def test_savez_loadz():
m = get_random_coo_matrix()
f, path = tempfile.mkstemp(suffix=".npz")
savez(m, path)
n = loadz(path)
os.remove(path)
assert_array_equal(n.toarray(), m.toarray())
def test_savez_loadz():
m = get_random_coo_matrix()
f, path = tempfile.mkstemp(suffix='.npz')
savez(m, path)
n = loadz(path)
os.remove(path)
assert_array_equal(n.toarray(), m.toarray())
def test_init_fast_sparse_matrix():
X = get_random_coo_matrix()
Y = X.tocsr()
Z = X.tocsc()
for M in [X, Y, Z]:
m = fast_sparse_matrix(M)
assert_array_equal(m.X.toarray(), M.toarray())
assert_equal(m.shape, M.shape)
def test_init_fast_sparse_matrix():
X = get_random_coo_matrix()
Y = X.tocsr()
Z = X.tocsc()
for M in [X, Y, Z]:
m = fast_sparse_matrix(M)
assert_array_equal(m.X.toarray(), M.toarray())
assert_equal(m.shape, M.shape)
def test_loadtxt():
X = get_random_coo_matrix()
f, path = tempfile.mkstemp(suffix='.npz')
with open(path, 'w') as f:
for i, j, v in zip(X.row, X.col, X.data):
print >> f, '{0}\t{1}\t{2}'.format(i + 1, j + 1, v)
Y = loadtxt(path)
os.remove(path)
assert_sparse_matrix_equal(X, Y)
def test_loadtxt():
X = get_random_coo_matrix()
f, path = tempfile.mkstemp(suffix=".npz")
with open(path, "w") as f:
for i, j, v in zip(X.row, X.col, X.data):
print >> f, "{0}\t{1}\t{2}".format(i + 1, j + 1, v)
Y = loadtxt(path)
os.remove(path)
assert_sparse_matrix_equal(X, Y)
def test_save_load():
"""Save to file as arrays in numpy binary format."""
X = get_random_coo_matrix()
m = fast_sparse_matrix(X)
f, path = tempfile.mkstemp(suffix='.npz')
m.save(path)
n = fast_sparse_matrix.load(path)
os.remove(path)
assert_equal(m.shape, n.shape)
assert_array_equal(m.X.toarray(), n.X.toarray())
assert_array_equal(m.col_view.toarray(), n.col_view.toarray())
def test_save_load():
"""Save to file as arrays in numpy binary format."""
X = get_random_coo_matrix()
m = fast_sparse_matrix(X)
f, path = tempfile.mkstemp(suffix=".npz")
m.save(path)
n = fast_sparse_matrix.load(path)
os.remove(path)
assert_equal(m.shape, n.shape)
assert_array_equal(m.X.toarray(), n.X.toarray())
assert_array_equal(m.col_view.toarray(), n.col_view.toarray())
def test_zero_known_item_scores():
train = get_random_coo_matrix().tocsr()
predictions = np.random.random_sample(train.shape)
r = BaseRecommender()
safe = r._zero_known_item_scores(predictions, train)
num_users, num_items = predictions.shape
for u in xrange(num_users):
for i in xrange(num_items):
if i in train[u].indices:
assert_less_equal(safe[u, i], 0)
else:
assert_equal(safe[u, i], predictions[u, i])
def test_zero_known_item_scores():
train = get_random_coo_matrix().tocsr()
predictions = np.random.random_sample(train.shape)
r = BaseRecommender()
safe = r._zero_known_item_scores(predictions,train)
num_users,num_items = predictions.shape
for u in xrange(num_users):
for i in xrange(num_items):
if i in train[u].indices:
assert_less_equal(safe[u,i],0)
else:
assert_equal(safe[u,i],predictions[u,i])
def test_save_load_sparse_matrix():
X = get_random_coo_matrix()
for fmt in ["tsv", "csv", "npz", "mm", "fsm"]:
if fmt == "mm":
suffix = ".mtx"
elif fmt == "npz" or fmt == "fsm":
suffix = ".npz"
else:
suffix = ""
f, path = tempfile.mkstemp(suffix=suffix)
save_sparse_matrix(X, fmt, path)
Y = load_sparse_matrix(fmt, path)
assert_sparse_matrix_equal(X, Y)
os.remove(path)
def test_save_load_sparse_matrix():
X = get_random_coo_matrix()
for fmt in ['tsv','csv','npz','mm','fsm']:
if fmt == 'mm':
suffix = '.mtx'
elif fmt == 'npz' or fmt == 'fsm':
suffix = '.npz'
else:
suffix = ''
f,path = tempfile.mkstemp(suffix=suffix)
save_sparse_matrix(X,fmt,path)
Y = load_sparse_matrix(fmt,path)
assert_sparse_matrix_equal(X,Y)
os.remove(path)
def test_fast_update_col():
X = get_random_coo_matrix().tocsc()
m = fast_sparse_matrix(X)
cols = X.shape[1]
for j in xrange(cols):
vals = m.fast_get_col(j).data
if (vals == 0).all():
continue
vals[vals != 0] += 1
m.fast_update_col(j, vals)
expected = X[:, j].toarray()
for i in xrange(expected.shape[0]):
if expected[i] != 0:
expected[i] += 1
assert_array_equal(m.fast_get_col(j).toarray(), expected)
def test_fast_update_col():
X = get_random_coo_matrix().tocsc()
m = fast_sparse_matrix(X)
cols = X.shape[1]
for j in xrange(cols):
vals = m.fast_get_col(j).data
if (vals == 0).all():
continue
vals[vals != 0] += 1
m.fast_update_col(j, vals)
expected = X[:, j].toarray()
for i in xrange(expected.shape[0]):
if expected[i] != 0:
expected[i] += 1
assert_array_equal(m.fast_get_col(j).toarray(), expected)
def test_fast_get_col():
X = get_random_coo_matrix().tocsc()
m = fast_sparse_matrix(X)
rows, cols = X.shape
for j in xrange(cols):
assert_array_equal(m.fast_get_col(j).toarray(), X[:, j].toarray())
def test_fast_get_col():
X = get_random_coo_matrix().tocsc()
m = fast_sparse_matrix(X)
rows, cols = X.shape
for j in xrange(cols):
assert_array_equal(m.fast_get_col(j).toarray(), X[:, j].toarray())
