diff options
| -rw-r--r-- | tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py | 482 |
1 files changed, 255 insertions, 227 deletions
diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py index 929e83523a..707f93b2da 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py @@ -39,18 +39,18 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'sparse_input_a': sparse_tensor.SparseTensorValue( + 'sparse_input_args_a': { # example 0, ids [2] # example 1, ids [0, 1] - indices=((0, 0), (1, 0), (1, 1)), - values=(2, 0, 1), - dense_shape=(2, 2)), - 'sparse_input_b': sparse_tensor.SparseTensorValue( + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': (2, 0, 1), + 'dense_shape': (2, 2)}, + 'sparse_input_args_b': { # example 0, ids [1] # example 1, ids [2, 0] - indices=((0, 0), (1, 0), (1, 1)), - values=(1, 2, 0), - dense_shape=(2, 2)), + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': (1, 2, 0), + 'dense_shape': (2, 2)}, 'expected_input_layer': [ # example 0, ids_a [2], ids_b [1] [[5., 6., 14., 15., 16.], [0., 0., 0., 0., 0.]], @@ -58,20 +58,20 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): [[1., 2., 17., 18., 19.], [3., 4., 11., 12., 13.]],], 'expected_sequence_length': [1, 2]}, {'testcase_name': '3D', - 'sparse_input_a': sparse_tensor.SparseTensorValue( + 'sparse_input_args_a': { # feature 0, ids [[2], [0, 1]] # feature 1, ids [[0, 0], [1]] - indices=( + 'indices': ( (0, 0, 0), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0)), - values=(2, 0, 1, 0, 0, 1), - dense_shape=(2, 2, 2)), - 'sparse_input_b': sparse_tensor.SparseTensorValue( + 'values': (2, 0, 1, 0, 0, 1), + 'dense_shape': (2, 2, 2)}, + 'sparse_input_args_b': { # feature 0, ids [[1, 1], [1]] # feature 1, ids [[2], [0]] - indices=((0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0), (1, 1, 0)), - values=(1, 1, 1, 2, 0), - dense_shape=(2, 2, 2)), + 'indices': ((0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0), (1, 1, 0)), + 'values': (1, 1, 1, 2, 0), + 'dense_shape': (2, 2, 2)}, 'expected_input_layer': [ # feature 0, [a: 2, -, b: 1, 1], [a: 0, 1, b: 1, -] [[5., 6., 14., 15., 16.], [2., 3., 14., 15., 16.]], @@ -80,9 +80,11 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): 'expected_sequence_length': [2, 2]}, ) def test_embedding_column( - self, sparse_input_a, sparse_input_b, expected_input_layer, + self, sparse_input_args_a, sparse_input_args_b, expected_input_layer, expected_sequence_length): + sparse_input_a = sparse_tensor.SparseTensorValue(**sparse_input_args_a) + sparse_input_b = sparse_tensor.SparseTensorValue(**sparse_input_args_b) vocabulary_size = 3 embedding_dimension_a = 2 embedding_values_a = ( @@ -261,18 +263,18 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'sparse_input_a': sparse_tensor.SparseTensorValue( + 'sparse_input_args_a': { # example 0, ids [2] # example 1, ids [0, 1] - indices=((0, 0), (1, 0), (1, 1)), - values=(2, 0, 1), - dense_shape=(2, 2)), - 'sparse_input_b': sparse_tensor.SparseTensorValue( + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': (2, 0, 1), + 'dense_shape': (2, 2)}, + 'sparse_input_args_b': { # example 0, ids [1] # example 1, ids [1, 0] - indices=((0, 0), (1, 0), (1, 1)), - values=(1, 1, 0), - dense_shape=(2, 2)), + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': (1, 1, 0), + 'dense_shape': (2, 2)}, 'expected_input_layer': [ # example 0, ids_a [2], ids_b [1] [[0., 0., 1., 0., 1.], [0., 0., 0., 0., 0.]], @@ -280,20 +282,20 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): [[1., 0., 0., 0., 1.], [0., 1., 0., 1., 0.]]], 'expected_sequence_length': [1, 2]}, {'testcase_name': '3D', - 'sparse_input_a': sparse_tensor.SparseTensorValue( + 'sparse_input_args_a': { # feature 0, ids [[2], [0, 1]] # feature 1, ids [[0, 0], [1]] - indices=( + 'indices': ( (0, 0, 0), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0)), - values=(2, 