[SPARK-11728] Replace example code in ml-ensembles.md using include_example
JIRA issue https://issues.apache.org/jira/browse/SPARK-11728. The ml-ensembles.md file contains `OneVsRestExample`. Instead of writing new code files of two `OneVsRestExample`s, I use two existing files in the examples directory, they are `OneVsRestExample.scala` and `JavaOneVsRestExample.scala`. Author: Xusen Yin <yinxusen@gmail.com> Closes #9716 from yinxusen/SPARK-11728.
This commit is contained in:
Xusen Yin
Xiangrui Meng
parent
2f191c66b6
commit
9154f89bef
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@ -115,194 +115,21 @@ We use two feature transformers to prepare the data; these help index categories
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.RandomForestClassifier) for more details.
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{% highlight scala %}
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import org.apache.spark.ml.Pipeline
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import org.apache.spark.ml.classification.RandomForestClassifier
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import org.apache.spark.ml.classification.RandomForestClassificationModel
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import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
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import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
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// Load and parse the data file, converting it to a DataFrame.
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val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
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// Index labels, adding metadata to the label column.
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// Fit on whole dataset to include all labels in index.
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val labelIndexer = new StringIndexer()
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.setInputCol("label")
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.setOutputCol("indexedLabel")
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.fit(data)
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// Automatically identify categorical features, and index them.
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// Set maxCategories so features with > 4 distinct values are treated as continuous.
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val featureIndexer = new VectorIndexer()
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.setInputCol("features")
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.setOutputCol("indexedFeatures")
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.setMaxCategories(4)
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.fit(data)
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// Split the data into training and test sets (30% held out for testing)
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val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
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// Train a RandomForest model.
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val rf = new RandomForestClassifier()
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.setLabelCol("indexedLabel")
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.setFeaturesCol("indexedFeatures")
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.setNumTrees(10)
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// Convert indexed labels back to original labels.
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val labelConverter = new IndexToString()
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.setInputCol("prediction")
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.setOutputCol("predictedLabel")
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.setLabels(labelIndexer.labels)
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// Chain indexers and forest in a Pipeline
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val pipeline = new Pipeline()
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.setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))
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// Train model. This also runs the indexers.
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val model = pipeline.fit(trainingData)
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// Make predictions.
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val predictions = model.transform(testData)
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// Select example rows to display.
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predictions.select("predictedLabel", "label", "features").show(5)
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// Select (prediction, true label) and compute test error
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val evaluator = new MulticlassClassificationEvaluator()
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.setLabelCol("indexedLabel")
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.setPredictionCol("prediction")
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.setMetricName("precision")
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val accuracy = evaluator.evaluate(predictions)
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println("Test Error = " + (1.0 - accuracy))
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val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
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println("Learned classification forest model:\n" + rfModel.toDebugString)
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{% endhighlight %}
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{% include_example scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/RandomForestClassifier.html) for more details.
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{% highlight java %}
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import org.apache.spark.ml.Pipeline;
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import org.apache.spark.ml.PipelineModel;
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import org.apache.spark.ml.PipelineStage;
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import org.apache.spark.ml.classification.RandomForestClassifier;
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import org.apache.spark.ml.classification.RandomForestClassificationModel;
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import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
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import org.apache.spark.ml.feature.*;
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import org.apache.spark.sql.DataFrame;
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// Load and parse the data file, converting it to a DataFrame.
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DataFrame data = sqlContext.read().format("libsvm")
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.load("data/mllib/sample_libsvm_data.txt");
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// Index labels, adding metadata to the label column.
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// Fit on whole dataset to include all labels in index.
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StringIndexerModel labelIndexer = new StringIndexer()
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.setInputCol("label")
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.setOutputCol("indexedLabel")
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.fit(data);
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// Automatically identify categorical features, and index them.
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// Set maxCategories so features with > 4 distinct values are treated as continuous.
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VectorIndexerModel featureIndexer = new VectorIndexer()
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.setInputCol("features")
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.setOutputCol("indexedFeatures")
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.setMaxCategories(4)
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.fit(data);
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// Split the data into training and test sets (30% held out for testing)
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DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
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DataFrame trainingData = splits[0];
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DataFrame testData = splits[1];
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// Train a RandomForest model.
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RandomForestClassifier rf = new RandomForestClassifier()
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.setLabelCol("indexedLabel")
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.setFeaturesCol("indexedFeatures");
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// Convert indexed labels back to original labels.
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IndexToString labelConverter = new IndexToString()
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.setInputCol("prediction")
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.setOutputCol("predictedLabel")
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.setLabels(labelIndexer.labels());
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// Chain indexers and forest in a Pipeline
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Pipeline pipeline = new Pipeline()
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.setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, labelConverter});
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// Train model. This also runs the indexers.
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PipelineModel model = pipeline.fit(trainingData);
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// Make predictions.
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DataFrame predictions = model.transform(testData);
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// Select example rows to display.
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predictions.select("predictedLabel", "label", "features").show(5);
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// Select (prediction, true label) and compute test error
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MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
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.setLabelCol("indexedLabel")
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.setPredictionCol("prediction")
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.setMetricName("precision");
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double accuracy = evaluator.evaluate(predictions);
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System.out.println("Test Error = " + (1.0 - accuracy));
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RandomForestClassificationModel rfModel =
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(RandomForestClassificationModel)(model.stages()[2]);
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System.out.println("Learned classification forest model:\n" + rfModel.toDebugString());
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{% endhighlight %}
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{% include_example java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier) for more details.
