Deprecate fetch_mldata (#11466)

jnothman · rth · commit fc56da504d8e · 2018-08-18T19:27:48.000+03:00
* API Deprecate fetch_mldata and update examples
* Use pytest's filterwarnings
* Rm unused import
* Remove broken doctest
* Refer user to openml URL
* DOC whatsnew tweak
diff --git a/doc/datasets/index.rst b/doc/datasets/index.rst
@@ -351,89 +351,6 @@ features::
 
  _`Faster API-compatible implementation`: https://github.com/mblondel/svmlight-loader
 
-..
-    For doctests:
-
-    >>> import numpy as np
-    >>> import os
-    >>> import tempfile
-    >>> # Create a temporary folder for the data fetcher
-    >>> custom_data_home = tempfile.mkdtemp()
-    >>> os.makedirs(os.path.join(custom_data_home, 'mldata'))
-
-
-.. _mldata:
-
-Downloading datasets from the mldata.org repository
----------------------------------------------------
-
-`mldata.org <http://mldata.org>`_ is a public repository for machine learning
-data, supported by the `PASCAL network <http://www.pascal-network.org>`_ .
-
-The ``sklearn.datasets`` package is able to directly download data
-sets from the repository using the function
-:func:`sklearn.datasets.fetch_mldata`.
-
-For example, to download the MNIST digit recognition database::
-
-  >>> from sklearn.datasets import fetch_mldata
-  >>> mnist = fetch_mldata('MNIST original', data_home=custom_data_home)
-
-The MNIST database contains a total of 70000 examples of handwritten digits
-of size 28x28 pixels, labeled from 0 to 9::
-
-  >>> mnist.data.shape
-  (70000, 784)
-  >>> mnist.target.shape
-  (70000,)
-  >>> np.unique(mnist.target)
-  array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
-
-After the first download, the dataset is cached locally in the path
-specified by the ``data_home`` keyword argument, which defaults to
-``~/scikit_learn_data/``::
-
-  >>> os.listdir(os.path.join(custom_data_home, 'mldata'))
-  ['mnist-original.mat']
-
-Data sets in `mldata.org <http://mldata.org>`_ do not adhere to a strict
-naming or formatting convention. :func:`sklearn.datasets.fetch_mldata` is
-able to make sense of the most common cases, but allows to tailor the
-defaults to individual datasets:
-
-* The data arrays in `mldata.org <http://mldata.org>`_ are most often
-  shaped as ``(n_features, n_samples)``. This is the opposite of the
-  ``scikit-learn`` convention, so :func:`sklearn.datasets.fetch_mldata`
-  transposes the matrix by default. The ``transpose_data`` keyword controls
-  this behavior::
-
-    >>> iris = fetch_mldata('iris', data_home=custom_data_home)
-    >>> iris.data.shape
-    (150, 4)
-    >>> iris = fetch_mldata('iris', transpose_data=False,
-    ...                     data_home=custom_data_home)
-    >>> iris.data.shape
-    (4, 150)
-
-* For datasets with multiple columns, :func:`sklearn.datasets.fetch_mldata`
-  tries to identify the target and data columns and rename them to ``target``
-  and ``data``. This is done by looking for arrays named ``label`` and
-  ``data`` in the dataset, and failing that by choosing the first array to be
-  ``target`` and the second to be ``data``. This behavior can be changed with
-  the ``target_name`` and ``data_name`` keywords, setting them to a specific
-  name or index number (the name and order of the columns in the datasets
-  can be found at its `mldata.org <http://mldata.org>`_ under the tab "Data"::
-
-    >>> iris2 = fetch_mldata('datasets-UCI iris', target_name=1, data_name=0,
-    ...                      data_home=custom_data_home)
-    >>> iris3 = fetch_mldata('datasets-UCI iris', target_name='class',
-    ...                      data_name='double0', data_home=custom_data_home)
-
-
-..
-    >>> import shutil
-    >>> shutil.rmtree(custom_data_home)
-
 .. _external_datasets:
 
 Loading from external datasets
diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
@@ -257,7 +257,6 @@ Loaders
    datasets.fetch_kddcup99
    datasets.fetch_lfw_pairs
    datasets.fetch_lfw_people
-   datasets.fetch_mldata
    datasets.fetch_olivetti_faces
    datasets.fetch_openml
    datasets.fetch_rcv1
@@ -1513,6 +1512,7 @@ To be removed in 0.22
    :template: deprecated_function.rst
 
    covariance.graph_lasso
+   datasets.fetch_mldata
 
 
 To be removed in 0.21
diff --git a/doc/whats_new/v0.20.rst b/doc/whats_new/v0.20.rst
@@ -209,6 +209,10 @@ Support for Python 3.3 has been officially dropped.
   data points could be generated. :issue:`10045` by :user:`Christian Braune
   <christianbraune79>`.
 
