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https://issues.apache.org/jira/browse/FLINK-2050?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14553896#comment-14553896
]
ASF GitHub Bot commented on FLINK-2050:
---------------------------------------
Github user thvasilo commented on a diff in the pull request:
https://github.com/apache/flink/pull/704#discussion_r30783065
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/preprocessing/StandardScaler.scala
---
@@ -22,38 +22,47 @@ import breeze.linalg
import breeze.numerics.sqrt
import breeze.numerics.sqrt._
import org.apache.flink.api.common.functions._
+import org.apache.flink.api.common.typeinfo.TypeInformation
import org.apache.flink.api.scala._
import org.apache.flink.configuration.Configuration
-import org.apache.flink.ml.common.{Parameter, ParameterMap, Transformer}
+import org.apache.flink.ml.common.{LabeledVector, Parameter, ParameterMap}
import org.apache.flink.ml.math.Breeze._
-import org.apache.flink.ml.math.Vector
+import org.apache.flink.ml.math.{BreezeVectorConverter, Vector}
+import org.apache.flink.ml.pipeline.{TransformOperation, FitOperation,
Transformer}
import org.apache.flink.ml.preprocessing.StandardScaler.{Mean, Std}
+import scala.reflect.ClassTag
+
/** Scales observations, so that all features have a user-specified mean
and standard deviation.
* By default for [[StandardScaler]] transformer mean=0.0 and std=1.0.
*
- * This transformer takes a [[Vector]] of values and maps it to a
- * scaled [[Vector]] such that each feature has a user-specified mean and
standard deviation.
+ * This transformer takes a subtype of [[Vector]] of values and maps it
to a
+ * scaled subtype of [[Vector]] such that each feature has a
user-specified mean and standard
+ * deviation.
*
* This transformer can be prepended to all [[Transformer]] and
- * [[org.apache.flink.ml.common.Learner]] implementations which expect an
input of
- * [[Vector]].
+ * [[org.apache.flink.ml.pipeline.Predictor]] implementations which
expect as input a subtype
+ * of [[Vector]].
*
* @example
* {{{
* val trainingDS: DataSet[Vector] = env.fromCollection(data)
* val transformer = StandardScaler().setMean(10.0).setStd(2.0)
*
- * transformer.transform(trainingDS)
+ * transformer.fit(trainingDS)
+ * val transformedDS = transformer.transform(trainingDS)
* }}}
*
* =Parameters=
*
- * - [[StandardScaler.Mean]]: The mean value of transformed data set; by
default equal to 0
- * - [[StandardScaler.Std]]: The standard deviation of the transformed
data set; by default
+ * - [[Mean]]: The mean value of transformed data set; by default equal
to 0
+ * - [[Std]]: The standard deviation of the transformed data set; by
default
* equal to 1
--- End diff --
Why use just the top-level type here, but the fully qualified one in the
ALS docstring?
> Add pipelining mechanism for chainable transformers and estimators
> ------------------------------------------------------------------
>
> Key: FLINK-2050
> URL: https://issues.apache.org/jira/browse/FLINK-2050
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Till Rohrmann
> Labels: ML
> Fix For: 0.9
>
>
> The key concept of an easy to use ML library is the quick and simple
> construction of data analysis pipelines. Scikit-learn's approach to define
> transformers and estimators seems to be a really good solution to this
> problem. I propose to follow a similar path, because it makes FlinkML
> flexible in terms of code reuse as well as easy for people coming from
> Scikit-learn to use the FlinkML.
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