译编来源：Apache Spark：ML Pipelines。维护方：Apache Software Foundation，原页未注明个人作者。本次读取的页面标为 Spark 4.2.0；Python 示例固定到官方仓库 v4.2.0。
一个文本分类任务通常不只包含分类器：先把文本拆成词，再把词转换成数值特征，最后根据特征和标签训练模型。ML Pipelines 为这些步骤提供基于 DataFrame 的统一高层接口，使处理流程能一起配置、拟合和复用。
本文完整整理原页概念与行为边界，代码统一采用 Python 版本；原页还提供功能相近的 Scala 和 Java 写法，其完整示例仍可从原文进入。本例只有四条人工训练记录，说明的是接口如何衔接，不能据此判断文本分类质量。
DataFrame、Transformer 与 Estimator
DataFrame 是这里的数据集载体：一张表中可以同时保留文本、数值特征向量、真实标签以及预测结果。Spark SQL 的基本和结构化类型之外，DataFrame 还支持机器学习用的 Vector 类型。DataFrame 可由 RDD 显式或隐式建立，列使用名称定位，例如 text、features 和 label。
Transformer 将一个 DataFrame 变成另一个 DataFrame，通常增加一列或多列。特征转换器读文本列、追加向量列；训练好的模型读特征列、追加预测列。两者都通过 transform() 工作。
Estimator 接收数据并拟合，fit() 返回 Model，而 Model 也是 Transformer。例如 LogisticRegression.fit() 训练后得到 LogisticRegressionModel。原文将 transform() 和 fit() 描述为无状态操作；每个组件实例都有唯一 ID，可用来精确关联其参数。
流水线怎样训练，又怎样推断
Pipeline 按顺序保存多个 PipelineStage，每个阶段可以是 Transformer 或 Estimator。训练时，Transformer 对当前数据调用 transform()；Estimator 对当前数据调用 fit()，得到对应的 Transformer。如果后面还有需要训练的阶段，再用这个已拟合 Transformer 处理数据并传下去。
在文本例子中，Tokenizer 把 text 拆成 words；HashingTF 从 words 生成 features；LogisticRegression 使用特征和标签训练分类器。Pipeline 自身是 Estimator，pipeline.fit(training) 的结果是 PipelineModel。
PipelineModel 的阶段数与原 Pipeline 相同，但其中原来的 Estimator 已替换为训练好的 Transformer。对测试数据调用 model.transform(test)，就会按顺序进行相同特征处理和模型预测。这样能避免训练和推断分别编写预处理时产生的不一致。
流水线不局限于直线结构。阶段的输入列和输出列隐式定义数据依赖，只要组成有向无环图，就可安排非线性流程；此时阶段数组必须按拓扑顺序排列。Pipeline 和 PipelineModel 根据 DataFrame schema 在运行前检查输入类型，不能把这种检查理解成编译期类型保证。
每个阶段还应是独立实例。不要把同一个 myHashingTF 对象在同一流水线放两次；若确实需要两个 HashingTF 阶段，应分别创建实例，使 ID 不同。
一个完整的 Python 文本分类例子
下面保留官方 pipeline_example.py 的完整代码和许可头，未改算法或参数。它建立 SparkSession、创建内嵌训练数据、拟合流水线，再为不含标签的测试文本生成结果，最后停止会话。需要已安装并配置匹配版本的 Spark/PySpark 和运行环境；本文未安装依赖、启动 Spark 或运行训练。
#
# 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.
#
"""
Pipeline Example.
"""
# $example on$
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import HashingTF, Tokenizer
# $example off$
from pyspark.sql import SparkSession
if __name__ == "__main__":
spark = SparkSession\
.builder\
.appName("PipelineExample")\
.getOrCreate()
# $example on$
# Prepare training documents from a list of (id, text, label) tuples.
training = spark.createDataFrame([
(0, "a b c d e spark", 1.0),
(1, "b d", 0.0),
(2, "spark f g h", 1.0),
(3, "hadoop mapreduce", 0.0)
], ["id", "text", "label"])
# Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10, regParam=0.001)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
# Fit the pipeline to training documents.
model = pipeline.fit(training)
# Prepare test documents, which are unlabeled (id, text) tuples.
test = spark.createDataFrame([
(4, "spark i j k"),
(5, "l m n"),
(6, "spark hadoop spark"),
(7, "apache hadoop")
], ["id", "text"])
