def test_read_simple(self):
f = tempfile.NamedTemporaryFile(delete=False)
f.write(simple_file.encode("ascii"))
f.close()
try:
X, Y, metas, attr_indices, class_indices, meta_indices = \
_io.sparse_read_float(f.name.encode("ascii"))
self.assertEqual(
attr_indices, {
b"abc": 0,
b"def": 1,
b"g": 2,
b"h": 3,
b"ij k": 4,
b"t": 5,
b"ij": 6,
b"kl": 7,
b"m": 8
})
np.testing.assert_almost_equal(
X.data, [1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 4, 1, 1])
np.testing.assert_equal(X.indices,
[0, 1, 2, 3, 4, 5, 1, 2, 3, 6, 7, 8, 1])
np.testing.assert_equal(X.indptr, [0, 6, 12, 13])
self.assertEqual(class_indices, {})
self.assertIsNone(Y)
self.assertEqual(meta_indices, {})
self.assertIsNone(metas)
finally:
os.remove(f.name)
def test_read_complex(self):
f = tempfile.NamedTemporaryFile(delete=False)
f.write(complex_file.encode("ascii"))
f.close()
try:
X, Y, metas, attr_indices, class_indices, meta_indices = \
_io.sparse_read_float(f.name.encode("ascii"))
self.assertEqual(attr_indices, {
b"abc": 0,
b"g": 1,
b"h": 2,
b"ij": 3
})
np.testing.assert_equal(X.data, [1, 1, 1, 1, 1, 1, 1])
np.testing.assert_equal(X.indices, [0, 1, 2, 3, 1, 2, 3])
np.testing.assert_equal(X.indptr, [0, 4, 7, 7])
self.assertEqual(class_indices, {b"k": 0, b"t": 1, b"kl": 2})
np.testing.assert_equal(Y.data, [5, 1, 1, 4])
np.testing.assert_equal(Y.indices, [0, 1, 0, 2])
np.testing.assert_equal(Y.indptr, [0, 2, 4, 4])
self.assertEqual(meta_indices, {b"m": 0, b"def": 1})
np.testing.assert_equal(metas.data, [1, 1])
np.testing.assert_equal(metas.indices, [0, 1])
np.testing.assert_equal(metas.indptr, [0, 0, 1, 2])
finally:
os.remove(f.name)
def test_read_complex(self):
f = tempfile.NamedTemporaryFile(delete=False)
f.write(complex_file.encode("ascii"))
f.close()
try:
X, Y, metas, attr_indices, class_indices, meta_indices = \
_io.sparse_read_float(f.name.encode("ascii"))
self.assertEqual(attr_indices,
{b"abc": 0, b"g": 1, b"h": 2, b"ij": 3})
np.testing.assert_equal(X.data, [1, 1, 1, 1, 1, 1, 1])
np.testing.assert_equal(X.indices, [0, 1, 2, 3, 1, 2, 3])
np.testing.assert_equal(X.indptr, [0, 4, 7, 7])
self.assertEqual(class_indices, {b"k": 0, b"t": 1, b"kl": 2})
np.testing.assert_equal(Y.data, [5, 1, 1, 4])
np.testing.assert_equal(Y.indices, [0, 1, 0, 2])
np.testing.assert_equal(Y.indptr, [0, 2, 4, 4])
self.assertEqual(meta_indices, {b"m": 0, b"def": 1})
np.testing.assert_equal(metas.data, [ 1, 1])
np.testing.assert_equal(metas.indices, [ 0, 1])
np.testing.assert_equal(metas.indptr, [0, 0, 1, 2])
finally:
os.remove(f.name)
def test_read_simple(self):
f = tempfile.NamedTemporaryFile(delete=False)
f.write(simple_file.encode("ascii"))
f.close()
try:
X, Y, metas, attr_indices, class_indices, meta_indices = \
_io.sparse_read_float(f.name.encode("ascii"))
self.assertEqual(attr_indices,
{b"abc": 0, b"def": 1, b"g": 2, b"h": 3, b"ij k": 4, b"t": 5,
b"ij": 6, b"kl": 7, b"m": 8})
np.testing.assert_almost_equal(X.data, [1, 1, 1, 1, 5, 1,
1, 1, 1, 1, 4, 1,
1])
np.testing.assert_equal(X.indices, [0, 1, 2, 3, 4, 5,
1, 2, 3, 6, 7, 8,
1])
np.testing.assert_equal(X.indptr, [0, 6, 12, 13])
self.assertEqual(class_indices, {})
self.assertIsNone(Y)
self.assertEqual(meta_indices, {})
self.assertIsNone(metas)
finally:
os.remove(f.name)
def read(self):
def constr_vars(inds):
if inds:
return [ContinuousVariable(x.decode("utf-8")) for _, x in
sorted((ind, name) for name, ind in inds.items())]
X, Y, metas, attr_indices, class_indices, meta_indices = \
_io.sparse_read_float(self.filename.encode(sys.getdefaultencoding()))
attrs = constr_vars(attr_indices)
classes = constr_vars(class_indices)
meta_attrs = constr_vars(meta_indices)
domain = Domain(attrs, classes, meta_attrs)
return Table.from_numpy(
domain, attrs and X, classes and Y, metas and meta_attrs)
def read_file(cls, filename, storage_class=None):
if storage_class is None:
from ..data import Table as storage_class
def constr_vars(inds):
if inds:
return [ContinuousVariable(x.decode("utf-8")) for _, x in
sorted((ind, name) for name, ind in inds.items())]
X, Y, metas, attr_indices, class_indices, meta_indices = \
_io.sparse_read_float(filename.encode(sys.getdefaultencoding()))
attrs = constr_vars(attr_indices)
classes = constr_vars(class_indices)
meta_attrs = constr_vars(meta_indices)
domain = Domain(attrs, classes, meta_attrs)
return storage_class.from_numpy(
domain, attrs and X, classes and Y, metas and meta_attrs)
def read_file(cls, filename, storage_class=None):
if storage_class is None:
from ..data import Table as storage_class
def constr_vars(inds):
if inds:
return [
ContinuousVariable(x.decode("utf-8")) for _, x in sorted(
(ind, name) for name, ind in inds.items())
]
X, Y, metas, attr_indices, class_indices, meta_indices = \
_io.sparse_read_float(filename.encode(sys.getdefaultencoding()))
attrs = constr_vars(attr_indices)
classes = constr_vars(class_indices)
meta_attrs = constr_vars(meta_indices)
domain = Domain(attrs, classes, meta_attrs)
return storage_class.from_numpy(domain, attrs and X, classes and Y,
metas and meta_attrs)