+- |API| Deprecated :func:`sklearn.datasets.fetch_mldata` to be removed in
+  version 0.22. mldata.org is no longer operational. Until removal it will
+  remain possible to load cached datasets. :issue:`11466` by `Joel Nothman`_.
+
 :mod:`sklearn.decomposition`
 ............................
 
diff --git a/examples/gaussian_process/plot_gpr_co2.py b/examples/gaussian_process/plot_gpr_co2.py
@@ -8,7 +8,7 @@
 hyperparameter optimization using gradient ascent on the
 log-marginal-likelihood. The data consists of the monthly average atmospheric
 CO2 concentrations (in parts per million by volume (ppmv)) collected at the
-Mauna Loa Observatory in Hawaii, between 1958 and 1997. The objective is to
+Mauna Loa Observatory in Hawaii, between 1958 and 2001. The objective is to
 model the CO2 concentration as a function of the time t.
 
 The kernel is composed of several terms that are responsible for explaining
@@ -57,24 +57,59 @@
 explained by the model. The figure shows also that the model makes very
 confident predictions until around 2015.
 """
-print(__doc__)
-
 # Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
 #
 # License: BSD 3 clause
 
+from __future__ import division, print_function
+
 import numpy as np
 
 from matplotlib import pyplot as plt
 
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels \
     import RBF, WhiteKernel, RationalQuadratic, ExpSineSquared
-from sklearn.datasets import fetch_mldata
+try:
+    from urllib.request import urlopen
+except ImportError:
+    # Python 2
+    from urllib2 import urlopen
+
+print(__doc__)
+
 
-data = fetch_mldata('mauna-loa-atmospheric-co2').data
-X = data[:, [1]]
-y = data[:, 0]
+def load_mauna_loa_atmospheric_c02():
+    url = ('http://cdiac.ess-dive.lbl.gov/'
+           'ftp/trends/co2/sio-keel-flask/maunaloa_c.dat')
+    months = []
+    ppmv_sums = []
+    counts = []
+    for line in urlopen(url):
+        line = line.decode('utf8')
+        if not line.startswith('MLO'):
+            # ignore headers
+            continue
+        station, date, weight, flag, ppmv = line.split()
+        y = date[:2]
+        m = date[2:4]
+        month_float = (int(('20' if y < '20' else '19') + y) +
+                       (int(m) - 1) / 12)
+        if not months or month_float != months[-1]:
+            months.append(month_float)
+            ppmv_sums.append(float(ppmv))
+            counts.append(1)
+        else:
+            # aggregate monthly sum to produce average
+            ppmv_sums[-1] += float(ppmv)
+            counts[-1] += 1
+
+    months = np.asarray(months).reshape(-1, 1)
+    avg_ppmvs = np.asarray(ppmv_sums) / counts
+    return months, avg_ppmvs
+
+
+X, y = load_mauna_loa_atmospheric_c02()
 
 # Kernel with parameters given in GPML book
 k1 = 66.0**2 * RBF(length_scale=67.0)  # long term smooth rising trend
diff --git a/examples/linear_model/plot_sgd_early_stopping.py b/examples/linear_model/plot_sgd_early_stopping.py
@@ -47,7 +47,7 @@
 import matplotlib.pyplot as plt
 
 from sklearn import linear_model
-from sklearn.datasets import fetch_mldata
+from sklearn.datasets import fetch_openml
 from sklearn.model_selection import train_test_split
 from sklearn.utils.testing import ignore_warnings
 from sklearn.exceptions import ConvergenceWarning
@@ -56,9 +56,10 @@
 print(__doc__)
 
 
-def load_mnist(n_samples=None, class_0=0, class_1=8):
+def load_mnist(n_samples=None, class_0='0', class_1='8'):
     """Load MNIST, select two classes, shuffle and return only n_samples."""
-    mnist = fetch_mldata('MNIST original')
+    # Load data from http://openml.org/d/554
+    mnist = fetch_openml('mnist_784', version=1)
 
     # take only two classes for binary classification
     mask = np.logical_or(mnist.target == class_0, mnist.target == class_1)
diff --git a/examples/linear_model/plot_sparse_logistic_regression_mnist.py b/examples/linear_model/plot_sparse_logistic_regression_mnist.py
@@ -20,7 +20,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from sklearn.datasets import fetch_mldata
+from sklearn.datasets import fetch_openml
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
@@ -35,9 +35,11 @@
 t0 = time.time()
 train_samples = 5000
 
-mnist = fetch_mldata('MNIST original')
-X = mnist.data.astype('float64')
+# Load data from https://www.openml.org/d/554
+mnist = fetch_openml('mnist_784', version=1)
+X = mnist.data
 y = mnist.target
+
 random_state = check_random_state(0)
 permutation = random_state.permutation(X.shape[0])
 X = X[permutation]
diff --git a/examples/multioutput/plot_classifier_chain_yeast.py b/examples/multioutput/plot_classifier_chain_yeast.py
@@ -32,24 +32,24 @@
 with randomly ordered chains).
 """
 
