uci_housing.py 2.7 KB
Newer Older
D
dangqingqing 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Y
Yu Yang 已提交
14 15 16 17 18
"""
UCI Housing dataset.

TODO(yuyang18): Complete comments.
"""
D
dangqingqing 已提交
19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91

import numpy as np
import os
from common import download

__all__ = ['train', 'test']

URL = 'https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data'
MD5 = 'd4accdce7a25600298819f8e28e8d593'
feature_names = [
    'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',
    'PTRATIO', 'B', 'LSTAT'
]

UCI_TRAIN_DATA = None
UCI_TEST_DATA = None


def feature_range(maximums, minimums):
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    fig, ax = plt.subplots()
    feature_num = len(maximums)
    ax.bar(range(feature_num), maximums - minimums, color='r', align='center')
    ax.set_title('feature scale')
    plt.xticks(range(feature_num), feature_names)
    plt.xlim([-1, feature_num])
    fig.set_figheight(6)
    fig.set_figwidth(10)
    if not os.path.exists('./image'):
        os.makedirs('./image')
    fig.savefig('image/ranges.png', dpi=48)
    plt.close(fig)


def load_data(filename, feature_num=14, ratio=0.8):
    global UCI_TRAIN_DATA, UCI_TEST_DATA
    if UCI_TRAIN_DATA is not None and UCI_TEST_DATA is not None:
        return

    data = np.fromfile(filename, sep=' ')
    data = data.reshape(data.shape[0] / feature_num, feature_num)
    maximums, minimums, avgs = data.max(axis=0), data.min(axis=0), data.sum(
        axis=0) / data.shape[0]
    feature_range(maximums[:-1], minimums[:-1])
    for i in xrange(feature_num - 1):
        data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i])
    offset = int(data.shape[0] * ratio)
    UCI_TRAIN_DATA = data[:offset]
    UCI_TEST_DATA = data[offset:]


def train():
    global UCI_TRAIN_DATA
    load_data(download(URL, 'uci_housing', MD5))

    def reader():
        for d in UCI_TRAIN_DATA:
            yield d[:-1], d[-1:]

    return reader


def test():
    global UCI_TEST_DATA
    load_data(download(URL, 'uci_housing', MD5))

    def reader():
        for d in UCI_TEST_DATA:
            yield d[:-1], d[-1:]

    return reader
Y
Yancey1989 已提交
92 93


94 95
def fetch():
    download(URL, 'uci_housing', MD5)