[SPARK-15605][ML][EXAMPLES] Fix broken ML JavaDeveloperApiExample.

## What changes were proposed in this pull request?
See [SPARK-15605](https://issues.apache.org/jira/browse/SPARK-15605) for the detail of this bug. This PR fix 2 major bugs in this example:
* The java example class use Param ```maxIter```, it will fail when calling ```Param.shouldOwn```. We need add a public method which return the ```maxIter``` Object. Because ```Params.params``` use java reflection to list all public method whose return type is ```Param```, and invoke them to get all defined param objects in the instance.
* The ```uid``` member defined in Java class will be initialized after Scala traits such as ```HasFeaturesCol```. So when ```HasFeaturesCol``` being constructed, they get ```uid``` with null, which will cause ```Param.shouldOwn``` check fail.

so, here is my changes:
* Add public method:
```public IntParam getMaxIterParam() {return maxIter;}```

* Use Java anonymous class overriding ```uid()``` to defined the ```uid```, and it solve the second problem described above.
* To make the ```getMaxIterParam ``` can be invoked using java reflection, we must make the two class (MyJavaLogisticRegression and MyJavaLogisticRegressionModel) public. so I make them become inner public static class.

## How was this patch tested?
Offline tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #13353 from yanboliang/spark-15605.

1 files changed, 0 insertions, 240 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
deleted file mode 100644
index 8971c0ac00..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
+++ /dev/null
@@ -1,240 +0,0 @@
-/*
- * 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.util.List;
-
-import com.google.common.collect.Lists;
-
-import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.ml.classification.Classifier;
-import org.apache.spark.ml.classification.ClassificationModel;
-import org.apache.spark.ml.feature.LabeledPoint;
-import org.apache.spark.ml.linalg.BLAS;
-import org.apache.spark.ml.linalg.Vector;
-import org.apache.spark.ml.linalg.Vectors;
-import org.apache.spark.ml.param.IntParam;
-import org.apache.spark.ml.param.ParamMap;
-import org.apache.spark.ml.util.Identifiable$;
-import org.apache.spark.sql.Dataset;
-import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SparkSession;
-
-
-/**
- * A simple example demonstrating how to write your own learning algorithm using Estimator,
- * Transformer, and other abstractions.
- * This mimics {@link org.apache.spark.ml.classification.LogisticRegression}.
- *
- * Run with
- * <pre>
- * bin/run-example ml.JavaDeveloperApiExample
- * </pre>
- */
-public class JavaDeveloperApiExample {
-
-  public static void main(String[] args) throws Exception {
-    SparkSession spark = SparkSession
-      .builder()
-      .appName("JavaDeveloperApiExample")
-      .getOrCreate();
-
-    // Prepare training data.
-    List<LabeledPoint> localTraining = Lists.newArrayList(
-        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)));
-    Dataset<Row> training = spark.createDataFrame(localTraining, LabeledPoint.class);
-
-    // Create a LogisticRegression instance. This instance is an Estimator.
-    MyJavaLogisticRegression lr = new MyJavaLogisticRegression();
-    // Print out the parameters, documentation, and any default values.
-    System.out.println("MyJavaLogisticRegression parameters:\n" + lr.explainParams() + "\n");
-
-    // We may set parameters using setter methods.
-    lr.setMaxIter(10);
-
-    // Learn a LogisticRegression model. This uses the parameters stored in lr.
-    MyJavaLogisticRegressionModel model = lr.fit(training);
-
-    // Prepare test data.
-    List<LabeledPoint> localTest = Lists.newArrayList(
-        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)));
-    Dataset<Row> test = spark.createDataFrame(localTest, LabeledPoint.class);
-
-    // Make predictions on test documents. cvModel uses the best model found (lrModel).
-    Dataset<Row> results = model.transform(test);
-    double sumPredictions = 0;
-    for (Row r : results.select("features", "label", "prediction").collectAsList()) {
-      sumPredictions += r.getDouble(2);
-    }
-    if (sumPredictions != 0.0) {
-      throw new Exception("MyJavaLogisticRegression predicted something other than 0," +
-          " even though all coefficients are 0!");
-    }
-
-    spark.stop();
-  }
-}
-
-/**
- * Example of defining a type of {@link Classifier}.
- *
- * Note: Some IDEs (e.g., IntelliJ) will complain that this will not compile due to
- *       {@link org.apache.spark.ml.param.Params#set} using incompatible return types.
- *       However, this should still compile and run successfully.
