def test_decode_errors(self):
input_schema = dataset_schema.from_feature_spec({
'b':
tf.FixedLenFeature(shape=[], dtype=tf.float32),
'a':
tf.FixedLenFeature(shape=[], dtype=tf.string),
})
coder = csv_coder.CsvCoder(column_names=['a', 'b'],
schema=input_schema)
# Test bad csv.
with self.assertRaisesRegexp(
csv_coder.DecodeError,
'\'int\' object has no attribute \'encode\': 123'):
coder.decode(123)
# Test extra column.
with self.assertRaisesRegexp(
csv_coder.DecodeError,
'Columns do not match specified csv headers'):
coder.decode('1,2,')
# Test missing column.
with self.assertRaisesRegexp(
csv_coder.DecodeError,
'Columns do not match specified csv headers'):
coder.decode('a_value')
# Test empty row.
with self.assertRaisesRegexp(
csv_coder.DecodeError,
'Columns do not match specified csv headers'):
coder.decode('')
def test_missing_data(self):
coder = csv_coder.CsvCoder(self._COLUMNS, self._INPUT_SCHEMA)
data = '12,,female,1,89.0,12.0'
with self.assertRaisesRegexp(ValueError,
'expected a value on column "text1"'):
coder.decode(data)
def test_bad_boolean_data(self):
coder = csv_coder.CsvCoder(self._COLUMNS, self._INPUT_SCHEMA)
data = '12,text value,categorical_value,1,89.0,12.0,0'
with self.assertRaisesRegexp(ValueError,
'expected "True" or "False" as inputs'):
coder.decode(data)
def test_csv_coder_with_unicode(self):
data = u'12,"this is a ,text",Hello κόσμε,1,89.0,12.0,20'
coder = csv_coder.CsvCoder(self._COLUMNS, self._INPUT_SCHEMA)
# Python types.
expected_decoded = {
'category1': [u'Hello κόσμε'.encode('utf-8')],
'numeric1': 12,
'numeric2': [89.0],
'numeric3': [20],
'text1': 'this is a ,text',
'y': ([1], [12.0])
}
self._assert_encode_decode(coder, data, expected_decoded)
# Numpy types.
expected_decoded = {
'category1': np.array([u'Hello κόσμε'.encode('utf-8')]),
'numeric1': np.array(12),
'numeric2': np.array([89.0]),
'numeric3': np.array([20]),
'text1': np.array(['this is a ,text']),
'y': (np.array(1), np.array([12.0]))
}
self._assert_encode_decode(coder, data, expected_decoded)
def test_csv_coder(self):
data = '12,"this is a ,text",categorical_value,1,89.0,12.0,20'
coder = csv_coder.CsvCoder(self._COLUMNS, self._INPUT_SCHEMA)
# Python types.
expected_decoded = {
'category1': ['categorical_value'],
'numeric1': 12,
'numeric2': [89.0],
'numeric3': [20],
'text1': 'this is a ,text',
'y': ([1], [12.0])
}
self._assert_encode_decode(coder, data, expected_decoded)
# Numpy types.
expected_decoded = {
'category1': np.array(['categorical_value']),
'numeric1': np.array(12),
'numeric2': np.array([89.0]),
'numeric3': np.array([20]),
'text1': np.array(['this is a ,text']),
'y': (np.array(1), np.array([12.0]))
}
self._assert_encode_decode(coder, data, expected_decoded)
def test_missing_data(self):
coder = csv_coder.CsvCoder(self._COLUMNS, self._INPUT_SCHEMA)
data = '12,,categorical_value,1,89.0,12.0,20'
with self.assertRaisesRegexp(ValueError,
'expected a value on column \'text1\''):
coder.decode(data)
def test_missing_numeric_data(self):
coder = csv_coder.CsvCoder(self._COLUMNS, self._INPUT_SCHEMA)