0, 1, 0, 0, 1), - dense_shape=(2, 2, 2)), - 'sparse_input_b': sparse_tensor.SparseTensorValue( + 'values': (2, 0, 1, 0, 0, 1), + 'dense_shape': (2, 2, 2)}, + 'sparse_input_args_b': { # feature 0, ids [[1, 1], [1]] # feature 1, ids [[1], [0]] - indices=((0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0), (1, 1, 0)), - values=(1, 1, 1, 1, 0), - dense_shape=(2, 2, 2)), + 'indices': ((0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0), (1, 1, 0)), + 'values': (1, 1, 1, 1, 0), + 'dense_shape': (2, 2, 2)}, 'expected_input_layer': [ # feature 0, [a: 2, -, b: 1, 1], [a: 0, 1, b: 1, -] [[0., 0., 1., 0., 2.], [1., 1., 0., 0., 1.]], @@ -302,8 +304,11 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): 'expected_sequence_length': [2, 2]}, ) def test_indicator_column( - self, sparse_input_a, sparse_input_b, expected_input_layer, + self, sparse_input_args_a, sparse_input_args_b, expected_input_layer, expected_sequence_length): + sparse_input_a = sparse_tensor.SparseTensorValue(**sparse_input_args_a) + sparse_input_b = sparse_tensor.SparseTensorValue(**sparse_input_args_b) + vocabulary_size_a = 3 vocabulary_size_b = 2 @@ -350,30 +355,32 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'sparse_input': sparse_tensor.SparseTensorValue( + 'sparse_input_args': { # example 0, values [0., 1] # example 1, [10.] - indices=((0, 0), (0, 1), (1, 0)), - values=(0., 1., 10.), - dense_shape=(2, 2)), + 'indices': ((0, 0), (0, 1), (1, 0)), + 'values': (0., 1., 10.), + 'dense_shape': (2, 2)}, 'expected_input_layer': [ [[0.], [1.]], [[10.], [0.]]], 'expected_sequence_length': [2, 1]}, {'testcase_name': '3D', - 'sparse_input': sparse_tensor.SparseTensorValue( + 'sparse_input_args': { # feature 0, ids [[20, 3], [5]] # feature 1, ids [[3], [8]] - indices=((0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0), (1, 1, 0)), - values=(20, 3, 5., 3., 8.), - dense_shape=(2, 2, 2)), + 'indices': ((0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0), (1, 1, 0)), + 'values': (20, 3, 5., 3., 8.), + 'dense_shape': (2, 2, 2)}, 'expected_input_layer': [ [[20.], [3.], [5.], [0.]], [[3.], [0.], [8.], [0.]]], 'expected_sequence_length': [2, 2]}, ) def test_numeric_column( - self, sparse_input, expected_input_layer, expected_sequence_length): + self, sparse_input_args, expected_input_layer, expected_sequence_length): + sparse_input = sparse_tensor.SparseTensorValue(**sparse_input_args) + numeric_column = sfc.sequence_numeric_column('aaa') input_layer, sequence_length = sfc.sequence_input_layer( @@ -387,27 +394,27 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'sparse_input': sparse_tensor.SparseTensorValue( + 'sparse_input_args': { # example 0, values [0., 1., 2., 3., 4., 5., 6., 7.] # example 1, [10., 11., 12., 13.] - indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), - (0, 7), (1, 0), (1, 1), (1, 2), (1, 3)), - values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), - dense_shape=(2, 8)), + 'indices': ((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), + (0, 7), (1, 0), (1, 1), (1, 2), (1, 3)), + 'values': (0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + 'dense_shape': (2, 8)}, 'expected_input_layer': [ # The output of numeric_column._get_dense_tensor should be flattened. [[0., 1., 2., 3.], [4., 5., 6., 7.]], [[10., 11., 12., 13.], [0., 0., 0., 0.]]], 'expected_sequence_length': [2, 1]}, {'testcase_name': '3D', - 'sparse_input': sparse_tensor.SparseTensorValue( + 'sparse_input_args': { # example 0, values [[0., 1., 2., 3.]], [[4., 5., 6., 7.]] # example 1, [[10., 11., 12., 13.], []] - indices=((0, 0, 0), (0, 0, 1), (0, 0, 2), (0, 0, 3), - (0, 1, 0), (0, 1, 1), (0, 1, 2), (0, 1, 3), - (1, 0, 0), (1, 0, 1), (1, 0, 2), (1, 0, 3)), - values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), - dense_shape=(2, 2, 4)), + 