Applying a Catboost Model in ClickHouse 

CatBoost is a free and open-source gradient boosting library developed at Yandex for machine learning.

With this instruction, you will learn to apply pre-trained models in ClickHouse by running model inference from SQL.

To apply a CatBoost model in ClickHouse:

  1. Create a Table.
  2. Insert the Data to the Table.
  3. Integrate CatBoost into ClickHouse (Optional step).
  4. Run the Model Inference from SQL.

For more information about training CatBoost models, see Training and applying models.

Prerequisites 

If you don’t have the Docker yet, install it.

Before applying a CatBoost model:

1. Pull the Docker image from the registry:

$ 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:

$ 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:

$ docker run -it -p 8888:8888 yandex/tutorial-catboost-clickhouse

1. Create a Table 

To create a ClickHouse table for the training sample:

1. Start ClickHouse console client in the interactive mode:

$ clickhouse client

2. Create the table using the command:

:) 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. Exit from ClickHouse console client:

:) exit

2. Insert the Data to the Table 

To insert the data:

1. Run the following command:

$ clickhouse client --host 127.0.0.1 --query 'INSERT INTO amazon_train FORMAT CSVWithNames' < ~/amazon/train.csv

2. Start ClickHouse console client in the interactive mode:

$ clickhouse client

3. Make sure the data has been uploaded:

:) SELECT count() FROM amazon_train

SELECT count()
FROM amazon_train

+-count()-+
|   65538 |
+-------+

3. Integrate CatBoost into ClickHouse 

To integrate CatBoost into ClickHouse:

1. Build the evaluation library.

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.

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:

<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. Add the path to CatBoost and the model configuration to the ClickHouse configuration:

<!-- 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. Run the Model Inference from SQL 

For test model run the ClickHouse client $ clickhouse client.

Let’s make sure that the model is working:

:) 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

Let’s predict the probability:

:) 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

Let’s calculate LogLoss on the sample:

:) 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
)

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