Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3099#discussion_r20113791
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -0,0 +1,139 @@
+/*
+ * 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.ml.classification
+
+import org.apache.spark.ml._
+import org.apache.spark.ml.param._
+import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
+import org.apache.spark.mllib.linalg.{BLAS, Vector, VectorUDT}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.Star
+import org.apache.spark.sql.catalyst.dsl._
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * Params for logistic regression.
+ */
+private[classification] trait LogisticRegressionParams extends Params
+ with HasRegParam with HasMaxIter with HasLabelCol with HasThreshold with
HasFeaturesCol
+ with HasScoreCol with HasPredictionCol
+
+/**
+ * Logistic regression.
+ */
+class LogisticRegression extends Estimator[LogisticRegressionModel] with
LogisticRegressionParams {
+
+ setRegParam(0.1)
+ setMaxIter(100)
+ setThreshold(0.5)
+
+ def setRegParam(value: Double): this.type = { set(regParam, value); this
}
+ def setMaxIter(value: Int): this.type = { set(maxIter, value); this }
+ def setLabelCol(value: String): this.type = { set(labelCol, value); this
}
+ def setThreshold(value: Double): this.type = { set(threshold, value);
this }
+ def setFeaturesCol(value: String): this.type = { set(featuresCol,
value); this }
+ def setScoreCol(value: String): this.type = { set(scoreCol, value); this
}
+ def setPredictionCol(value: String): this.type = { set(predictionCol,
value); this }
+
+ override def fit(dataset: SchemaRDD, paramMap: ParamMap):
LogisticRegressionModel = {
+ transform(dataset.schema, paramMap, logging = true)
+ import dataset.sqlContext._
+ val map = this.paramMap ++ paramMap
+ val instances = dataset.select(map(labelCol).attr,
map(featuresCol).attr)
+ .map { case Row(label: Double, features: Vector) =>
+ LabeledPoint(label, features)
+ }.persist(StorageLevel.MEMORY_AND_DISK)
+ val lr = new LogisticRegressionWithLBFGS
+ lr.optimizer
+ .setRegParam(map(regParam))
+ .setNumIterations(map(maxIter))
+ val lrm = new LogisticRegressionModel(this, map,
lr.run(instances).weights)
+ instances.unpersist()
+ // copy model params
+ Params.copyValues(this, lrm)
+ lrm
+ }
+
+ override def transform(schema: StructType, paramMap: ParamMap):
StructType = {
+ val map = this.paramMap ++ paramMap
+ val featuresType = schema(map(featuresCol)).dataType
+ // TODO: Support casting Array[Double] and Array[Float] to Vector.
+ require(featuresType.isInstanceOf[VectorUDT],
+ s"Features column ${map(featuresCol)} must be a vector column but
got $featuresType.")
+ val labelType = schema(map(labelCol)).dataType
+ require(labelType == DoubleType,
+ s"Cannot convert label column ${map(labelCol)} of type $labelType to
a double column.")
+ val fieldNames = schema.fieldNames
+ require(!fieldNames.contains(map(scoreCol)), s"Score column
${map(scoreCol)} already exists.")
+ require(!fieldNames.contains(map(predictionCol)),
+ s"Prediction column ${map(predictionCol)} already exists.")
+ val outputFields = schema.fields ++ Seq(
+ StructField(map(scoreCol), DoubleType, false),
+ StructField(map(predictionCol), DoubleType, false))
+ StructType(outputFields)
+ }
+}
+
+/**
+ * Model produced by [[LogisticRegression]].
+ */
+class LogisticRegressionModel private[ml] (
+ override val parent: LogisticRegression,
+ override val fittingParamMap: ParamMap,
+ val weights: Vector) extends Model[LogisticRegressionModel] with
LogisticRegressionParams {
+
+ def setThreshold(value: Double): this.type = { set(threshold, value);
this }
+ def setFeaturesCol(value: String): this.type = { set(featuresCol,
value); this }
+ def setScoreCol(value: String): this.type = { set(scoreCol, value); this
}
+ def setPredictionCol(value: String): this.type = { set(predictionCol,
value); this }
+
+ override def transform(schema: StructType, paramMap: ParamMap):
StructType = {
+ val map = this.paramMap ++ paramMap
+ val featuresType = schema(map(featuresCol)).dataType
+ // TODO: Support casting Array[Double] and Array[Float] to Vector.
+ require(featuresType.isInstanceOf[VectorUDT],
+ s"Features column ${map(featuresCol)} must be a vector column but
got $featuresType.")
+ val fieldNames = schema.fieldNames
+ require(!fieldNames.contains(map(scoreCol)), s"Score column
${map(scoreCol)} already exists.")
+ require(!fieldNames.contains(map(predictionCol)),
+ s"Prediction column ${map(predictionCol)} already exists.")
+ val outputFields = schema.fields ++ Seq(
+ StructField(map(scoreCol), DoubleType, false),
+ StructField(map(predictionCol), DoubleType, false))
+ StructType(outputFields)
+ }
+
+ override def transform(dataset: SchemaRDD, paramMap: ParamMap):
SchemaRDD = {
+ transform(dataset.schema, paramMap, logging = true)
--- End diff --
Use "map" (from below) instead of paramMap?
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