'indices': ((0, 0, 0), (0, 0, 1), (0, 0, 2), (0, 0, 3), + (0, 1, 0), (0, 1, 1), (0, 1, 2), (0, 1, 3), + (1, 0, 0), (1, 0, 1), (1, 0, 2), (1, 0, 3)), + 'values': (0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + 'dense_shape': (2, 2, 4)}, 'expected_input_layer': [ # The output of numeric_column._get_dense_tensor should be flattened. [[0., 1., 2., 3.], [4., 5., 6., 7.]], @@ -415,8 +422,10 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): 'expected_sequence_length': [2, 1]}, ) def test_numeric_column_multi_dim( - self, sparse_input, expected_input_layer, expected_sequence_length): + self, sparse_input_args, expected_input_layer, expected_sequence_length): """Tests sequence_input_layer for multi-dimensional numeric_column.""" + sparse_input = sparse_tensor.SparseTensorValue(**sparse_input_args) + numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) input_layer, sequence_length = sfc.sequence_input_layer( @@ -460,28 +469,29 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'sparse_input': sparse_tensor.SparseTensorValue( + 'sparse_input_args': { # example 0, values [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]] # example 1, [[[10., 11.], [12., 13.]]] - indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), - (0, 7), (1, 0), (1, 1), (1, 2), (1, 3)), - values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), - dense_shape=(2, 8)), + 'indices': ((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), + (0, 7), (1, 0), (1, 1), (1, 2), (1, 3)), + 'values': (0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + 'dense_shape': (2, 8)}, 'expected_shape': [2, 2, 4]}, {'testcase_name': '3D', - 'sparse_input': sparse_tensor.SparseTensorValue( + 'sparse_input_args': { # example 0, values [[0., 1., 2., 3.]], [[4., 5., 6., 7.]] # example 1, [[10., 11., 12., 13.], []] - indices=((0, 0, 0), (0, 0, 1), (0, 0, 2), (0, 0, 3), - (0, 1, 0), (0, 1, 1), (0, 1, 2), (0, 1, 2), - (1, 0, 0), (1, 0, 1), (1, 0, 2), (1, 0, 3)), - values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), - dense_shape=(2, 2, 4)), + 'indices': ((0, 0, 0), (0, 0, 1), (0, 0, 2), (0, 0, 3), + (0, 1, 0), (0, 1, 1), (0, 1, 2), (0, 1, 2), + (1, 0, 0), (1, 0, 1), (1, 0, 2), (1, 0, 3)), + 'values': (0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + 'dense_shape': (2, 2, 4)}, 'expected_shape': [2, 2, 4]}, ) def test_static_shape_from_tensors_numeric( - self, sparse_input, expected_shape): + self, sparse_input_args, expected_shape): """Tests that we return a known static shape when we have one.""" + sparse_input = sparse_tensor.SparseTensorValue(**sparse_input_args) numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) input_layer, _ = sfc.sequence_input_layer( @@ -492,30 +502,31 @@ class SequenceInputLayerTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'sparse_input': sparse_tensor.SparseTensorValue( + 'sparse_input_args': { # example 0, ids [2] # example 1, ids [0, 1] # example 2, ids [] # example 3, ids [1] - indices=((0, 0), (1, 0), (1, 1), (3, 0)), - values=(2, 0, 1, 1), - dense_shape=(4, 2)), + 'indices': ((0, 0), (1, 0), (1, 1), (3, 0)), + 'values': (2, 0, 1, 1), + 'dense_shape': (4, 2)}, 'expected_shape': [4, 2, 3]}, {'testcase_name': '3D', - 'sparse_input': sparse_tensor.SparseTensorValue( + 'sparse_input_args': { # example 0, ids [[2]] # example 1, ids [[0, 1], [2]] # example 2, ids [] # example 3, ids [[1], [0, 2]] - indices=((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0), - (3, 0, 0), (3, 1, 0), (3, 1, 1)), - values=(2, 0, 1, 2, 1, 0, 2), - dense_shape=(4, 2, 2)), + 'indices': ((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0), + (3, 0, 0), (3, 1, 0), (3, 1, 1)), + 'values': (2, 0, 1, 2, 1, 0, 2), + 'dense_shape': (4, 2, 2)}, 'expected_shape': [4, 2, 3]} ) def test_static_shape_from_tensors_indicator( - self, sparse_input, expected_shape): + self, sparse_input_args, expected_shape): """Tests