit(dataset);

// Print the coefficients and intercept for generalized linear regression model System.out.println(“Coefficients: “ + model.coefficients()); System.out.println(“Intercept: “ + model.intercept());

// Summarize the model over the training set and print out some metrics GeneralizedLinearRegressionTrainingSummary summary = model.summary(); System.out.println(“Coefficient Standard Errors: “ + Arrays.toString(summary.coefficientStandardErrors())); System.out.println(“T Values: “ + Arrays.toString(summary.tValues())); System.out.println(“P Values: “ + Arrays.toString(summary.pValues())); System.out.println(“Dispersion: “ + summary.dispersion()); System.out.println(“Null Deviance: “ + summary.nullDeviance()); System.out.println(“Residual Degree Of Freedom Null: “ + summary.residualDegreeOfFreedomNull()); System.out.println(“Deviance: “ + summary.deviance()); System.out.println(“Residual Degree Of Freedom: “ + summary.residualDegreeOfFreedom()); System.out.println(“AIC: “ + summary.aic()); System.out.println(“Deviance Residuals: “); summary.residuals().show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaGeneralizedLinearRegressionExample.java" in the Spark repo.

Refer to the Python API docs for more details.

from pyspark.ml.regression import GeneralizedLinearRegression

# Load training data dataset = spark.read.format(“libsvm”)\ .load(“data/mllib/sample_linear_regression_data.txt”)

glr = GeneralizedLinearRegression(family=“gaussian”, link=“identity”, maxIter=10, regParam=0.3)

# Fit the model model = glr.fit(dataset)

# Print the coefficients and intercept for generalized linear regression model print(“Coefficients: “ + str(model.coefficients)) print(“Intercept: “ + str(model.intercept))

# Summarize the model over the training set and print out some metrics summary = model.summary print(“Coefficient Standard Errors: “ + str(summary.coefficientStandardErrors)) print(“T Values: “ + str(summary.tValues)) print(“P Values: “ + str(summary.pValues)) print(“Dispersion: “ + str(summary.dispersion)) print(“Null Deviance: “ + str(summary.nullDeviance)) print(“Residual Degree Of Freedom Null: “ + str(summary.residualDegreeOfFreedomNull)) print(“Deviance: “ + str(summary.deviance)) print(“Residual Degree Of Freedom: “ + str(summary.residualDegreeOfFreedom)) print(“AIC: “ + str(summary.aic)) print(“Deviance Residuals: “) summary.residuals().show()

Find full example code at "examples/src/main/python/ml/generalized_linear_regression_example.py" in the Spark repo.

Refer to the R API docs for more details.

training <- read.df(“data/mllib/sample_multiclass_classification_data.txt”, source = “libsvm”) # Fit a generalized linear model of family “gaussian” with spark.glm df_list <- randomSplit(training, c(7, 3), 2) gaussianDF <- df_list[[1]] gaussianTestDF <- df_list[[2]] gaussianGLM <- spark.glm(gaussianDF, label ~ features, family = “gaussian”)

</span># Model summary summary(gaussianGLM)

</span># Prediction gaussianPredictions <- predict(gaussianGLM, gaussianTestDF) head(gaussianPredictions)

</span># Fit a generalized linear model with glm (R-compliant) gaussianGLM2 <- glm(label ~ features, gaussianDF, family = “gaussian”) summary(gaussianGLM2)

</span># Fit a generalized linear model of family “binomial” with spark.glm training2 <- read.df(“data/mllib/sample_multiclass_classification_data.txt”, source = “libsvm”) training2 <- transform(training2, label = cast(training2$label > 1, “integer”)) df_list2 <- randomSplit(training2, c(7, 3), 2) binomialDF <- df_list2[[1]] binomialTestDF <- df_list2[[2]] binomialGLM <- spark.glm(binomialDF, label ~ features, family = “binomial”)

</span># Model summary summary(binomialGLM)

</span># Prediction binomialPredictions <- predict(binomialGLM, binomialTestDF) head(binomialPredictions)

</span># Fit a generalized linear model of family “tweedie” with spark.glm training3 <- read.df(“data/mllib/sample_multiclass_classification_data.txt”, source = “libsvm”) tweedieDF <- transform(training3, label = training3$label * exp(randn(10))) tweedieGLM <- spark.glm(tweedieDF, label ~ features, family = “tweedie”, var.power = 1.2, link.power = 0)

</span># Model summary summary(tweedieGLM)

</span><div>Find full example code at “examples/src/main/r/ml/glm.R” in the Spark repo.</div>

Decision tree regression

Decision trees are a popular family of classification and regression methods. More information about the spark.ml implementation can be found further in the section on decision trees.

Examples

The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. We use a feature transformer to index categorical features, adding metadata to the DataFrame which the Decision Tree algorithm can recognize.

