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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.
*/
// scalastyle:off println
package org.apache.spark.examples.ml
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.classification.{ClassificationModel, Classifier, ClassifierParams}
import org.apache.spark.ml.param.{IntParam, ParamMap}
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.sql.{DataFrame, Dataset, 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.implicits._
// Prepare training data.
val training = sc.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.toDF())
// Prepare test data.
val test = sc.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.toDF())
.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 coefficients are 0!")
sc.stop()
}
}
/**
* 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
*
* NOTE: The usual way to add a parameter to a model or algorithm is to include:
* - val myParamName: ParamType
* - def getMyParamName
* - def setMyParamName
* Here, we have a trait to be mixed in with the Estimator and Model (MyLogisticRegression
* and MyLogisticRegressionModel). We place the setter (setMaxIter) method in the Estimator
* class since the maxIter parameter is only used during training (not in the Model).
*/
val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations")
def getMaxIter: Int = $(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(override val uid: String)
extends Classifier[Vector, MyLogisticRegression, MyLogisticRegressionModel]
with MyLogisticRegressionParams {
def this() = this(Identifiable.randomUID("myLogReg"))
setMaxIter(100) // Initialize
// The parameter setter is in this class since it should return type MyLogisticRegression.
def setMaxIter(value: Int): this.type = set(maxIter, value)
// This method is used by fit()
override protected def train(dataset: Dataset[_]): MyLogisticRegressionModel = {
// Extract columns from data using helper method.
val oldDataset = extractLabeledPoints(dataset)
// Do learning to estimate the coefficients vector.
val numFeatures = oldDataset.take(1)(0).features.size
val coefficients = Vectors.zeros(numFeatures) // Learning would happen here.
// Create a model, and return it.
new MyLogisticRegressionModel(uid, coefficients).setParent(this)
}
override def copy(extra: ParamMap): MyLogisticRegression = defaultCopy(extra)
}
/**
* 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 uid: String,
val coefficients: 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, coefficients)
// 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
/** Number of features the model was trained on. */
override val numFeatures: Int = coefficients.size
/**
* 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 default implementation of [[transform()]].
*/
override def copy(extra: ParamMap): MyLogisticRegressionModel = {
copyValues(new MyLogisticRegressionModel(uid, coefficients), extra).setParent(parent)
}
}
// scalastyle:on println
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