def test_weight_column_should_not_be_used_as_feature(self):
with self.assertRaisesRegexp(ValueError,
'weight_column should not be used as feature'):
parsing_utils.regressor_parse_example_spec(
feature_columns=[fc.numeric_column('a')],
label_key='b',
weight_column=fc.numeric_column('a'))
def test_weight_column_should_not_be_used_as_feature(self):
with self.assertRaisesRegexp(ValueError,
'weight_column should not be used as feature'):
parsing_utils.regressor_parse_example_spec(
feature_columns=[fc.numeric_column('a')],
label_key='b',
weight_column=fc.numeric_column('a'))
def test_weight_column_should_be_a_numeric_column(self):
with self.assertRaisesRegexp(ValueError,
'tf.feature_column.numeric_column'):
not_a_numeric_column = 3
parsing_utils.regressor_parse_example_spec(
feature_columns=[fc.numeric_column('a')],
label_key='b',
weight_column=not_a_numeric_column)
def test_weight_column_should_be_a_numeric_column(self):
with self.assertRaisesRegexp(ValueError,
'tf.feature_column.numeric_column'):
not_a_numeric_column = 3
parsing_utils.regressor_parse_example_spec(
feature_columns=[fc.numeric_column('a')],
label_key='b',
weight_column=not_a_numeric_column)
def test_defaults(self):
parsing_spec = parsing_utils.regressor_parse_example_spec(
feature_columns=[fc.numeric_column('a')], label_key='b')
expected_spec = {
'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
'b': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
}
self.assertDictEqual(expected_spec, parsing_spec)
def test_defaults(self):
parsing_spec = parsing_utils.regressor_parse_example_spec(
feature_columns=[fc.numeric_column('a')], label_key='b')
expected_spec = {
'a': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
'b': parsing_ops.FixedLenFeature((1,), dtype=dtypes.float32),
}
self.assertDictEqual(expected_spec, parsing_spec)