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{% highlight python %}
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from pyspark.ml import Pipeline
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from pyspark.ml.classification import RandomForestClassifier
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from pyspark.ml.feature import StringIndexer, VectorIndexer
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from pyspark.ml.evaluation import MulticlassClassificationEvaluator
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# Load and parse the data file, converting it to a DataFrame.
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data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
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# Index labels, adding metadata to the label column.
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# Fit on whole dataset to include all labels in index.
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labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
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# Automatically identify categorical features, and index them.
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# Set maxCategories so features with > 4 distinct values are treated as continuous.
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featureIndexer =\
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VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
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# Split the data into training and test sets (30% held out for testing)
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(trainingData, testData) = data.randomSplit([0.7, 0.3])
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# Train a RandomForest model.
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rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")
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# Chain indexers and forest in a Pipeline
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pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf])
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# Train model. This also runs the indexers.
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model = pipeline.fit(trainingData)
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# Make predictions.
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predictions = model.transform(testData)
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# Select example rows to display.
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predictions.select("prediction", "indexedLabel", "features").show(5)
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# Select (prediction, true label) and compute test error
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evaluator = MulticlassClassificationEvaluator(
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labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
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accuracy = evaluator.evaluate(predictions)
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print "Test Error = %g" % (1.0 - accuracy)
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rfModel = model.stages[2]
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print rfModel # summary only
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{% endhighlight %}
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{% include_example python/ml/random_forest_classifier_example.py %}
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</div>
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</div>
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@ -316,167 +143,21 @@ We use a feature transformer to index categorical features, adding metadata to t
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.RandomForestRegressor) for more details.
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{% highlight scala %}
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import org.apache.spark.ml.Pipeline
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import org.apache.spark.ml.regression.RandomForestRegressor
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import org.apache.spark.ml.regression.RandomForestRegressionModel
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import org.apache.spark.ml.feature.VectorIndexer
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import org.apache.spark.ml.evaluation.RegressionEvaluator
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// Load and parse the data file, converting it to a DataFrame.
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val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
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// Automatically identify categorical features, and index them.
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// Set maxCategories so features with > 4 distinct values are treated as continuous.
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val featureIndexer = new VectorIndexer()
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.setInputCol("features")
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.setOutputCol("indexedFeatures")
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.setMaxCategories(4)
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.fit(data)
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// Split the data into training and test sets (30% held out for testing)
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val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
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// Train a RandomForest model.
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val rf = new RandomForestRegressor()
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.setLabelCol("label")
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.setFeaturesCol("indexedFeatures")
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// Chain indexer and forest in a Pipeline
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val pipeline = new Pipeline()
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.setStages(Array(featureIndexer, rf))
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// Train model. This also runs the indexer.
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val model = pipeline.fit(trainingData)
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// Make predictions.
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val predictions = model.transform(testData)
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// Select example rows to display.
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predictions.select("prediction", "label", "features").show(5)
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// Select (prediction, true label) and compute test error
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val evaluator = new RegressionEvaluator()
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.setLabelCol("label")
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.setPredictionCol("prediction")
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.setMetricName("rmse")
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val rmse = evaluator.evaluate(predictions)
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println("Root Mean Squared Error (RMSE) on test data = " + rmse)
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val rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel]
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println("Learned regression forest model:\n" + rfModel.toDebugString)
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{% endhighlight %}
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{% include_example scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/RandomForestRegressor.html) for more details.
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{% highlight java %}
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import org.apache.spark.ml.Pipeline;
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import org.apache.spark.ml.PipelineModel;
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import org.apache.spark.ml.PipelineStage;
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import org.apache.spark.ml.evaluation.RegressionEvaluator;
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import org.apache.spark.ml.feature.VectorIndexer;
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import org.apache.spark.ml.feature.VectorIndexerModel;
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import org.apache.spark.ml.regression.RandomForestRegressionModel;
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import org.apache.spark.ml.regression.RandomForestRegressor;
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import org.apache.spark.sql.DataFrame;
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// Load and parse the data file, converting it to a DataFrame.
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DataFrame data = sqlContext.read().format("libsvm")
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.load("data/mllib/sample_libsvm_data.txt");
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// Automatically identify categorical features, and index them.
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// Set maxCategories so features with > 4 distinct values are treated as continuous.
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VectorIndexerModel featureIndexer = new VectorIndexer()
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.setInputCol("features")
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.setOutputCol("indexedFeatures")
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.setMaxCategories(4)
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.fit(data);
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// Split the data into training and test sets (30% held out for testing)
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DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
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DataFrame trainingData = splits[0];
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DataFrame testData = splits[1];
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// Train a RandomForest model.
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RandomForestRegressor rf = new RandomForestRegressor()
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.setLabelCol("label")
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.setFeaturesCol("indexedFeatures");
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// Chain indexer and forest in a Pipeline
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Pipeline pipeline = new Pipeline()
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.setStages(new PipelineStage[] {featureIndexer, rf});
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// Train model. This also runs the indexer.
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PipelineModel model = pipeline.fit(trainingData);
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// Make predictions.
