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241 lines
7.0 KiB
Markdown
241 lines
7.0 KiB
Markdown
---
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toc_priority: 41
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toc_title: Applying CatBoost Models
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---
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# Applying a Catboost Model in ClickHouse {#applying-catboost-model-in-clickhouse}
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[CatBoost](https://catboost.ai) is a free and open-source gradient boosting library developed at [Yandex](https://yandex.com/company/) for machine learning.
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With this instruction, you will learn to apply pre-trained models in ClickHouse by running model inference from SQL.
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To apply a CatBoost model in ClickHouse:
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1. [Create a Table](#create-table).
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2. [Insert the Data to the Table](#insert-data-to-table).
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3. [Integrate CatBoost into ClickHouse](#integrate-catboost-into-clickhouse) (Optional step).
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4. [Run the Model Inference from SQL](#run-model-inference).
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For more information about training CatBoost models, see [Training and applying models](https://catboost.ai/docs/features/training.html#training).
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## Prerequisites {#prerequisites}
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If you do not have the [Docker](https://docs.docker.com/install/) yet, install it.
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!!! note "Note"
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[Docker](https://www.docker.com) is a software platform that allows you to create containers that isolate a CatBoost and ClickHouse installation from the rest of the system.
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Before applying a CatBoost model:
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**1.** Pull the [Docker image](https://hub.docker.com/r/yandex/tutorial-catboost-clickhouse) from the registry:
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``` bash
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$ docker pull yandex/tutorial-catboost-clickhouse
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```
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This Docker image contains everything you need to run CatBoost and ClickHouse: code, runtime, libraries, environment variables, and configuration files.
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**2.** Make sure the Docker image has been successfully pulled:
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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.** Start a Docker container based on this image:
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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. Create a Table {#create-table}
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To create a ClickHouse table for the training sample:
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**1.** Start ClickHouse console client in the interactive mode:
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``` bash
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$ clickhouse client
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```
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!!! note "Note"
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The ClickHouse server is already running inside the Docker container.
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**2.** Create the table using the command:
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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.** Exit from ClickHouse console client:
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``` sql
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:) exit
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```
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## 2. Insert the Data to the Table {#insert-data-to-table}
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To insert the data:
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**1.** Run the following command:
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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.** Start ClickHouse console client in the interactive mode:
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``` bash
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$ clickhouse client
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```
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**3.** Make sure the data has been uploaded:
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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. Integrate CatBoost into ClickHouse {#integrate-catboost-into-clickhouse}
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!!! note "Note"
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**Optional step.** The Docker image contains everything you need to run CatBoost and ClickHouse.
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To integrate CatBoost into ClickHouse:
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**1.** Build the evaluation library.
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The fastest way to evaluate a CatBoost model is compile `libcatboostmodel.<so|dll|dylib>` library. For more information about how to build the library, see [CatBoost documentation](https://catboost.ai/docs/concepts/c-plus-plus-api_dynamic-c-pluplus-wrapper.html).
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**2.** Create a new directory anywhere and with any name, for example, `data` and put the created library in it. The Docker image already contains the library `data/libcatboostmodel.so`.
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**3.** Create a new directory for config model anywhere and with any name, for example, `models`.
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**4.** Create a model configuration file with any name, for example, `models/amazon_model.xml`.
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**5.** Describe the model configuration:
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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.** Add the path to CatBoost and the model configuration to the ClickHouse configuration:
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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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!!! note "Note"
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You can change path to the CatBoost model configuration later without restarting server.
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## 4. Run the Model Inference from SQL {#run-model-inference}
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For test model run the ClickHouse client `$ clickhouse client`.
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Let’s make sure that the model is working:
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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 "Note"
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Function [modelEvaluate](../sql-reference/functions/other-functions.md#function-modelevaluate) returns tuple with per-class raw predictions for multiclass models.
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Let’s predict the probability:
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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 "Note"
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More info about [exp()](../sql-reference/functions/math-functions.md) function.
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Let’s calculate LogLoss on the sample:
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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 "Note"
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More info about [avg()](../sql-reference/aggregate-functions/reference/avg.md#agg_function-avg) and [log()](../sql-reference/functions/math-functions.md) functions.
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[Original article](https://clickhouse.tech/docs/en/guides/apply_catboost_model/) <!--hide-->
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