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author | Joseph K. Bradley <joseph@databricks.com> | 2015-08-12 14:27:13 -0700 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-08-12 14:27:13 -0700 |
commit | 551def5d6972440365bd7436d484a67138d9a8f3 (patch) | |
tree | af2280c3849497b4236099ec84fe7b4b64d63f2e /mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala | |
parent | 762bacc16ac5e74c8b05a7c1e3e367d1d1633cef (diff) | |
download | spark-551def5d6972440365bd7436d484a67138d9a8f3.tar.gz spark-551def5d6972440365bd7436d484a67138d9a8f3.tar.bz2 spark-551def5d6972440365bd7436d484a67138d9a8f3.zip |
[SPARK-9789] [ML] Added logreg threshold param back
Reinstated LogisticRegression.threshold Param for binary compatibility. Param thresholds overrides threshold, if set.
CC: mengxr dbtsai feynmanliang
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #8079 from jkbradley/logreg-reinstate-threshold.
Diffstat (limited to 'mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala | 127 |
1 files changed, 104 insertions, 23 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index f55134d258..5bcd7117b6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -34,8 +34,7 @@ import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{DataFrame, Row, SQLContext} -import org.apache.spark.sql.functions.{col, udf} +import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.storage.StorageLevel /** @@ -43,44 +42,115 @@ import org.apache.spark.storage.StorageLevel */ private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams with HasRegParam with HasElasticNetParam with HasMaxIter with HasFitIntercept with HasTol - with HasStandardization { + with HasStandardization with HasThreshold { /** - * Version of setThresholds() for binary classification, available for backwards - * compatibility. + * Set threshold in binary classification, in range [0, 1]. * - * Calling this with threshold p will effectively call `setThresholds(Array(1-p, p))`. + * If the estimated probability of class label 1 is > threshold, then predict 1, else 0. + * A high threshold encourages the model to predict 0 more often; + * a low threshold encourages the model to predict 1 more often. + * + * Note: Calling this with threshold p is equivalent to calling `setThresholds(Array(1-p, p))`. + * When [[setThreshold()]] is called, any user-set value for [[thresholds]] will be cleared. + * If both [[threshold]] and [[thresholds]] are set in a ParamMap, then they must be + * equivalent. + * + * Default is 0.5. + * @group setParam + */ + def setThreshold(value: Double): this.type = { + if (isSet(thresholds)) clear(thresholds) + set(threshold, value) + } + + /** + * Get threshold for binary classification. + * + * If [[threshold]] is set, returns that value. + * Otherwise, if [[thresholds]] is set with length 2 (i.e., binary classification), + * this returns the equivalent threshold: {{{1 / (1 + thresholds(0) / thresholds(1))}}}. + * Otherwise, returns [[threshold]] default value. + * + * @group getParam + * @throws IllegalArgumentException if [[thresholds]] is set to an array of length other than 2. + */ + override def getThreshold: Double = { + checkThresholdConsistency() + if (isSet(thresholds)) { + val ts = $(thresholds) + require(ts.length == 2, "Logistic Regression getThreshold only applies to" + + " binary classification, but thresholds has length != 2. thresholds: " + ts.mkString(",")) + 1.0 / (1.0 + ts(0) / ts(1)) + } else { + $(threshold) + } + } + + /** + * Set thresholds in multiclass (or binary) classification to adjust the probability of + * predicting each class. Array must have length equal to the number of classes, with values >= 0. + * The class with largest value p/t is predicted, where p is the original probability of that + * class and t is the class' threshold. + * + * Note: When [[setThresholds()]] is called, any user-set value for [[threshold]] will be cleared. + * If both [[threshold]] and [[thresholds]] are set in a ParamMap, then they must be + * equivalent. * - * Default is effectively 0.5. * @group setParam */ - def setThreshold(value: Double): this.type = set(thresholds, Array(1.0 - value, value)) + def setThresholds(value: Array[Double]): this.type = { + if (isSet(threshold)) clear(threshold) + set(thresholds, value) + } /** - * Version of [[getThresholds()]] for binary classification, available for backwards - * compatibility. + * Get thresholds for binary or multiclass classification. + * + * If [[thresholds]] is set, return its