More details on parameters can be found in the Scala API documentation.

import org.apache.spark.ml.Pipeline import org.apache.spark.ml.evaluation.RegressionEvaluator import org.apache.spark.ml.feature.VectorIndexer import org.apache.spark.ml.regression.DecisionTreeRegressionModel import org.apache.spark.ml.regression.DecisionTreeRegressor

// Load the data stored in LIBSVM format as a DataFrame. val data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

// Automatically identify categorical features, and index them. // Here, we treat features with > 4 distinct values as continuous. val featureIndexer = new VectorIndexer() .setInputCol(“features”) .setOutputCol(“indexedFeatures”) .setMaxCategories(4) .fit(data)

// Split the data into training and test sets (30% held out for testing). val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

// Train a DecisionTree model. val dt = new DecisionTreeRegressor() .setLabelCol(“label”) .setFeaturesCol(“indexedFeatures”)

// Chain indexer and tree in a Pipeline. val pipeline = new Pipeline() .setStages(Array(featureIndexer, dt))

// Train model. This also runs the indexer. val model = pipeline.fit(trainingData)

// Make predictions. val predictions = model.transform(testData)

// Select example rows to display. predictions.select(“prediction”, “label”, “features”).show(5)

// Select (prediction, true label) and compute test error. val evaluator = new RegressionEvaluator() .setLabelCol(“label”) .setPredictionCol(“prediction”) .setMetricName(“rmse”) val rmse = evaluator.evaluate(predictions) println(s“Root Mean Squared Error (RMSE) on test data = $rmse”)

val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel] println(s“Learned regression tree model:\n ${treeModel.toDebugString}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala" in the Spark repo.

More details on parameters can be found in the Java API documentation.

import org.apache.spark.ml.Pipeline; import org.apache.spark.ml.PipelineModel; import org.apache.spark.ml.PipelineStage; import org.apache.spark.ml.evaluation.RegressionEvaluator; import org.apache.spark.ml.feature.VectorIndexer; import org.apache.spark.ml.feature.VectorIndexerModel; import org.apache.spark.ml.regression.DecisionTreeRegressionModel; import org.apache.spark.ml.regression.DecisionTreeRegressor; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load the data stored in LIBSVM format as a DataFrame. Dataset<Row> data = spark.read().format(“libsvm”) .load(“data/mllib/sample_libsvm_data.txt”);

// Automatically identify categorical features, and index them. // Set maxCategories so features with > 4 distinct values are treated as continuous. VectorIndexerModel featureIndexer = new VectorIndexer() .setInputCol(“features”) .setOutputCol(“indexedFeatures”) .setMaxCategories(4) .fit(data);

// Train a DecisionTree model. DecisionTreeRegressor dt = new DecisionTreeRegressor() .setFeaturesCol(“indexedFeatures”);

// Chain indexer and tree in a Pipeline. Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[]{featureIndexer, dt});

// Train model. This also runs the indexer. PipelineModel model = pipeline.fit(trainingData);

// Make predictions. Dataset<Row> predictions = model.transform(testData);

// Select example rows to display. predictions.select(“label”, “features”).show(5);

// Select (prediction, true label) and compute test error. RegressionEvaluator evaluator = new RegressionEvaluator() .setLabelCol(“label”) .setPredictionCol(“prediction”) .setMetricName(“rmse”); double rmse = evaluator.evaluate(predictions); System.out.println(“Root Mean Squared Error (RMSE) on test data = “ + rmse);

DecisionTreeRegressionModel treeModel = (DecisionTreeRegressionModel) (model.stages()[1]); System.out.println(“Learned regression tree model:\n” + treeModel.toDebugString());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java" in the Spark repo.

More details on parameters can be found in the Python API documentation.

from pyspark.ml import Pipeline from pyspark.ml.regression import DecisionTreeRegressor from pyspark.ml.feature import VectorIndexer from pyspark.ml.evaluation import RegressionEvaluator

# Load the data stored in LIBSVM format as a DataFrame. data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

# Automatically identify categorical features, and index them.

We specify maxCategories so features with > 4 distinct values are treated as continuous.

</span>featureIndexer =\ VectorIndexer(inputCol=“features”, outputCol=“indexedFeatures”, maxCategories=4).fit(data)

# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a DecisionTree model. dt = DecisionTreeRegressor(featuresCol=“indexedFeatures”)

# Chain indexer and tree in a Pipeline pipeline = Pipeline(stages=[featureIndexer, dt])

# Train model. This also runs the indexer. model = pipeline.fit(trainingData)

# Make predictions. predictions = model.transform(testData)

# Select example rows to display. predictions.select(“prediction”, “label”, “features”).show(5)

# Select (prediction, true label) and compute test error evaluator = RegressionEvaluator( labelCol=“label”, predictionCol=“prediction”, metricName=“rmse”) rmse = evaluator.evaluate(predictions) print(“Root Mean Squared Error (RMSE) on test data = %g” % rmse)

treeModel = model.stages[1] # summary only print(treeModel)

Find full example code at "examples/src/main/python/ml/decision_tree_regression_example.py" in the Spark repo.

