Iris

docs/new_dataset.rst

@@ -235,7 +235,7 @@ Try downloading and converting the data file:
     cd $FUEL_DATA_PATH
     fuel-download iris
     fuel-convert iris
-    fuel-download --clear iris
+    fuel-download iris --clear
     cd -
 
 You can now use the Iris dataset like you would use any other built-in dataset:
@@ -300,7 +300,7 @@ You can now use the Iris dataset like you would use any other built-in dataset:
     ...         delimiter=',')
     ...     numpy.random.shuffle(data)
     ...     features = data[:, :-1].astype('float32')
-    ...     targets = data[:, -1:].astype('uint8')
+    ...     targets = data[:, -1:].astype('uint8').reshape((-1, 1))
     ...     train_features = features[:100]
     ...     train_targets = targets[:100]
     ...     valid_features = features[100:120]

fuel/converters/__init__.py

@@ -14,6 +14,7 @@
 from fuel.converters import caltech101_silhouettes
 from fuel.converters import cifar10
 from fuel.converters import cifar100
+from fuel.converters import iris
 from fuel.converters import mnist
 from fuel.converters import svhn
 
@@ -23,5 +24,6 @@
     ('caltech101_silhouettes', caltech101_silhouettes.fill_subparser),
     ('cifar10', cifar10.fill_subparser),
     ('cifar100', cifar100.fill_subparser),
+    ('iris', iris.fill_subparser),
     ('mnist', mnist.fill_subparser),
     ('svhn', svhn.fill_subparser))

fuel/converters/iris.py

@@ -0,0 +1,58 @@
+import os
+
+import h5py
+import numpy
+
+from fuel.converters.base import fill_hdf5_file
+
+
+def convert_iris(directory, output_directory, output_filename='iris.hdf5'):
+    """Convert the Iris dataset to HDF5.
+
+    Converts the Iris dataset to an HDF5 dataset compatible with
+    :class:`fuel.datasets.Iris`. The converted dataset is
+    saved as 'iris.hdf5'.
+    This method assumes the existence of the file `iris.data`.
+
+    Parameters
+    ----------
+    directory : str
+        Directory in which input files reside.
+    output_directory : str
+        Directory in which to save the converted dataset.
+    output_filename : str, optional
+        Name of the saved dataset. Defaults to `None`, in which case a name
+        based on `dtype` will be used.
+
+    Returns
+    -------
+    output_paths : tuple of str
+        Single-element tuple containing the path to the converted dataset.
+
+    """
+    classes = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}
+    data = numpy.loadtxt(
+        os.path.join(directory, 'iris.data'),
+        converters={4: lambda x: classes[x]},
+        delimiter=',')
+    features = data[:, :-1].astype('float32')
+    targets = data[:, -1].astype('uint8').reshape((-1, 1))
+    data = (('all', 'features', features),
+            ('all', 'targets', targets))
+
+    output_path = os.path.join(output_directory, output_filename)
+    h5file = h5py.File(output_path, mode='w')
+    fill_hdf5_file(h5file, data)
+    h5file['features'].dims[0].label = 'batch'
+    h5file['features'].dims[1].label = 'feature'
+    h5file['targets'].dims[0].label = 'batch'
+    h5file['targets'].dims[1].label = 'index'
+
+    h5file.flush()
+    h5file.close()
+
+    return (output_path,)
+
+
+def fill_subparser(subparser):
+    subparser.set_defaults(func=convert_iris)

fuel/datasets/__init__.py

@@ -7,6 +7,7 @@
 from fuel.datasets.cifar10 import CIFAR10
 from fuel.datasets.cifar100 import CIFAR100
 from fuel.datasets.caltech101_silhouettes import CalTech101Silhouettes
+from fuel.datasets.iris import Iris
 from fuel.datasets.mnist import MNIST
 from fuel.datasets.svhn import SVHN
 from fuel.datasets.text import TextFile

