Skip to main content

IbisML is a library for building scalable ML pipelines using Ibis.

Project description

IbisML

Build status Docs License PyPI

What is IbisML?

IbisML is a library for building scalable ML pipelines using Ibis:

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)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ibis_ml-0.1.3.tar.gz (28.8 kB view hashes)

Uploaded Source

Built Distribution

ibis_ml-0.1.3-py3-none-any.whl (35.4 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page