Refer to the R API docs for more details.

# Load training data df <- read.df(“data/mllib/sample_linear_regression_data.txt”, source = “libsvm”) training <- df test <- df

</span># Fit a DecisionTree regression model with spark.decisionTree model <- spark.decisionTree(training, label ~ features, “regression”)

</span># Model summary summary(model)

</span># Prediction predictions <- predict(model, test) head(predictions)

</span><div>Find full example code at “examples/src/main/r/ml/decisionTree.R” in the Spark repo.</div>

Random forest regression

Random forests are a popular family of classification and regression methods. More information about the spark.ml implementation can be found further in the section on random forests.

Examples

Refer to the Scala API docs for more details.

import org.apache.spark.ml.Pipeline import org.apache.spark.ml.evaluation.RegressionEvaluator import org.apache.spark.ml.feature.VectorIndexer import org.apache.spark.ml.regression.{RandomForestRegressionModel, RandomForestRegressor}

// Load and parse the data file, converting it to a DataFrame. val data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

// Automatically identify categorical features, and index them. // Set maxCategories so features with > 4 distinct values are treated as continuous. val featureIndexer = new VectorIndexer() .setInputCol(“features”) .setOutputCol(“indexedFeatures”) .setMaxCategories(4) .fit(data)

// Split the data into training and test sets (30% held out for testing). val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

// Train a RandomForest model. val rf = new RandomForestRegressor() .setLabelCol(“label”) .setFeaturesCol(“indexedFeatures”)

// Chain indexer and forest in a Pipeline. val pipeline = new Pipeline() .setStages(Array(featureIndexer, rf))

// Train model. This also runs the indexer. val model = pipeline.fit(trainingData)

// Make predictions. val predictions = model.transform(testData)

// Select example rows to display. predictions.select(“prediction”, “label”, “features”).show(5)

val rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel] println(s“Learned regression forest model:\n ${rfModel.toDebugString}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala" in the Spark repo.

Refer to the Java API docs for more details.

import org.apache.spark.ml.Pipeline; import org.apache.spark.ml.PipelineModel; import org.apache.spark.ml.PipelineStage; import org.apache.spark.ml.evaluation.RegressionEvaluator; import org.apache.spark.ml.feature.VectorIndexer; import org.apache.spark.ml.feature.VectorIndexerModel; import org.apache.spark.ml.regression.RandomForestRegressionModel; import org.apache.spark.ml.regression.RandomForestRegressor; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load and parse the data file, converting it to a DataFrame. Dataset<Row> data = spark.read().format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”);

// Train a RandomForest model. RandomForestRegressor rf = new RandomForestRegressor() .setLabelCol(“label”) .setFeaturesCol(“indexedFeatures”);

// Chain indexer and forest in a Pipeline Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[] {featureIndexer, rf});

// Train model. This also runs the indexer. PipelineModel model = pipeline.fit(trainingData);

// Make predictions. Dataset<Row> predictions = model.transform(testData);

// Select example rows to display. predictions.select(“prediction”, “label”, “features”).show(5);

// Select (prediction, true label) and compute test error RegressionEvaluator evaluator = new RegressionEvaluator() .setLabelCol(“label”) .setPredictionCol(“prediction”) .setMetricName(“rmse”); double rmse = evaluator.evaluate(predictions); System.out.println(“Root Mean Squared Error (RMSE) on test data = “ + rmse);

RandomForestRegressionModel rfModel = (RandomForestRegressionModel)(model.stages()[1]); System.out.println(“Learned regression forest model:\n” + rfModel.toDebugString());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java" in the Spark repo.

Refer to the Python API docs for more details.

from pyspark.ml import Pipeline from pyspark.ml.regression import RandomForestRegressor from pyspark.ml.feature import VectorIndexer from pyspark.ml.evaluation import RegressionEvaluator

# Load and parse the data file, converting it to a DataFrame. data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

# Automatically identify categorical features, and index them.

Set maxCategories so features with > 4 distinct values are treated as continuous.

</span>featureIndexer =\ VectorIndexer(inputCol=“features”, outputCol=“indexedFeatures”, maxCategories=4).fit(data)

# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a RandomForest model. rf = RandomForestRegressor(featuresCol=“indexedFeatures”)

# Chain indexer and forest in a Pipeline pipeline = Pipeline(stages=[featureIndexer, rf])

# Train model. This also runs the indexer. model = pipeline.fit(trainingData)

# Make predictions. predictions = model.transform(testData)

# Select example rows to display. predictions.select(“prediction”, “label”, “features”).show(5)

rfModel = model.stages[1] print(rfModel) # summary only

Find full example code at "examples/src/main/python/ml/random_forest_regressor_example.py" in the Spark repo.

