# Applying a Catboost Model in ClickHouse {#applying-catboost-model-in-clickhouse} [CatBoost](https://catboost.ai) is a free and open-source gradient boosting library developed at [Yandex](https://yandex.com/company/) for machine learning. With this instruction, you will learn to apply pre-trained models in ClickHouse: as a result, you run the model inference from SQL. To apply a CatBoost model in ClickHouse: 1. [Create a Table](#create-table). 2. [Insert the Data to the Table](#insert-data-to-table). 3. [Integrate CatBoost into ClickHouse](#integrate-catboost-into-clickhouse) (Optional step). 4. [Run the Model Inference from SQL](#run-model-inference). For more information about training CatBoost models, see [Training and applying models](https://catboost.ai/docs/features/training.html#training). ## Prerequisites {#prerequisites} If you don't have the [Docker](https://docs.docker.com/install/) yet, install it. !!! note "Note" [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. Before applying a CatBoost model: **1.** Pull the [Docker image](https://hub.docker.com/r/yandex/tutorial-catboost-clickhouse) from the registry: ```bash $ docker pull yandex/tutorial-catboost-clickhouse ``` This Docker image contains everything you need to run CatBoost and ClickHouse: code, runtime, libraries, environment variables, and configuration files. **2.** Make sure the Docker image has been successfully pulled: ```bash $ docker image ls REPOSITORY TAG IMAGE ID CREATED SIZE yandex/tutorial-catboost-clickhouse latest 622e4d17945b 22 hours ago 1.37GB ``` **3.** Start a Docker container based on this image: ```bash $ docker run -it -p 8888:8888 yandex/tutorial-catboost-clickhouse ``` ## 1. Create a Table {#create-table} To create a ClickHouse table for the train sample: **1.** Start ClickHouse console client in interactive mode: ```bash $ clickhouse client ``` !!! note "Note" The ClickHouse server is already running inside the Docker container. **2.** Create the table using the command: ```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() ``` **3.** Exit from ClickHouse console client: ```sql :) exit ``` ## 2. Insert the Data to the Table {#insert-data-to-table} To insert the data: **1.** Run the following command: ```bash $ clickhouse client --host 127.0.0.1 --query 'INSERT INTO amazon_train FORMAT CSVWithNames' < ~/amazon/train.csv ``` **2.** Start ClickHouse console client in interactive mode: ```bash $ clickhouse client ``` **3.** Make sure the data has been uploaded: ```sql :) SELECT count() FROM amazon_train SELECT count() FROM amazon_train +-count()-+ | 65538 | +---------+ ``` ## 3. Integrate CatBoost into ClickHouse {#integrate-catboost-into-clickhouse} !!! note "Note" **Optional step.** The Docker image contains everything you need to run CatBoost and ClickHouse. To integrate CatBoost into ClickHouse: **1.** Build the evaluation library. The fastest way to evaluate a CatBoost model is compile `libcatboostmodel.` 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). **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`. **3.** Create a new directory for config model anywhere and with any name, for example, `models`. **4.** Create a model configuration file with any name, for example, `models/amazon_model.xml`. **5.** Describe the model configuration: ```xml catboost amazon /home/catboost/tutorial/catboost_model.bin 0 ``` **6.** Add the path to CatBoost and the model configuration to the ClickHouse configuration: ```xml /home/catboost/data/libcatboostmodel.so /home/catboost/models/*_model.xml ``` ## 4. Run the Model Inference from SQL {#run-model-inference} For test model run the ClickHouse client `$ clickhouse client`. Let's make sure that the model is working: ```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 "Note" Function [modelEvaluate](../query_language/functions/other_functions.md#function-modelevaluate) returns tuple with per-class raw predictions for multiclass models. Let's predict probability: ```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 "Note" More info about [exp()](../query_language/functions/math_functions.md) function. Let's calculate LogLoss on the sample: ```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 "Note" More info about [avg()](../query_language/agg_functions/reference.md#agg_function-avg) and [log()](../query_language/functions/math_functions.md) functions.