Boston Housing Regression

This example solves a regression problem using a pipeline with the following steps:

  • Feature augmentation with PCA and Fast ICA,

  • A Pre-regression using an ensemble containing gradient boosted, and a KMeans clustering for even more features in the stacking,

  • The model stacking using a ridge regression.

This example also prints the shapes of the objects between the pipeline elements.


/home/gui/Documents/GIT/ FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.

    The Boston housing prices dataset has an ethical problem. You can refer to
    the documentation of this function for further details.

    The scikit-learn maintainers therefore strongly discourage the use of this
    dataset unless the purpose of the code is to study and educate about
    ethical issues in data science and machine learning.

    In this special case, you can fetch the dataset from the original

        import pandas as pd
        import numpy as np

        data_url = ""
        raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
        target = raw_df.values[1::2, 2]

    Alternative datasets include the California housing dataset (i.e.
    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing
    dataset. You can load the datasets as follows::

        from sklearn.datasets import fetch_california_housing
        housing = fetch_california_housing()

    for the California housing dataset and::

        from sklearn.datasets import fetch_openml
        housing = fetch_openml(name="house_prices", as_frame=True)

    for the Ames housing dataset.
  warnings.warn(msg, category=FutureWarning)
Fitting on train:
NumpyShapePrinter: (379, 13)
/home/gui/Documents/GIT/ FutureWarning: From version 1.3 whiten='unit-variance' will be used by default.
NumpyShapePrinter: (379, 17)

Transforming train and test:
NumpyShapePrinter: (379, 13)
NumpyShapePrinter: (379, 17)
NumpyShapePrinter: (379,)
NumpyShapePrinter: (127, 13)
NumpyShapePrinter: (127, 17)
NumpyShapePrinter: (127,)

Evaluating transformed train:
R2 regression score: 0.9997216058471721

Evaluating transformed test:
R2 regression score: 0.9121752499875265

import numpy as np
from sklearn.cluster import KMeans
from sklearn.datasets import load_boston
from sklearn.decomposition import PCA, FastICA
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle

from neuraxle.pipeline import Pipeline
from neuraxle.steps.numpy import NumpyShapePrinter
from neuraxle.steps.sklearn import RidgeModelStacking
from neuraxle.union import AddFeatures

def main():
    boston = load_boston()
    X, y = shuffle(,, random_state=13)
    X = X.astype(np.float32)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, shuffle=False)

    p = Pipeline([

    print("Fitting on train:")
    p =, y_train)
    print("Transforming train and test:")
    y_train_predicted = p.predict(X_train)
    y_test_predicted = p.predict(X_test)
    print("Evaluating transformed train:")
    score_train = r2_score(y_train_predicted, y_train)
    print('R2 regression score:', score_train)
    print("Evaluating transformed test:")
    score_test = r2_score(y_test_predicted, y_test)
    print('R2 regression score:', score_test)

    assert y_train_predicted.shape == (379,)
    assert y_test_predicted.shape == (127,)
    assert isinstance(score_train, float)
    assert isinstance(score_test, float)

    return y_train_predicted, y_test_predicted, score_train, score_test

if __name__ == "__main__":

Total running time of the script: ( 0 minutes 0.651 seconds)

Gallery generated by Sphinx-Gallery