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_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_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())