Refer to the R API docs for more details.

# Load training data df <- read.df(“data/mllib/sample_linear_regression_data.txt”, source = “libsvm”) training <- df test <- df

</span># Fit a random forest regression model with spark.randomForest model <- spark.randomForest(training, label ~ features, “regression”, numTrees = 10)

</span># Model summary summary(model)

</span># Prediction predictions <- predict(model, test) head(predictions)

</span><div>Find full example code at “examples/src/main/r/ml/randomForest.R” in the Spark repo.</div>

Gradient-boosted tree regression

Gradient-boosted trees (GBTs) are a popular regression method using ensembles of decision trees. More information about the spark.ml implementation can be found further in the section on GBTs.

Examples

Note: For this example dataset, GBTRegressor actually only needs 1 iteration, but that will not be true in general.

Refer to the Scala API docs for more details.

// Load and parse the data file, converting it to a DataFrame. val data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

// Split the data into training and test sets (30% held out for testing). val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

// Train a GBT model. val gbt = new GBTRegressor() .setLabelCol(“label”) .setFeaturesCol(“indexedFeatures”) .setMaxIter(10)

// Chain indexer and GBT in a Pipeline. val pipeline = new Pipeline() .setStages(Array(featureIndexer, gbt))

// Train model. This also runs the indexer. val model = pipeline.fit(trainingData)

// Make predictions. val predictions = model.transform(testData)

// Select example rows to display. predictions.select(“prediction”, “label”, “features”).show(5)

val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel] println(s“Learned regression GBT model:\n ${gbtModel.toDebugString}”)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala" in the Spark repo.

Refer to the Java API docs for more details.

import org.apache.spark.ml.Pipeline; import org.apache.spark.ml.PipelineModel; import org.apache.spark.ml.PipelineStage; import org.apache.spark.ml.evaluation.RegressionEvaluator; import org.apache.spark.ml.feature.VectorIndexer; import org.apache.spark.ml.feature.VectorIndexerModel; import org.apache.spark.ml.regression.GBTRegressionModel; import org.apache.spark.ml.regression.GBTRegressor; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession;

// Load and parse the data file, converting it to a DataFrame. Dataset<Row> data = spark.read().format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”);

// Split the data into training and test sets (30% held out for testing). Dataset<Row>[] splits = data.randomSplit(new double[] {0.7, 0.3}); Dataset<Row> trainingData = splits[0]; Dataset<Row> testData = splits[1];

// Train a GBT model. GBTRegressor gbt = new GBTRegressor() .setLabelCol(“label”) .setFeaturesCol(“indexedFeatures”) .setMaxIter(10);

// Chain indexer and GBT in a Pipeline. Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] {featureIndexer, gbt});

// Train model. This also runs the indexer. PipelineModel model = pipeline.fit(trainingData);

// Make predictions. Dataset<Row> predictions = model.transform(testData);

// Select example rows to display. predictions.select(“prediction”, “label”, “features”).show(5);

GBTRegressionModel gbtModel = (GBTRegressionModel)(model.stages()[1]); System.out.println(“Learned regression GBT model:\n” + gbtModel.toDebugString());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java" in the Spark repo.

Refer to the Python API docs for more details.

from pyspark.ml import Pipeline from pyspark.ml.regression import GBTRegressor from pyspark.ml.feature import VectorIndexer from pyspark.ml.evaluation import RegressionEvaluator

# Load and parse the data file, converting it to a DataFrame. data = spark.read.format(“libsvm”).load(“data/mllib/sample_libsvm_data.txt”)

# Automatically identify categorical features, and index them.

Set maxCategories so features with > 4 distinct values are treated as continuous.

</span>featureIndexer =\ VectorIndexer(inputCol=“features”, outputCol=“indexedFeatures”, maxCategories=4).fit(data)

# Split the data into training and test sets (30% held out for testing) (trainingData, testData) = data.randomSplit([0.7, 0.3])

# Train a GBT model. gbt = GBTRegressor(featuresCol=“indexedFeatures”, maxIter=10)

# Chain indexer and GBT in a Pipeline pipeline = Pipeline(stages=[featureIndexer, gbt])

# Train model. This also runs the indexer. model = pipeline.fit(trainingData)

# Make predictions. predictions = model.transform(testData)

# Select example rows to display. predictions.select(“prediction”, “label”, “features”).show(5)

gbtModel = model.stages[1] print(gbtModel) # summary only

Find full example code at "examples/src/main/python/ml/gradient_boosted_tree_regressor_example.py" in the Spark repo.

Refer to the R API docs for more details.

