def test_relation_sparse_2d_dataview_digest():
row = np.array([0, 3, 1, 0])
col = np.array([0, 3, 1, 2])
data = np.array([4, 5, 7, 9])
m = coo_matrix((data, (row, col)), shape=(4, 4))
view = sparse_2d_dataview(m)
view1 = sparse_2d_dataview(m)
assert_equals(_hexdigest(view), _hexdigest(view1))
row1 = np.array([0, 3, 1, 0])
col1 = np.array([0, 3, 1, 2])
data1 = np.array([4, 5, 7, 1])
m1 = coo_matrix((data1, (row1, col1)), shape=(4, 4))
view = sparse_2d_dataview(m)
view1 = sparse_2d_dataview(m1)
assert_not_equals(_hexdigest(view), _hexdigest(view1))
def test_relation_sparse_2d_dataview_digest():
row = np.array([0, 3, 1, 0])
col = np.array([0, 3, 1, 2])
data = np.array([4, 5, 7, 9])
m = coo_matrix((data, (row, col)), shape=(4, 4))
view = sparse_2d_dataview(m)
view1 = sparse_2d_dataview(m)
assert_equals(_hexdigest(view), _hexdigest(view1))
row1 = np.array([0, 3, 1, 0])
col1 = np.array([0, 3, 1, 2])
data1 = np.array([4, 5, 7, 1])
m1 = coo_matrix((data1, (row1, col1)), shape=(4, 4))
view = sparse_2d_dataview(m)
view1 = sparse_2d_dataview(m1)
assert_not_equals(_hexdigest(view), _hexdigest(view1))
def test_relation_sparse_2d_dataview_build_from_csc():
row = np.array([0, 3, 1, 0])
col = np.array([0, 3, 1, 2])
data = np.array([4, 5, 7, 9])
m = coo_matrix((data, (row, col)), shape=(4, 4))
csc = m.tocsc()
assert isinstance(csc, csc_matrix)
view = sparse_2d_dataview(csc)
assert view.shape() == (4, 4)
def test_relation_sparse_2d_dataview_build_from_csc():
row = np.array([0, 3, 1, 0])
col = np.array([0, 3, 1, 2])
data = np.array([4, 5, 7, 9])
m = coo_matrix((data, (row, col)), shape=(4, 4))
csc = m.tocsc()
assert isinstance(csc, csc_matrix)
view = sparse_2d_dataview(csc)
assert view.shape() == (4, 4)
def test_relation_sparse_2d_dataview_pickle():
def sparsify(y, fn=lambda x: x):
inds = it.product(range(y.shape[0]), range(y.shape[1]))
ijv = [(i, j, y[i, j]) for i, j in inds if fn(y[i, j])]
args = (map(op.itemgetter(2),
ijv), (map(op.itemgetter(0),
ijv), map(op.itemgetter(1), ijv)))
return coo_matrix(args, shape=y.shape)
y = np.random.randint(-2, 2, size=(10, 10))
view = sparse_2d_dataview(sparsify(y))
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
assert_equals((view.tocsr() != view1.tocsr()).nnz, 0)
y = np.random.uniform(size=(4, 3))
view = sparse_2d_dataview(sparsify(y, fn=lambda x: x >= 0.4 and x <= 0.6))
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
assert_almost_equals(np.abs(
(view.tocsr() - view1.tocsr()).todense()).max(),
0.,
places=3)
def test_relation_sparse_2d_dataview_pickle():
def sparsify(y, fn=lambda x: x):
inds = it.product(range(y.shape[0]), range(y.shape[1]))
ijv = [(i, j, y[i, j]) for i, j in inds if fn(y[i, j])]
args = (
map(op.itemgetter(2), ijv),
(map(op.itemgetter(0), ijv), map(op.itemgetter(1), ijv))
)
return coo_matrix(args, shape=y.shape)
y = np.random.randint(-2, 2, size=(10, 10))
view = sparse_2d_dataview(sparsify(y))
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
assert_equals((view.tocsr() != view1.tocsr()).nnz, 0)
y = np.random.uniform(size=(4, 3))
view = sparse_2d_dataview(sparsify(y, fn=lambda x: x >= 0.4 and x <= 0.6))
bstr = pickle.dumps(view)
view1 = pickle.loads(bstr)
assert_almost_equals(
np.abs((view.tocsr() - view1.tocsr()).todense()).max(), 0., places=3)