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https://github.com/omnata-labs/dbt-ml-preprocessing
A SQL port of python's scikit-learn preprocessing module, provided as cross-database dbt macros.
https://github.com/omnata-labs/dbt-ml-preprocessing
bigquery dbt redshift scikit-learn snowflake
Last synced: about 1 month ago
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A SQL port of python's scikit-learn preprocessing module, provided as cross-database dbt macros.
- Host: GitHub
- URL: https://github.com/omnata-labs/dbt-ml-preprocessing
- Owner: omnata-labs
- License: mit
- Created: 2021-01-08T09:48:01.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-03T21:38:18.000Z (12 months ago)
- Last Synced: 2024-02-07T03:38:39.979Z (4 months ago)
- Topics: bigquery, dbt, redshift, scikit-learn, snowflake
- Language: Python
- Homepage: https://omnata-labs.github.io/dbt-ml-preprocessing/
- Size: 1.65 MB
- Stars: 175
- Watchers: 8
- Forks: 17
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-dbt - dbt-ml-preprocessing - A SQL port of python's scikit-learn preprocessing module, provided as cross-database dbt macros. (Packages)
- awesome-dbt - dbt-ml-preprocessing - A SQL port of python's scikit-learn preprocessing module, provided as cross-database dbt macros. (Packages)
- awesome-dbt - dbt-ml-preprocessing - A SQL port of python's scikit-learn preprocessing module, provided as cross-database dbt macros. (Packages)
README
# dbt-ml-preprocessing
A package for dbt which enables standardization of data sets. You can use it to build a feature store in your data warehouse, without using external libraries like Spark's mllib or Python's scikit-learn.
The package contains a set of macros that mirror the functionality of the [scikit-learn preprocessing module](https://scikit-learn.org/stable/modules/preprocessing.html). Originally they were developed as part of the 2019 Medium article [Feature Engineering in Snowflake](https://medium.com/omnata/feature-engineering-in-snowflake-4312032e0d53).
Currently they have been tested in Snowflake, Redshift , BigQuery, SQL Server and PostgreSQL 13.2. The test case expectations have been built using scikit-learn (see *.py in [integration_tests/data/sql](integration_tests/data/sql)), so you can expect behavioural parity with it.
| :warning: There are now several better alternatives to this package. If you're using Snowflake, they now offer the [snowflake-ml-python](https://docs.snowflake.com/en/developer-guide/snowpark-ml/index) package which is fully supported and much more comprehensive. Within dbt, the Python models feature allows Snowflake, BigQuery and Databricks users to use scikit-learn directly |
| --- |
The macros are:
| scikit-learn function | macro name | Snowflake | BigQuery | Redshift | MSSQL | PostgreSQL | Example |
| --- | --- | --- | --- | --- | --- | --- | --- |
| [KBinsDiscretizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html#sklearn.preprocessing.KBinsDiscretizer)| k_bins_discretizer | Y | Y | Y | Y | Y |  |
| [LabelEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder)| label_encoder | Y | Y | Y | Y | Y |  |
| [MaxAbsScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler) | max_abs_scaler | Y | Y | Y | Y | Y | [](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#maxabsscaler) |
| [MinMaxScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler) | min_max_scaler | Y | Y | Y | Y | Y | [](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#minmaxscaler) |
| [Normalizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html#sklearn.preprocessing.Normalizer) | normalizer | Y | Y | Y | Y | Y | [](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#normalizer) |
| [OneHotEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder) | one_hot_encoder | Y | Y | Y | Y | Y |  |
| [QuantileTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.QuantileTransformer.html#sklearn.preprocessing.QuantileTransformer) | quantile_transformer | Y | Y | N | N | Y | [](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#quantiletransformer-uniform-output) |
| [RobustScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html#sklearn.preprocessing.RobustScaler) | robust_scaler | Y | Y | Y | Y | Y | [](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#robustscaler) |
| [StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler) | standard_scaler | Y | Y | Y | N | Y | [](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#standardscaler) |
_\* 2D charts taken from [scikit-learn.org](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html), GIFs are my own_
## Installation
To use this in your dbt project, create or modify packages.yml to include:
```
packages:
- package: "omnata-labs/dbt_ml_preprocessing"
version: [">=1.0.2"]
```
_(replace the revision number with the latest)_
Then run:
```dbt deps``` to import the package.
### dbt 1.0.0 compatibility
dbt-ml-preprocessing version 1.2.0 is the first version to support (and require) dbt 1.0.0.
If you are not ready to upgrade to dbt 1.0.0, please use dbt-ml-preprocessing version 1.0.2.
## Usage
To read the macro documentation and see examples, simply [generate your docs](https://docs.getdbt.com/reference/commands/cmd-docs/), and you'll see macro documentation in the Projects tree under ```dbt_ml_preprocessing```:
