Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/6757#discussion_r38370214
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tuning/SlidingWindowCrossValidator.scala
---
@@ -0,0 +1,261 @@
+/*
+ * 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.tuning
+
+import com.github.fommil.netlib.F2jBLAS
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml._
+import org.apache.spark.ml.evaluation.Evaluator
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.types.StructType
+
+/**
+ * Params for [[SlidingWindowCrossValidator]] and
[[SlidingWindowCrossValidatorModel]].
+ */
+private[ml] trait SlidingWindowCrossValidatorParams extends Params {
+
+ /**
+ * param for the estimator to be cross-validated
+ * @group param
+ */
+ val estimator: Param[Estimator[_]] = new Param(this, "estimator",
"estimator for selection")
+
+ /** @group getParam */
+ def getEstimator: Estimator[_] = $(estimator)
+
+ /**
+ * param for estimator param maps
+ * @group param
+ */
+ val estimatorParamMaps: Param[Array[ParamMap]] =
+ new Param(this, "estimatorParamMaps", "param maps for the estimator")
+
+ /** @group getParam */
+ def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps)
+
+ /**
+ * param for the evaluator used to select hyper-parameters that maximize
the cross-validated
+ * metric
+ * @group param
+ */
+ val evaluator: Param[Evaluator] = new Param(this, "evaluator",
+ "evaluator used to select hyper-parameters that maximize the
cross-validated metric")
+
+ /** @group getParam */
+ def getEvaluator: Evaluator = $(evaluator)
+
+ /**
+ * Param for number of folds for cross validation. Must be >= 2.
+ * Default: 3
+ * @group param
+ */
+ val numFolds: IntParam = new IntParam(this, "numFolds",
+ "number of folds for cross validation (>= 2)", ParamValidators.gtEq(2))
+
+ /** @group getParam */
+ def getNumFolds: Int = $(numFolds)
+
+ setDefault(numFolds -> 3)
+
+ /**
+ * Param for the cutoff index (exclusive) for the first training data.
Must be > 0.
+ * Default: 1
+ * @group param
+ */
+ val firstCutoffIndex: LongParam = new LongParam(this, "firstCutoffIndex",
+ "cutoff index (exclusive) for the first training data",
ParamValidators.gt(0))
+
+ setDefault(firstCutoffIndex -> 1L)
+
+ /** @group getParam */
+ def getFirstCutoffIndex: Long = $(firstCutoffIndex)
+
+ /**
+ * Param for the size of the validation data window. Must be > 0.
+ * Default: 1
+ * @group param
+ */
+ val windowSize: LongParam = new LongParam(this, "windowSize",
+ "size of the validation data window", ParamValidators.gt(0))
+
+ setDefault(windowSize -> 1L)
+
+ /** @group getParam */
+ def getWindowSize: Long = $(windowSize)
+
+ /**
+ * Param for index column name.
+ * Default: "index"
+ * @group param
+ */
+ val indexCol: Param[String] = new Param[String](this, "indexCol", "index
column name")
+
+ setDefault(indexCol, "index")
+
+ /** @group getParam */
+ def getIndexCol: String = $(indexCol)
+}
+
+private [tuning] object SlidingWindowCrossValidator {
+ def split(dataset: DataFrame, firstCutoffIndex: Long, windowSize: Long,
numFolds: Int,
+ indexCol: String): Array[(DataFrame, DataFrame)] = {
+ (0 until numFolds).map { idx =>
+ val trainingUntil = firstCutoffIndex + idx * windowSize
+ val validationUntil = firstCutoffIndex + (idx + 1) * windowSize
+ val trainingSet = dataset.filter(dataset(indexCol) < trainingUntil)
+ val validationSet = dataset.filter(
+ dataset(indexCol) >= trainingUntil && dataset(indexCol) <
validationUntil)
+ (trainingSet, validationSet)
+ }
+ .toArray
+ }
+}
+
+
+/**
+ * :: Experimental ::
+ * Sliding window cross validation.
+ *
+ * [[SlidingWindowCrossValidator]] provides a cross-validation method by
splitting the data set by
+ * a cutoff index. The cutoff index is used to separate training and
validation data set. Rows
+ * smaller than the cutoff index go to the training set and rows larger
than it go to the validation
+ * set. This splitting method is useful for time-sensitive problem like
fraud detection and stock
+ * price prediction.
+ *
+ * 3 parameters govern the splitting: numFolds is the number of splits,
firstCutoffIndex denotes the
+ * the cutoff index of the first training data, and windowSize defines the
index range of a
+ * validation set. In particular, training data is constructed with index
range in [-inf, firstCutoffIndex
+ * + i * windowSize) and validation data is constructed with index range
in [firstCutoffIndex +
+ * i * windowSize, firstCutoffIndex + (i + 1) * windowSize).
+ */
+@Experimental
+class SlidingWindowCrossValidator(override val uid: String)
+ extends Estimator[SlidingWindowCrossValidatorModel]
+ with SlidingWindowCrossValidatorParams with Logging {
+
+ def this() = this(Identifiable.randomUID("swcv"))
+
+ private val f2jBLAS = new F2jBLAS
+
+ /** @group setParam */
+ def setEstimator(value: Estimator[_]): this.type = set(estimator, value)
+
+ /** @group setParam */
+ def setEstimatorParamMaps(value: Array[ParamMap]): this.type =
set(estimatorParamMaps, value)
+
+ /** @group setParam */
+ def setEvaluator(value: Evaluator): this.type = set(evaluator, value)
+
+ /** @group setParam */
+ def setNumFolds(value: Int): this.type = set(numFolds, value)
+
+ /** @group setParam */
+ def setFirstCutoffIndex(value: Long): this.type = set(firstCutoffIndex,
value)
+
+ /** @group setParam */
+ def setWindowSize(value: Long): this.type = set(windowSize, value)
+
+ /** @group setParam */
+ def setIndexCol(value: String): this.type = set(indexCol, value)
+
+ override def fit(dataset: DataFrame): SlidingWindowCrossValidatorModel =
{
--- End diff --
There is a lot of duplication between this and `CrossValidator`; perhaps we
can think about subclassing here (i.e. defining a splitting method which
defaults to `MLUtils.kFold` in `CrossValidator` and is overridden by
`SlidingWindowCrossValidator.split` here)
---
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 [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
