1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
|
/*
* 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.
*/
// The content here should be in sync with `package.scala`.
/**
* <h2>ML attributes</h2>
*
* The ML pipeline API uses {@link org.apache.spark.sql.DataFrame}s as ML datasets.
* Each dataset consists of typed columns, e.g., string, double, vector, etc.
* However, knowing only the column type may not be sufficient to handle the data properly.
* For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices,
* which cannot be treated as numeric values in ML algorithms, and, for another instance, we may
* want to know the names and types of features stored in a vector column.
* ML attributes are used to provide additional information to describe columns in a dataset.
*
* <h3>ML columns</h3>
*
* A column with ML attributes attached is called an ML column.
* The data in ML columns are stored as double values, i.e., an ML column is either a scalar column
* of double values or a vector column.
* Columns of other types must be encoded into ML columns using transformers.
* We use {@link org.apache.spark.ml.attribute.Attribute} to describe a scalar ML column, and
* {@link org.apache.spark.ml.attribute.AttributeGroup} to describe a vector ML column.
* ML attributes are stored in the metadata field of the column schema.
*/
package org.apache.spark.ml.attribute;
|
