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ibis-ml

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

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# IbisML

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## What is IbisML?

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

- Preprocess your data at scale on any [Ibis](https://ibis-project.org/)-supported
  backend.
- Compose [`Recipe`](/reference/core.html#ibis_ml.Recipe)s with other scikit-learn
  estimators using
  [`Pipeline`](https://scikit-learn.org/stable/modules/compose.html#pipeline-chaining-estimators)s.
- Seamlessly integrate with [scikit-learn](https://scikit-learn.org/stable/),
  [XGBoost](https://xgboost.readthedocs.io/en/stable/python/sklearn_estimator.html), and
  [PyTorch](https://skorch.readthedocs.io/en/stable/) models.

## How do I install IbisML?

```bash
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.

```python
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`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)
like any other
[transformer](https://scikit-learn.org/stable/glossary.html#term-transformer).

```python
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.

```python
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)
```

ibis-ml

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