Uploaded Test files

venv/Lib/site-packages/sklearn/datasets/_california_housing.py

@@ -0,0 +1,193 @@
+"""California housing dataset.
+
+The original database is available from StatLib
+
+    http://lib.stat.cmu.edu/datasets/
+
+The data contains 20,640 observations on 9 variables.
+
+This dataset contains the average house value as target variable
+and the following input variables (features): average income,
+housing average age, average rooms, average bedrooms, population,
+average occupation, latitude, and longitude in that order.
+
+References
+----------
+
+Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
+Statistics and Probability Letters, 33 (1997) 291-297.
+
+"""
+# Authors: Peter Prettenhofer
+# License: BSD 3 clause
+
+from os.path import dirname, exists, join
+from os import makedirs, remove
+import tarfile
+
+import numpy as np
+import logging
+
+import joblib
+
+from . import get_data_home
+from ._base import _convert_data_dataframe
+from ._base import _fetch_remote
+from ._base import _pkl_filepath
+from ._base import RemoteFileMetadata
+from ..utils import Bunch
+from ..utils.validation import _deprecate_positional_args
+
+
+# The original data can be found at:
+# https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz
+ARCHIVE = RemoteFileMetadata(
+    filename='cal_housing.tgz',
+    url='https://ndownloader.figshare.com/files/5976036',
+    checksum=('aaa5c9a6afe2225cc2aed2723682ae40'
+              '3280c4a3695a2ddda4ffb5d8215ea681'))
+
+logger = logging.getLogger(__name__)
+
+
+@_deprecate_positional_args
+def fetch_california_housing(*, data_home=None, download_if_missing=True,
+                             return_X_y=False, as_frame=False):
+    """Load the California housing dataset (regression).
+
+    ==============   ==============
+    Samples total             20640
+    Dimensionality                8
+    Features                   real
+    Target           real 0.15 - 5.
+    ==============   ==============
+
+    Read more in the :ref:`User Guide <california_housing_dataset>`.
+
+    Parameters
+    ----------
+    data_home : optional, default: None
+        Specify another download and cache folder for the datasets. By default
+        all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
+
+    download_if_missing : optional, default=True
+        If False, raise a IOError if the data is not locally available
+        instead of trying to download the data from the source site.
+
+
+    return_X_y : boolean, default=False.
+        If True, returns ``(data.data, data.target)`` instead of a Bunch
+        object.
+
+        .. versionadded:: 0.20
+
+    as_frame : boolean, default=False
+        If True, the data is a pandas DataFrame including columns with
+        appropriate dtypes (numeric, string or categorical). The target is
+        a pandas DataFrame or Series depending on the number of target_columns.
+
+        .. versionadded:: 0.23
+
+    Returns
+    -------
+    dataset : :class:`~sklearn.utils.Bunch`
+        Dictionary-like object, with the following attributes.
+
+        data : ndarray, shape (20640, 8)
+            Each row corresponding to the 8 feature values in order.
+            If ``as_frame`` is True, ``data`` is a pandas object.
+        target : numpy array of shape (20640,)
+            Each value corresponds to the average
+            house value in units of 100,000.
+            If ``as_frame`` is True, ``target`` is a pandas object.
+        feature_names : list of length 8
+            Array of ordered feature names used in the dataset.
+        DESCR : string
+            Description of the California housing dataset.
+
+    (data, target) : tuple if ``return_X_y`` is True
+
+        .. versionadded:: 0.20
+
+    frame : pandas DataFrame
+        Only present when `as_frame=True`. DataFrame with ``data`` and
+        ``target``.
+
+        .. versionadded:: 0.23
+
+    Notes
+    -----
+
+    This dataset consists of 20,640 samples and 9 features.
+    """
+    data_home = get_data_home(data_home=data_home)
+    if not exists(data_home):
+        makedirs(data_home)
+
+    filepath = _pkl_filepath(data_home, 'cal_housing.pkz')
+    if not exists(filepath):
+        if not download_if_missing:
+            raise IOError("Data not found and `download_if_missing` is False")
+
+        logger.info('Downloading Cal. housing from {} to {}'.format(
+            ARCHIVE.url, data_home))
+
+        archive_path = _fetch_remote(ARCHIVE, dirname=data_home)
+
+        with tarfile.open(mode="r:gz", name=archive_path) as f:
+            cal_housing = np.loadtxt(
+                f.extractfile('CaliforniaHousing/cal_housing.data'),
+                delimiter=',')
+            # Columns are not in the same order compared to the previous
+            # URL resource on lib.stat.cmu.edu
+            columns_index = [8, 7, 2, 3, 4, 5, 6, 1, 0]
+            cal_housing = cal_housing[:, columns_index]
+
+            joblib.dump(cal_housing, filepath, compress=6)
+        remove(archive_path)
+
+    else:
+        cal_housing = joblib.load(filepath)
+
+    feature_names = ["MedInc", "HouseAge", "AveRooms", "AveBedrms",
+                     "Population", "AveOccup", "Latitude", "Longitude"]
+
+    target, data = cal_housing[:, 0], cal_housing[:, 1:]
+
+    # avg rooms = total rooms / households
+    data[:, 2] /= data[:, 5]
+
+    # avg bed rooms = total bed rooms / households
+    data[:, 3] /= data[:, 5]
+
+    # avg occupancy = population / households
+    data[:, 5] = data[:, 4] / data[:, 5]
+
+    # target in units of 100,000
+    target = target / 100000.0
+
+    module_path = dirname(__file__)
+    with open(join(module_path, 'descr', 'california_housing.rst')) as dfile:
+        descr = dfile.read()
+
+    X = data
+    y = target
+
+    frame = None
+    target_names = ["MedHouseVal", ]
+    if as_frame:
+        frame, X, y = _convert_data_dataframe("fetch_california_housing",
+                                              data,
+                                              target,
+                                              feature_names,
+                                              target_names)
+
+    if return_X_y:
+        return X, y
+
+    return Bunch(data=X,
+                 target=y,
+                 frame=frame,
+                 target_names=target_names,
+                 feature_names=feature_names,
+                 DESCR=descr)