sqlgbm

⚠️ Warning: This library is in a very early development stage. The API and functionality will improve significantly over time. Not ready for production use yet.

sqlgbm is a Python library that converts tree-based machine learning models into SQL queries. This allows you to deploy your ML models directly in your database without any additional infrastructure.

documentation

You can find the documentation here.

installation

pip install sqlgbm

overview

sqlgbm takes your trained tree-based models and generates SQL code that reproduces the model's predictions. This enables you to:

• Run predictions directly in your database
• Eliminate latency from API calls between your database and ML serving infrastructure
• Simplify your production architecture by removing additional serving components

supported models

Currently supported models:

• LightGBM
• XGBoost

usage

basic example

from sqlgbm import SQLGBM
import lightgbm as lgb
import pandas as pd

# Load titanic dataset
titanic = pd.read_csv('titanic.csv')
features = ['pclass', 'sex', 'age', 'fare']
X = titanic[features]
X['sex'] = X['sex'].astype('category')
y = titanic['survived']

# Train model
clf = lgb.LGBMClassifier(n_estimators=3, max_depth=3)
clf.fit(X, y, categorical_feature=['sex'])

# Convert to SQL
sqlgbm = SQLGBM(clf, cat_features=['sex'])
sql = sqlgbm.generate_query('titanic', 'probability')

print(sql)

xgboost example

import xgboost as xgb
from sqlgbm import SQLGBM

# Prepare data and train model
# ...

# Convert XGBoost model to SQL
model = xgb.XGBClassifier(n_estimators=3, max_depth=3, base_score=0.5)
model.fit(X, y)

sqlgbm = SQLGBM(model, X=X)  # X used to infer categorical features
sql = sqlgbm.generate_query('my_table', 'all')

output types

sqlgbm supports different output formats through the output_type parameter:

• raw: Returns the raw model output
• probability: Returns the probability (after sigmoid transformation)
• prediction: Returns the binary prediction (0 or 1) based on a 0.5 threshold
• all: Returns all three outputs

Additional options:

• fast_sigmoid: Use a faster approximation of the sigmoid function

roadmap

• Add support for CatBoost
• Optimize SQL generation for large models
• Add support for multiclass classification
• Provide specialized optimizations for different database engines

license

MIT