Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3637#discussion_r22983383
--- 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)
+}
+
+/**
+ * Example of defining a type of [[Classifier]].
+ *
+ * NOTE: This is private since it is an example. In practice, you may not
want it to be private.
+ */
+private class MyLogisticRegression
+ extends Classifier[Vector, MyLogisticRegression,
MyLogisticRegressionModel]
+ with MyLogisticRegressionParams {
+
+ setMaxIter(100) // Initialize
+
+ def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+ override def fit(dataset: SchemaRDD, paramMap: ParamMap):
MyLogisticRegressionModel = {
+ // Check schema (types). This allows early failure before running the
algorithm.
+ transformSchema(dataset.schema, paramMap, logging = true)
+
+ // Extract columns from data using helper method.
+ val oldDataset = extractLabeledPoints(dataset, paramMap)
+
+ // Combine given parameters with the embedded parameters, where the
given paramMap overrides
+ // any embedded settings.
+ val map = this.paramMap ++ paramMap
+
+ // Do learning to estimate the weight vector.
+ val numFeatures = oldDataset.take(1)(0).features.size
+ val weights = Vectors.zeros(numFeatures) // Learning would happen here.
+
+ // Create a model to return.
+ val lrm = new MyLogisticRegressionModel(this, map, weights)
+
+ // Copy model params.
+ // An Estimator stores the parameters for the Model it produces, and
this copies any relevant
+ // parameters to the model.
+ Params.inheritValues(map, this, lrm)
+
+ // Return the learned model.
+ lrm
+ }
+
+ /**
+ * Returns the SQL DataType corresponding to the FeaturesType type
parameter.
+ * This is used by [[ClassifierParams.validateAndTransformSchema()]] to
check the input data.
+ */
+ override protected def featuresDataType: DataType = new VectorUDT
+}
+
+/**
+ * Example of defining a type of [[ClassificationModel]].
+ *
+ * NOTE: This is private since it is an example. In practice, you may not
want it to be private.
+ */
+private class MyLogisticRegressionModel(
+ override val parent: MyLogisticRegression,
+ override val fittingParamMap: ParamMap,
+ val weights: Vector)
+ extends ClassificationModel[Vector, MyLogisticRegressionModel]
+ with MyLogisticRegressionParams {
+
+ // 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.
+ */
+ override protected def predictRaw(features: Vector): Vector = {
+ val margin = BLAS.dot(features, weights)
+ // There are 2 classes (binary classification), so we return a
length-2 vector,
+ // where index i corresponds to class i (i = 0, 1).
+ Vectors.dense(-margin, margin)
+ }
+
+ /** Number of classes the label can take. 2 indicates binary
classification. */
+ override val numClasses: Int = 2
+
+ /**
+ * Create a copy of the model.
+ * The copy is shallow, except for the embedded paramMap, which gets a
deep copy.
+ *
+ * This is used for the defaul implementation of [[transform()]].
+ */
+ override protected def copy(): MyLogisticRegressionModel = {
--- End diff --
copy() is used by the default implementation of transform() in Predictor.
Someone can call transform() and pass in new model parameters (such as
threshold for LogisticRegression). We want to call Predictor.predict() using
these new parameters, but we don't want to modify the original instance of the
model.
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