def test_matrix_sdm(self):
matrix_filename = "test_matrix_sdm.sdm"
matrix_relative_path = "{}/{}".format(self.TEMP_DIR_NAME,
matrix_filename)
expected_matrix = scipy.sparse.rand(10, 20, 0.5)
matrix_io.write_matrix(matrix_relative_path, expected_matrix)
actual_matrix = matrix_io.read_matrix(matrix_relative_path)
self.assertTrue((expected_matrix != actual_matrix).nnz == 0)
def test_dense_matrix_csv(self):
matrix_filename = "test_dense_matrix_csv.csv"
matrix_relative_path = "{}/{}".format(self.TEMP_DIR_NAME,
matrix_filename)
expected_matrix = numpy.random.randn(10, 20)
matrix_io.write_matrix(matrix_relative_path, expected_matrix)
actual_matrix = matrix_io.read_matrix(matrix_relative_path)
self.assertTrue(numpy.allclose(actual_matrix, expected_matrix))
def test_matrix_ddm(self):
matrix_filename = "test_matrix_ddt.ddm"
matrix_relative_path = "{}/{}".format(self.TEMP_DIR_NAME,
matrix_filename)
expected_matrix = numpy.random.randn(10, 20)
matrix_io.write_matrix(matrix_relative_path, expected_matrix)
actual_matrix = matrix_io.read_matrix(matrix_relative_path)
self.assertTrue(numpy.array_equal(actual_matrix, expected_matrix))
def test_matrix_sparse_mtx(self):
matrix_filename = "test_matrix_sparse_mtx.mtx"
matrix_relative_path = "{}/{}".format(self.TEMP_DIR_NAME,
matrix_filename)
expected_matrix = scipy.sparse.rand(10, 20, 0.5)
matrix_io.write_matrix(matrix_relative_path, expected_matrix)
actual_matrix = matrix_io.read_matrix(matrix_relative_path)
self.assertTrue(
numpy.allclose(actual_matrix.todense(), expected_matrix.todense()))
def test_matrix_sbm(self):
matrix_filename = "test_matrix_sbm.sbm"
matrix_relative_path = "{}/{}".format(self.TEMP_DIR_NAME,
matrix_filename)
expected_dense_matrix = numpy.random.randint(0, 2, size=(10, 20))
expected_sparse_matrix = scipy.sparse.coo_matrix(expected_dense_matrix)
matrix_io.write_matrix(matrix_relative_path, expected_sparse_matrix)
actual_matrix = matrix_io.read_matrix(matrix_relative_path)
self.assertTrue((expected_sparse_matrix != actual_matrix).nnz == 0)
def af_cmd_oom_prediction(root_dir, sideinfo, test):
tmpdir = mkdtemp()
outfile = join(tmpdir, "predictions-average.ddm")
sideinfo_file = join(tmpdir, "sideinfo.mm")
mio.write_matrix(sideinfo_file, sideinfo.todense())
exec_file = os.path.abspath("./af_predict")
cmd = "%s --modeldir %s/ --out %s --features %s" % (exec_file, root_dir,
outfile, sideinfo_file)
subprocess.call(cmd, shell=True)
return calc_rmse(outfile, test)
m = m - mean
elif (args.mode == "none"):
pass
elif (args.mode != "none"):
raise ValueError("Unknown centering mode: %s" % (args.mode))
return m
train = mio.read_matrix(args.train)
test = mio.read_matrix(args.test)
assert train.shape == test.shape
mean_train = mean(train, args.mode)
centered_train = center(train, args.mode, mean_train)
centered_test = center(test, args.mode, mean_train)
mio.write_matrix(os.path.join(args.output, os.path.basename(args.train)),
centered_train)
mio.write_matrix(os.path.join(args.output, os.path.basename(args.test)),
centered_test)
features = []
if args.col_features: features += args.col_features
if args.row_features: features += args.row_features
for fname in features:
m = mio.read_matrix(fname)
if (args.mode == "global"):
mean_feat = mean_train
else:
mean_feat = mean(m, args.mode)
centered_feat = center(m, args.mode, mean_feat)
mio.write_matrix(os.path.join(args.output, os.path.basename(fname)),