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DataFrame predictions = model.transform(testData);
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// Select example rows to display.
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predictions.select("prediction", "label", "features").show(5);
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// Select (prediction, true label) and compute test error
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RegressionEvaluator evaluator = new RegressionEvaluator()
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.setLabelCol("label")
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.setPredictionCol("prediction")
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.setMetricName("rmse");
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double rmse = evaluator.evaluate(predictions);
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System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);
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RandomForestRegressionModel rfModel =
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(RandomForestRegressionModel)(model.stages()[1]);
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System.out.println("Learned regression forest model:\n" + rfModel.toDebugString());
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{% endhighlight %}
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{% include_example java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor) for more details.
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{% highlight python %}
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from pyspark.ml import Pipeline
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from pyspark.ml.regression import RandomForestRegressor
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from pyspark.ml.feature import VectorIndexer
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from pyspark.ml.evaluation import RegressionEvaluator
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# Load and parse the data file, converting it to a DataFrame.
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data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
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# Automatically identify categorical features, and index them.
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# Set maxCategories so features with > 4 distinct values are treated as continuous.
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featureIndexer =\
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VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
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# Split the data into training and test sets (30% held out for testing)
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(trainingData, testData) = data.randomSplit([0.7, 0.3])
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# Train a RandomForest model.
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rf = RandomForestRegressor(featuresCol="indexedFeatures")
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# Chain indexer and forest in a Pipeline
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pipeline = Pipeline(stages=[featureIndexer, rf])
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# Train model. This also runs the indexer.
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model = pipeline.fit(trainingData)
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# Make predictions.
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predictions = model.transform(testData)
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# Select example rows to display.
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predictions.select("prediction", "label", "features").show(5)
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# Select (prediction, true label) and compute test error
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evaluator = RegressionEvaluator(
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labelCol="label", predictionCol="prediction", metricName="rmse")
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rmse = evaluator.evaluate(predictions)
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print "Root Mean Squared Error (RMSE) on test data = %g" % rmse
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rfModel = model.stages[1]
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print rfModel # summary only
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{% endhighlight %}
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{% include_example python/ml/random_forest_regressor_example.py %}
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</div>
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</div>
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@ -560,194 +241,21 @@ We use two feature transformers to prepare the data; these help index categories
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.GBTClassifier) for more details.
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{% highlight scala %}
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import org.apache.spark.ml.Pipeline
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import org.apache.spark.ml.classification.GBTClassifier
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import org.apache.spark.ml.classification.GBTClassificationModel
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import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
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import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
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// Load and parse the data file, converting it to a DataFrame.
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val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
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// Index labels, adding metadata to the label column.
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// Fit on whole dataset to include all labels in index.
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val labelIndexer = new StringIndexer()
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.setInputCol("label")
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.setOutputCol("indexedLabel")
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.fit(data)
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// Automatically identify categorical features, and index them.
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// Set maxCategories so features with > 4 distinct values are treated as continuous.
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val featureIndexer = new VectorIndexer()
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.setInputCol("features")
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.setOutputCol("indexedFeatures")
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.setMaxCategories(4)
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.fit(data)
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// Split the data into training and test sets (30% held out for testing)
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val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
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// Train a GBT model.
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val gbt = new GBTClassifier()
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.setLabelCol("indexedLabel")
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.setFeaturesCol("indexedFeatures")
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.setMaxIter(10)
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// Convert indexed labels back to original labels.
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val labelConverter = new IndexToString()
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.setInputCol("prediction")
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.setOutputCol("predictedLabel")
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.setLabels(labelIndexer.labels)
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// Chain indexers and GBT in a Pipeline
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val pipeline = new Pipeline()
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.setStages(Array(labelIndexer, featureIndexer, gbt, labelConverter))
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// Train model. This also runs the indexers.
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val model = pipeline.fit(trainingData)
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// Make predictions.
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val predictions = model.transform(testData)
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// Select example rows to display.
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predictions.select("predictedLabel", "label", "features").show(5)
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// Select (prediction, true label) and compute test error
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val evaluator = new MulticlassClassificationEvaluator()
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.setLabelCol("indexedLabel")
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.setPredictionCol("prediction")
|
||||
.setMetricName("precision")
|
||||
val accuracy = evaluator.evaluate(predictions)
|
||||
println("Test Error = " + (1.0 - accuracy))
|
||||
|
||||
val gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel]
|
||||
println("Learned classification GBT model:\n" + gbtModel.toDebugString)
|
||||
{% endhighlight %}
|
||||
{% include_example scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala %}
|
||||
</div>
|
||||
|
||||
<div data-lang="java" markdown="1">
|
||||
|
||||
Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/GBTClassifier.html) for more details.
|
||||
|
||||
{% highlight java %}
|
||||
import org.apache.spark.ml.Pipeline;
|
||||
import org.apache.spark.ml.PipelineModel;
|
||||
import org.apache.spark.ml.PipelineStage;
|
||||
import org.apache.spark.ml.classification.GBTClassifier;
|
||||
import org.apache.spark.ml.classification.GBTClassificationModel;
|
||||
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
|
||||
import org.apache.spark.ml.feature.*;
|
||||
import org.apache.spark.sql.DataFrame;
|
||||
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
DataFrame data sqlContext.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)
|
||||
DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
|
||||
DataFrame trainingData = splits[0];
|
||||
DataFrame 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.