# Load training data df <- read.df(“data/mllib/sample_linear_regression_data.txt”, source = “libsvm”) training <- df test <- df

</span># Fit a GBT regression model with spark.gbt model <- spark.gbt(training, label ~ features, “regression”, maxIter = 10)

</span># Model summary summary(model)

</span># Prediction predictions <- predict(model, test) head(predictions)

</span><div>Find full example code at “examples/src/main/r/ml/gbt.R” in the Spark repo.</div>

Survival regression

In spark.ml, we implement the Accelerated failure time (AFT) model which is a parametric survival regression model for censored data. It describes a model for the log of survival time, so it’s often called a log-linear model for survival analysis. Different from a Proportional hazards model designed for the same purpose, the AFT model is easier to parallelize because each instance contributes to the objective function independently.

Given the values of the covariates $x^{‘}$, for random lifetime $t_{i}$ of subjects i = 1, …, n, with possible right-censoring, the likelihood function under the AFT model is given as: \[ L(\beta,\sigma)=\prod_{i=1}^n[\frac{1}{\sigma}f_{0}(\frac{\log{t_{i}}-x^{'}\beta}{\sigma})]^{\delta_{i}}S_{0}(\frac{\log{t_{i}}-x^{'}\beta}{\sigma})^{1-\delta_{i}} \] Where $\delta_{i}$ is the indicator of the event has occurred i.e. uncensored or not. Using $\epsilon_{i}=\frac{\log{t_{i}}-x^{‘}\beta}{\sigma}$, the log-likelihood function assumes the form: \[ \iota(\beta,\sigma)=\sum_{i=1}^{n}[-\delta_{i}\log\sigma+\delta_{i}\log{f_{0}}(\epsilon_{i})+(1-\delta_{i})\log{S_{0}(\epsilon_{i})}] \] Where $S_{0}(\epsilon_{i})$ is the baseline survivor function, and $f_{0}(\epsilon_{i})$ is the corresponding density function.

The most commonly used AFT model is based on the Weibull distribution of the survival time. The Weibull distribution for lifetime corresponds to the extreme value distribution for the log of the lifetime, and the $S_{0}(\epsilon)$ function is: \[ S_{0}(\epsilon_{i})=\exp(-e^{\epsilon_{i}}) \] the $f_{0}(\epsilon_{i})$ function is: \[ f_{0}(\epsilon_{i})=e^{\epsilon_{i}}\exp(-e^{\epsilon_{i}}) \] The log-likelihood function for AFT model with a Weibull distribution of lifetime is: \[ \iota(\beta,\sigma)= -\sum_{i=1}^n[\delta_{i}\log\sigma-\delta_{i}\epsilon_{i}+e^{\epsilon_{i}}] \] Due to minimizing the negative log-likelihood equivalent to maximum a posteriori probability, the loss function we use to optimize is $-\iota(\beta,\sigma)$. The gradient functions for $\beta$ and $\log\sigma$ respectively are: \[ \frac{\partial (-\iota)}{\partial \beta}=\sum_{1=1}^{n}[\delta_{i}-e^{\epsilon_{i}}]\frac{x_{i}}{\sigma} \] \[ \frac{\partial (-\iota)}{\partial (\log\sigma)}=\sum_{i=1}^{n}[\delta_{i}+(\delta_{i}-e^{\epsilon_{i}})\epsilon_{i}] \]

The AFT model can be formulated as a convex optimization problem, i.e. the task of finding a minimizer of a convex function $-\iota(\beta,\sigma)$ that depends on the coefficients vector $\beta$ and the log of scale parameter $\log\sigma$. The optimization algorithm underlying the implementation is L-BFGS. The implementation matches the result from R’s survival function survreg

When fitting AFTSurvivalRegressionModel without intercept on dataset with constant nonzero column, Spark MLlib outputs zero coefficients for constant nonzero columns. This behavior is different from R survival::survreg.

Examples

Refer to the Scala API docs for more details.

import org.apache.spark.ml.linalg.Vectors import org.apache.spark.ml.regression.AFTSurvivalRegression

val training = spark.createDataFrame(Seq( (1.218, 1.0, Vectors.dense(1.560, -0.605)), (2.949, 0.0, Vectors.dense(0.346, 2.158)), (3.627, 0.0, Vectors.dense(1.380, 0.231)), (0.273, 1.0, Vectors.dense(0.520, 1.151)), (4.199, 0.0, Vectors.dense(0.795, -0.226)) )).toDF(“label”, “censor”, “features”) val quantileProbabilities = Array(0.3, 0.6) val aft = new AFTSurvivalRegression() .setQuantileProbabilities(quantileProbabilities) .setQuantilesCol(“quantiles”)

val model = aft.fit(training)

// Print the coefficients, intercept and scale parameter for AFT survival regression println(s“Coefficients: ${model.coefficients}”) println(s“Intercept: ${model.intercept}”) println(s“Scale: ${model.scale}”) model.transform(training).show(false)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/AFTSurvivalRegressionExample.scala" in the Spark repo.

