chore: import upstream snapshot with attribution

python/paddle/dataset/uci_housing.py

@@ -0,0 +1,190 @@
+# 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.
+"""
+UCI Housing dataset.
+
+This module will download dataset from
+https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ and
+parse training set and test set into paddle reader creators.
+"""
+
+import os
+import tarfile
+import tempfile
+
+import numpy as np
+
+import paddle.dataset.common
+from paddle.utils import deprecated
+
+__all__ = []
+
+URL = 'http://paddlemodels.bj.bcebos.com/uci_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
+
+FLUID_URL_MODEL = 'https://github.com/PaddlePaddle/book/raw/develop/01.fit_a_line/fluid/fit_a_line.fluid.tar'
+FLUID_MD5_MODEL = '6e6dd637ccd5993961f68bfbde46090b'
+
+
+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(
+        list(range(feature_num)), maximums - minimums, color='r', align='center'
+    )
+    ax.set_title('feature scale')
+    plt.xticks(list(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],
+    )
+    # if you want to print the distribution of input data, you could use function of feature_range
+    # feature_range(maximums[:-1], minimums[:-1])
+    for i in range(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:]
+
+
+@deprecated(
+    since="2.0.0",
+    update_to="paddle.text.datasets.UCIHousing",
+    level=1,
+    reason="Please use new dataset API which supports paddle.io.DataLoader",
+)
+def train():
+    """
+    UCI_HOUSING training set creator.
+
+    It returns a reader creator, each sample in the reader is features after
+    normalization and price number.
+
+    :return: Training reader creator
+    :rtype: callable
+    """
+    global UCI_TRAIN_DATA
+    load_data(paddle.dataset.common.download(URL, 'uci_housing', MD5))
+
+    def reader():
+        for d in UCI_TRAIN_DATA:
+            yield d[:-1], d[-1:]
+
+    return reader
+
+
+@deprecated(
+    since="2.0.0",
+    update_to="paddle.text.datasets.UCIHousing",
+    level=1,
+    reason="Please use new dataset API which supports paddle.io.DataLoader",
+)
+def test():
+    """
+    UCI_HOUSING test set creator.
+
+    It returns a reader creator, each sample in the reader is features after
+    normalization and price number.
+
+    :return: Test reader creator
+    :rtype: callable
+    """
+    global UCI_TEST_DATA
+    load_data(paddle.dataset.common.download(URL, 'uci_housing', MD5))
+
+    def reader():
+        for d in UCI_TEST_DATA:
+            yield d[:-1], d[-1:]
+
+    return reader
+
+
+def fluid_model():
+    parameter_tar = paddle.dataset.common.download(
+        FLUID_URL_MODEL, 'uci_housing', FLUID_MD5_MODEL, 'fit_a_line.fluid.tar'
+    )
+
+    tar = tarfile.TarFile(parameter_tar, mode='r')
+    dirpath = tempfile.mkdtemp()
+    tar.extractall(path=dirpath)
+
+    return dirpath
+
+
+@deprecated(
+    since="2.0.0",
+    update_to="paddle.text.datasets.UCIHousing",
+    level=1,
+    reason="Please use new dataset API which supports paddle.io.DataLoader",
+)
+def predict_reader():
+    """
+    It returns just one tuple data to do inference.
+
+    :return: one tuple data
+    :rtype: tuple
+    """
+    global UCI_TEST_DATA
+    load_data(paddle.dataset.common.download(URL, 'uci_housing', MD5))
+    return (UCI_TEST_DATA[0][:-1],)
+
+
+@deprecated(
+    since="2.0.0",
+    update_to="paddle.text.datasets.UCIHousing",
+    level=1,
+    reason="Please use new dataset API which supports paddle.io.DataLoader",
+)
+def fetch():
+    paddle.dataset.common.download(URL, 'uci_housing', MD5)