- */
-class MyJavaLogisticRegression
-  extends Classifier<Vector, MyJavaLogisticRegression, MyJavaLogisticRegressionModel> {
-
-  MyJavaLogisticRegression() {
-    init();
-  }
-
-  MyJavaLogisticRegression(String uid) {
-    this.uid_ = uid;
-    init();
-  }
-
-  private String uid_ = Identifiable$.MODULE$.randomUID("myJavaLogReg");
-
-  @Override
-  public String uid() {
-    return uid_;
-  }
-
-  /**
-   * Param for max number of iterations
-   * <p>
-   * NOTE: The usual way to add a parameter to a model or algorithm is to include:
-   * - val myParamName: ParamType
-   * - def getMyParamName
-   * - def setMyParamName
-   */
-  IntParam maxIter = new IntParam(this, "maxIter", "max number of iterations");
-
-  int getMaxIter() { return (Integer) getOrDefault(maxIter); }
-
-  private void init() {
-    setMaxIter(100);
-  }
-
-  // The parameter setter is in this class since it should return type MyJavaLogisticRegression.
-  MyJavaLogisticRegression setMaxIter(int value) {
-    return (MyJavaLogisticRegression) set(maxIter, value);
-  }
-
-  // This method is used by fit().
-  // In Java, we have to make it public since Java does not understand Scala's protected modifier.
-  public MyJavaLogisticRegressionModel train(Dataset<?> dataset) {
-    // Extract columns from data using helper method.
-    JavaRDD<LabeledPoint> oldDataset = extractLabeledPoints(dataset).toJavaRDD();
-
-    // Do learning to estimate the coefficients vector.
-    int numFeatures = oldDataset.take(1).get(0).features().size();
-    Vector coefficients = Vectors.zeros(numFeatures); // Learning would happen here.
-
-    // Create a model, and return it.
-    return new MyJavaLogisticRegressionModel(uid(), coefficients).setParent(this);
-  }
-
-  @Override
-  public MyJavaLogisticRegression copy(ParamMap extra) {
-    return defaultCopy(extra);
-  }
-}
-
-/**
- * Example of defining a type of {@link ClassificationModel}.
- *
- * Note: Some IDEs (e.g., IntelliJ) will complain that this will not compile due to
- *       {@link org.apache.spark.ml.param.Params#set} using incompatible return types.
- *       However, this should still compile and run successfully.
- */
-class MyJavaLogisticRegressionModel
-  extends ClassificationModel<Vector, MyJavaLogisticRegressionModel> {
-
-  private Vector coefficients_;
-  public Vector coefficients() { return coefficients_; }
-
-  MyJavaLogisticRegressionModel(String uid, Vector coefficients) {
-    this.uid_ = uid;
-    this.coefficients_ = coefficients;
-  }
-
-  private String uid_ = Identifiable$.MODULE$.randomUID("myJavaLogReg");
-
-  @Override
-  public String uid() {
-    return uid_;
-  }
-
-  // This uses the default implementation of transform(), which reads column "features" and outputs
-  // columns "prediction" and "rawPrediction."
-
-  // This uses the default implementation of predict(), which chooses the label corresponding to
-  // the maximum value returned by [[predictRaw()]].
-
-  /**
-   * Raw prediction for each possible label.
-   * The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives
-   * a measure of confidence in each possible label (where larger = more confident).
-   * This internal method is used to implement [[transform()]] and output [[rawPredictionCol]].
-   *
-   * @return vector where element i is the raw prediction for label i.
-   * This raw prediction may be any real number, where a larger value indicates greater
-   * confidence for that label.
-   *
-   * In Java, we have to make this method public since Java does not understand Scala's protected
-   * modifier.
-   */
-  public Vector predictRaw(Vector features) {
-    double margin = BLAS.dot(features, coefficients_);
-    // There are 2 classes (binary classification), so we return a length-2 vector,
-    // where index i corresponds to class i (i = 0, 1).
-    return Vectors.dense(-margin, margin);
-  }
-
-  /**
-   * Number of classes the label can take. 2 indicates binary classification.
-   */
-  public int numClasses() { return 2; }
-
-  /**
-   * Number of features the model was trained on.
-   */
-  public int numFeatures() { return coefficients_.size(); }
-
-  /**
-   * Create a copy of the model.
-   * The copy is shallow, except for the embedded paramMap, which gets a deep copy.
-   * <p>
-   * This is used for the default implementation of [[transform()]].
-   *
-   * In Java, we have to make this method public since Java does not understand Scala's protected
-   * modifier.
-   */
-  @Override
-  public MyJavaLogisticRegressionModel copy(ParamMap extra) {
-    return copyValues(new MyJavaLogisticRegressionModel(uid(), coefficients_), extra)
-      .setParent(parent());
-  }
-}