|
||||
DataFrame 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("precision");
|
||||
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());
|
||||
{% endhighlight %}
|
||||
{% include_example java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java %}
|
||||
</div>
|
||||
|
||||
<div data-lang="python" markdown="1">
|
||||
|
||||
Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.GBTClassifier) for more details.
|
||||
|
||||
{% highlight python %}
|
||||
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 = sqlContext.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="precision")
|
||||
accuracy = evaluator.evaluate(predictions)
|
||||
print "Test Error = %g" % (1.0 - accuracy)
|
||||
|
||||
gbtModel = model.stages[2]
|
||||
print gbtModel # summary only
|
||||
{% endhighlight %}
|
||||
{% include_example python/ml/gradient_boosted_tree_classifier_example.py %}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
|
@ -761,168 +269,21 @@ be true in general.
|
|||
|
||||
Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.GBTRegressor) for more details.
|
||||
|
||||
{% highlight scala %}
|
||||
import org.apache.spark.ml.Pipeline
|
||||
import org.apache.spark.ml.regression.GBTRegressor
|
||||
import org.apache.spark.ml.regression.GBTRegressionModel
|
||||
import org.apache.spark.ml.feature.VectorIndexer
|
||||
import org.apache.spark.ml.evaluation.RegressionEvaluator
|
||||
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
val data = sqlContext.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("Root Mean Squared Error (RMSE) on test data = " + rmse)
|
||||
|
||||
val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel]
|
||||
println("Learned regression GBT model:\n" + gbtModel.toDebugString)
|
||||
{% endhighlight %}
|
||||
{% include_example scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala %}
|
||||
</div>
|
||||
|
||||
<div data-lang="java" markdown="1">
|
||||
|
||||
Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/GBTRegressor.html) for more details.
|
||||
|
||||
{% highlight java %}
|
||||
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.DataFrame;
|
||||
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
DataFrame data = sqlContext.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)
|
||||
DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
|
||||
DataFrame trainingData = splits[0];
|
||||
DataFrame 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.
|
||||
DataFrame 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());
|
||||
{% endhighlight %}
|
||||
{% include_example java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java %}
|
||||
</div>
|
||||
|
||||
<div data-lang="python" markdown="1">
|
||||
|
||||
Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.GBTRegressor) for more details.
|
||||
|
||||
{% highlight python %}
|
||||
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 = sqlContext.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
|
||||
{% endhighlight %}
|
||||
{% include_example python/ml/gradient_boosted_tree_regressor_example.py %}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
|
@ -945,100 +306,13 @@ The example below demonstrates how to load the
|
|||
|
||||
Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest) for more details.
|
||||
|
||||
{% highlight scala %}
|
||||
import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest}
|
||||
import org.apache.spark.mllib.evaluation.MulticlassMetrics
|
||||
import org.apache.spark.sql.{Row, SQLContext}
|
||||
|
||||
val sqlContext = new SQLContext(sc)
|
||||
|
||||
// parse data into dataframe
|
||||
val data = sqlContext.read.format("libsvm")
|
||||
.load("data/mllib/sample_multiclass_classification_data.txt")
|
||||
val Array(train, test) = data.randomSplit(Array(0.7, 0.3))
|
||||
|
||||
// instantiate multiclass learner and train
|
||||
val ovr = new OneVsRest().setClassifier(new LogisticRegression)
|
||||
|
||||
val ovrModel = ovr.fit(train)
|
||||
|
||||
// score model on test data
|
||||
val predictions = ovrModel.transform(test).select("prediction", "label")
|
||||
val predictionsAndLabels = predictions.map {case Row(p: Double, l: Double) => (p, l)}
|
||||
|
||||
// compute confusion matrix
|
||||
val metrics = new MulticlassMetrics(predictionsAndLabels)
|
||||
println(metrics.confusionMatrix)
|
||||
|
||||
// the Iris DataSet has three classes
|
||||
val numClasses = 3
|
||||
|
||||
println("label\tfpr\n")
|
||||
(0 until numClasses).foreach { index =>
|
||||
val label = index.toDouble
|
||||
println(label + "\t" + metrics.falsePositiveRate(label))
|
||||
}
|
||||
{% endhighlight %}
|
||||
{% include_example scala/org/apache/spark/examples/ml/OneVsRestExample.scala %}
|
||||
</div>
|
||||
|
||||
<div data-lang="java" markdown="1">
|
||||
|
||||
Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/OneVsRest.html) for more details.
