A simple patsy to scikit-learn adaptor. Be aware that the content of this package is pretty experimental. Feedback welcome.
This package provides two classes:
- A scikit-learn meta-estimator PatsyModel, that feeds the design matrix created by patsy into a scikit-learn estimator.
- A scikit-learn transformer, that uses a patsy formula to transform and select features.
Both classes work on pandas DataFrames. If you want to use PatsyModel for a supervised estiamator, the input dataframe is expected to contain both the data and the target.
# put the iris dataset into a pandas dataframe
from sklearn.datasets import load_iris
import pandas as pd
iris = load_iris()
names = [f_name.replace(" ", "_").strip("_(cm)") for f_name in iris.feature_names]
iris_df = pd.DataFrame(iris.data, columns=names)
iris_df['species'] = iris.target
# create logistic regression with two features
from patsylearn import PatsyModel
from sklearn.linear_model import LogisticRegression
model = PatsyModel(LogisticRegression(), "species ~ sepal_length + petal_length")
# model is an sklearn estimator.
from sklearn.cross_validation import train_test_split
data_train, data_test = train_test_split(iris_df)
model.fit(data_train)
print(model.score(data_test))
# use PatsyTransformer to just select and transform features
transformer = PatsyTransformer("sepal_length + np.log(petal_length) + petal_length:sepal_width")
# transformer is a scikit-learn transformer
transformer.fit(data)
print(transformer.transform(data).shape)