mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-11-30 03:22:14 +00:00
244 lines
6.6 KiB
Markdown
244 lines
6.6 KiB
Markdown
---
|
||
machine_translated: true
|
||
machine_translated_rev: 72537a2d527c63c07aa5d2361a8829f3895cf2bd
|
||
toc_priority: 41
|
||
toc_title: "\u5E94\u7528CatBoost\u6A21\u578B"
|
||
---
|
||
|
||
# 在ClickHouse中应用Catboost模型 {#applying-catboost-model-in-clickhouse}
|
||
|
||
[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。
|
||
|
||
|
||
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。
|
||
|
||
|
||
在ClickHouse中应用CatBoost模型的一般过程:
|
||
|
||
1. [创建数据表](#create-table).
|
||
2. [将数据插入到表中](#insert-data-to-table).
|
||
3. [将CatBoost集成到ClickHouse中](#integrate-catboost-into-clickhouse) （可跳过）。
|
||
4. [从SQL运行模型推断](#run-model-inference).
|
||
|
||
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).
|
||
|
||
## 先决条件 {#prerequisites}
|
||
|
||
请先安装 [Docker](https://docs.docker.com/install/)。
|
||
|
||
!!! note "注"
|
||
[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。
|
||
|
||
在应用CatBoost模型之前:
|
||
|
||
**1.** 从容器仓库拉取docker映像 (https://hub.docker.com/r/yandex/tutorial-catboost-clickhouse) :
|
||
|
||
``` bash
|
||
$ docker pull yandex/tutorial-catboost-clickhouse
|
||
```
|
||
|
||
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。
|
||
|
||
**2.** 确保已成功拉取Docker映像:
|
||
|
||
``` bash
|
||
$ docker image ls
|
||
REPOSITORY TAG IMAGE ID CREATED SIZE
|
||
yandex/tutorial-catboost-clickhouse latest 622e4d17945b 22 hours ago 1.37GB
|
||
```
|
||
|
||
**3.** 基于此映像启动一个Docker容器:
|
||
|
||
``` bash
|
||
$ docker run -it -p 8888:8888 yandex/tutorial-catboost-clickhouse
|
||
```
|
||
|
||
## 1. 创建数据表 {#create-table}
|
||
|
||
为训练样本创建ClickHouse表:
|
||
|
||
**1.** 在交互模式下启动ClickHouse控制台客户端:
|
||
|
||
``` bash
|
||
$ clickhouse client
|
||
```
|
||
|
||
!!! note "注"
|
||
ClickHouse服务器已经在Docker容器内运行。
|
||
|
||
**2.** 使用以下命令创建表:
|
||
|
||
``` sql
|
||
:) CREATE TABLE amazon_train
|
||
(
|
||
date Date MATERIALIZED today(),
|
||
ACTION UInt8,
|
||
RESOURCE UInt32,
|
||
MGR_ID UInt32,
|
||
ROLE_ROLLUP_1 UInt32,
|
||
ROLE_ROLLUP_2 UInt32,
|
||
ROLE_DEPTNAME UInt32,
|
||
ROLE_TITLE UInt32,
|
||
ROLE_FAMILY_DESC UInt32,
|
||
ROLE_FAMILY UInt32,
|
||
ROLE_CODE UInt32
|
||
)
|
||
ENGINE = MergeTree ORDER BY date
|
||
```
|
||
|
||
**3.** 从ClickHouse控制台客户端退出:
|
||
|
||
``` sql
|
||
:) exit
|
||
```
|
||
|
||
## 2. 将数据插入到表中 {#insert-data-to-table}
|
||
|
||
插入数据:
|
||
|
||
**1.** 运行以下命令:
|
||
|
||
``` bash
|
||
$ clickhouse client --host 127.0.0.1 --query 'INSERT INTO amazon_train FORMAT CSVWithNames' < ~/amazon/train.csv
|
||
```
|
||
|
||
**2.** 在交互模式下启动ClickHouse控制台客户端:
|
||
|
||
``` bash
|
||
$ clickhouse client
|
||
```
|
||
|
||
**3.** 确保数据已上传:
|
||
|
||
``` sql
|
||
:) SELECT count() FROM amazon_train
|
||
|
||
SELECT count()
|
||
FROM amazon_train
|
||
|
||
+-count()-+
|
||
| 65538 |
|
||
+-------+
|
||
```
|
||
|
||
## 3. 将CatBoost集成到ClickHouse中 {#integrate-catboost-into-clickhouse}
|
||
|
||
!!! note "注"
|
||
**可跳过。** Docker映像包含运行CatBoost和ClickHouse所需的所有内容。
|
||
|
||
CatBoost集成到ClickHouse步骤:
|
||
|
||
**1.** 构建评估库。
|
||
|
||
评估CatBoost模型的最快方法是编译 `libcatboostmodel.<so|dll|dylib>` 库文件.
