Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3637#discussion_r21498969
--- Diff: mllib/src/main/scala/org/apache/spark/ml/LabeledPoint.scala ---
@@ -0,0 +1,52 @@
+/*
+ * 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
+
+import scala.beans.BeanInfo
+
+import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.mllib.linalg.Vector
+
+/**
+ * :: AlphaComponent ::
+ * Class that represents an instance (data point) for prediction tasks.
+ *
+ * @param label Label to predict
+ * @param features List of features describing this instance
+ * @param weight Instance weight
+ */
+@AlphaComponent
+@BeanInfo
+case class LabeledPoint(label: Double, features: Vector, weight: Double) {
--- End diff --
Do you mean Vector would be replaced with Array[Feature] where Feature
would have subclasses like:
* ContinuousFeature(f: Double)
* CategoricalFeature(f: Int)
That loses a lot of the benefits of Vector (fewer Java Objects).
Or do you mean Vector would be replaced with Features, which has subclasses
like:
* ContinuousFeatures(f: Vector)
* CategoricalFeatures(f: Array[Int])
* MixedFeatures(contFeat: Vector, catFeat: Array[Int])
That would be reasonably efficient but would be a bit more awkward for both
developers (APIs + casting) and users (casting data loaded from elsewhere).
W.r.t. generic types, I agree it would be unusual to want more than real
values and categorical values, but I could imagine weak learning algorithms
specific to images or text which operate on special types. (On a related note,
I'm debating whether boosting and bagging should support this typed API at all.
They will need types for labels but not for features.)
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