Github user hhbyyh commented on a diff in the pull request:
https://github.com/apache/spark/pull/6039#discussion_r32949758
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala ---
@@ -0,0 +1,166 @@
+/*
+ * 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.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.param.{DoubleParam, Params}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
+import org.apache.spark.mllib.stat.Statistics
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{StructField, StructType}
+
+/**
+ * Params for [[MinMaxScaler]] and [[MinMaxScalerModel]].
+ */
+private[feature] trait MinMaxScalerParams extends Params with HasInputCol
with HasOutputCol {
+
+ /**
+ * lower bound after transformation, shared by all features
+ * Default: 0.0
+ * @group param
+ */
+ val min: DoubleParam = new DoubleParam(this, "min",
+ "lower bound of the output feature range")
+
+ /**
+ * upper bound after transformation, shared by all features
+ * Default: 1.0
+ * @group param
+ */
+ val max: DoubleParam = new DoubleParam(this, "max",
+ "upper bound of the output feature range")
+
+ /** Validates and transforms the input schema. */
+ protected def validateAndTransformSchema(schema: StructType): StructType
= {
+ val inputType = schema($(inputCol)).dataType
+ require(inputType.isInstanceOf[VectorUDT],
+ s"Input column ${$(inputCol)} must be a vector column")
+ require(!schema.fieldNames.contains($(outputCol)),
+ s"Output column ${$(outputCol)} already exists.")
+ val outputFields = schema.fields :+ StructField($(outputCol), new
VectorUDT, false)
+ StructType(outputFields)
+ }
+}
+
+/**
+ * :: Experimental ::
+ * Rescale each feature individually to a common range [min, max] linearly
using column summary
+ * statistics, which is also known as min-max normalization or Rescaling.
The rescaled value for
+ * feature E is calculated as,
+ *
+ * Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) +
min
+ *
+ * For the case E_{max} == E_{min}, Rescaled(e_i) = 0.5 * (max + min)
+ */
+@Experimental
+class MinMaxScaler(override val uid: String)
+ extends Estimator[MinMaxScalerModel] with MinMaxScalerParams {
+
+ def this() = this(Identifiable.randomUID("minMaxScal"))
+
+ setDefault(min -> 0.0, max -> 1.0)
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ /** @group setParam */
+ def setMin(value: Double): this.type = set(min, value)
+
+ /** @group setParam */
+ def setMax(value: Double): this.type = set(max, value)
+
+ override def fit(dataset: DataFrame): MinMaxScalerModel = {
+ transformSchema(dataset.schema, logging = true)
+ val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v
}
+ val summary = Statistics.colStats(input)
+ copyValues(new MinMaxScalerModel(uid, summary.min,
summary.max).setParent(this))
+ }
+
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema)
+ }
+
+ override def validateParams(): Unit = {
+ require($(min) < $(max), s"The specified min(${$(min)}) is larger or
equal to max(${$(max)})")
+ }
+
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by [[MinMaxScaler]].
+ */
+@Experimental
+class MinMaxScalerModel private[ml] (
+ override val uid: String,
+ val originalMin: Vector,
+ val originalMax: Vector)
+ extends Model[MinMaxScalerModel] with MinMaxScalerParams {
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ /** @group setParam */
+ def setMin(value: Double): this.type = set(min, value)
+
+ /** @group setParam */
+ def setMax(value: Double): this.type = set(max, value)
+
+ override def validateParams(): Unit = {
+ require($(min) < $(max), s"The specified min(${$(min)}) is larger or
equal to max(${$(max)})")
+ }
+
+ override def transform(dataset: DataFrame): DataFrame = {
+ val outputSchema = transformSchema(dataset.schema, logging = true)
+
+ val originalRange = (originalMax.toBreeze -
originalMin.toBreeze).toArray
+ val minArray = originalMin.toArray
+
+ val reScale = udf { (vector: Vector) =>
+ val scale = $(max) - $(min)
+
+ // 0 in sparse vector will probably be rescaled to non-zero
+ val values = vector.toArray
+ val size = values.size
+ var i = 0
+ while (i < size) {
+ val raw = if (originalRange(i) != 0) (values(i) - minArray(i)) /
originalRange(i) else 0.5
+ values(i) = raw * scale + $(min)
+ i += 1
+ }
+ Vectors.dense(values)
+ }
+
+ val metadata = outputSchema($(outputCol)).metadata
--- End diff --
Not sure how to clean the metadata properly. I've made an update using
withColumn. Is that what's in your mind?
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