# Make predictions on test documents and print columns of interest.
prediction = model.transform(test)
selected = prediction.select("id", "text", "probability", "prediction")
for row in selected.collect():
rid, text, prob, prediction = row
print(
"(%d, %s) --> prob=%s, prediction=%f" % (
rid, text, str(prob), prediction
)
)
# $example off$
spark.stop()
id 用于识别记录，text 是输入文本，label 只出现在训练数据中。probability 是模型输出的类别概率向量，prediction 是预测类别。示例打印这些值，但本文没有生成或编造实际概率输出。
这里的 Python 示例没有显式设置 HashingTF 的 numFeatures，沿用当前版本默认值；原页 Scala 和 Java 的流水线示例则显式设为 1000。这一差异应在跨语言迁移时主动统一。训练和推断必须保持分词、特征列和哈希维度一致。
统一参数 API 与覆盖关系
所有 Transformer 和 Estimator 使用统一的参数 API。一个 Param 有名称和说明，ParamMap 则保存参数与值的对应关系。可以直接为实例设参数，例如构造逻辑回归时传 maxIter=10、regParam=0.01，或调用 setter；也可在 fit()、transform() 时传入 ParamMap。后者会覆盖实例此前设置的同名参数。
参数属于具体实例，而不是仅靠字符串名称定位。两个逻辑回归对象可以分别设置自己的 maxIter。Python 的参数映射使用字典，原文例子先设迭代次数 20，随后覆盖成 30，再指定正则参数 0.1、阈值 0.55，并把概率输出列改名为 myProbability。
# 摘自官方参数例；依赖原例已创建的 lr 和 training。
paramMap = {lr.maxIter: 20}
paramMap[lr.maxIter] = 30
paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55})
paramMap2 = {lr.probabilityCol: "myProbability"}
paramMapCombined = paramMap.copy()
paramMapCombined.update(paramMap2)
model2 = lr.fit(training, paramMapCombined)因此检查训练参数时，应以最终传入的映射为准。原例还用 explainParams() 查看参数说明和默认值，用 extractParamMap() 查看模型使用的参数，并对新的向量数据调用 transform()。输出选择的是改名后的 myProbability，继续读取 probability 就会与当前设置不符。完整参数示例见下方附录及官方版本化文件。
保存模型时同时考虑版本
Spark 1.6 为 Pipeline API 加入了模型导入导出；原文说明，自 Spark 2.3 起，基于 DataFrame 的 spark.ml 和 pyspark.ml 已完整覆盖持久化。Scala、Java、Python 的持久化可跨语言使用；R 在原文描述中仍采用调整过的格式，R 保存的模型只能由 R 加载，相关限制指向 SPARK-15572。
持久化可保存训练好的 PipelineModel，也可保存尚未训练的 Pipeline。原页 Scala 示例使用 write.overwrite().save(...) 写到两个固定的 /tmp 路径，然后用 PipelineModel.load(...) 加载。编辑风险提示：overwrite() 明确允许覆盖既有目标；迁移到业务代码时应使用独立且已确认的输出目录，不能照抄固定路径去覆盖正在使用的模型。
原文对兼容性的承诺有边界：跨大版本尽力兼容，但不保证；小版本和补丁版本支持向后加载兼容。持久化文件的内部格式本身不承诺稳定。模型行为在小版本和补丁版本之间通常保持一致，但错误修复可能改变行为。破坏兼容性的变动应在发行说明中列出；这些说法都不能代替你对目标版本的验证。
流水线的另一个作用是把超参数优化放进统一流程。自动模型选择的完整用法在原文链接的 ML Tuning Guide 中；本文不把四条记录扩展成未做过的交叉验证或准确率报告。
静态审核与使用边界
本次只读取文档、源码与许可，未执行示例。内嵌字符串作为数据送入 DataFrame，没有在这段代码中发现动态执行、命令拼接或硬编码凭据；这不表示整个运行环境或 Spark 没有安全问题。
collect() 把所选数据全部取回驱动进程；示例只有四条测试数据，换成大数据集可能耗尽驱动内存。结果还会打印原始文本，因此不应把敏感业务文本直接送入可共享日志。SparkSession.getOrCreate() 使用环境中的会话或配置，运行前需要确认连接目标是隔离测试环境，不能把文中调用理解成必然只在本机运行。
本稿的编辑提示不改变原示例。没有加载任何外部模型、访问生产集群，也没有测试性能、精度、跨语言模型加载或版本兼容性。
完整参数示例：Estimator、Transformer 与 Param
#
# 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.
#
"""
Estimator Transformer Param Example.