-print(__doc__)
-
 # Author: Adam Kleczewski
 # License: BSD 3 clause
 
 import numpy as np
 import matplotlib.pyplot as plt
+from sklearn.datasets import fetch_openml
 from sklearn.multioutput import ClassifierChain
 from sklearn.model_selection import train_test_split
 from sklearn.multiclass import OneVsRestClassifier
 from sklearn.metrics import jaccard_similarity_score
 from sklearn.linear_model import LogisticRegression
-from sklearn.datasets import fetch_mldata
 
-# Load a multi-label dataset
-yeast = fetch_mldata('yeast')
-X = yeast['data']
-Y = yeast['target'].transpose().toarray()
+print(__doc__)
+
+# Load a multi-label dataset from https://www.openml.org/d/40597
+yeast = fetch_openml('yeast', version=4)
+X = yeast.data
+Y = yeast.target == 'TRUE'
 X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.2,
                                                     random_state=0)
 
diff --git a/examples/neural_networks/plot_mnist_filters.py b/examples/neural_networks/plot_mnist_filters.py
@@ -20,15 +20,18 @@
 for a very short time. Training longer would result in weights with a much
 smoother spatial appearance.
 """
-print(__doc__)
-
 import matplotlib.pyplot as plt
-from sklearn.datasets import fetch_mldata
+from sklearn.datasets import fetch_openml
 from sklearn.neural_network import MLPClassifier
 
-mnist = fetch_mldata("MNIST original")
+print(__doc__)
+
+# Load data from https://www.openml.org/d/554
+mnist = fetch_openml('mnist_784', version=1)
+X = mnist.data
+y = mnist.target
+
 # rescale the data, use the traditional train/test split
-X, y = mnist.data / 255., mnist.target
 X_train, X_test = X[:60000], X[60000:]
 y_train, y_test = y[:60000], y[60000:]
 
diff --git a/sklearn/datasets/mldata.py b/sklearn/datasets/mldata.py
@@ -25,13 +25,19 @@
 
 from .base import get_data_home
 from ..utils import Bunch
+from ..utils import deprecated
 
 MLDATA_BASE_URL = "http://mldata.org/repository/data/download/matlab/%s"
 
 
+@deprecated('mldata_filename was deprecated in version 0.20 and will be '
+            'removed in version 0.22')
 def mldata_filename(dataname):
     """Convert a raw name for a data set in a mldata.org filename.
 
+    .. deprecated:: 0.20
+        Will be removed in version 0.22
+
     Parameters
     ----------
     dataname : str
@@ -46,10 +52,14 @@ def mldata_filename(dataname):
     return re.sub(r'[().]', '', dataname)
 
 
+@deprecated('fetch_mldata was deprecated in version 0.20 and will be removed '
+            'in version 0.22')
 def fetch_mldata(dataname, target_name='label', data_name='data',
                  transpose_data=True, data_home=None):
     """Fetch an mldata.org data set
 
+    mldata.org is no longer operational.
+
     If the file does not exist yet, it is downloaded from mldata.org .
 
     mldata.org does not have an enforced convention for storing data or
@@ -70,6 +80,9 @@ def fetch_mldata(dataname, target_name='label', data_name='data',
     mldata.org data sets may have multiple columns, which are stored in the
     Bunch object with their original name.
 
+    .. deprecated:: 0.20
+        Will be removed in version 0.22
+
     Parameters
     ----------
 
@@ -99,40 +112,6 @@ def fetch_mldata(dataname, target_name='label', data_name='data',
         'data', the data to learn, 'target', the classification labels,
         'DESCR', the full description of the dataset, and
         'COL_NAMES', the original names of the dataset columns.
-
-    Examples
-    --------
-    Load the 'iris' dataset from mldata.org:
-
-    >>> from sklearn.datasets.mldata import fetch_mldata
-    >>> import tempfile
-    >>> test_data_home = tempfile.mkdtemp()
-
-    >>> iris = fetch_mldata('iris', data_home=test_data_home)
-    >>> iris.target.shape
-    (150,)
-    >>> iris.data.shape
-    (150, 4)
-
-    Load the 'leukemia' dataset from mldata.org, which needs to be transposed
-    to respects the scikit-learn axes convention:
-
-    >>> leuk = fetch_mldata('leukemia', transpose_data=True,
-    ...                     data_home=test_data_home)
-    >>> leuk.data.shape
-    (72, 7129)
-
-    Load an alternative 'iris' dataset, which has different names for the
-    columns:
-
-    >>> iris2 = fetch_mldata('datasets-UCI iris', target_name=1,
-    ...                      data_name=0, data_home=test_data_home)
-    >>> iris3 = fetch_mldata('datasets-UCI iris',
-    ...                      target_name='class', data_name='double0',
-    ...                      data_home=test_data_home)
-
-    >>> import shutil
-    >>> shutil.rmtree(test_data_home)
     """
 
     # normalize dataset name
diff --git a/sklearn/datasets/tests/test_mldata.py b/sklearn/datasets/tests/test_mldata.py
diff --git a/sklearn/utils/testing.py b/sklearn/utils/testing.py
