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"""Testing utilities."""
# Copyright (c) 2011 Pietro Berkes
# License: Simplified BSD
from .fixes import savemat
import urllib2
from StringIO import StringIO
import scipy as sp
try:
from nose.tools import assert_in, assert_not_in
except ImportError:
# Nose < 1.0.0
from nose.tools import assert_true, assert_false
def assert_in(x, container):
assert_true(x in container, msg="%r in %r" % (x, container))
def assert_not_in(x, container):
assert_false(x in container, msg="%r in %r" % (x, container))
def _assert_less(a, b, msg=None):
message = "%r is not lower than %r" % (a, b)
if msg is not None:
message += ": " + msg
assert a < b, message
def _assert_greater(a, b, msg=None):
message = "%r is not greater than %r" % (a, b)
if msg is not None:
message += ": " + msg
assert a > b, message
try:
from nose.tools import assert_less
except ImportError:
assert_less = _assert_less
try:
from nose.tools import assert_greater
except ImportError:
assert_greater = _assert_greater
def fake_mldata_cache(columns_dict, dataname, matfile, ordering=None):
"""Create a fake mldata data set in the cache_path.
Parameters
----------
columns_dict: contains data as
columns_dict[column_name] = array of data
dataname: name of data set
matfile: file-like object or file name
ordering: list of column_names, determines the ordering in the data set
Note: this function transposes all arrays, while fetch_mldata only
transposes 'data', keep that into account in the tests.
"""
datasets = dict(columns_dict)
# transpose all variables
for name in datasets:
datasets[name] = datasets[name].T
if ordering is None:
ordering = sorted(list(datasets.keys()))
# NOTE: setting up this array is tricky, because of the way Matlab
# re-packages 1D arrays
datasets['mldata_descr_ordering'] = sp.empty((1, len(ordering)),
dtype='object')
for i, name in enumerate(ordering):
datasets['mldata_descr_ordering'][0, i] = name
savemat(matfile, datasets, oned_as='column')
class mock_urllib2(object):
def __init__(self, mock_datasets):
"""Object that mocks the urllib2 module to fake requests to mldata.
`mock_datasets` is a dictionary of {dataset_name: data_dict}, or
{dataset_name: (data_dict, ordering).
`data_dict` itself is a dictionary of {column_name: data_array},
and `ordering` is a list of column_names to determine the ordering
in the data set (see `fake_mldata_cache` for details).
When requesting a dataset with a name that is in mock_datasets,
this object creates a fake dataset in a StringIO object and
returns it. Otherwise, it raises an URLError.
"""
self.mock_datasets = mock_datasets
class HTTPError(urllib2.URLError):
code = 404
def urlopen(self, urlname):
dataset_name = urlname.split('/')[-1]
if dataset_name in self.mock_datasets:
resource_name = '_' + dataset_name
matfile = StringIO()
dataset = self.mock_datasets[dataset_name]
ordering = None
if isinstance(dataset, tuple):
dataset, ordering = dataset
fake_mldata_cache(dataset, resource_name, matfile, ordering)
matfile.seek(0)
return matfile
else:
raise mock_urllib2.HTTPError('%s not found.' % urlname)
def quote(self, string, safe='/'):
return urllib2.quote(string, safe)
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