that we return a known static shape when we have one.""" + sparse_input = sparse_tensor.SparseTensorValue(**sparse_input_args) categorical_column = sfc.sequence_categorical_column_with_identity( key='aaa', num_buckets=3) indicator_column = fc.indicator_column(categorical_column) @@ -546,11 +557,12 @@ class ConcatenateContextInputTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': 'rank_lt_3', - 'seq_input': ops.convert_to_tensor(np.arange(100).reshape(10, 10))}, + 'seq_input_arg': np.arange(100).reshape(10, 10)}, {'testcase_name': 'rank_gt_3', - 'seq_input': ops.convert_to_tensor(np.arange(100).reshape(5, 5, 2, 2))} + 'seq_input_arg': np.arange(100).reshape(5, 5, 2, 2)} ) - def test_sequence_input_throws_error(self, seq_input): + def test_sequence_input_throws_error(self, seq_input_arg): + seq_input = ops.convert_to_tensor(seq_input_arg) context_input = ops.convert_to_tensor(np.arange(100).reshape(10, 10)) seq_input = math_ops.cast(seq_input, dtype=dtypes.float32) context_input = math_ops.cast(context_input, dtype=dtypes.float32) @@ -559,11 +571,12 @@ class ConcatenateContextInputTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': 'rank_lt_2', - 'context_input': ops.convert_to_tensor(np.arange(100))}, + 'context_input_arg': np.arange(100)}, {'testcase_name': 'rank_gt_2', - 'context_input': ops.convert_to_tensor(np.arange(100).reshape(5, 5, 4))} + 'context_input_arg': np.arange(100).reshape(5, 5, 4)} ) - def test_context_input_throws_error(self, context_input): + def test_context_input_throws_error(self, context_input_arg): + context_input = ops.convert_to_tensor(context_input_arg) seq_input = ops.convert_to_tensor(np.arange(100).reshape(5, 5, 4)) seq_input = math_ops.cast(seq_input, dtype=dtypes.float32) context_input = math_ops.cast(context_input, dtype=dtypes.float32) @@ -657,25 +670,27 @@ class SequenceCategoricalColumnWithIdentityTest( @parameterized.named_parameters( {'testcase_name': '2D', - 'inputs': sparse_tensor.SparseTensorValue( - indices=((0, 0), (1, 0), (1, 1)), - values=(1, 2, 0), - dense_shape=(2, 2)), - 'expected': sparse_tensor.SparseTensorValue( - indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), - values=np.array((1, 2, 0), dtype=np.int64), - dense_shape=(2, 2, 1))}, + 'inputs_args': { + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': (1, 2, 0), + 'dense_shape': (2, 2)}, + 'expected_args': { + 'indices': ((0, 0, 0), (1, 0, 0), (1, 1, 0)), + 'values': np.array((1, 2, 0), dtype=np.int64), + 'dense_shape': (2, 2, 1)}}, {'testcase_name': '3D', - 'inputs': sparse_tensor.SparseTensorValue( - indices=((0, 0, 2), (1, 0, 0), (1, 2, 0)), - values=(6, 7, 8), - dense_shape=(2, 2, 2)), - 'expected': sparse_tensor.SparseTensorValue( - indices=((0, 0, 2), (1, 0, 0), (1, 2, 0)), - values=(6, 7, 8), - dense_shape=(2, 2, 2))} + 'inputs_args': { + 'indices': ((0, 0, 2), (1, 0, 0), (1, 2, 0)), + 'values': (6, 7, 8), + 'dense_shape': (2, 2, 2)}, + 'expected_args': { + 'indices': ((0, 0, 2), (1, 0, 0), (1, 2, 0)), + 'values': (6, 7, 8), + 'dense_shape': (2, 2, 2)}} ) - def test_get_sparse_tensors(self, inputs, expected): + def test_get_sparse_tensors(self, inputs_args, expected_args): + inputs = sparse_tensor.SparseTensorValue(**inputs_args) + expected = sparse_tensor.SparseTensorValue(**expected_args) column = sfc.sequence_categorical_column_with_identity('aaa', num_buckets=9) id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs})) @@ -691,27 +706,29 @@ class SequenceCategoricalColumnWithHashBucketTest( @parameterized.named_parameters( {'testcase_name': '2D', - 'inputs': sparse_tensor.SparseTensorValue( - indices=((0, 0), (1, 0), (1, 1)), - values=('omar', 'stringer', 'marlo'), - dense_shape=(2, 2)), - 'expected': sparse_tensor.SparseTensorValue( - indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), + 