|
||||
|
||||
{% highlight java %}
|
||||
import org.apache.spark.SparkConf;
|
||||
import org.apache.spark.api.java.JavaSparkContext;
|
||||
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.mllib.evaluation.MulticlassMetrics;
|
||||
import org.apache.spark.mllib.linalg.Matrix;
|
||||
import org.apache.spark.sql.DataFrame;
|
||||
import org.apache.spark.sql.SQLContext;
|
||||
|
||||
SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample");
|
||||
JavaSparkContext jsc = new JavaSparkContext(conf);
|
||||
SQLContext jsql = new SQLContext(jsc);
|
||||
|
||||
DataFrame dataFrame = sqlContext.read().format("libsvm")
|
||||
.load("data/mllib/sample_multiclass_classification_data.txt");
|
||||
|
||||
DataFrame[] splits = dataFrame.randomSplit(new double[] {0.7, 0.3}, 12345);
|
||||
DataFrame train = splits[0];
|
||||
DataFrame test = splits[1];
|
||||
|
||||
// instantiate the One Vs Rest Classifier
|
||||
OneVsRest ovr = new OneVsRest().setClassifier(new LogisticRegression());
|
||||
|
||||
// train the multiclass model
|
||||
OneVsRestModel ovrModel = ovr.fit(train.cache());
|
||||
|
||||
// score the model on test data
|
||||
DataFrame predictions = ovrModel
|
||||
.transform(test)
|
||||
.select("prediction", "label");
|
||||
|
||||
// obtain metrics
|
||||
MulticlassMetrics metrics = new MulticlassMetrics(predictions);
|
||||
Matrix confusionMatrix = metrics.confusionMatrix();
|
||||
|
||||
// output the Confusion Matrix
|
||||
System.out.println("Confusion Matrix");
|
||||
System.out.println(confusionMatrix);
|
||||
|
||||
// compute the false positive rate per label
|
||||
System.out.println();
|
||||
System.out.println("label\tfpr\n");
|
||||
|
||||
// the Iris DataSet has three classes
|
||||
int numClasses = 3;
|
||||
for (int index = 0; index < numClasses; index++) {
|
||||
double label = (double) index;
|
||||
System.out.print(label);
|
||||
System.out.print("\t");
|
||||
System.out.print(metrics.falsePositiveRate(label));
|
||||
System.out.println();
|
||||
}
|
||||
{% endhighlight %}
|
||||
{% include_example java/org/apache/spark/examples/ml/JavaOneVsRestExample.java %}
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -0,0 +1,102 @@
|
|||
/*
|
||||
* Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
* contributor license agreements. See the NOTICE file distributed with
|
||||
* this work for additional information regarding copyright ownership.
|
||||
* The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
* (the "License"); you may not use this file except in compliance with
|
||||
* the License. You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.apache.spark.examples.ml;
|
||||
|
||||
import org.apache.spark.SparkConf;
|
||||
import org.apache.spark.api.java.JavaSparkContext;
|
||||
// $example on$
|
||||
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.DataFrame;
|
||||
import org.apache.spark.sql.SQLContext;
|
||||
// $example off$
|
||||
|
||||
public class JavaGradientBoostedTreeClassifierExample {
|
||||
public static void main(String[] args) {
|
||||
SparkConf conf = new SparkConf().setAppName("JavaGradientBoostedTreeClassifierExample");
|
||||
JavaSparkContext jsc = new JavaSparkContext(conf);
|
||||
SQLContext sqlContext = new SQLContext(jsc);
|
||||
|
||||
// $example on$
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
DataFrame data = sqlContext.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)
|
||||
DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
|
||||
DataFrame trainingData = splits[0];
|
||||
DataFrame 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.
|
||||
DataFrame 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("precision");
|
||||
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());
|
||||
// $example off$
|
||||
|
||||
jsc.stop();
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
/*
|
||||
* Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
* contributor license agreements. See the NOTICE file distributed with
|
||||
* this work for additional information regarding copyright ownership.
|
||||
* The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
* (the "License"); you may not use this file except in compliance with
|
||||
* the License. You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.apache.spark.examples.ml;
|
||||
|
||||
import org.apache.spark.SparkConf;
|
||||
import org.apache.spark.api.java.JavaSparkContext;
|
||||
// $example on$
|
||||
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.DataFrame;
|
||||
import org.apache.spark.sql.SQLContext;
|
||||
// $example off$
|
||||
|
||||
public class JavaGradientBoostedTreeRegressorExample {
|
||||
public static void main(String[] args) {
|
||||
SparkConf conf = new SparkConf().setAppName("JavaGradientBoostedTreeRegressorExample");
|
||||
JavaSparkContext jsc = new JavaSparkContext(conf);
|
||||
SQLContext sqlContext = new SQLContext(jsc);
|
||||
|
||||
// $example on$
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
DataFrame data = sqlContext.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)
|
||||
DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
|
||||
DataFrame trainingData = splits[0];
|
||||
DataFrame 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.
|
||||
DataFrame 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());
|
||||
// $example off$
|
||||
|
||||
jsc.stop();
|
||||
}
|
||||
}
|
||||
|
|
@ -21,6 +21,7 @@ import org.apache.commons.cli.*;
|
|||
|
||||
import org.apache.spark.SparkConf;
|
||||
import org.apache.spark.api.java.JavaSparkContext;
|
||||
// $example on$
|
||||
import org.apache.spark.ml.classification.LogisticRegression;
|
||||
import org.apache.spark.ml.classification.OneVsRest;
|
||||
import org.apache.spark.ml.classification.OneVsRestModel;
|
||||
|
|
@ -31,6 +32,7 @@ import org.apache.spark.mllib.linalg.Vector;
|
|||
import org.apache.spark.sql.DataFrame;
|
||||
import org.apache.spark.sql.SQLContext;
|
||||
import org.apache.spark.sql.types.StructField;
|
||||
// $example off$
|
||||
|
||||
/**
|
||||
* An example runner for Multiclass to Binary Reduction with One Vs Rest.