fuel/datasets/iris.py

@@ -0,0 +1,31 @@
+from fuel.datasets import H5PYDataset
+from fuel.utils import find_in_data_path
+
+
+class Iris(H5PYDataset):
+    u"""Iris dataset.
+
+    Iris [LBBH] is a simple pattern recognition dataset, which consist of
+    3 classes of 50 examples each having 4 real-valued features each, where
+    each class refers to a type of iris plant. It is accessible through the
+    UCI Machine Learning repository [UCI].
+
+    .. [IRIS] Ronald A. Fisher, *The use of multiple measurements in
+       taxonomic problems*, Annual Eugenics, 7, Part II, 179-188,
+       September 1936.
+    .. [UCI] https://archive.ics.uci.edu/ml/datasets/Iris
+
+    Parameters
+    ----------
+    which_sets : tuple of str
+        Which split to load. Valid value is 'all'
+        corresponding to 150 examples.
+
+    """
+    filename = 'iris.hdf5'
+
+    def __init__(self, which_sets, **kwargs):
+        kwargs.setdefault('load_in_memory', True)
+        super(Iris, self).__init__(
+            file_or_path=find_in_data_path(self.filename),
+            which_sets=which_sets, **kwargs)

fuel/downloaders/__init__.py

@@ -10,6 +10,7 @@
 from fuel.downloaders import caltech101_silhouettes
 from fuel.downloaders import cifar10
 from fuel.downloaders import cifar100
+from fuel.downloaders import iris
 from fuel.downloaders import mnist
 from fuel.downloaders import svhn
 
@@ -18,5 +19,6 @@
     ('caltech101_silhouettes', caltech101_silhouettes.fill_subparser),
     ('cifar10', cifar10.fill_subparser),
     ('cifar100', cifar100.fill_subparser),
+    ('iris', iris.fill_subparser),
     ('mnist', mnist.fill_subparser),
     ('svhn', svhn.fill_subparser))

fuel/downloaders/iris.py

@@ -0,0 +1,22 @@
+from fuel.downloaders.base import default_downloader
+
+
+def fill_subparser(subparser):
+    """Set up a subparser to download the Iris dataset file.
+
+    The Iris dataset file `iris.data` is downloaded from the UCI
+    Machine Learning Repository [UCI].
+
+    .. [UCI] https://archive.ics.uci.edu/ml/datasets/Iris
+
+    Parameters
+    ----------
+    subparser : :class:`argparse.ArgumentParser`
+        Subparser handling the iris command.
+
+    """
+    subparser.set_defaults(
+        func=default_downloader,
+        urls=['https://archive.ics.uci.edu/ml/machine-learning-databases/'
+              'iris/iris.data'],
+        filenames=['iris.data'])

fuel/downloaders/mnist.py

@@ -4,7 +4,7 @@
 def fill_subparser(subparser):
     """Sets up a subparser to download the MNIST dataset files.
 
-    The following MNIST dataset files are downoladed from Yann LeCun's
+    The following MNIST dataset files are downloaded from Yann LeCun's
     website [LECUN]:
     `train-images-idx3-ubyte.gz`, `train-labels-idx1-ubyte.gz`,
     `t10k-images-idx3-ubyte.gz`, `t10k-labels-idx1-ubyte.gz`.

tests/test_converters.py

@@ -19,8 +19,9 @@
 from fuel.converters.base import (fill_hdf5_file, check_exists,
                                   MissingInputFiles)
 from fuel.converters import (binarized_mnist, caltech101_silhouettes,
-                             cifar10, cifar100, mnist, svhn)
+                             iris, cifar10, cifar100, mnist, svhn)
 from fuel.downloaders.caltech101_silhouettes import silhouettes_downloader
+from fuel.downloaders.base import default_downloader
 
 if six.PY3:
     getbuffer = memoryview
@@ -424,6 +425,50 @@ def test_download_and_convert(self, size=16):
             assert h5['targets'].shape == (8641, 1)
 