'inputs_args': { + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': ('omar', 'stringer', 'marlo'), + 'dense_shape': (2, 2)}, + 'expected_args': { + 'indices': ((0, 0, 0), (1, 0, 0), (1, 1, 0)), # Ignored to avoid hash dependence in test. - values=np.array((0, 0, 0), dtype=np.int64), - dense_shape=(2, 2, 1))}, + 'values': np.array((0, 0, 0), dtype=np.int64), + 'dense_shape': (2, 2, 1)}}, {'testcase_name': '3D', - 'inputs': sparse_tensor.SparseTensorValue( - indices=((0, 0, 2), (1, 0, 0), (1, 2, 0)), - values=('omar', 'stringer', 'marlo'), - dense_shape=(2, 2, 2)), - 'expected': sparse_tensor.SparseTensorValue( - indices=((0, 0, 2), (1, 0, 0), (1, 2, 0)), + 'inputs_args': { + 'indices': ((0, 0, 2), (1, 0, 0), (1, 2, 0)), + 'values': ('omar', 'stringer', 'marlo'), + 'dense_shape': (2, 2, 2)}, + 'expected_args': { + 'indices': ((0, 0, 2), (1, 0, 0), (1, 2, 0)), # Ignored to avoid hash dependence in test. - values=np.array((0, 0, 0), dtype=np.int64), - dense_shape=(2, 2, 2))} + 'values': np.array((0, 0, 0), dtype=np.int64), + 'dense_shape': (2, 2, 2)}} ) - def test_get_sparse_tensors(self, inputs, expected): + def test_get_sparse_tensors(self, inputs_args, expected_args): + inputs = sparse_tensor.SparseTensorValue(**inputs_args) + expected = sparse_tensor.SparseTensorValue(**expected_args) column = sfc.sequence_categorical_column_with_hash_bucket( 'aaa', hash_bucket_size=10) @@ -742,25 +759,27 @@ class SequenceCategoricalColumnWithVocabularyFileTest( @parameterized.named_parameters( {'testcase_name': '2D', - 'inputs': sparse_tensor.SparseTensorValue( - indices=((0, 0), (1, 0), (1, 1)), - values=('marlo', 'skywalker', 'omar'), - dense_shape=(2, 2)), - 'expected': sparse_tensor.SparseTensorValue( - indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), - values=np.array((2, -1, 0), dtype=np.int64), - dense_shape=(2, 2, 1))}, + 'inputs_args': { + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': ('marlo', 'skywalker', 'omar'), + 'dense_shape': (2, 2)}, + 'expected_args': { + 'indices': ((0, 0, 0), (1, 0, 0), (1, 1, 0)), + 'values': np.array((2, -1, 0), dtype=np.int64), + 'dense_shape': (2, 2, 1)}}, {'testcase_name': '3D', - 'inputs': sparse_tensor.SparseTensorValue( - indices=((0, 0, 2), (1, 0, 0), (1, 2, 0)), - values=('omar', 'skywalker', 'marlo'), - dense_shape=(2, 2, 2)), - 'expected': sparse_tensor.SparseTensorValue( - indices=((0, 0, 2), (1, 0, 0), (1, 2, 0)), - values=np.array((0, -1, 2), dtype=np.int64), - dense_shape=(2, 2, 2))} + 'inputs_args': { + 'indices': ((0, 0, 2), (1, 0, 0), (1, 2, 0)), + 'values': ('omar', 'skywalker', 'marlo'), + 'dense_shape': (2, 2, 2)}, + 'expected_args': { + 'indices': ((0, 0, 2), (1, 0, 0), (1, 2, 0)), + 'values': np.array((0, -1, 2), dtype=np.int64), + 'dense_shape': (2, 2, 2)}} ) - def test_get_sparse_tensors(self, inputs, expected): + def test_get_sparse_tensors(self, inputs_args, expected_args): + inputs = sparse_tensor.SparseTensorValue(**inputs_args) + expected = sparse_tensor.SparseTensorValue(**expected_args) column = sfc.sequence_categorical_column_with_vocabulary_file( key='aaa', vocabulary_file=self._wire_vocabulary_file_name, @@ -779,25 +798,27 @@ class SequenceCategoricalColumnWithVocabularyListTest( @parameterized.named_parameters( {'testcase_name': '2D', - 'inputs': sparse_tensor.SparseTensorValue( - indices=((0, 0), (1, 0), (1, 1)), - values=('marlo', 'skywalker', 'omar'), - dense_shape=(2, 2)), - 'expected': sparse_tensor.SparseTensorValue( - indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)), - values=np.array((2, -1, 0), dtype=np.int64), - dense_shape=(2, 2, 1))}, + 'inputs_args': { + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': ('marlo', 'skywalker', 'omar'), + 'dense_shape': (2, 2)}, + 'expected_args': { + 'indices': ((0, 0, 0), (1, 0, 0), (1, 1, 