IbisML is a library for building scalable ML pipelines using Ibis.
Project description
IbisML
What is IbisML?
IbisML is a library for building scalable ML pipelines using Ibis:
- Preprocess your data at scale on any Ibis-supported backend.
- Compose
Recipe
s with other scikit-learn estimators usingPipeline
s. - Seamlessly integrate with scikit-learn, XGBoost, and PyTorch models.
How do I install IbisML?
pip install ibis-ml
How do I use IbisML?
With recipes, you can define sequences of feature engineering steps to get your data ready for modeling. For example, create a recipe to replace missing values using the mean of each numeric column and then normalize numeric data to have a standard deviation of one and a mean of zero.
import ibis_ml as ml
imputer = ml.ImputeMean(ml.numeric())
scaler = ml.ScaleStandard(ml.numeric())
rec = ml.Recipe(imputer, scaler)
A recipe can be chained in a
Pipeline
like any other
transformer.
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
pipe = Pipeline([("rec", rec), ("svc", SVC())])
The pipeline can be used as any other estimator and avoids leaking the test set into the train set.
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
pipe.fit(X_train, y_train).score(X_test, y_test)
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