[SPARK-13012] [Documentation] Replace example code in ml-guide.md using include_example

examples/src/main/java/org/apache/spark/examples/ml/JavaDocument.java

@@ -0,0 +1,43 @@
+/*
+ * 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 java.io.Serializable;
+
+/**
+ * Unlabeled instance type, Spark SQL can infer schema from Java Beans.
+ */
+@SuppressWarnings("serial")
+public class JavaDocument implements Serializable {
+
+  private long id;
+  private String text;
+
+  public JavaDocument(long id, String text) {
+    this.id = id;
+    this.text = text;
+  }
+
+  public long getId() {
+    return this.id;
+  }
+
+  public String getText() {
+    return this.text;
+  }
+}

examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java

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+/*
+ * 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;
+
+// $example on$
+import java.util.Arrays;
+// $example off$
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+// $example on$
+import org.apache.spark.ml.classification.LogisticRegression;
+import org.apache.spark.ml.classification.LogisticRegressionModel;
+import org.apache.spark.ml.param.ParamMap;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+// $example off$
+import org.apache.spark.sql.SQLContext;
+
+/**
+ * Java example for Estimator, Transformer, and Param.
+ */
+public class JavaEstimatorTransformerParamExample {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf()
+      .setAppName("JavaEstimatorTransformerParamExample");
+    SparkContext sc = new SparkContext(conf);
+    SQLContext sqlContext = new SQLContext(sc);
+
+    // $example on$
+    // Prepare training data.
+    // We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans into
+    // DataFrames, where it uses the bean metadata to infer the schema.
+    DataFrame training = sqlContext.createDataFrame(
+      Arrays.asList(
+        new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
+        new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
+        new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
+        new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))
+      ), LabeledPoint.class);
+
+    // Create a LogisticRegression instance. This instance is an Estimator.
+    LogisticRegression lr = new LogisticRegression();
+    // Print out the parameters, documentation, and any default values.
+    System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n");
+
+    // We may set parameters using setter methods.
+    lr.setMaxIter(10).setRegParam(0.01);
+
+    // Learn a LogisticRegression model. This uses the parameters stored in lr.
+    LogisticRegressionModel model1 = lr.fit(training);
+    // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
+    // we can view the parameters it used during fit().
+    // This prints the parameter (name: value) pairs, where names are unique IDs for this
+    // LogisticRegression instance.
+    System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap());
+
+    // We may alternatively specify parameters using a ParamMap.
+    ParamMap paramMap = new ParamMap()
+      .put(lr.maxIter().w(20))  // Specify 1 Param.
+      .put(lr.maxIter(), 30)  // This overwrites the original maxIter.
+      .put(lr.regParam().w(0.1), lr.threshold().w(0.55));  // Specify multiple Params.
+
+    // One can also combine ParamMaps.
+    ParamMap paramMap2 = new ParamMap()
+      .put(lr.probabilityCol().w("myProbability"));  // Change output column name
+    ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);
+
+    // Now learn a new model using the paramMapCombined parameters.
+    // paramMapCombined overrides all parameters set earlier via lr.set* methods.
+    LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
+    System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
+
+    // Prepare test documents.
+    DataFrame test = sqlContext.createDataFrame(Arrays.asList(
+      new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
+      new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
+      new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))
+    ), LabeledPoint.class);
+
+    // Make predictions on test documents using the Transformer.transform() method.
+    // LogisticRegression.transform will only use the 'features' column.
+    // Note that model2.transform() outputs a 'myProbability' column instead of the usual
+    // 'probability' column since we renamed the lr.probabilityCol parameter previously.
+    DataFrame results = model2.transform(test);
+    for (Row r : results.select("features", "label", "myProbability", "prediction").collect()) {
+      System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
+        + ", prediction=" + r.get(3));
+    }
+    // $example off$
+
+    sc.stop();
+  }
+}

examples/src/main/java/org/apache/spark/examples/ml/JavaLabeledDocument.java

@@ -0,0 +1,38 @@
+/*
+ * 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 java.io.Serializable;
+
+/**
+ * Labeled instance type, Spark SQL can infer schema from Java Beans.
+ */
+@SuppressWarnings("serial")
+public class JavaLabeledDocument extends JavaDocument implements Serializable {
+
+  private double label;
+
+  public JavaLabeledDocument(long id, String text, double label) {
+    super(id, text);
+    this.label = label;
+  }
+
+  public double getLabel() {
+    return this.label;
+  }
+}

examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java

@@ -0,0 +1,122 @@
+/*
+ * 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;
+
+// $example on$
+import java.util.Arrays;
+// $example off$
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+// $example on$
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.classification.LogisticRegression;
+import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
+import org.apache.spark.ml.feature.HashingTF;
+import org.apache.spark.ml.feature.Tokenizer;
+import org.apache.spark.ml.param.ParamMap;
+import org.apache.spark.ml.tuning.CrossValidator;
+import org.apache.spark.ml.tuning.CrossValidatorModel;
+import org.apache.spark.ml.tuning.ParamGridBuilder;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+// $example off$
+import org.apache.spark.sql.SQLContext;
+
+/**
+ * Java example for Model Selection via Cross Validation.
+ */
+public class JavaModelSelectionViaCrossValidationExample {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf()
+      .setAppName("JavaModelSelectionViaCrossValidationExample");
+    SparkContext sc = new SparkContext(conf);
+    SQLContext sqlContext = new SQLContext(sc);
+
+    // $example on$
+    // Prepare training documents, which are labeled.
+    DataFrame training = sqlContext.createDataFrame(Arrays.asList(
+      new JavaLabeledDocument(0L, "a b c d e spark", 1.0),
+      new JavaLabeledDocument(1L, "b d", 0.0),
+      new JavaLabeledDocument(2L,"spark f g h", 1.0),
+      new JavaLabeledDocument(3L, "hadoop mapreduce", 0.0),
+      new JavaLabeledDocument(4L, "b spark who", 1.0),
+      new JavaLabeledDocument(5L, "g d a y", 0.0),
+      new JavaLabeledDocument(6L, "spark fly", 1.0),
+      new JavaLabeledDocument(7L, "was mapreduce", 0.0),
+      new JavaLabeledDocument(8L, "e spark program", 1.0),
+      new JavaLabeledDocument(9L, "a e c l", 0.0),
+      new JavaLabeledDocument(10L, "spark compile", 1.0),
+      new JavaLabeledDocument(11L, "hadoop software", 0.0)
+    ), JavaLabeledDocument.class);
+
+    // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
+    Tokenizer tokenizer = new Tokenizer()
+      .setInputCol("text")
+      .setOutputCol("words");
+    HashingTF hashingTF = new HashingTF()
+      .setNumFeatures(1000)
+      .setInputCol(tokenizer.getOutputCol())
+      .setOutputCol("features");
+    LogisticRegression lr = new LogisticRegression()
+      .setMaxIter(10)
+      .setRegParam(0.01);
+    Pipeline pipeline = new Pipeline()
+      .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
+
+    // We use a ParamGridBuilder to construct a grid of parameters to search over.
+    // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
+    // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
+    ParamMap[] paramGrid = new ParamGridBuilder()
+      .addGrid(hashingTF.numFeatures(), new int[] {10, 100, 1000})
+      .addGrid(lr.regParam(), new double[] {0.1, 0.01})
+      .build();
+
+    // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
+    // This will allow us to jointly choose parameters for all Pipeline stages.
+    // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+    // Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
+    // is areaUnderROC.
+    CrossValidator cv = new CrossValidator()
+      .setEstimator(pipeline)
+      .setEvaluator(new BinaryClassificationEvaluator())
+      .setEstimatorParamMaps(paramGrid).setNumFolds(2);  // Use 3+ in practice
+
+    // Run cross-validation, and choose the best set of parameters.
+    CrossValidatorModel cvModel = cv.fit(training);
+
+    // Prepare test documents, which are unlabeled.
+    DataFrame test = sqlContext.createDataFrame(Arrays.asList(
+      new JavaDocument(4L, "spark i j k"),
+      new JavaDocument(5L, "l m n"),
+      new JavaDocument(6L, "mapreduce spark"),
+      new JavaDocument(7L, "apache hadoop")
+    ), JavaDocument.class);
+
+    // Make predictions on test documents. cvModel uses the best model found (lrModel).
+    DataFrame predictions = cvModel.transform(test);
+    for (Row r : predictions.select("id", "text", "probability", "prediction").collect()) {
+      System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
+        + ", prediction=" + r.get(3));
+    }
+    // $example off$
+
+    sc.stop();
+  }
+}