Github user tomerk commented on a diff in the pull request: https://github.com/apache/spark/pull/3637#discussion_r22752063 --- 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 -- Why do we need to override copy here?
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