|
||||
|
|
@ -61,6 +63,7 @@ public class JavaOneVsRestExample {
|
|||
JavaSparkContext jsc = new JavaSparkContext(conf);
|
||||
SQLContext jsql = new SQLContext(jsc);
|
||||
|
||||
// $example on$
|
||||
// configure the base classifier
|
||||
LogisticRegression classifier = new LogisticRegression()
|
||||
.setMaxIter(params.maxIter)
|
||||
|
|
@ -125,6 +128,7 @@ public class JavaOneVsRestExample {
|
|||
System.out.println(confusionMatrix);
|
||||
System.out.println();
|
||||
System.out.println(results);
|
||||
// $example off$
|
||||
|
||||
jsc.stop();
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,101 @@
|
|||
/*
|
||||
* Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
* contributor license agreements. See the NOTICE file distributed with
|
||||
* this work for additional information regarding copyright ownership.
|
||||
* The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
* (the "License"); you may not use this file except in compliance with
|
||||
* the License. You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.apache.spark.examples.ml;
|
||||
|
||||
import org.apache.spark.SparkConf;
|
||||
import org.apache.spark.api.java.JavaSparkContext;
|
||||
// $example on$
|
||||
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.DataFrame;
|
||||
import org.apache.spark.sql.SQLContext;
|
||||
// $example off$
|
||||
|
||||
public class JavaRandomForestClassifierExample {
|
||||
public static void main(String[] args) {
|
||||
SparkConf conf = new SparkConf().setAppName("JavaRandomForestClassifierExample");
|
||||
JavaSparkContext jsc = new JavaSparkContext(conf);
|
||||
SQLContext sqlContext = new SQLContext(jsc);
|
||||
|
||||
// $example on$
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
DataFrame data = sqlContext.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)
|
||||
DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
|
||||
DataFrame trainingData = splits[0];
|
||||
DataFrame 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.
|
||||
DataFrame 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("precision");
|
||||
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());
|
||||
// $example off$
|
||||
|
||||
jsc.stop();
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
/*
|
||||
* Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
* contributor license agreements. See the NOTICE file distributed with
|
||||
* this work for additional information regarding copyright ownership.
|
||||
* The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
* (the "License"); you may not use this file except in compliance with
|
||||
* the License. You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.apache.spark.examples.ml;
|
||||
|
||||
import org.apache.spark.SparkConf;
|
||||
import org.apache.spark.api.java.JavaSparkContext;
|
||||
// $example on$
|
||||
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.DataFrame;
|
||||
import org.apache.spark.sql.SQLContext;
|
||||
// $example off$
|
||||
|
||||
public class JavaRandomForestRegressorExample {
|
||||
public static void main(String[] args) {
|
||||
SparkConf conf = new SparkConf().setAppName("JavaRandomForestRegressorExample");
|
||||
JavaSparkContext jsc = new JavaSparkContext(conf);
|
||||
SQLContext sqlContext = new SQLContext(jsc);
|
||||
|
||||
// $example on$
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
DataFrame data = sqlContext.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)
|
||||
DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
|
||||
DataFrame trainingData = splits[0];
|
||||
DataFrame 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.
|
||||
DataFrame 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());
|
||||
// $example off$
|
||||
|
||||
jsc.stop();
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,77 @@
|
|||
#
|
||||
# Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
# contributor license agreements. See the NOTICE file distributed with
|
||||
# this work for additional information regarding copyright ownership.
|
||||
# The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
# (the "License"); you may not use this file except in compliance with
|
||||
# the License. You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
"""
|
||||
Gradient Boosted Tree Classifier Example.
|
||||
"""
|
||||
from __future__ import print_function
|
||||
|
||||
import sys
|
||||
|
||||
from pyspark import SparkContext, SQLContext
|
||||
# $example on$
|
||||
from pyspark.ml import Pipeline
|
||||
from pyspark.ml.classification import GBTClassifier
|
||||
from pyspark.ml.feature import StringIndexer, VectorIndexer
|
||||
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
|
||||
# $example off$
|
||||
|
||||
if __name__ == "__main__":
|
||||
sc = SparkContext(appName="gradient_boosted_tree_classifier_example")
|
||||
sqlContext = SQLContext(sc)
|
||||
|
||||
# $example on$
|
||||
# Load and parse the data file, converting it to a DataFrame.
|
||||
data = sqlContext.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="precision")
|
||||
accuracy = evaluator.evaluate(predictions)
|
||||
print("Test Error = %g" % (1.0 - accuracy))
|
||||
|
||||
gbtModel = model.stages[2]
|
||||
print(gbtModel) # summary only
|
||||
# $example off$
|
||||
|
||||
sc.stop()
|
||||
|
|
@ -0,0 +1,74 @@
|
|||
#
|
||||
# Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
# contributor license agreements. See the NOTICE file distributed with
|
||||
# this work for additional information regarding copyright ownership.
|
||||
# The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
# (the "License"); you may not use this file except in compliance with
|
||||
# the License. You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
"""
|
||||
Gradient Boosted Tree Regressor Example.
