89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Boston housing price regression dataset."""
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import numpy as np
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from keras.utils.data_utils import get_file
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.datasets.boston_housing.load_data")
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def load_data(path="boston_housing.npz", test_split=0.2, seed=113):
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"""Loads the Boston Housing dataset.
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This is a dataset taken from the StatLib library which is maintained at
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Carnegie Mellon University.
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**WARNING:** This dataset has an ethical problem: the authors of this
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dataset included a variable, "B", that may appear to assume that racial
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self-segregation influences house prices. As such, we strongly discourage
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the use of this dataset, unless in the context of illustrating ethical
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issues in data science and machine learning.
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Samples contain 13 attributes of houses at different locations around the
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Boston suburbs in the late 1970s. Targets are the median values of
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the houses at a location (in k$).
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The attributes themselves are defined in the
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[StatLib website](http://lib.stat.cmu.edu/datasets/boston).
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Args:
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path: path where to cache the dataset locally
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(relative to `~/.keras/datasets`).
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test_split: fraction of the data to reserve as test set.
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seed: Random seed for shuffling the data
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before computing the test split.
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Returns:
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Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
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**x_train, x_test**: numpy arrays with shape `(num_samples, 13)`
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containing either the training samples (for x_train),
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or test samples (for y_train).
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**y_train, y_test**: numpy arrays of shape `(num_samples,)` containing the
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target scalars. The targets are float scalars typically between 10 and
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50 that represent the home prices in k$.
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"""
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assert 0 <= test_split < 1
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origin_folder = (
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"https://storage.googleapis.com/tensorflow/tf-keras-datasets/"
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)
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path = get_file(
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path,
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origin=origin_folder + "boston_housing.npz",
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file_hash=( # noqa: E501
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"f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5"
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),
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)
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with np.load(path, allow_pickle=True) as f:
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x = f["x"]
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y = f["y"]
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rng = np.random.RandomState(seed)
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indices = np.arange(len(x))
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rng.shuffle(indices)
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x = x[indices]
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y = y[indices]
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x_train = np.array(x[: int(len(x) * (1 - test_split))])
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y_train = np.array(y[: int(len(x) * (1 - test_split))])
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x_test = np.array(x[int(len(x) * (1 - test_split)) :])
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y_test = np.array(y[int(len(x) * (1 - test_split)) :])
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return (x_train, y_train), (x_test, y_test)
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