# The numbers are missing.
data = ',"this is a ,text",female,1,89.0,12.0'
with self.assertRaisesRegexp(ValueError,
'expected a value on column "numeric1"'):
coder.decode(data)
def test_constructor_error(self,
columns,
feature_spec,
error_msg,
error_type=ValueError,
**kwargs):
schema = dataset_schema.from_feature_spec(feature_spec)
with self.assertRaisesRegexp(error_type, error_msg):
csv_coder.CsvCoder(columns, schema, **kwargs)
def testDecode(self):
for csv_line, value, multivalent, feature_spec in (
self._ENCODE_DECODE_CASES + self._DECODE_ONLY_CASES):
schema = dataset_schema.from_feature_spec({'x': feature_spec})
if isinstance(feature_spec, tf.SparseFeature):
columns = [feature_spec.index_key, feature_spec.value_key]
else:
columns = 'x'
if multivalent:
coder = csv_coder.CsvCoder(columns,
schema,
secondary_delimiter='|',
multivalent_columns=columns)
else:
coder = csv_coder.CsvCoder(columns, schema)
np.testing.assert_equal(
coder.decode(csv_line), {'x': value},
self._msg_for_decode_case(csv_line, feature_spec))
def testEncode(self):
for csv_line, value, multivalent, feature_spec in self._ENCODE_DECODE_CASES:
schema = dataset_schema.from_feature_spec({'x': feature_spec})
if isinstance(feature_spec, tf.SparseFeature):
columns = [feature_spec.index_key, feature_spec.value_key]
else:
columns = 'x'
if multivalent:
coder = csv_coder.CsvCoder(columns,
schema,
secondary_delimiter='|',
multivalent_columns=columns)
else:
coder = csv_coder.CsvCoder(columns, schema)
self.assertEqual(coder.encode({'x': value}),
csv_line,
msg=self._msg_for_encode_case(
value, feature_spec))
def test_decode_error(self,
columns,
feature_spec,
csv_line,
error_msg,
error_type=ValueError,
**kwargs):
schema = schema_utils.schema_from_feature_spec(feature_spec)
coder = csv_coder.CsvCoder(columns, schema, **kwargs)
with self.assertRaisesRegexp(error_type, error_msg):
coder.decode(csv_line)
def test_encode_error(self,
columns,
feature_spec,
instance,
error_msg,
error_type=ValueError,
**kwargs):
schema = dataset_schema.from_feature_spec(feature_spec)
coder = csv_coder.CsvCoder(columns, schema, **kwargs)
with self.assertRaisesRegexp(error_type, error_msg):
coder.encode(instance)
def test_fixed_length_missing_values(self):
input_schema = dataset_schema.from_feature_spec({
'b':
tf.FixedLenFeature(shape=[], dtype=tf.float32, default_value=-1),
'a':
tf.FixedLenFeature(shape=[], dtype=tf.string, default_value=''),
})
coder = csv_coder.CsvCoder(column_names=['a', 'b'],
schema=input_schema)
self.assertEqual(coder.decode('a_value,'), {'a': 'a_value', 'b': -1.0})
self.assertEqual(coder.decode(',1.0'), {'a': '', 'b': 1.0})
self.assertEqual(coder.decode(','), {'a': '', 'b': -1.0})
def testEncodeErrors(self):
for value, error_type, error_msg, multivalent, feature_spec in (
self._ENCODE_ERROR_CASES):
schema = dataset_schema.from_feature_spec({'x': feature_spec})
if isinstance(feature_spec, tf.SparseFeature):
columns = [feature_spec.index_key, feature_spec.value_key]
else:
columns = 'x'
with self.assertRaisesRegexp(error_type,
error_msg,
msg=self._msg_for_encode_case(
value, feature_spec)):
if multivalent:
coder = csv_coder.CsvCoder(columns,
schema,
secondary_delimiter='|',
multivalent_columns=columns)
else:
coder = csv_coder.CsvCoder(columns, schema)
coder.encode({'x': value})
def test_data_types(self):
# The numbers are strings.
data = '"12","this is a ,text",female,"1","89.0","12.0"'
coder = csv_coder.CsvCoder(self._COLUMNS, self._INPUT_SCHEMA)
expected_decoded = {
'category1': ['female'],
'numeric1': 12,
'numeric2': [89.0],
'text1': 'this is a ,text',
'y': ([12.0], [1])
}
self._assert_encode_not_equal_decode(coder, data, expected_decoded)
def test_fixed_length_missing_values_no_default(self):
input_schema = dataset_schema.from_feature_spec({
'b':
tf.FixedLenFeature(shape=[], dtype=tf.float32),
'a':
tf.FixedLenFeature(shape=[], dtype=tf.string),
})
coder = csv_coder.CsvCoder(column_names=['a', 'b'],
schema=input_schema)
with self.assertRaisesRegexp(ValueError,
'expected a value on column "b"'):
coder.decode('a_value,')
def test_bad_row(self):
coder = csv_coder.CsvCoder(self._COLUMNS, self._INPUT_SCHEMA)