Refer to the Java API docs for more details.

import java.util.Arrays; import java.util.List;

import org.apache.spark.ml.regression.AFTSurvivalRegression; import org.apache.spark.ml.regression.AFTSurvivalRegressionModel; import org.apache.spark.ml.linalg.VectorUDT; import org.apache.spark.ml.linalg.Vectors; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.RowFactory; import org.apache.spark.sql.SparkSession; import org.apache.spark.sql.types.DataTypes; import org.apache.spark.sql.types.Metadata; import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType;

List<Row> data = Arrays.asList( RowFactory.create(1.218, 1.0, Vectors.dense(1.560, -0.605)), RowFactory.create(2.949, 0.0, Vectors.dense(0.346, 2.158)), RowFactory.create(3.627, 0.0, Vectors.dense(1.380, 0.231)), RowFactory.create(0.273, 1.0, Vectors.dense(0.520, 1.151)), RowFactory.create(4.199, 0.0, Vectors.dense(0.795, -0.226)) ); StructType schema = new StructType(new StructField[]{ new StructField(“label”, DataTypes.DoubleType, false, Metadata.empty()), new StructField(“censor”, DataTypes.DoubleType, false, Metadata.empty()), new StructField(“features”, new VectorUDT(), false, Metadata.empty()) }); Dataset<Row> training = spark.createDataFrame(data, schema); double[] quantileProbabilities = new double[]{0.3, 0.6}; AFTSurvivalRegression aft = new AFTSurvivalRegression() .setQuantileProbabilities(quantileProbabilities) .setQuantilesCol(“quantiles”);

AFTSurvivalRegressionModel model = aft.fit(training);

// Print the coefficients, intercept and scale parameter for AFT survival regression System.out.println(“Coefficients: “ + model.coefficients()); System.out.println(“Intercept: “ + model.intercept()); System.out.println(“Scale: “ + model.scale()); model.transform(training).show(false);

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java" in the Spark repo.

Refer to the Python API docs for more details.

from pyspark.ml.regression import AFTSurvivalRegression from pyspark.ml.linalg import Vectors

training = spark.createDataFrame([ (1.218, 1.0, Vectors.dense(1.560, -0.605)), (2.949, 0.0, Vectors.dense(0.346, 2.158)), (3.627, 0.0, Vectors.dense(1.380, 0.231)), (0.273, 1.0, Vectors.dense(0.520, 1.151)), (4.199, 0.0, Vectors.dense(0.795, -0.226))], [“label”, “censor”, “features”]) quantileProbabilities = [0.3, 0.6] aft = AFTSurvivalRegression(quantileProbabilities=quantileProbabilities, quantilesCol=“quantiles”)

model = aft.fit(training)

# Print the coefficients, intercept and scale parameter for AFT survival regression print(“Coefficients: “ + str(model.coefficients)) print(“Intercept: “ + str(model.intercept)) print(“Scale: “ + str(model.scale)) model.transform(training).show(truncate=False)

Find full example code at "examples/src/main/python/ml/aft_survival_regression.py" in the Spark repo.

Refer to the R API docs for more details.

# Use the ovarian dataset available in R survival package library(survival)

</span># Fit an accelerated failure time (AFT) survival regression model with spark.survreg ovarianDF <- suppressWarnings(createDataFrame(ovarian)) aftDF <- ovarianDF aftTestDF <- ovarianDF aftModel <- spark.survreg(aftDF, Surv(futime, fustat) ~ ecog_ps + rx)

</span># Model summary summary(aftModel)

</span># Prediction aftPredictions <- predict(aftModel, aftTestDF) head(aftPredictions)

</span><div>Find full example code at “examples/src/main/r/ml/survreg.R” in the Spark repo.</div>

Isotonic regression

Isotonic regression belongs to the family of regression algorithms. Formally isotonic regression is a problem where given a finite set of real numbers $Y = {y_1, y_2, ..., y_n}$ representing observed responses and $X = {x_1, x_2, ..., x_n}$ the unknown response values to be fitted finding a function that minimizes

\begin{equation} f(x) = \sum_{i=1}^n w_i (y_i - x_i)^2 \end{equation}

with respect to complete order subject to $x_1\le x_2\le ...\le x_n$ where $w_i$ are positive weights. The resulting function is called isotonic regression and it is unique. It can be viewed as least squares problem under order restriction. Essentially isotonic regression is a monotonic function best fitting the original data points.

We implement a pool adjacent violators algorithm which uses an approach to parallelizing isotonic regression. The training input is a DataFrame which contains three columns label, features and weight. Additionally, IsotonicRegression algorithm has one optional parameter called $isotonic$ defaulting to true. This argument specifies if the isotonic regression is isotonic (monotonically increasing) or antitonic (monotonically decreasing).

