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244 lines
6.6 KiB
Markdown
244 lines
6.6 KiB
Markdown
---
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machine_translated: true
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machine_translated_rev: 72537a2d527c63c07aa5d2361a8829f3895cf2bd
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toc_priority: 41
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toc_title: "\u5E94\u7528CatBoost\u6A21\u578B"
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---
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# 在ClickHouse中应用Catboost模型 {#applying-catboost-model-in-clickhouse}
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[CatBoost](https://catboost.ai) 是一个由[Yandex](https://yandex.com/company/)开发的开源免费机器学习库。
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通过这篇指导，您将学会如何用SQL建模，使用ClickHouse预先训练好的模型来推断数据。
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在ClickHouse中应用CatBoost模型的一般过程:
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1. [创建数据表](#create-table).
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2. [将数据插入到表中](#insert-data-to-table).
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3. [将CatBoost集成到ClickHouse中](#integrate-catboost-into-clickhouse) （可跳过）。
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4. [从SQL运行模型推断](#run-model-inference).
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有关训练CatBoost模型的详细信息，请参阅 [训练和模型应用](https://catboost.ai/docs/features/training.html#training).
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## 先决条件 {#prerequisites}
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请先安装 [Docker](https://docs.docker.com/install/)。
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!!! note "注"
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[Docker](https://www.docker.com) 是一个软件平台，用户可以用来创建独立于其余系统、集成CatBoost和ClickHouse的容器。
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在应用CatBoost模型之前:
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**1.** 从容器仓库拉取docker映像 (https://hub.docker.com/r/yandex/tutorial-catboost-clickhouse) :
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``` bash
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$ docker pull yandex/tutorial-catboost-clickhouse
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```
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此Docker映像包含运行CatBoost和ClickHouse所需的所有内容：代码、运行环境、库、环境变量和配置文件。
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**2.** 确保已成功拉取Docker映像:
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``` bash
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$ docker image ls
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REPOSITORY TAG IMAGE ID CREATED SIZE
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yandex/tutorial-catboost-clickhouse latest 622e4d17945b 22 hours ago 1.37GB
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```
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**3.** 基于此映像启动一个Docker容器:
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``` bash
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$ docker run -it -p 8888:8888 yandex/tutorial-catboost-clickhouse
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```
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## 1. 创建数据表 {#create-table}
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为训练样本创建ClickHouse表:
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**1.** 在交互模式下启动ClickHouse控制台客户端:
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``` bash
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$ clickhouse client
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```
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!!! note "注"
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ClickHouse服务器已经在Docker容器内运行。
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**2.** 使用以下命令创建表:
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``` sql
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:) CREATE TABLE amazon_train
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(
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date Date MATERIALIZED today(),
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ACTION UInt8,
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RESOURCE UInt32,
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MGR_ID UInt32,
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ROLE_ROLLUP_1 UInt32,
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ROLE_ROLLUP_2 UInt32,
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ROLE_DEPTNAME UInt32,
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ROLE_TITLE UInt32,
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ROLE_FAMILY_DESC UInt32,
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ROLE_FAMILY UInt32,
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ROLE_CODE UInt32
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)
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ENGINE = MergeTree ORDER BY date
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```
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**3.** 从ClickHouse控制台客户端退出:
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``` sql
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:) exit
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```
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## 2. 将数据插入到表中 {#insert-data-to-table}
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插入数据:
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**1.** 运行以下命令:
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``` bash
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$ clickhouse client --host 127.0.0.1 --query 'INSERT INTO amazon_train FORMAT CSVWithNames' < ~/amazon/train.csv
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```
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**2.** 在交互模式下启动ClickHouse控制台客户端:
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``` bash
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$ clickhouse client
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```
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**3.** 确保数据已上传:
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``` sql
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:) SELECT count() FROM amazon_train
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SELECT count()
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FROM amazon_train
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+-count()-+
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| 65538 |
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+-------+
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```
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## 3. 将CatBoost集成到ClickHouse中 {#integrate-catboost-into-clickhouse}
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!!! note "注"
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**可跳过。** Docker映像包含运行CatBoost和ClickHouse所需的所有内容。
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CatBoost集成到ClickHouse步骤:
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**1.** 构建评估库。
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评估CatBoost模型的最快方法是编译 `libcatboostmodel.<so|dll|dylib>` 库文件.