|
||||
"""
|
||||
from __future__ import print_function
|
||||
|
||||
import sys
|
||||
|
||||
from pyspark import SparkContext, SQLContext
|
||||
# $example on$
|
||||
from pyspark.ml import Pipeline
|
||||
from pyspark.ml.regression import GBTRegressor
|
||||
from pyspark.ml.feature import VectorIndexer
|
||||
from pyspark.ml.evaluation import RegressionEvaluator
|
||||
# $example off$
|
||||
|
||||
if __name__ == "__main__":
|
||||
sc = SparkContext(appName="gradient_boosted_tree_regressor_example")
|
||||
sqlContext = SQLContext(sc)
|
||||
|
||||
# $example on$
|
||||
# Load and parse the data file, converting it to a DataFrame.
|
||||
data = sqlContext.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
|
||||
# $example off$
|
||||
|
||||
sc.stop()
|
||||
|
|
@ -0,0 +1,77 @@
|
|||
#
|
||||
# Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
# contributor license agreements. See the NOTICE file distributed with
|
||||
# this work for additional information regarding copyright ownership.
|
||||
# The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
# (the "License"); you may not use this file except in compliance with
|
||||
# the License. You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
"""
|
||||
Random Forest Classifier Example.
|
||||
"""
|
||||
from __future__ import print_function
|
||||
|
||||
import sys
|
||||
|
||||
from pyspark import SparkContext, SQLContext
|
||||
# $example on$
|
||||
from pyspark.ml import Pipeline
|
||||
from pyspark.ml.classification import RandomForestClassifier
|
||||
from pyspark.ml.feature import StringIndexer, VectorIndexer
|
||||
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
|
||||
# $example off$
|
||||
|
||||
if __name__ == "__main__":
|
||||
sc = SparkContext(appName="random_forest_classifier_example")
|
||||
sqlContext = SQLContext(sc)
|
||||
|
||||
# $example on$
|
||||
# Load and parse the data file, converting it to a DataFrame.
|
||||
data = sqlContext.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")
|
||||
|
||||
# Chain indexers and forest in a Pipeline
|
||||
pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf])
|
||||
|
||||
# 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="precision")
|
||||
accuracy = evaluator.evaluate(predictions)
|
||||
print("Test Error = %g" % (1.0 - accuracy))
|
||||
|
||||
rfModel = model.stages[2]
|
||||
print(rfModel) # summary only
|
||||
# $example off$
|
||||
|
||||
sc.stop()
|
||||
|
|
@ -0,0 +1,74 @@
|
|||
#
|
||||
# Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
# contributor license agreements. See the NOTICE file distributed with
|
||||
# this work for additional information regarding copyright ownership.
|
||||
# The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
# (the "License"); you may not use this file except in compliance with
|
||||
# the License. You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
"""
|
||||
Random Forest Regressor Example.
|
||||
"""
|
||||
from __future__ import print_function
|
||||
|
||||
import sys
|
||||
|
||||
from pyspark import SparkContext, SQLContext
|
||||
# $example on$
|
||||
from pyspark.ml import Pipeline
|
||||
from pyspark.ml.regression import RandomForestRegressor
|
||||
from pyspark.ml.feature import VectorIndexer
|
||||
from pyspark.ml.evaluation import RegressionEvaluator
|
||||
# $example off$
|
||||
|
||||
if __name__ == "__main__":
|
||||
sc = SparkContext(appName="random_forest_regressor_example")
|
||||
sqlContext = SQLContext(sc)
|
||||
|
||||
# $example on$
|
||||
# Load and parse the data file, converting it to a DataFrame.
|
||||
data = sqlContext.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
|
||||
# $example off$
|
||||
|
||||
sc.stop()
|
||||
|
|
@ -0,0 +1,97 @@
|
|||
/*
|
||||
* Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
* contributor license agreements. See the NOTICE file distributed with
|
||||
* this work for additional information regarding copyright ownership.
|
||||
* The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
* (the "License"); you may not use this file except in compliance with
|
||||
* the License. You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
// scalastyle:off println
|
||||
package org.apache.spark.examples.ml
|
||||
|
||||
import org.apache.spark.sql.SQLContext
|
||||
import org.apache.spark.{SparkConf, SparkContext}
|
||||
// $example on$
|
||||
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}
|
||||
// $example off$
|
||||
|
||||
object GradientBoostedTreeClassifierExample {
|
||||
def main(args: Array[String]): Unit = {
|
||||
val conf = new SparkConf().setAppName("GradientBoostedTreeClassifierExample")
|
||||
val sc = new SparkContext(conf)
|
||||
val sqlContext = new SQLContext(sc)
|
||||
|
||||
// $example on$
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
val data = sqlContext.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)
|
||||
|
||||
// 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("precision")
|
||||
val accuracy = evaluator.evaluate(predictions)
|
||||
println("Test Error = " + (1.0 - accuracy))
|
||||
|
||||
val gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel]
|
||||
println("Learned classification GBT model:\n" + gbtModel.toDebugString)
|
||||
// $example off$
|
||||
|
||||
sc.stop()
|
||||
}
|
||||
}
|
||||
// scalastyle:on println
|
||||
|
|
@ -0,0 +1,85 @@
|
|||
/*
|
||||
* Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
* contributor license agreements. See the NOTICE file distributed with
|
||||
* this work for additional information regarding copyright ownership.
