def test_splitter():
ds = give_data()
# split with defaults
spl1 = Splitter('chunks')
assert_raises(NotImplementedError, spl1, ds)
splits = list(spl1.generate(ds))
assert_equal(len(splits), len(ds.sa['chunks'].unique))
for split in splits:
# it should have perform basic slicing!
assert_true(split.samples.base is ds.samples)
assert_equal(len(split.sa['chunks'].unique), 1)
assert_true('lastsplit' in split.a)
assert_true(splits[-1].a.lastsplit)
# now again, more customized
spl2 = Splitter('targets', attr_values = [0,1,1,2,3,3,3], count=4,
noslicing=True)
splits = list(spl2.generate(ds))
assert_equal(len(splits), 4)
for split in splits:
# it should NOT have perform basic slicing!
assert_false(split.samples.base is ds.samples)
assert_equal(len(split.sa['targets'].unique), 1)
assert_equal(len(split.sa['chunks'].unique), 10)
assert_true(splits[-1].a.lastsplit)
# two should be identical
assert_array_equal(splits[1].samples, splits[2].samples)
# now go wild and split by feature attribute
ds.fa['roi'] = np.repeat([0,1], 5)
# splitter should auto-detect that this is a feature attribute
spl3 = Splitter('roi')
splits = list(spl3.generate(ds))
assert_equal(len(splits), 2)
for split in splits:
assert_true(split.samples.base is ds.samples)
assert_equal(len(split.fa['roi'].unique), 1)
assert_equal(split.shape, (100, 5))
# and finally test chained splitters
cspl = ChainNode([spl2, spl3, spl1])
splits = list(cspl.generate(ds))
# 4 target splits and 2 roi splits each and 10 chunks each
assert_equal(len(splits), 80)
def test_sifter():
# somewhat duplicating the doctest
ds = Dataset(samples=np.arange(8).reshape((4,2)),
sa={'chunks': [ 0 , 1 , 2 , 3 ],
'targets': ['c', 'c', 'p', 'p']})
par = ChainNode([NFoldPartitioner(cvtype=2, attr='chunks'),
Sifter([('partitions', 2),
('targets', ['c', 'p'])])
])
dss = list(par.generate(ds))
assert_equal(len(dss), 4)
for ds_ in dss:
testing = ds[ds_.sa.partitions == 2]
assert_array_equal(np.unique(testing.sa.targets), ['c', 'p'])
# and we still have both targets present in training
training = ds[ds_.sa.partitions == 1]
assert_array_equal(np.unique(training.sa.targets), ['c', 'p'])
def test_discarded_boundaries(self):
ds = datasets['hollow']
# four runs
ds.sa['chunks'] = np.repeat(np.arange(4), 10)
# do odd even splitting for lots of boundaries in few splits
part = ChainNode([OddEvenPartitioner(),
StripBoundariesSamples('chunks', 1, 2)])
parts = [d.samples.sid for d in part.generate(ds)]
# both dataset should have the same samples, because the boundaries are
# identical and the same sample should be stripped
assert_array_equal(parts[0], parts[1])
# we strip 3 samples per boundary
assert_equal(len(parts[0]), len(ds) - (3 * 3))
for i in [9, 10, 11, 19, 20, 21, 29, 30, 31]:
assert_false(i in parts[0])
def plot_feature_hist(dataset, xlim=None, noticks=True,
targets_attr='targets', chunks_attr=None,
**kwargs):
"""Plot histograms of feature values for each labels.
Parameters
----------
dataset : Dataset
xlim : None or 2-tuple
Common x-axis limits for all histograms.
noticks : bool
If True, no axis ticks will be plotted. This is useful to save
space in large plots.
targets_attr : string, optional
Name of samples attribute to be used as targets
chunks_attr : None or string
If a string, a histogram will be plotted per each target and each
chunk (as defined in sa named `chunks_attr`), resulting is a
histogram grid (targets x chunks).
**kwargs
Any additional arguments are passed to matplotlib's hist().
"""
lsplit = ChainNode([NFoldPartitioner(1, attr=targets_attr),
Splitter('partitions', attr_values=[2])])
csplit = ChainNode([NFoldPartitioner(1, attr=chunks_attr),
Splitter('partitions', attr_values=[2])])
nrows = len(dataset.sa[targets_attr].unique)
ncols = len(dataset.sa[chunks_attr].unique)
def doplot(data):
"""Just a little helper which plots the histogram and removes
ticks etc"""
pl.hist(data, **kwargs)
if xlim is not None:
pl.xlim(xlim)
if noticks:
pl.yticks([])
pl.xticks([])
fig = 1
# for all labels
for row, ds in enumerate(lsplit.generate(dataset)):
if chunks_attr:
for col, d in enumerate(csplit.generate(ds)):
pl.subplot(nrows, ncols, fig)
doplot(d.samples.ravel())
if row == 0:
pl.title('C:' + str(d.sa[chunks_attr].unique[0]))
if col == 0:
pl.ylabel('L:' + str(d.sa[targets_attr].unique[0]))
fig += 1
else:
pl.subplot(1, nrows, fig)
doplot(ds.samples)
pl.title('L:' + str(ds.sa[targets_attr].unique[0]))
fig += 1