Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/8740#discussion_r39596988
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/package-info.java ---
@@ -0,0 +1,112 @@
+/*
+ * 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 java.util.List;
+ *
+ * import org.apache.spark.api.java.JavaRDD;
+ * // Import factory methods provided by DataTypes.
--- End diff --
the comment is not necessary
---
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]