Training returns an IsotonicRegressionModel that can be used to predict labels for both known and unknown features. The result of isotonic regression is treated as piecewise linear function. The rules for prediction therefore are:

If the prediction input exactly matches a training feature then associated prediction is returned. In case there are multiple predictions with the same feature then one of them is returned. Which one is undefined (same as java.util.Arrays.binarySearch).
If the prediction input is lower or higher than all training features then prediction with lowest or highest feature is returned respectively. In case there are multiple predictions with the same feature then the lowest or highest is returned respectively.
If the prediction input falls between two training features then prediction is treated as piecewise linear function and interpolated value is calculated from the predictions of the two closest features. In case there are multiple values with the same feature then the same rules as in previous point are used.

Examples

Refer to the IsotonicRegression Scala docs for details on the API.

import org.apache.spark.ml.regression.IsotonicRegression

// Loads data. val dataset = spark.read.format(“libsvm”) .load(“data/mllib/sample_isotonic_regression_libsvm_data.txt”)

// Trains an isotonic regression model. val ir = new IsotonicRegression() val model = ir.fit(dataset)

println(s“Boundaries in increasing order: ${model.boundaries}\n”) println(s“Predictions associated with the boundaries: ${model.predictions}\n”)

// Makes predictions. model.transform(dataset).show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/IsotonicRegressionExample.scala" in the Spark repo.

Refer to the IsotonicRegression Java docs for details on the API.

import org.apache.spark.ml.regression.IsotonicRegression; import org.apache.spark.ml.regression.IsotonicRegressionModel; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row;

// Loads data. Dataset<Row> dataset = spark.read().format(“libsvm”) .load(“data/mllib/sample_isotonic_regression_libsvm_data.txt”);

// Trains an isotonic regression model. IsotonicRegression ir = new IsotonicRegression(); IsotonicRegressionModel model = ir.fit(dataset);

System.out.println(“Boundaries in increasing order: “ + model.boundaries() + “\n”); System.out.println(“Predictions associated with the boundaries: “ + model.predictions() + “\n”);

// Makes predictions. model.transform(dataset).show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaIsotonicRegressionExample.java" in the Spark repo.

Refer to the IsotonicRegression Python docs for more details on the API.

from pyspark.ml.regression import IsotonicRegression

# Loads data. dataset = spark.read.format(“libsvm”)\ .load(“data/mllib/sample_isotonic_regression_libsvm_data.txt”)

# Trains an isotonic regression model. model = IsotonicRegression().fit(dataset) print(“Boundaries in increasing order: %s\n” % str(model.boundaries)) print(“Predictions associated with the boundaries: %s\n” % str(model.predictions))

# Makes predictions. model.transform(dataset).show()

Find full example code at "examples/src/main/python/ml/isotonic_regression_example.py" in the Spark repo.

Refer to the IsotonicRegression R API docs for more details on the API.

# Load training data df <- read.df(“data/mllib/sample_isotonic_regression_libsvm_data.txt”, source = “libsvm”) training <- df test <- df

</span># Fit an isotonic regression model with spark.isoreg model <- spark.isoreg(training, label ~ features, isotonic = FALSE)

</span># Model summary summary(model)

</span># Prediction predictions <- predict(model, test) head(predictions)

</span><div>Find full example code at “examples/src/main/r/ml/isoreg.R” in the Spark repo.</div>

Linear methods

We implement popular linear methods such as logistic regression and linear least squares with $L_1$ or $L_2$ regularization. Refer to the linear methods guide for the RDD-based API for details about implementation and tuning; this information is still relevant.

We also include a DataFrame API for Elastic net, a hybrid of $L_1$ and $L_2$ regularization proposed in Zou et al, Regularization and variable selection via the elastic net. Mathematically, it is defined as a convex combination of the $L_1$ and the $L_2$ regularization terms: \[ \alpha \left( \lambda \|\wv\|_1 \right) + (1-\alpha) \left( \frac{\lambda}{2}\|\wv\|_2^2 \right) , \alpha \in [0, 1], \lambda \geq 0 \] By setting $\alpha$ properly, elastic net contains both $L_1$ and $L_2$ regularization as special cases. For example, if a linear regression model is trained with the elastic net parameter $\alpha$ set to $1$, it is equivalent to a Lasso model. On the other hand, if $\alpha$ is set to $0$, the trained model reduces to a ridge regression model. We implement Pipelines API for both linear regression and logistic regression with elastic net regularization.

Decision trees

Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression. Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions. Tree ensemble algorithms such as random forests and boosting are among the top performers for classification and regression tasks.

The spark.ml implementation supports decision trees for binary and multiclass classification and for regression, using both continuous and categorical features. The implementation partitions data by rows, allowing distributed training with millions or even billions of instances.