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有关如何构建库文件的详细信息，请参阅 [CatBoost文件](https://catboost.ai/docs/concepts/c-plus-plus-api_dynamic-c-pluplus-wrapper.html).
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**2.** 创建一个新目录（位置与名称可随意指定）, 如 `data` 并将创建的库文件放入其中。 Docker映像已经包含了库 `data/libcatboostmodel.so`.
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**3.** 创建一个新目录来放配置模型, 如 `models`.
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**4.** 创建一个模型配置文件，如 `models/amazon_model.xml`.
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**5.** 描述模型配置:
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``` xml
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<models>
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<model>
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<!-- Model type. Now catboost only. -->
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<type>catboost</type>
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<!-- Model name. -->
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<name>amazon</name>
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<!-- Path to trained model. -->
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<path>/home/catboost/tutorial/catboost_model.bin</path>
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<!-- Update interval. -->
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<lifetime>0</lifetime>
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</model>
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</models>
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```
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**6.** 将CatBoost库文件的路径和模型配置添加到ClickHouse配置:
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``` xml
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<!-- File etc/clickhouse-server/config.d/models_config.xml. -->
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<catboost_dynamic_library_path>/home/catboost/data/libcatboostmodel.so</catboost_dynamic_library_path>
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<models_config>/home/catboost/models/*_model.xml</models_config>
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```
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## 4. 运行从SQL推断的模型 {#run-model-inference}
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测试模型是否正常，运行ClickHouse客户端 `$ clickhouse client`.
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让我们确保模型能正常工作:
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``` sql
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:) SELECT
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modelEvaluate('amazon',
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RESOURCE,
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MGR_ID,
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ROLE_ROLLUP_1,
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ROLE_ROLLUP_2,
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ROLE_DEPTNAME,
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ROLE_TITLE,
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ROLE_FAMILY_DESC,
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ROLE_FAMILY,
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ROLE_CODE) > 0 AS prediction,
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ACTION AS target
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FROM amazon_train
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LIMIT 10
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```
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!!! note "注"
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函数 [modelEvaluate](../sql-reference/functions/other-functions.md#function-modelevaluate) 返回带有多类模型的每类原始预测的元组。
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执行预测:
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``` sql
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:) SELECT
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modelEvaluate('amazon',
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RESOURCE,
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MGR_ID,
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ROLE_ROLLUP_1,
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ROLE_ROLLUP_2,
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ROLE_DEPTNAME,
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ROLE_TITLE,
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ROLE_FAMILY_DESC,
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ROLE_FAMILY,
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ROLE_CODE) AS prediction,
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1. / (1 + exp(-prediction)) AS probability,
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ACTION AS target
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FROM amazon_train
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LIMIT 10
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```
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!!! note "注"
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查看函数说明 [exp()](../sql-reference/functions/math-functions.md) 。
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让我们计算样本的LogLoss:
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``` sql
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:) SELECT -avg(tg * log(prob) + (1 - tg) * log(1 - prob)) AS logloss
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FROM
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(
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SELECT
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modelEvaluate('amazon',
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RESOURCE,
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MGR_ID,
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ROLE_ROLLUP_1,
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ROLE_ROLLUP_2,
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ROLE_DEPTNAME,
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ROLE_TITLE,
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ROLE_FAMILY_DESC,
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ROLE_FAMILY,
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ROLE_CODE) AS prediction,
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1. / (1. + exp(-prediction)) AS prob,
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ACTION AS tg
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FROM amazon_train
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)
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```
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!!! note "注"
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查看函数说明 [avg()](../sql-reference/aggregate-functions/reference/avg.md#agg_function-avg) 和 [log()](../sql-reference/functions/math-functions.md) 。
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[原始文章](https://clickhouse.tech/docs/en/guides/apply_catboost_model/) <!--hide-->
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