spark DataValidators 源码
spark DataValidators 代码
文件路径：/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala
/*
* 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.mllib.util
import org.apache.spark.annotation.Since
import org.apache.spark.internal.Logging
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.rdd.RDD
/**
* A collection of methods used to validate data before applying ML algorithms.
*/
@Since("0.8.0")
object DataValidators extends Logging {
/**
* Function to check if labels used for classification are either zero or one.
*
* @return True if labels are all zero or one, false otherwise.
*/
@Since("1.0.0")
val binaryLabelValidator: RDD[LabeledPoint] => Boolean = { data =>
val numInvalid = data.filter(x => x.label != 1.0 && x.label != 0.0).count()
if (numInvalid != 0) {
logError("Classification labels should be 0 or 1. Found " + numInvalid + " invalid labels")
}
numInvalid == 0
}
/**
* Function to check if labels used for k class multi-label classification are
* in the range of {0, 1, ..., k - 1}.
*
* @return True if labels are all in the range of {0, 1, ..., k-1}, false otherwise.
*/
@Since("1.3.0")
def multiLabelValidator(k: Int): RDD[LabeledPoint] => Boolean = { data =>
val numInvalid = data.filter(x =>
x.label - x.label.toInt != 0.0 || x.label < 0 || x.label > k - 1).count()
if (numInvalid != 0) {
logError("Classification labels should be in {0 to " + (k - 1) + "}. " +
"Found " + numInvalid + " invalid labels")
}
numInvalid == 0
}
}
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