# The data has a more columns than expected.
data = '12,"this is a ,text",female,1,89.0,12.0,"oh no, I\'m an error"'
with self.assertRaisesRegexp(
Exception, 'Columns do not match specified csv headers'):
coder.decode(data)
# The data has a fewer columns than expected.
data = '12,"this is a ,text",female"'
with self.assertRaisesRegexp(
Exception, 'Columns do not match specified csv headers'):
coder.decode(data)
def test_var_length_missing_values(self):
input_schema = dataset_schema.from_feature_spec({
'b':
tf.VarLenFeature(dtype=tf.float32),
'a':
tf.VarLenFeature(dtype=tf.string),
})
coder = csv_coder.CsvCoder(column_names=['a', 'b'],
schema=input_schema)
self.assertEqual(coder.decode('a_value,'), {'a': ['a_value'], 'b': []})
self.assertEqual(coder.decode(',0'), {'a': [], 'b': [0.0]})
self.assertEqual(coder.decode(',1.0'), {'a': [], 'b': [1.0]})
self.assertEqual(coder.decode(','), {'a': [], 'b': []})
def test_tsv_coder(self):
data = '12\t"this is a \ttext"\tfemale\t1\t89.0\t12.0'
coder = csv_coder.CsvCoder(self._COLUMNS,
self._INPUT_SCHEMA,
delimiter='\t')
expected_decoded = {
'category1': ['female'],
'numeric1': 12,
'numeric2': [89.0],
'text1': 'this is a \ttext',
'y': ([12.0], [1])
}
self._assert_encode_decode(coder, data, expected_decoded)
def testDecodeErrors(self):
for csv_line, error_type, error_msg, multivalent, feature_spec in (
self._DECODE_ERROR_CASES):
schema = dataset_schema.from_feature_spec({'x': feature_spec})
if isinstance(feature_spec, tf.SparseFeature):
columns = [feature_spec.index_key, feature_spec.value_key]
else:
columns = 'x'
with self.assertRaisesRegexp(error_type,
error_msg,
msg=self._msg_for_decode_case(
csv_line, feature_spec)):
# We don't distinguish between errors in the coder constructor and in
# the decode method.
if multivalent:
coder = csv_coder.CsvCoder(columns,
schema,
secondary_delimiter='|',
multivalent_columns=columns)
else:
coder = csv_coder.CsvCoder(columns, schema)
coder.decode(csv_line)
def test_picklable(self):
csv_line = '12,"this is a ,text",categorical_value,1,89.0,12.0,20'
instance = {
'category1': [b'categorical_value'],
'numeric1': 12,
'numeric2': [89.0],
'numeric3': [20],
'text1': b'this is a ,text',
'y': ([1], [12.0])
}
schema = dataset_schema.from_feature_spec(_FEATURE_SPEC)
coder = csv_coder.CsvCoder(_COLUMNS, schema)
# Repeat twice to ensure the act of encoding/decoding doesn't break
# pickling.
for _ in range(2):
coder = pickle.loads(pickle.dumps(coder))
self.assertEqual(coder.decode(csv_line), instance)
self.assertEqual(coder.encode(instance), csv_line.encode('utf-8'))
def test_sparse_feature_incorrect_values(self):
input_schema = dataset_schema.from_feature_spec({
'a':
tf.SparseFeature('idx', 'value', tf.float32, 10),
})
coder = csv_coder.CsvCoder(column_names=['idx', 'value'],
schema=input_schema)
# Index negative.
with self.assertRaisesRegexp(ValueError, 'has index -1 out of range'):
coder.decode('-1,12.0')
# Index equal to size.
with self.assertRaisesRegexp(ValueError, 'has index 10 out of range'):
coder.decode('10,12.0')
