chore: import upstream snapshot with attribution

python/paddle/text/datasets/uci_housing.py

@@ -0,0 +1,160 @@
+#   Copyright (c) 2020 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.
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Literal
+
+import numpy as np
+
+import paddle
+from paddle.dataset.common import _check_exists_and_download
+from paddle.io import Dataset
+
+if TYPE_CHECKING:
+    import numpy.typing as npt
+
+    from paddle._typing.dtype_like import _DTypeLiteral
+
+    _UciHousingDataSetMode = Literal["train", "test"]
+__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',
+]
+
+
+class UCIHousing(Dataset):
+    """
+    Implementation of `UCI housing <https://archive.ics.uci.edu/ml/datasets/Housing>`_
+    dataset
+
+    Args:
+        data_file(str|None): path to data file, can be set None if
+            :attr:`download` is True. Default None.
+        mode(str): 'train' or 'test' mode. Default 'train'.
+        download(bool): whether to download dataset automatically if
+            :attr:`data_file` is not set. Default True.
+
+    Returns:
+        Dataset: instance of UCI housing dataset.
+
+    Examples:
+
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> from paddle.text.datasets import UCIHousing
+
+            >>> class SimpleNet(paddle.nn.Layer):
+            ...     def __init__(self):
+            ...         super().__init__()
+            ...
+            ...     def forward(self, feature, target):
+            ...         return paddle.sum(feature), target
+
+            >>> paddle.disable_static()
+
+            >>> uci_housing = UCIHousing(mode='train')
+
+            >>> for i in range(10):
+            ...     feature, target = uci_housing[i]
+            ...     feature = paddle.to_tensor(feature)
+            ...     target = paddle.to_tensor(target)
+            ...
+            ...     model = SimpleNet()
+            ...     feature, target = model(feature, target)
+            ...     print(feature.shape, target.numpy())
+            paddle.Size([]) [24.]
+            paddle.Size([]) [21.6]
+            paddle.Size([]) [34.7]
+            paddle.Size([]) [33.4]
+            paddle.Size([]) [36.2]
+            paddle.Size([]) [28.7]
+            paddle.Size([]) [22.9]
+            paddle.Size([]) [27.1]
+            paddle.Size([]) [16.5]
+            paddle.Size([]) [18.9]
+
+    """
+
+    mode: _UciHousingDataSetMode
+    data_file: str | None
+    dtype: _DTypeLiteral
+
+    def __init__(
+        self,
+        data_file: str | None = None,
+        mode: _UciHousingDataSetMode = 'train',
+        download: bool = True,
+    ) -> None:
+        assert mode.lower() in [
+            'train',
+            'test',
+        ], f"mode should be 'train' or 'test', but got {mode}"
+        self.mode = mode.lower()
+
+        self.data_file = data_file
+        if self.data_file is None:
+            assert download, (
+                "data_file is not set and downloading automatically is disabled"
+            )
+            self.data_file = _check_exists_and_download(
+                data_file, URL, MD5, 'uci_housing', download
+            )
+
+        # read dataset into memory
+        self._load_data()
+
+        self.dtype = paddle.get_default_dtype()
+
+    def _load_data(self, feature_num: int = 14, ratio: float = 0.8) -> None:
+        data = np.fromfile(self.data_file, 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],
+        )
+        for i in range(feature_num - 1):
+            data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i])
+        offset = int(data.shape[0] * ratio)
+        if self.mode == 'train':
+            self.data = data[:offset]
+        elif self.mode == 'test':
+            self.data = data[offset:]
+
+    def __getitem__(
+        self, idx: int
+    ) -> tuple[npt.NDArray[Any], npt.NDArray[Any]]:
+        data = self.data[idx]
+        return np.array(data[:-1]).astype(self.dtype), np.array(
+            data[-1:]
+        ).astype(self.dtype)
+
+    def __len__(self) -> int:
+        return len(self.data)