|
||||
* The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
* (the "License"); you may not use this file except in compliance with
|
||||
* the License. You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
// scalastyle:off println
|
||||
package org.apache.spark.examples.ml
|
||||
|
||||
import org.apache.spark.sql.SQLContext
|
||||
import org.apache.spark.{SparkConf, SparkContext}
|
||||
// $example on$
|
||||
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}
|
||||
// $example off$
|
||||
|
||||
object GradientBoostedTreeRegressorExample {
|
||||
def main(args: Array[String]): Unit = {
|
||||
val conf = new SparkConf().setAppName("GradientBoostedTreeRegressorExample")
|
||||
val sc = new SparkContext(conf)
|
||||
val sqlContext = new SQLContext(sc)
|
||||
|
||||
// $example on$
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
val data = sqlContext.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("Root Mean Squared Error (RMSE) on test data = " + rmse)
|
||||
|
||||
val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel]
|
||||
println("Learned regression GBT model:\n" + gbtModel.toDebugString)
|
||||
// $example off$
|
||||
|
||||
sc.stop()
|
||||
}
|
||||
}
|
||||
// scalastyle:on println
|
||||
|
|
@ -23,12 +23,14 @@ import java.util.concurrent.TimeUnit.{NANOSECONDS => NANO}
|
|||
import scopt.OptionParser
|
||||
|
||||
import org.apache.spark.{SparkContext, SparkConf}
|
||||
// $example on$
|
||||
import org.apache.spark.examples.mllib.AbstractParams
|
||||
import org.apache.spark.ml.classification.{OneVsRest, LogisticRegression}
|
||||
import org.apache.spark.ml.util.MetadataUtils
|
||||
import org.apache.spark.mllib.evaluation.MulticlassMetrics
|
||||
import org.apache.spark.mllib.linalg.Vector
|
||||
import org.apache.spark.sql.DataFrame
|
||||
// $example off$
|
||||
import org.apache.spark.sql.SQLContext
|
||||
|
||||
/**
|
||||
|
|
@ -112,6 +114,7 @@ object OneVsRestExample {
|
|||
val sc = new SparkContext(conf)
|
||||
val sqlContext = new SQLContext(sc)
|
||||
|
||||
// $example on$
|
||||
val inputData = sqlContext.read.format("libsvm").load(params.input)
|
||||
// compute the train/test split: if testInput is not provided use part of input.
|
||||
val data = params.testInput match {
|
||||
|
|
@ -172,6 +175,7 @@ object OneVsRestExample {
|
|||
println("label\tfpr")
|
||||
|
||||
println(fprs.map {case (label, fpr) => label + "\t" + fpr}.mkString("\n"))
|
||||
// $example off$
|
||||
|
||||
sc.stop()
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,97 @@
|
|||
/*
|
||||
* Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
* contributor license agreements. See the NOTICE file distributed with
|
||||
* this work for additional information regarding copyright ownership.
|
||||
* The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
* (the "License"); you may not use this file except in compliance with
|
||||
* the License. You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
// scalastyle:off println
|
||||
package org.apache.spark.examples.ml
|
||||
|
||||
import org.apache.spark.sql.SQLContext
|
||||
import org.apache.spark.{SparkConf, SparkContext}
|
||||
// $example on$
|
||||
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}
|
||||
// $example off$
|
||||
|
||||
object RandomForestClassifierExample {
|
||||
def main(args: Array[String]): Unit = {
|
||||
val conf = new SparkConf().setAppName("RandomForestClassifierExample")
|
||||
val sc = new SparkContext(conf)
|
||||
val sqlContext = new SQLContext(sc)
|
||||
|
||||
// $example on$
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
val data = sqlContext.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("precision")
|
||||
val accuracy = evaluator.evaluate(predictions)
|
||||
println("Test Error = " + (1.0 - accuracy))
|
||||
|
||||
val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
|
||||
println("Learned classification forest model:\n" + rfModel.toDebugString)
|
||||
// $example off$
|
||||
|
||||
sc.stop()
|
||||
}
|
||||
}
|
||||
// scalastyle:on println
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
/*
|
||||
* Licensed to the Apache Software Foundation (ASF) under one or more
|
||||
* contributor license agreements. See the NOTICE file distributed with
|
||||
* this work for additional information regarding copyright ownership.
|
||||
* The ASF licenses this file to You under the Apache License, Version 2.0
|
||||
* (the "License"); you may not use this file except in compliance with
|
||||
* the License. You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
// scalastyle:off println
|
||||
package org.apache.spark.examples.ml
|
||||
|
||||
import org.apache.spark.sql.SQLContext
|
||||
import org.apache.spark.{SparkConf, SparkContext}
|
||||
// $example on$
|
||||
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}
|
||||
// $example off$
|
||||
|
||||
object RandomForestRegressorExample {
|
||||
def main(args: Array[String]): Unit = {
|
||||
val conf = new SparkConf().setAppName("RandomForestRegressorExample")
|
||||
val sc = new SparkContext(conf)
|
||||
val sqlContext = new SQLContext(sc)
|
||||
|
||||
// $example on$
|
||||
// Load and parse the data file, converting it to a DataFrame.
|
||||
val data = sqlContext.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("Root Mean Squared Error (RMSE) on test data = " + rmse)
|
||||
|
||||
val rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel]
|
||||
println("Learned regression forest model:\n" + rfModel.toDebugString)
|
||||
// $example off$
|
||||
|
||||
sc.stop()
|
||||
}
|
||||
}
|
||||
// scalastyle:on println
|
||||
Loading…
Reference in a new issue