def testNDimension(self):
with self.cached_session() as sess:
content = [["1 1:3.4 2:0.5 4:0.231", "1 1:3.4 2:0.5 4:0.231"],
["1 2:2.5 3:inf 5:0.503", "1 2:2.5 3:inf 5:0.503"],
["2 3:2.5 2:nan 1:0.105", "2 3:2.5 2:nan 1:0.105"]]
sparse_features, labels = libsvm_io.decode_libsvm(
content, num_features=6, label_dtype=dtypes.float64)
features = sparse.to_dense(sparse_features, validate_indices=False)
self.assertAllEqual(labels.get_shape().as_list(), [3, 2])
features, labels = sess.run([features, labels])
self.assertAllEqual(labels, [[1, 1], [1, 1], [2, 2]])
self.assertAllClose(features, [
[[0, 3.4, 0.5, 0, 0.231, 0], [0, 3.4, 0.5, 0, 0.231, 0]],
[[0, 0, 2.5, np.inf, 0, 0.503], [0, 0, 2.5, np.inf, 0, 0.503]],
[[0, 0.105, np.nan, 2.5, 0, 0], [0, 0.105, np.nan, 2.5, 0, 0]]
])
def test_basic(self):
"""test_basic"""
with self.cached_session() as sess:
content = [
"1 1:3.4 2:0.5 4:0.231", "1 2:2.5 3:inf 5:0.503",
"2 3:2.5 2:nan 1:0.105"
]
sparse_features, labels = libsvm_io.decode_libsvm(content,
num_features=6)
features = sparse.to_dense(sparse_features, validate_indices=False)
self.assertAllEqual(labels.get_shape().as_list(), [3])
features, labels = sess.run([features, labels])
self.assertAllEqual(labels, [1, 1, 2])
self.assertAllClose(
features,
[[0, 3.4, 0.5, 0, 0.231, 0], [0, 0, 2.5, np.inf, 0, 0.503],
[0, 0.105, np.nan, 2.5, 0, 0]])