"""Boston housing price regression dataset.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from ..utils.data_utils import get_file
import numpy as np


def load_data(path='boston_housing.npz', test_split=0.2, seed=113):
    """Loads the Boston Housing dataset.

    # Arguments
        path: path where to cache the dataset locally
            (relative to ~/.keras/datasets).
        test_split: fraction of the data to reserve as test set.
        seed: Random seed for shuffling the data
            before computing the test split.

    # Returns
        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
    """
    assert 0 <= test_split < 1
    path = get_file(
        path,
        origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',
        file_hash='f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')
    with np.load(path, allow_pickle=True) as f:
        x = f['x']
        y = f['y']

    rng = np.random.RandomState(seed)
    indices = np.arange(len(x))
    rng.shuffle(indices)
    x = x[indices]
    y = y[indices]

    x_train = np.array(x[:int(len(x) * (1 - test_split))])
    y_train = np.array(y[:int(len(x) * (1 - test_split))])
    x_test = np.array(x[int(len(x) * (1 - test_split)):])
    y_test = np.array(y[int(len(x) * (1 - test_split)):])
    return (x_train, y_train), (x_test, y_test)