# Index greater than size.
with self.assertRaisesRegexp(ValueError, 'has index 11 out of range'):
coder.decode('11,12.0')
def test_valency(self):
data = '11|12,"this is a ,text",female|male,1|3,89.0|91.0,12.0|15.0'
feature_spec = self._INPUT_SCHEMA.as_feature_spec().copy()
feature_spec['numeric1'] = tf.FixedLenFeature(shape=[2],
dtype=tf.int32)
schema = dataset_schema.from_feature_spec(feature_spec)
multivalent_columns = ['numeric1', 'numeric2', 'y']
coder = csv_coder.CsvCoder(self._COLUMNS,
schema,
delimiter=',',
secondary_delimiter='|',
multivalent_columns=multivalent_columns)
expected_decoded = {
'category1': ['female|male'],
'numeric1': [11, 12],
'numeric2': [89.0, 91.0],
'text1': 'this is a ,text',
'y': ([12.0, 15.0], [1, 3])
}
self._assert_encode_decode(coder, data, expected_decoded)
def test_picklable(self):
encoded_data = '12,"this is a ,text",female,1,89.0,12.0'
expected_decoded = {
'category1': ['female'],
'numeric1': 12,
'numeric2': [89.0],
'text1': 'this is a ,text',
'y': ([12.0], [1])
}
coder = csv_coder.CsvCoder(self._COLUMNS, self._INPUT_SCHEMA)
# Ensure we can pickle right away.
coder = pickle.loads(pickle.dumps(coder))
self._assert_encode_decode(coder, encoded_data, expected_decoded)
# And after use.
coder = pickle.loads(pickle.dumps(coder))
self._assert_encode_decode(coder, encoded_data, expected_decoded)
def test_sparse_feature_missing_values(self):
input_schema = dataset_schema.from_feature_spec({
'a':
tf.SparseFeature('idx', 'value', tf.float32, 10),
})
coder = csv_coder.CsvCoder(column_names=['idx', 'value'],
schema=input_schema)
# Missing both value and index (which is allowed).
self.assertEqual(coder.decode(','), {'a': ([], [])})
# Missing index only (not allowed).
with self.assertRaisesRegexp(ValueError,
'expected an index in column "idx"'):
coder.decode(',12.0')
# Missing value only (not allowed).
with self.assertRaisesRegexp(ValueError,
'expected a value in column "value"'):
coder.decode('1,')
def test_all_values_present(self):
columns = ['a', 'b', 'c', 'd', 'e']
input_schema = dataset_schema.from_feature_spec({
'b':
tf.FixedLenFeature(shape=[], dtype=tf.float32),
'a':
tf.FixedLenFeature(shape=[], dtype=tf.string),
'c':
tf.VarLenFeature(dtype=tf.string),
'y':
tf.SparseFeature('d', 'e', tf.float32, 10),
})
coder = csv_coder.CsvCoder(column_names=columns, schema=input_schema)
self.assertEqual(
coder.decode('a_value,1.0,0,1,12.0'),
# Column 'c' is specified as a string so the value is not casted.
{
'a': 'a_value',
'b': 1.0,
'c': ['0'],
'y': ([12.0], [1])
})
def test_decode_errors(self):
input_schema = dataset_schema.from_feature_spec({
'b':
tf.FixedLenFeature(shape=[], dtype=tf.float32),
'a':
tf.FixedLenFeature(shape=[], dtype=tf.string),
})
coder = csv_coder.CsvCoder(column_names=['a', 'b'],
schema=input_schema)
# Test non-numerical column value.
with self.assertRaisesRegexp(
ValueError, 'could not convert string to float: b_value'):
coder.decode('a_value, b_value')
# Test bad csv.
with self.assertRaisesRegexp(csv_coder.DecodeError,
'string or Unicode object, int found'):
coder.decode(123)
# Test extra column.
with self.assertRaisesRegexp(
csv_coder.DecodeError,
'Columns do not match specified csv headers'):
coder.decode('1,2,')
# Test missing column.
with self.assertRaisesRegexp(
csv_coder.DecodeError,
'Columns do not match specified csv headers'):
coder.decode('a_value')
# Test empty row.
with self.assertRaisesRegexp(
csv_coder.DecodeError,
'Columns do not match specified csv headers'):
coder.decode('')
def test_column_not_found(self):
with self.assertRaisesRegexp(ValueError, 'Column not found: '):
csv_coder.CsvCoder([], self._INPUT_SCHEMA)
def test_encode(self, columns, feature_spec, csv_line, instance, **kwargs):
schema = dataset_schema.from_feature_spec(feature_spec)
coder = csv_coder.CsvCoder(columns, schema, **kwargs)
self.assertEqual(coder.encode(instance), csv_line.encode('utf-8'))
def test_decode(self, columns, feature_spec, csv_line, instance, **kwargs): schema = dataset_schema.from_feature_spec(feature_spec) coder = csv_coder.CsvCoder(columns, schema, **kwargs) np.testing.assert_equal(coder.decode(csv_line), instance)
