Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/8740#discussion_r39530506
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/package-info.java ---
@@ -0,0 +1,92 @@
+/*
+ * 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 org.apache.spark.sql.RowFactory;
+ * import org.apache.spark.ml.feature.*;
+ * import org.apache.spark.ml.Pipeline;
+ *
+ * // a DataFrame with three columns: id (integer), text (string), and
rating (double).
+ * DataFrame df = sqlContext.createDataFrame(Arrays.asList(
+ * RowFactory.create(0, "Hi I heard about Spark", 3.0),
+ * RowFactory.create(1, "I wish Java could use case classes", 4.0),
+ * RowFactory.create(2, "Logistic regression models are neat", 4.0)
+ * )).toDF("id", "text", "rating");
+ *
+ * // define feature transformers
+ * RegexTokenizer tok = new RegexTokenizer()
+ * .setInputCol("text")
+ * .setOutputCol("words");
+ * StopWordsRemover sw = new StopWordsRemover()
+ * .setInputCol("words")
+ * .setOutputCol("filtered_words");
+ * HashingTF tf = new HashingTF()
+ * .setInputCol("filtered_words")
+ * .setOutputCol("tf")
+ * .setNumFeatures(10000);
+ * IDF idf = new IDF()
+ * .setInputCol("tf")
+ * .setOutputCol("tf_idf");
+ * VectorAssembler assembler = new VectorAssembler()
+ * .setInputCols(new String[] {"tf_idf", "rating"})
+ * .setOutputCol("features");
+ *
+ * // assemble and fit the feature transformation pipeline
+ * Pipeline pipeline = new Pipeline()
+ * .setStages(new PipelineStage[] {tok, sw, tf, idf, assembler});
+ * val model = pipeline.fit(df);
+ *
+ * // save transformed features with raw data
+ * model.transform(df)
+ * .select("id", "text", "rating", "features")
+ * .write.format("parquet").save("/output/path");
+ * </code>
+ * </pre>
+ *
+ * Some feature transformers implemented in MLlib are inspired by those
implemented in scikit-learn.
+ * The major difference is that most scikit-learn feature transformers
operate eagerly on the entire
+ * input dataset, while MLlib's feature transformers operate lazily on
individual columns,
+ * which is more efficient and flexible to handle large and complex
datasets.
+ *
+ * @see <a
href="http://scikit-learn.org/stable/modules/preprocessing.html";>scikit-learn.preprocessing</a>
--- End diff --
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