Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6039#discussion_r32689854
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala ---
@@ -0,0 +1,171 @@
+/*
+ * 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.AlphaComponent
+import org.apache.spark.ml._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
+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 expected 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 expected feature range")
+}
+
+/**
+ * :: AlphaComponent ::
+ * 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
+ */
+@AlphaComponent
--- End diff --
Please change this and other AlphaComponent annotations to be Experimental
instead.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