Users can find more information about the decision tree algorithm in the MLlib Decision Tree guide. The main differences between this API and the original MLlib Decision Tree API are:

support for ML Pipelines
separation of Decision Trees for classification vs. regression
use of DataFrame metadata to distinguish continuous and categorical features

The Pipelines API for Decision Trees offers a bit more functionality than the original API.
In particular, for classification, users can get the predicted probability of each class (a.k.a. class conditional probabilities); for regression, users can get the biased sample variance of prediction.

Ensembles of trees (Random Forests and Gradient-Boosted Trees) are described below in the Tree ensembles section.

Inputs and Outputs

We list the input and output (prediction) column types here. All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.

Input Columns

Param name	Type(s)	Default	Description
labelCol	Double	"label"	Label to predict
featuresCol	Vector	"features"	Feature vector

Output Columns

Param name	Type(s)	Default	Description	Notes
predictionCol	Double	"prediction"	Predicted label
rawPredictionCol	Vector	"rawPrediction"	Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction	Classification only
probabilityCol	Vector	"probability"	Vector of length # classes equal to rawPrediction normalized to a multinomial distribution	Classification only
varianceCol	Double		The biased sample variance of prediction	Regression only

Tree Ensembles

The DataFrame API supports two major tree ensemble algorithms: Random Forests and Gradient-Boosted Trees (GBTs). Both use spark.ml decision trees as their base models.

Users can find more information about ensemble algorithms in the MLlib Ensemble guide.
In this section, we demonstrate the DataFrame API for ensembles.

The main differences between this API and the original MLlib ensembles API are:

support for DataFrames and ML Pipelines
separation of classification vs. regression
use of DataFrame metadata to distinguish continuous and categorical features
more functionality for random forests: estimates of feature importance, as well as the predicted probability of each class (a.k.a. class conditional probabilities) for classification.

Random Forests

Random forests are ensembles of decision trees. Random forests combine many decision trees in order to reduce the risk of overfitting. The spark.ml implementation supports random forests for binary and multiclass classification and for regression, using both continuous and categorical features.

For more information on the algorithm itself, please see the spark.mllib documentation on random forests.

Inputs and Outputs

We list the input and output (prediction) column types here. All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.

Input Columns

Param name	Type(s)	Default	Description
labelCol	Double	"label"	Label to predict
featuresCol	Vector	"features"	Feature vector

Output Columns (Predictions)

Param name	Type(s)	Default	Description	Notes
predictionCol	Double	"prediction"	Predicted label
rawPredictionCol	Vector	"rawPrediction"	Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction	Classification only
probabilityCol	Vector	"probability"	Vector of length # classes equal to rawPrediction normalized to a multinomial distribution	Classification only

Gradient-Boosted Trees (GBTs)

Gradient-Boosted Trees (GBTs) are ensembles of decision trees. GBTs iteratively train decision trees in order to minimize a loss function. The spark.ml implementation supports GBTs for binary classification and for regression, using both continuous and categorical features.

For more information on the algorithm itself, please see the spark.mllib documentation on GBTs.

Inputs and Outputs

We list the input and output (prediction) column types here. All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.

Input Columns

Param name	Type(s)	Default	Description
labelCol	Double	"label"	Label to predict
featuresCol	Vector	"features"	Feature vector

Note that GBTClassifier currently only supports binary labels.

Output Columns (Predictions)

Param name	Type(s)	Default	Description	Notes
predictionCol	Double	"prediction"	Predicted label

In the future, GBTClassifier will also output columns for rawPrediction and probability, just as RandomForestClassifier does.

MLlib: Main Guide

MLlib: RDD-based API Guide

Classification and regression

Classification

Logistic regression

Binomial logistic regression

in the earlier example

Multinomial logistic regression

Decision tree classifier

Fit on whole dataset to include all labels in index.

We specify maxCategories so features with > 4 distinct values are treated as continuous.

Random forest classifier

Fit on whole dataset to include all labels in index.

Set maxCategories so features with > 4 distinct values are treated as continuous.

Gradient-boosted tree classifier

Fit on whole dataset to include all labels in index.

Set maxCategories so features with > 4 distinct values are treated as continuous.

Multilayer perceptron classifier

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

and output of size 3 (classes)

Linear Support Vector Machine

One-vs-Rest classifier (a.k.a. One-vs-All)

Naive Bayes

Regression

Linear regression

Generalized linear regression

Available families

Decision tree regression

We specify maxCategories so features with > 4 distinct values are treated as continuous.

Random forest regression

Set maxCategories so features with > 4 distinct values are treated as continuous.

Gradient-boosted tree regression

Set maxCategories so features with > 4 distinct values are treated as continuous.

Survival regression

Isotonic regression

Linear methods

Decision trees

Inputs and Outputs

Input Columns

Output Columns

Tree Ensembles

Random Forests

Inputs and Outputs

Input Columns

Output Columns (Predictions)

Gradient-Boosted Trees (GBTs)

Inputs and Outputs

Input Columns

Output Columns (Predictions)