sqlgbm 0.1.0

Convert tree-based ML models to SQL queries

sqlgbm

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.

Installation

pip install sqlgbm

Usage

sqlgbm currently supports LightGBM models and can convert them to SQL queries:

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)

Output Types

sqlgbm supports different output formats:

• 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

License

MIT