spark LibSVMDataSource 源码
spark LibSVMDataSource 代码
文件路径：/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMDataSource.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.ml.source.libsvm
/**
* `libsvm` package implements Spark SQL data source API for loading LIBSVM data as `DataFrame`.
* The loaded `DataFrame` has two columns: `label` containing labels stored as doubles and
* `features` containing feature vectors stored as `Vector`s.
*
* To use LIBSVM data source, you need to set "libsvm" as the format in `DataFrameReader` and
* optionally specify options, for example:
* {{{
* // Scala
* val df = spark.read.format("libsvm")
* .option("numFeatures", "780")
* .load("data/mllib/sample_libsvm_data.txt")
*
* // Java
* Dataset<Row> df = spark.read().format("libsvm")
* .option("numFeatures, "780")
* .load("data/mllib/sample_libsvm_data.txt");
* }}}
*
* LIBSVM data source supports the following options:
* - "numFeatures": number of features.
* If unspecified or nonpositive, the number of features will be determined automatically at the
* cost of one additional pass.
* This is also useful when the dataset is already split into multiple files and you want to load
* them separately, because some features may not present in certain files, which leads to
* inconsistent feature dimensions.
* - "vectorType": feature vector type, "sparse" (default) or "dense".
*
* @note This class is public for documentation purpose. Please don't use this class directly.
* Rather, use the data source API as illustrated above.
*
* @see <a href="https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/">LIBSVM datasets</a>
*/
class LibSVMDataSource private() {}
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