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213 lines
5.6 KiB
Markdown
213 lines
5.6 KiB
Markdown
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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 for machine learning.
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To apply a CatBoost model in ClickHouse:
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1. [Create a table for the train sample](#create-a-table).
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1. [Insert the data to the table](#insert-the-data-to-the-table).
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1. [Configure the model](#configure-the-model).
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1. [Test the trained model](#test-the-trained-model).
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## Before you start {#before-you-start}
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If you don't have the [Docker](https://docs.docker.com/install/) yet, install it.
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> **Note:** [Docker](https://www.docker.com) uses containers to create virtual environments that isolate a CatBoost and ClickHouse installation from the rest of the system. CatBoost and ClickHouse programs are run within this virtual environment.
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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 an application: code, runtime, libraries, environment variables, and configuration files.
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**3.** Make sure the Docker image has been 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 3e5ad9fae997 19 months ago 1.58GB
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```
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**2.** Start the Docker-configured 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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> **Note:** Example running a Jupyter Notebook with this manual materials to [http://localhost:8888](http://localhost:8888).
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## 1. Create a table {#create-a-table}
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To create a ClickHouse table for the train sample:
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**1.** Start a ClickHouse client:
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```bash
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$ clickhouse client
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```
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> **Note:** 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(date, date, 8192)
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```
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## 2. Insert the data to the table {#insert-the-data-to-the-table}
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To insert the data:
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**1.** Exit from ClickHouse:
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```sql
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:) exit
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```
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**2.** Upload the data:
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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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**3.** Make sure the data has been uploaded:
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```sql
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$ clickhouse client
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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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+---------+
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```
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## 3. Configure the model to work with the trained model {#configure-the-model}
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This step is optional: the Docker container contains all configuration files.
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**1.** Create a config file (for example, `config_model.xml`) with 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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> **Note:** To show contents of the config file in the Docker container, run `cat models/amazon_model.xml`.
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**2.** Add the following lines to the `/etc/clickhouse-server/config.xml` file:
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```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:** To show contents of the ClickHouse config file in the Docker container, run `cat ../../etc/clickhouse-server/config.xml`.
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**3.** Restart ClickHouse server:
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```bash
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$ sudo service clickhouse-server restart
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```
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## 4. Test the trained model {#test-the-trained-model}
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For test 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:** Function `modelEvaluate` returns tuple with per-class raw predictions for multiclass models.
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- Let's predict 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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- 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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