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From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-10077][DOCS][ML] Add package info for j...
Date Wed, 16 Sep 2015 11:38:42 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/8740#discussion_r39618817
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/package-info.java ---
    @@ -0,0 +1,109 @@
    +/*
    + * 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.
    + */
    +
    +
    +/**
    + * Feature transformers
    + *
    + * The `ml.feature` package provides common feature transformers that help convert raw
data or
    + * features into more suitable forms for model fitting.
    + * Most feature transformers are implemented as {@link org.apache.spark.ml.Transformer}s,
which
    + * transforms one {@link org.apache.spark.sql.DataFrame} into another, e.g.,
    + * {@link org.apache.spark.feature.HashingTF}.
    + * Some feature transformers are implemented as {@link org.apache.spark.ml.Estimator}}s,
because the
    + * transformation requires some aggregated information of the dataset, e.g., document
    + * frequencies in {@link org.apache.spark.ml.feature.IDF}.
    + * For those feature transformers, calling {@link org.apache.spark.ml.Estimator#fit}
is required to
    + * obtain the model first, e.g., {@link org.apache.spark.ml.feature.IDFModel}, in order
to apply
    + * transformation.
    + * The transformation is usually done by appending new columns to the input
    + * {@link org.apache.spark.sql.DataFrame}, so all input columns are carried over.
    + *
    + * We try to make each transformer minimal, so it becomes flexible to assemble feature
    + * transformation pipelines.
    + * {@link org.apache.spark.ml.Pipeline} can be used to chain feature transformers, and
    + * {@link org.apache.spark.ml.feature.VectorAssembler} can be used to combine multiple
feature
    + * transformations, for example:
    + *
    + * <pre>
    + * <code>
    + *   import java.util.Arrays;
    + *
    + *   import org.apache.spark.api.java.JavaRDD;
    + *   import static org.apache.spark.sql.types.DataTypes.*;
    + *   import org.apache.spark.sql.types.StructType;
    + *   import org.apache.spark.sql.types.StructField;
    + *   import org.apache.spark.sql.DataFrame;
    + *   import org.apache.spark.sql.RowFactory;
    + *   import org.apache.spark.sql.Row;
    + *
    + *   import org.apache.spark.ml.feature.*;
    + *   import org.apache.spark.ml.Pipeline;
    + *   import org.apache.spark.ml.PipelineStage;
    + *   import org.apache.spark.ml.PipelineModel;
    + *
    + *  // a DataFrame with three columns: id (integer), text (string), and rating (double).
    + *  StructType schema = createStructType(
    + *      Arrays.asList(
    --- End diff --
    
    fix indentation (2-space)


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