Classification and regression
\[
\newcommand{\R}{\mathbb{R}}
\newcommand{\E}{\mathbb{E}}
\newcommand{\x}{\mathbf{x}}
\newcommand{\y}{\mathbf{y}}
\newcommand{\wv}{\mathbf{w}}
\newcommand{\av}{\mathbf{\alpha}}
\newcommand{\bv}{\mathbf{b}}
\newcommand{\N}{\mathbb{N}}
\newcommand{\id}{\mathbf{I}}
\newcommand{\ind}{\mathbf{1}}
\newcommand{\0}{\mathbf{0}}
\newcommand{\unit}{\mathbf{e}}
\newcommand{\one}{\mathbf{1}}
\newcommand{\zero}{\mathbf{0}}
\]
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
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}")
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());
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))
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
# Fit an binomial logistic regression model with spark.logit
model <- spark.logit(training, label ~ features, maxIter = 10, regParam = 0.3, elasticNetParam = 0.8)
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
head(predictions)
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)
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.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);
LogisticRegressionTrainingSummary
provides a summary for a
LogisticRegressionModel
.
In the case of binary classification, certain additional metrics are
available, e.g. ROC curve. See BinaryLogisticRegressionTrainingSummary
.
Continuing the earlier example:
from pyspark.ml.classification import LogisticRegression
# Extract the summary from the returned LogisticRegressionModel instance trained
# in the earlier example
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)
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 asinterceptVector
.
coefficients
andintercept
methods on a logistic regression model trained with multinomial family are not supported. UsecoefficientMatrix
andinterceptVector
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")
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);
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))
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
# Fit a multinomial logistic regression model with spark.logit
model <- spark.logit(training, label ~ features, maxIter = 10, regParam = 0.3, elasticNetParam = 0.8)
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
head(predictions)
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.labels)
// 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}")
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");
// 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.
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.labels());
// 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());
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.
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.
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)
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
# Fit a DecisionTree classification model with spark.decisionTree
model <- spark.decisionTree(training, label ~ features, "classification")
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
head(predictions)
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
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 tree-based algorithms can recognize.
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.labels)
// 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)
// 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 rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
println(s"Learned classification forest model:\n ${rfModel.toDebugString}")
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");
// Convert indexed labels back to original labels.
IndexToString labelConverter = new IndexToString()
.setInputCol("prediction")
.setOutputCol("predictedLabel")
.setLabels(labelIndexer.labels());
// 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());
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.
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.
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)
# 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))
rfModel = model.stages[2]
print(rfModel) # summary only
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
# Fit a random forest classification model with spark.randomForest
model <- spark.randomForest(training, label ~ features, "classification", numTrees = 10)
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
head(predictions)
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
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 tree-based algorithms can recognize.
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.labels)
// 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)
// 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 gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel]
println(s"Learned classification GBT model:\n ${gbtModel.toDebugString}")
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");
// 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 GBT model.
GBTClassifier gbt = new GBTClassifier()
.setLabelCol("indexedLabel")
.setFeaturesCol("indexedFeatures")
.setMaxIter(10);
// Convert indexed labels back to original labels.
IndexToString labelConverter = new IndexToString()
.setInputCol("prediction")
.setOutputCol("predictedLabel")
.setLabels(labelIndexer.labels());
// 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);
// 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));
GBTClassificationModel gbtModel = (GBTClassificationModel)(model.stages()[2]);
System.out.println("Learned classification GBT model:\n" + gbtModel.toDebugString());
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.
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.
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)
# 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))
gbtModel = model.stages[2]
print(gbtModel) # summary only
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
# Fit a GBT classification model with spark.gbt
model <- spark.gbt(training, label ~ features, "classification", maxIter = 10)
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
head(predictions)
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)}")
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));
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)
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)))
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
# 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)
# 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)
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
head(predictions)
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}")
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());
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))
Refer to the R API docs for more details.
# load training data
t <- as.data.frame(Titanic)
training <- createDataFrame(t)
# fit Linear SVM model
model <- spark.svmLinear(training, Survived ~ ., regParam = 0.01, maxIter = 10)
# Model summary
summary(model)
# Prediction
prediction <- predict(model, training)
showDF(prediction)
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}")
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));
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))
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 both multinomial naive Bayes and Bernoulli naive Bayes.
Input data: These 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 Naive Bayes) or a zero or one indicating whether the term was found in the document (in Bernoulli Naive Bayes). Feature values must be non-negative. The model type is selected with an optional parameter “multinomial” or “bernoulli” 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")
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);
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))
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)
# Model summary
summary(nbModel)
# Prediction
nbPredictions <- predict(nbModel, nbTestDF)
head(nbPredictions)
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}")
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());
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)
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:
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:
where the parameter of interest $\theta_i$ is related to the expected value of the response variable $\mu_i$ by
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 $\eta_i$:
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.
A GLM finds the regression coefficients $\vec{\beta}$ which maximize the likelihood function.
where the parameter of interest $\theta_i$ is related to the regression coefficients $\vec{\beta}$ by
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()
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();
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()
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")
# Model summary
summary(gaussianGLM)
# Prediction
gaussianPredictions <- predict(gaussianGLM, gaussianTestDF)
head(gaussianPredictions)
# Fit a generalized linear model with glm (R-compliant)
gaussianGLM2 <- glm(label ~ features, gaussianDF, family = "gaussian")
summary(gaussianGLM2)
# 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")
# Model summary
summary(binomialGLM)
# Prediction
binomialPredictions <- predict(binomialGLM, binomialTestDF)
head(binomialPredictions)
# 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)
# Model summary
summary(tweedieGLM)
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}")
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);
// 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.
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());
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.
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)
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
# Fit a DecisionTree regression model with spark.decisionTree
model <- spark.decisionTree(training, label ~ features, "regression")
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
head(predictions)
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
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 tree-based algorithms can recognize.
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)
// 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 rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel]
println(s"Learned regression forest model:\n ${rfModel.toDebugString}")
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");
// 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.
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());
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.
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)
# 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)
rfModel = model.stages[1]
print(rfModel) # summary only
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
# Fit a random forest regression model with spark.randomForest
model <- spark.randomForest(training, label ~ features, "regression", numTrees = 10)
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
head(predictions)
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.
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.{GBTRegressionModel, GBTRegressor}
// 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 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)
// 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 gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel]
println(s"Learned regression GBT model:\n ${gbtModel.toDebugString}")
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");
// 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 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);
// 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);
GBTRegressionModel gbtModel = (GBTRegressionModel)(model.stages()[1]);
System.out.println("Learned regression GBT model:\n" + gbtModel.toDebugString());
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.
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)
# 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)
gbtModel = model.stages[1]
print(gbtModel) # summary only
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
# Fit a GBT regression model with spark.gbt
model <- spark.gbt(training, label ~ features, "regression", maxIter = 10)
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
head(predictions)
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)
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);
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)
Refer to the R API docs for more details.
# Use the ovarian dataset available in R survival package
library(survival)
# 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)
# Model summary
summary(aftModel)
# Prediction
aftPredictions <- predict(aftModel, aftTestDF)
head(aftPredictions)
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()
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();
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()
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
# Fit an isotonic regression model with spark.isoreg
model <- spark.isoreg(training, label ~ features, isotonic = FALSE)
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
head(predictions)
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.