 
+class TestIris(object):
+    def setUp(self):
+        self.tempdir = tempfile.mkdtemp()
+
+    def tearDown(self):
+        shutil.rmtree(self.tempdir)
+
+    def test_download_and_convert(self):
+        tempdir = self.tempdir
+
+        cwd = os.getcwd()
+        os.chdir(tempdir)
+
+        assert_raises(IOError,
+                      iris.convert_iris,
+                      directory=tempdir,
+                      output_directory=tempdir)
+
+        default_downloader(
+            directory=tempdir,
+            urls=['https://archive.ics.uci.edu/ml/machine-learning-databases/'
+                  'iris/iris.data'],
+            filenames=['iris.data'])
+
+        classes = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}
+        data = numpy.loadtxt(
+            os.path.join(tempdir, 'iris.data'),
+            converters={4: lambda x: classes[x]},
+            delimiter=',')
+        features = data[:, :-1].astype('float32')
+        targets = data[:, -1].astype('uint8').reshape((-1, 1))
+
+        iris.convert_iris(directory=tempdir,
+                          output_directory=tempdir)
+
+        os.chdir(cwd)
+
+        output_file = "iris.hdf5"
+        output_file = os.path.join(tempdir, output_file)
+        with h5py.File(output_file, 'r') as h5:
+            assert numpy.allclose(h5['features'], features)
+            assert numpy.allclose(h5['targets'], targets)
+
+
 class TestSVHN(object):
     def setUp(self):
         numpy.random.seed(9 + 5 + 2015)

tests/test_downloaders.py

@@ -8,7 +8,7 @@
 from numpy.testing import assert_equal, assert_raises
 
 from fuel.downloaders import (binarized_mnist, caltech101_silhouettes,
-                              cifar10, cifar100, mnist, svhn)
+                              cifar10, cifar100, iris, mnist, svhn)
 from fuel.downloaders.base import (download, default_downloader,
                                    filename_from_url, NeedURLPrefix,
                                    ensure_directory_exists)
@@ -128,6 +128,19 @@ def test_caltech101_silhouettes():
     assert args.func is caltech101_silhouettes.silhouettes_downloader
 
 
+def test_iris():
+    parser = argparse.ArgumentParser()
+    subparsers = parser.add_subparsers()
+    iris.fill_subparser(subparsers.add_parser('iris'))
+    args = parser.parse_args(['iris'])
+    urls = ['https://archive.ics.uci.edu/ml/machine-learning-databases/'
+            'iris/iris.data']
+    filenames = ['iris.data']
+    assert_equal(args.filenames, filenames)
+    assert_equal(args.urls, urls)
+    assert args.func is default_downloader
+
+
 def test_cifar10():
     parser = argparse.ArgumentParser()
     subparsers = parser.add_subparsers()

tests/test_iris.py

@@ -0,0 +1,37 @@
+import numpy
+
+from numpy.testing import assert_raises, assert_equal, assert_allclose
+
+from fuel import config
+from fuel.datasets import Iris
+from tests import skip_if_not_available
+
+
+def test_iris_all():
+    skip_if_not_available(datasets=['iris.hdf5'])
+
+    dataset = Iris(('all',), load_in_memory=False)
+    handle = dataset.open()
+    data, labels = dataset.get_data(handle, slice(0, 10))
+    assert data.dtype == config.floatX
+    assert data.shape == (10, 4)
+    assert labels.shape == (10, 1)
+    known = numpy.array([5.1, 3.5, 1.4, 0.2])
+    assert_allclose(data[0], known)
+    assert labels[0][0] == 0
+    assert dataset.num_examples == 150
+    dataset.close(handle)
+
+
+def test_iris_axes():
+    skip_if_not_available(datasets=['iris.hdf5'])
+
+    dataset = Iris(('all',), load_in_memory=False)
+    assert_equal(dataset.axis_labels['features'],
+                 ('batch', 'feature'))
+
+
+def test_iris_invalid_split():
+    skip_if_not_available(datasets=['iris.hdf5'])
+
+    assert_raises(ValueError, Iris, ('dummy',))