0)), + 'values': np.array((2, -1, 0), dtype=np.int64), + 'dense_shape': (2, 2, 1)}}, {'testcase_name': '3D', - 'inputs': sparse_tensor.SparseTensorValue( - indices=((0, 0, 2), (1, 0, 0), (1, 2, 0)), - values=('omar', 'skywalker', 'marlo'), - dense_shape=(2, 2, 2)), - 'expected': sparse_tensor.SparseTensorValue( - indices=((0, 0, 2), (1, 0, 0), (1, 2, 0)), - values=np.array((0, -1, 2), dtype=np.int64), - dense_shape=(2, 2, 2))} + 'inputs_args': { + 'indices': ((0, 0, 2), (1, 0, 0), (1, 2, 0)), + 'values': ('omar', 'skywalker', 'marlo'), + 'dense_shape': (2, 2, 2)}, + 'expected_args': { + 'indices': ((0, 0, 2), (1, 0, 0), (1, 2, 0)), + 'values': np.array((0, -1, 2), dtype=np.int64), + 'dense_shape': (2, 2, 2)}} ) - def test_get_sparse_tensors(self, inputs, expected): + def test_get_sparse_tensors(self, inputs_args, expected_args): + inputs = sparse_tensor.SparseTensorValue(**inputs_args) + expected = sparse_tensor.SparseTensorValue(**expected_args) column = sfc.sequence_categorical_column_with_vocabulary_list( key='aaa', vocabulary_list=('omar', 'stringer', 'marlo')) @@ -815,14 +836,14 @@ class SequenceEmbeddingColumnTest( @parameterized.named_parameters( {'testcase_name': '2D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [2] # example 1, ids [0, 1] # example 2, ids [] # example 3, ids [1] - indices=((0, 0), (1, 0), (1, 1), (3, 0)), - values=(2, 0, 1, 1), - dense_shape=(4, 2)), + 'indices': ((0, 0), (1, 0), (1, 1), (3, 0)), + 'values': (2, 0, 1, 1), + 'dense_shape': (4, 2)}, 'expected': [ # example 0, ids [2] [[7., 11.], [0., 0.]], @@ -833,15 +854,15 @@ class SequenceEmbeddingColumnTest( # example 3, ids [1] [[3., 5.], [0., 0.]]]}, {'testcase_name': '3D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [[2]] # example 1, ids [[0, 1], [2]] # example 2, ids [] # example 3, ids [[1], [0, 2]] - indices=((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0), - (3, 0, 0), (3, 1, 0), (3, 1, 1)), - values=(2, 0, 1, 2, 1, 0, 2), - dense_shape=(4, 2, 2)), + 'indices': ((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0), + (3, 0, 0), (3, 1, 0), (3, 1, 1)), + 'values': (2, 0, 1, 2, 1, 0, 2), + 'dense_shape': (4, 2, 2)}, 'expected': [ # example 0, ids [[2]] [[7., 11.], [0., 0.]], @@ -852,7 +873,8 @@ class SequenceEmbeddingColumnTest( # example 3, ids [[1], [0, 2]] [[3., 5.], [4., 6.5]]]} ) - def test_get_sequence_dense_tensor(self, inputs, expected): + def test_get_sequence_dense_tensor(self, inputs_args, expected): + inputs = sparse_tensor.SparseTensorValue(**inputs_args) vocabulary_size = 3 embedding_dimension = 2 embedding_values = ( @@ -884,23 +906,24 @@ class SequenceEmbeddingColumnTest( @parameterized.named_parameters( {'testcase_name': '2D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [2] # example 1, ids [0, 1] - indices=((0, 0), (1, 0), (1, 1)), - values=(2, 0, 1), - dense_shape=(2, 2)), + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': (2, 0, 1), + 'dense_shape': (2, 2)}, 'expected_sequence_length': [1, 2]}, {'testcase_name': '3D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [[2]] # example 1, ids [[0, 1], [2]] - indices=((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0)), - values=(2, 0, 1, 2), - dense_shape=(2, 2, 2)), + 'indices': ((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0)), + 'values': (2, 0, 1, 2), + 'dense_shape': (2, 2, 2)}, 'expected_sequence_length': [1, 2]} ) - def test_sequence_length(self, inputs, expected_sequence_length): + def test_sequence_length(self, inputs_args, expected_sequence_length): + inputs = sparse_tensor.SparseTensorValue(**inputs_args) vocabulary_size = 3 categorical_column = sfc.sequence_categorical_column_with_identity( @@ -1124,14 +1147,14 @@ class SequenceIndicatorColumnTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [2] # example 1, ids [0, 1] # example 2, ids [] # example 3, ids [1] - indices=((0, 0), (1, 0), (1, 1), (3, 0)), - values=(2, 0, 1, 1), - dense_shape=(4, 2)), + 'indices': ((0, 0), (1, 0), (1, 1), (3, 0)), + 'values': (2, 0, 1, 1), + 'dense_shape': (4, 2)}, 'expected': [ # example 0, ids [2] [[0., 0., 1.], [0., 0., 0.]], @@ -1142,15 +1165,15 @@ class SequenceIndicatorColumnTest(test.TestCase, parameterized.TestCase): # example 3, ids [1] [[0., 1., 0.], [0., 0., 0.]]]