value. + * Otherwise, if [[threshold]] is set, return the equivalent thresholds for binary + * classification: (1-threshold, threshold). + * If neither are set, throw an exception. * - * Param thresholds must have length 2 (or not be specified). - * This returns {{{1 / (1 + thresholds(0) / thresholds(1))}}}. * @group getParam */ - def getThreshold: Double = { - if (isDefined(thresholds)) { - val thresholdValues = $(thresholds) - assert(thresholdValues.length == 2, "Logistic Regression getThreshold only applies to" + - " binary classification, but thresholds has length != 2." + - s" thresholds: ${thresholdValues.mkString(",")}") - 1.0 / (1.0 + thresholdValues(0) / thresholdValues(1)) + override def getThresholds: Array[Double] = { + checkThresholdConsistency() + if (!isSet(thresholds) && isSet(threshold)) { + val t = $(threshold) + Array(1-t, t) } else { - 0.5 + $(thresholds) + } + } + + /** + * If [[threshold]] and [[thresholds]] are both set, ensures they are consistent. + * @throws IllegalArgumentException if [[threshold]] and [[thresholds]] are not equivalent + */ + protected def checkThresholdConsistency(): Unit = { + if (isSet(threshold) && isSet(thresholds)) { + val ts = $(thresholds) + require(ts.length == 2, "Logistic Regression found inconsistent values for threshold and" + + s" thresholds. Param threshold is set (${$(threshold)}), indicating binary" + + s" classification, but Param thresholds is set with length ${ts.length}." + + " Clear one Param value to fix this problem.") + val t = 1.0 / (1.0 + ts(0) / ts(1)) + require(math.abs($(threshold) - t) < 1E-5, "Logistic Regression getThreshold found" + + s" inconsistent values for threshold (${$(threshold)}) and thresholds (equivalent to $t)") } } + + override def validateParams(): Unit = { + checkThresholdConsistency() + } } /** * :: Experimental :: * Logistic regression. - * Currently, this class only supports binary classification. + * Currently, this class only supports binary classification. It will support multiclass + * in the future. */ @Experimental class LogisticRegression(override val uid: String) @@ -128,7 +198,7 @@ class LogisticRegression(override val uid: String) * Whether to fit an intercept term. * Default is true. * @group setParam - * */ + */ def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value) setDefault(fitIntercept -> true) @@ -140,7 +210,7 @@ class LogisticRegression(override val uid: String) * is applied. In R's GLMNET package, the default behavior is true as well. * Default is true. * @group setParam - * */ + */ def setStandardization(value: Boolean): this.type = set(standardization, value) setDefault(standardization -> true) @@ -148,6 +218,10 @@ class LogisticRegression(override val uid: String) override def getThreshold: Double = super.getThreshold + override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value) + + override def getThresholds: Array[Double] = super.getThresholds + override protected def train(dataset: DataFrame): LogisticRegressionModel = { // Extract columns from data. If dataset is persisted, do not persist oldDataset. val instances = extractLabeledPoints(dataset).map { @@ -314,6 +388,10 @@ class LogisticRegressionModel private[ml] ( override def getThreshold: Double = super.getThreshold + override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value) + + override def getThresholds: Array[Double] = super.getThresholds + /** Margin (rawPrediction) for class label 1. For binary classification only. */ private val margin: Vector => Double = (features) => { BLAS.dot(features, weights) + intercept @@ -364,6 +442,7 @@ class LogisticRegressionModel private[ml] ( * The behavior of this can be adjusted using [[thresholds]]. */ override protected def predict(features: Vector): Double = { + // Note: We should use getThreshold instead of $(threshold) since getThreshold is overridden. if (score(features) > getThreshold) 1 else 0 } @@ -393,6 +472,7 @@ class LogisticRegressionModel private[ml] ( } override protected def raw2prediction(rawPrediction: Vector): Double = { + // Note: We should use getThreshold instead of $(threshold) since getThreshold is overridden. val t = getThreshold val rawThreshold = if (t == 0.0) { Double.NegativeInfinity @@ -405,6 +485,7 @@ class LogisticRegressionModel private[ml] ( } override protected def probability2prediction(probability: Vector): Double = { + // Note: We should use getThreshold instead of $(threshold) since getThreshold is overridden. if (probability(1) > getThreshold) 1 else 0 } } |