orbitalml 0.1.0

Allow SKLearn predictions to run on database systems in pure SQL.

OrbitalML

Convert SKLearn pipelines into SQL queries for execution in a database without the need for a Python environment.

See examples directory for example pipelines and Documentation

Warning:

This is a work in progress.
You might encounter bugs or missing features.

Note:

Not all transformations and models can be represented as SQL queries,
so OrbitalML might not be able to implement the specific pipeline you are using.

Getting Started

Install OrbitalML:

$ pip install orbitalml

Prepare some data:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

COLUMNS = ["sepal.length", "sepal.width", "petal.length", "petal.width"]

iris = load_iris(as_frame=True)
iris_x = iris.data.set_axis(COLUMNS, axis=1)

# SQL and OrbitalML don't like dots in column names, replace them with underscores
iris_x.columns = COLUMNS = [cname.replace(".", "_") for cname in COLUMNS]

X_train, X_test, y_train, y_test = train_test_split(
    iris_x, iris.target, test_size=0.2, random_state=42
)

Define a Scikit-Learn pipeline and train it:

from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

pipeline = Pipeline(
    [
        ("preprocess", ColumnTransformer([("scaler", StandardScaler(with_std=False), COLUMNS)],
                                        remainder="passthrough")),
        ("linear_regression", LinearRegression()),
    ]
)
pipeline.fit(X_train, y_train)

Convert the pipeline to OrbitalML:

import orbitalml
import orbitalml.types

orbitalml_pipeline = orbitalml.parse_pipeline(pipeline, features={
    "sepal_length": orbitalml.types.DoubleColumnType(),
    "sepal_width": orbitalml.types.DoubleColumnType(),
    "petal_length": orbitalml.types.DoubleColumnType(),
    "petal_width": orbitalml.types.DoubleColumnType(),
})

You can print the pipeline to see the result:

>>> print(orbitalml_pipeline)

ParsedPipeline(
    features={
        sepal_length: DoubleColumnType()
        sepal_width: DoubleColumnType()
        petal_length: DoubleColumnType()
        petal_width: DoubleColumnType()
    },
    steps=[
        merged_columns=Concat(
            inputs: sepal_length, sepal_width, petal_length, petal_width,
            attributes: 
             axis=1
        )
        variable1=Sub(
            inputs: merged_columns, Su_Subcst=[5.809166666666666, 3.0616666666666665, 3.7266666666666666, 1.18333333...,
            attributes: 
        )
        multiplied=MatMul(
            inputs: variable1, coef=[-0.11633479416518255, -0.05977785171980231, 0.25491374699772246, 0.5475959...,
            attributes: 
        )
        resh=Add(
            inputs: multiplied, intercept=[0.9916666666666668],
            attributes: 
        )
        variable=Reshape(
            inputs: resh, shape_tensor=[-1, 1],
            attributes: 
        )
    ],
)

Now we can generate the SQL from the pipeline:

sql = orbitalml.export_sql("DATA_TABLE", orbitalml_pipeline, dialect="duckdb")

And check the resulting query:

>>> print(sql)

SELECT ("t0"."sepal_length" - 5.809166666666666) * -0.11633479416518255 + 0.9916666666666668 +  
       ("t0"."sepal_width" - 3.0616666666666665) * -0.05977785171980231 + 
       ("t0"."petal_length" - 3.7266666666666666) * 0.25491374699772246 + 
       ("t0"."petal_width" - 1.1833333333333333) * 0.5475959809777828 
AS "variable" FROM "DATA_TABLE" AS "t0"

Once the SQL is generate, you can use it to run the pipeline on a database. From here on the SQL can be exported and reused in other places:

>>> print("\nPrediction with SQL")
>>> duckdb.register("DATA_TABLE", X_test)
>>> print(duckdb.sql(sql).df()["variable"][:5].to_numpy())

Prediction with SQL
[ 1.23071715 -0.04010441  2.21970287  1.34966889  1.28429336]

We can verify that the prediction matches the one done by Scikit-Learn by running the scikitlearn pipeline on the same set of data:

>>> print("\nPrediction with SciKit-Learn")
>>> print(pipeline.predict(X_test)[:5])

Prediction with SciKit-Learn
[ 1.23071715 -0.04010441  2.21970287  1.34966889  1.28429336 ]

Supported Models

OrbitalML currently supports the following models:

• Linear Regression
• Logistic Regression
• Lasso Regression
• Elastic Net
• Decision Tree Regressor
• Decision Tree Classifier
• Random Forest Classifier
• Gradient Boosting Regressor
• Gradient Boosting Classifier

Testing

Setup testing environment:

$ uv sync --no-dev --extra test

Run Tests:

$ uv run pytest -v

Try Examples:

$ uv run examples/pipeline_lineareg.py

Development

Setup a development environment:

$ uv sync