}, {'testcase_name': '3D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [[2]] # example 1, ids [[0, 1], [2]] # example 2, ids [] # example 3, ids [[1], [2, 2]] - indices=((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0), - (3, 0, 0), (3, 1, 0), (3, 1, 1)), - values=(2, 0, 1, 2, 1, 2, 2), - dense_shape=(4, 2, 2)), + 'indices': ((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0), + (3, 0, 0), (3, 1, 0), (3, 1, 1)), + 'values': (2, 0, 1, 2, 1, 2, 2), + 'dense_shape': (4, 2, 2)}, 'expected': [ # example 0, ids [[2]] [[0., 0., 1.], [0., 0., 0.]], @@ -1161,7 +1184,8 @@ class SequenceIndicatorColumnTest(test.TestCase, parameterized.TestCase): # example 3, ids [[1], [2, 2]] [[0., 1., 0.], [0., 0., 2.]]]} ) - def test_get_sequence_dense_tensor(self, inputs, expected): + def test_get_sequence_dense_tensor(self, inputs_args, expected): + inputs = sparse_tensor.SparseTensorValue(**inputs_args) vocabulary_size = 3 categorical_column = sfc.sequence_categorical_column_with_identity( @@ -1176,23 +1200,24 @@ class SequenceIndicatorColumnTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [2] # example 1, ids [0, 1] - indices=((0, 0), (1, 0), (1, 1)), - values=(2, 0, 1), - dense_shape=(2, 2)), + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': (2, 0, 1), + 'dense_shape': (2, 2)}, 'expected_sequence_length': [1, 2]}, {'testcase_name': '3D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [[2]] # example 1, ids [[0, 1], [2]] - indices=((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0)), - values=(2, 0, 1, 2), - dense_shape=(2, 2, 2)), + 'indices': ((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0)), + 'values': (2, 0, 1, 2), + 'dense_shape': (2, 2, 2)}, 'expected_sequence_length': [1, 2]} ) - def test_sequence_length(self, inputs, expected_sequence_length): + def test_sequence_length(self, inputs_args, expected_sequence_length): + inputs = sparse_tensor.SparseTensorValue(**inputs_args) vocabulary_size = 3 categorical_column = sfc.sequence_categorical_column_with_identity( @@ -1269,27 +1294,28 @@ class SequenceNumericColumnTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, values [0., 1] # example 1, [10.] - indices=((0, 0), (0, 1), (1, 0)), - values=(0., 1., 10.), - dense_shape=(2, 2)), + 'indices': ((0, 0), (0, 1), (1, 0)), + 'values': (0., 1., 10.), + 'dense_shape': (2, 2)}, 'expected': [ [[0.], [1.]], [[10.], [0.]]]}, {'testcase_name': '3D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # feature 0, ids [[20, 3], [5]] # feature 1, ids [[3], [8]] - indices=((0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0), (1, 1, 0)), - values=(20, 3, 5., 3., 8.), - dense_shape=(2, 2, 2)), + 'indices': ((0, 0, 0), (0, 0, 1), (0, 1, 0), (1, 0, 0), (1, 1, 0)), + 'values': (20, 3, 5., 3., 8.), + 'dense_shape': (2, 2, 2)}, 'expected': [ [[20.], [3.], [5.], [0.]], [[3.], [0.], [8.], [0.]]]