"""
# $example on$
from pyspark.ml.linalg import Vectors
from pyspark.ml.classification import LogisticRegression
# $example off$
from pyspark.sql import SparkSession
if __name__ == "__main__":
spark = SparkSession\
.builder\
.appName("EstimatorTransformerParamExample")\
.getOrCreate()
# $example on$
# Prepare training data from a list of (label, features) tuples.
training = spark.createDataFrame([
(1.0, Vectors.dense([0.0, 1.1, 0.1])),
(0.0, Vectors.dense([2.0, 1.0, -1.0])),
(0.0, Vectors.dense([2.0, 1.3, 1.0])),
(1.0, Vectors.dense([0.0, 1.2, -0.5]))], ["label", "features"])
# Create a LogisticRegression instance. This instance is an Estimator.
lr = LogisticRegression(maxIter=10, regParam=0.01)
# Print out the parameters, documentation, and any default values.
print("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
# Learn a LogisticRegression model. This uses the parameters stored in lr.
model1 = lr.fit(training)
# Since model1 is a Model (i.e., a transformer produced by an Estimator),
# we can view the parameters it used during fit().
# This prints the parameter (name: value) pairs, where names are unique IDs for this
# LogisticRegression instance.
print("Model 1 was fit using parameters: ")
print(model1.extractParamMap())
# We may alternatively specify parameters using a Python dictionary as a paramMap
paramMap = {lr.maxIter: 20}
paramMap[lr.maxIter] = 30 # Specify 1 Param, overwriting the original maxIter.
# Specify multiple Params.
paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55}) # type: ignore
# You can combine paramMaps, which are python dictionaries.
# Change output column name
paramMap2 = {lr.probabilityCol: "myProbability"}
paramMapCombined = paramMap.copy()
paramMapCombined.update(paramMap2) # type: ignore
# Now learn a new model using the paramMapCombined parameters.
# paramMapCombined overrides all parameters set earlier via lr.set* methods.
model2 = lr.fit(training, paramMapCombined)
print("Model 2 was fit using parameters: ")
print(model2.extractParamMap())
# Prepare test data
test = spark.createDataFrame([
(1.0, Vectors.dense([-1.0, 1.5, 1.3])),
(0.0, Vectors.dense([3.0, 2.0, -0.1])),
(1.0, Vectors.dense([0.0, 2.2, -1.5]))], ["label", "features"])
# Make predictions on test data using the Transformer.transform() method.
# LogisticRegression.transform will only use the 'features' column.
# Note that model2.transform() outputs a "myProbability" column instead of the usual
# 'probability' column since we renamed the lr.probabilityCol parameter previously.
prediction = model2.transform(test)
result = prediction.select("features", "label", "myProbability", "prediction") \
.collect()
for row in result:
print("features=%s, label=%s -> prob=%s, prediction=%s"
% (row.features, row.label, row.myProbability, row.prediction))
# $example off$
spark.stop()
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-----------------------------------------------------------
(see LICENSE-CC0.txt)
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上游 NOTICE
Apache Spark
Copyright 2014 and onwards The Apache Software Foundation.
This product includes software developed at
The Apache Software Foundation (http://www.apache.org/).
Export Control Notice
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This distribution includes cryptographic software. The country in which you currently reside may have
restrictions on the import, possession, use, and/or re-export to another country, of encryption software.
BEFORE using any encryption software, please check your country's laws, regulations and policies concerning
the import, possession, or use, and re-export of encryption software, to see if this is permitted. See
<http://www.wassenaar.org/> for more information.
The U.S. Government Department of Commerce, Bureau of Industry and Security (BIS), has classified this
software as Export Commodity Control Number (ECCN) 5D002.C.1, which includes information security software
using or performing cryptographic functions with asymmetric algorithms. The form and manner of this Apache
Software Foundation distribution makes it eligible for export under the License Exception ENC Technology
Software Unrestricted (TSU) exception (see the BIS Export Administration Regulations, Section 740.13) for
both object code and source code.
The following provides more details on the included cryptographic software:
This software uses Apache Commons Crypto (https://commons.apache.org/proper/commons-crypto/) to
support authentication, and encryption and decryption of data sent across the network between
services.
Metrics
Copyright 2010-2013 Coda Hale and Yammer, Inc.
This product includes software developed by Coda Hale and Yammer, Inc.
This product includes code derived from the JSR-166 project (ThreadLocalRandom, Striped64,
LongAdder), which was released with the following comments:
Written by Doug Lea with assistance from members of JCP JSR-166
Expert Group and released to the public domain, as explained at
http://creativecommons.org/publicdomain/zero/1.0/
© 版权声明
文章版权归作者所有，未经允许请勿转载。
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