Github user etrain commented on a diff in the pull request:
https://github.com/apache/spark/pull/3637#discussion_r22691031
--- Diff:
examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
---
@@ -0,0 +1,195 @@
+/*
+ * 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, SparkContext}
+import org.apache.spark.SparkContext._
+import org.apache.spark.ml.classification.{Classifier, ClassifierParams,
ClassificationModel}
+import org.apache.spark.ml.param.{Params, IntParam, ParamMap}
+import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors, VectorUDT}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.sql.{DataType, SchemaRDD, Row, SQLContext}
+
+/**
+ * A simple example demonstrating how to write your own learning algorithm
using Estimator,
+ * Transformer, and other abstractions.
+ * This mimics [[org.apache.spark.ml.classification.LogisticRegression]].
+ * Run with
+ * {{{
+ * bin/run-example ml.DeveloperApiExample
+ * }}}
+ */
+object DeveloperApiExample {
+
+ def main(args: Array[String]) {
+ val conf = new SparkConf().setAppName("DeveloperApiExample")
+ val sc = new SparkContext(conf)
+ val sqlContext = new SQLContext(sc)
+ import sqlContext._
+
+ // Prepare training data.
+ val training = sparkContext.parallelize(Seq(
+ LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
+ LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
+ LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
+ LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
+
+ // Create a LogisticRegression instance. This instance is an
Estimator.
+ val lr = new MyLogisticRegression()
+ // Print out the parameters, documentation, and any default values.
+ println("MyLogisticRegression 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.
+ val model = lr.fit(training)
+
+ // Prepare test data.
+ val test = sparkContext.parallelize(Seq(
+ LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
+ LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
+ LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))))
+
+ // Make predictions on test data.
+ val sumPredictions: Double = model.transform(test)
+ .select('features, 'label, 'prediction)
+ .collect()
+ .map { case Row(features: Vector, label: Double, prediction: Double)
=>
+ prediction
+ }.sum
+ assert(sumPredictions == 0.0,
+ "MyLogisticRegression predicted something other than 0, even though
all weights are 0!")
+ }
+}
+
+/**
+ * Example of defining a parameter trait for a user-defined type of
[[Classifier]].
+ *
+ * NOTE: This is private since it is an example. In practice, you may not
want it to be private.
+ */
+private trait MyLogisticRegressionParams extends ClassifierParams {
+
+ /** param for max number of iterations */
+ val maxIter: IntParam = new IntParam(this, "maxIter", "max number of
iterations")
+ def getMaxIter: Int = get(maxIter)
--- End diff --
I'm a little confused here - do I understand correctly that you need to
specify a getter by convention? Or is this just showing that you *can* specify
a getter?
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