}, ) - def test_get_sequence_dense_tensor(self, inputs, expected): + def test_get_sequence_dense_tensor(self, inputs_args, expected): + inputs = sparse_tensor.SparseTensorValue(**inputs_args) numeric_column = sfc.sequence_numeric_column('aaa') dense_tensor, _ = numeric_column._get_sequence_dense_tensor( @@ -1335,23 +1361,23 @@ class SequenceNumericColumnTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'sparse_input': sparse_tensor.SparseTensorValue( + 'sparse_input_args': { # example 0, values [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]] # example 1, [[[10., 11.], [12., 13.]]] - indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), - (0, 7), (1, 0), (1, 1), (1, 2), (1, 3)), - values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), - dense_shape=(2, 8)), + 'indices': ((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), + (0, 7), (1, 0), (1, 1), (1, 2), (1, 3)), + 'values': (0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + 'dense_shape': (2, 8)}, 'expected_dense_tensor': [ [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]], [[[10., 11.], [12., 13.]], [[0., 0.], [0., 0.]]]]}, {'testcase_name': '3D', - 'sparse_input': sparse_tensor.SparseTensorValue( - indices=((0, 0, 0), (0, 0, 2), (0, 0, 4), (0, 0, 6), - (0, 1, 0), (0, 1, 2), (0, 1, 4), (0, 1, 6), - (1, 0, 0), (1, 0, 2), (1, 0, 4), (1, 0, 6)), - values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), - dense_shape=(2, 2, 8)), + 'sparse_input_args': { + 'indices': ((0, 0, 0), (0, 0, 2), (0, 0, 4), (0, 0, 6), + (0, 1, 0), (0, 1, 2), (0, 1, 4), (0, 1, 6), + (1, 0, 0), (1, 0, 2), (1, 0, 4), (1, 0, 6)), + 'values': (0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.), + 'dense_shape': (2, 2, 8)}, 'expected_dense_tensor': [ [[[0., 0.], [1., 0.]], [[2., 0.], [3., 0.]], [[4., 0.], [5., 0.]], [[6., 0.], [7., 0.]]], @@ -1359,8 +1385,9 @@ class SequenceNumericColumnTest(test.TestCase, parameterized.TestCase): [[0., 0.], [0., 0.]], [[0., 0.], [0., 0.]]]]}, ) def test_get_dense_tensor_multi_dim( - self, sparse_input, expected_dense_tensor): + self, sparse_input_args, expected_dense_tensor): """Tests get_sequence_dense_tensor for multi-dim numeric_column.""" + sparse_input = sparse_tensor.SparseTensorValue(**sparse_input_args) numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2)) dense_tensor, _ = numeric_column._get_sequence_dense_tensor( @@ -1372,43 +1399,44 @@ class SequenceNumericColumnTest(test.TestCase, parameterized.TestCase): @parameterized.named_parameters( {'testcase_name': '2D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [2] # example 1, ids [0, 1] - indices=((0, 0), (1, 0), (1, 1)), - values=(2., 0., 1.), - dense_shape=(2, 2)), + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': (2., 0., 1.), + 'dense_shape': (2, 2)}, 'expected_sequence_length': [1, 2], 'shape': (1,)}, {'testcase_name': '3D', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [[2]] # example 1, ids [[0, 1], [2]] - indices=((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0)), - values=(2., 0., 1., 2.), - dense_shape=(2, 2, 2)), + 'indices': ((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0)), + 'values': (2., 0., 1., 2.), + 'dense_shape': (2, 2, 2)}, 'expected_sequence_length': [1, 2], 'shape': (1,)}, {'testcase_name': '2D_with_shape', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [2] # example 1, ids [0, 1] - indices=((0, 0), (1, 0), (1, 1)), - values=(2., 0., 1.), - dense_shape=(2, 2)), + 'indices': ((0, 0), (1, 0), (1, 1)), + 'values': (2., 0., 1.), + 'dense_shape': (2, 2)}, 'expected_sequence_length': [1, 1], 'shape': (2,)}, {'testcase_name': '3D_with_shape', - 'inputs': sparse_tensor.SparseTensorValue( + 'inputs_args': { # example 0, ids [[2]] # example 1, ids [[0, 1], [2]] - indices=((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0)), - values=(2., 0., 1., 2.), - dense_shape=(2, 2, 2)), + 'indices': ((0, 0, 0), (1, 0, 0), (1, 0, 1), (1, 1, 0)), + 'values': (2., 0., 1., 2.), + 'dense_shape': (2, 2, 2)}, 'expected_sequence_length': [1, 2], 'shape': (2,)}, ) - def test_sequence_length(self, inputs, expected_sequence_length, shape): + def test_sequence_length(self, inputs_args, expected_sequence_length, shape): + inputs = sparse_tensor.SparseTensorValue(**inputs_args) numeric_column = sfc.sequence_numeric_column('aaa', shape=shape) _, sequence_length = numeric_column._get_sequence_dense_tensor( |