|
||
|
||
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).
|
||
|
||
**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`.
|
||
|
||
**3.** 创建一个新目录来放配置模型, 如 `models`.
|
||
|
||
**4.** 创建一个模型配置文件，如 `models/amazon_model.xml`.
|
||
|
||
**5.** 描述模型配置:
|
||
|
||
``` xml
|
||
<models>
|
||
<model>
|
||
<!-- Model type. Now catboost only. -->
|
||
<type>catboost</type>
|
||
<!-- Model name. -->
|
||
<name>amazon</name>
|
||
<!-- Path to trained model. -->
|
||
<path>/home/catboost/tutorial/catboost_model.bin</path>
|
||
<!-- Update interval. -->
|
||
<lifetime>0</lifetime>
|
||
</model>
|
||
</models>
|
||
```
|
||
|
||
**6.** 将CatBoost库文件的路径和模型配置添加到ClickHouse配置:
|
||
|
||
``` xml
|
||
<!-- File etc/clickhouse-server/config.d/models_config.xml. -->
|
||
<catboost_dynamic_library_path>/home/catboost/data/libcatboostmodel.so</catboost_dynamic_library_path>
|
||
<models_config>/home/catboost/models/*_model.xml</models_config>
|
||
```
|
||
|
||
## 4. 运行从SQL推断的模型 {#run-model-inference}
|
||
|
||
测试模型是否正常，运行ClickHouse客户端 `$ clickhouse client`.
|
||
|
||
让我们确保模型能正常工作:
|
||
|
||
``` sql
|
||
:) SELECT
|
||
modelEvaluate('amazon',
|
||
RESOURCE,
|
||
MGR_ID,
|
||
ROLE_ROLLUP_1,
|
||
ROLE_ROLLUP_2,
|
||
ROLE_DEPTNAME,
|
||
ROLE_TITLE,
|
||
ROLE_FAMILY_DESC,
|
||
ROLE_FAMILY,
|
||
ROLE_CODE) > 0 AS prediction,
|
||
ACTION AS target
|
||
FROM amazon_train
|
||
LIMIT 10
|
||
```
|
||
|
||
!!! note "注"
|
||
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。
|
||
|
||
执行预测:
|
||
|
||
``` sql
|
||
:) SELECT
|
||
modelEvaluate('amazon',
|
||
RESOURCE,
|
||
MGR_ID,
|
||
ROLE_ROLLUP_1,
|
||
ROLE_ROLLUP_2,
|
||
ROLE_DEPTNAME,
|
||
ROLE_TITLE,
|
||
ROLE_FAMILY_DESC,
|
||
ROLE_FAMILY,
|
||
ROLE_CODE) AS prediction,
|
||
1. / (1 + exp(-prediction)) AS probability,
|
||
ACTION AS target
|
||
FROM amazon_train
|
||
LIMIT 10
|
||
```
|
||
|
||
!!! note "注"
|
||
查看函数说明 [exp()](../sql-reference/functions/math-functions.md) 。
|
||
|
||
让我们计算样本的LogLoss:
|
||
|
||
``` sql
|
||
:) SELECT -avg(tg * log(prob) + (1 - tg) * log(1 - prob)) AS logloss
|
||
FROM
|
||
(
|
||
SELECT
|
||
modelEvaluate('amazon',
|
||
RESOURCE,
|
||
MGR_ID,
|
||
ROLE_ROLLUP_1,
|
||
ROLE_ROLLUP_2,
|
||
ROLE_DEPTNAME,
|
||
ROLE_TITLE,
|
||
ROLE_FAMILY_DESC,
|
||
ROLE_FAMILY,
|
||
ROLE_CODE) AS prediction,
|
||
1. / (1. + exp(-prediction)) AS prob,
|
||
ACTION AS tg
|
||
FROM amazon_train
|
||
)
|
||
```
|
||
|
||
!!! note "注"
|
||
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) 。
|
||
|
||
[原始文章](https://clickhouse.tech/docs/en/guides/apply_catboost_model/) <!--hide-->
|