def test_csv_decoder_with_schema(self):
input_lines = ['1,1,2.0,hello', '5,5,12.34,world']
column_names = [
'int_feature_parsed_as_float', 'int_feature', 'float_feature',
'str_feature'
]
schema = text_format.Parse(
"""
feature { name: "int_feature_parsed_as_float" type: FLOAT }
feature { name: "int_feature" type: INT }
feature { name: "float_feature" type: FLOAT }
feature { name: "str_feature" type: BYTES }
""", schema_pb2.Schema())
expected_result = [
pa.Table.from_arrays([
pa.array([[1], [5]], pa.list_(pa.float32())),
pa.array([[1], [5]], pa.list_(pa.int64())),
pa.array([[2.0], [12.34]], pa.list_(pa.float32())),
pa.array([[b'hello'], [b'world']], pa.list_(pa.binary())),
], [
'int_feature_parsed_as_float', 'int_feature', 'float_feature',
'str_feature'
])
]
with beam.Pipeline() as p:
result = (p | beam.Create(input_lines)
| csv_decoder.DecodeCSV(column_names=column_names,
schema=schema,
infer_type_from_schema=True))
util.assert_that(
result,
test_util.make_arrow_tables_equal_fn(self, expected_result))
def test_batch_examples(self):
examples = [{
'a': np.array([1.0, 2.0], dtype=np.float32),
'b': np.array(['a', 'b', 'c', 'e'])
}, {
'a': np.array([3.0, 4.0, 5.0], dtype=np.float32),
}, {
'b': np.array(['d', 'e', 'f']),
'd': np.array([10, 20, 30], dtype=np.int64),
}, {
'b': np.array(['a', 'b', 'c'])
}, {
'c': np.array(['d', 'e', 'f'])
}]
expected_tables = [
pa.Table.from_arrays([
pa.array([[1.0, 2.0], [3.0, 4.0, 5.0]],
type=pa.list_(pa.float32())),
pa.array([['a', 'b', 'c', 'e'], None])
], ['a', 'b']),
pa.Table.from_arrays([
pa.array([['d', 'e', 'f'], ['a', 'b', 'c']]),
pa.array([[10, 20, 30], None], type=pa.list_(pa.int64()))
], ['b', 'd']),
pa.Table.from_arrays([pa.array([['d', 'e', 'f']])], ['c']),
]
with beam.Pipeline() as p:
result = (
p
| beam.Create(examples)
| batch_util.BatchExamplesToArrowTables(desired_batch_size=2))
util.assert_that(
result,
test_util.make_arrow_tables_equal_fn(self, expected_tables))
def test_csv_decoder_with_int_and_float_in_same_column(self):
input_lines = ['2,1.5', '1.5,2']
column_names = ['float_feature1', 'float_feature2']
expected_result = [
pa.Table.from_arrays([
pa.array([[2.0], [1.5]], pa.list_(pa.float32())),
pa.array([[1.5], [2.0]], pa.list_(pa.float32())),
], ['float_feature1', 'float_feature2'])
]
with beam.Pipeline() as p:
result = (p | beam.Create(input_lines)
| csv_decoder.DecodeCSV(column_names=column_names))
util.assert_that(
result,
test_util.make_arrow_tables_equal_fn(self, expected_result))
def test_csv_decoder_consider_blank_line(self):
input_lines = ['', '1,2']
column_names = ['float_feature1', 'float_feature2']
expected_result = [
pa.Table.from_arrays([
pa.array([None, [1.0]], pa.list_(pa.float32())),
pa.array([None, [2.0]], pa.list_(pa.float32())),
], ['float_feature1', 'float_feature2'])
]
with beam.Pipeline() as p:
result = (p | beam.Create(input_lines) | csv_decoder.DecodeCSV(
column_names=column_names, skip_blank_lines=False))
util.assert_that(
result,
test_util.make_arrow_tables_equal_fn(self, expected_result))
def testNumberArrayShouldShareBuffer(self):
float_array = pa.array([1, 2, np.NaN], pa.float32())
np_array = arrow_util.primitive_array_to_numpy(float_array)
self.assertEqual(np_array.dtype, np.float32)
self.assertEqual(np_array.shape, (3, ))
# Check that they share the same buffer.
self.assertEqual(np_array.ctypes.data,
float_array.buffers()[1].address)
def test_csv_decoder_missing_values(self):
input_lines = ['1,,hello', ',12.34,']
column_names = ['int_feature', 'float_feature', 'str_feature']
expected_result = [
pa.Table.from_arrays([
pa.array([[1.0], None], pa.list_(pa.float32())),
pa.array([None, [12.34]], pa.list_(pa.float32())),
pa.array([[b'hello'], None], pa.list_(pa.binary())),
], ['int_feature', 'float_feature', 'str_feature'])
]
with beam.Pipeline() as p:
result = (p | beam.Create(input_lines)
| csv_decoder.DecodeCSV(column_names=column_names))
util.assert_that(
result,
test_util.make_arrow_tables_equal_fn(self, expected_result))
def test_basic_stats_generator_only_nan(self):
b1 = pa.Table.from_arrays(
[pa.array([[np.NaN]], type=pa.list_(pa.float32()))], ['a'])
batches = [b1]
expected_result = {
types.FeaturePath(['a']):
text_format.Parse(
"""
path {
step: 'a'
}
type: FLOAT
num_stats {
common_stats {
num_non_missing: 1
min_num_values: 1
max_num_values: 1
avg_num_values: 1.0
tot_num_values: 1
num_values_histogram {
buckets {
low_value: 1.0
high_value: 1.0
sample_count: 0.5
}
buckets {
low_value: 1.0
high_value: 1.0
sample_count: 0.5
}
type: QUANTILES
}
}
histograms {
num_nan: 1
type: STANDARD
}
histograms {
num_nan: 1
type: QUANTILES
}
}
""", statistics_pb2.FeatureNameStatistics())
}
generator = basic_stats_generator.BasicStatsGenerator(
num_values_histogram_buckets=2,
num_histogram_buckets=3,
num_quantiles_histogram_buckets=4)
self.assertCombinerOutputEqual(batches, generator, expected_result)
def test_csv_decoder_empty_row(self):
input_lines = [',,', '1,2.0,hello']
column_names = ['int_feature', 'float_feature', 'str_feature']
expected_result = [
pa.Table.from_arrays([
pa.array([None, [1]], pa.list_(pa.int64())),
pa.array([None, [2.0]], pa.list_(pa.float32())),
pa.array([None, [b'hello']], pa.list_(pa.binary())),
], ['int_feature', 'float_feature', 'str_feature'])
]
with beam.Pipeline() as p:
result = (p | beam.Create(input_lines)
| csv_decoder.DecodeCSV(column_names=column_names))
util.assert_that(
result,
test_util.make_arrow_tables_equal_fn(self, expected_result))
def test_csv_decoder(self):
input_lines = ['1,2.0,hello', '5,12.34,world']
column_names = ['int_feature', 'float_feature', 'str_feature']
expected_result = [
pa.Table.from_arrays([
pa.array([[1], [5]], pa.list_(pa.int64())),
pa.array([[2.0], [12.34]], pa.list_(pa.float32())),
pa.array([[b'hello'], [b'world']], pa.list_(pa.binary())),
], ['int_feature', 'float_feature', 'str_feature'])
]
with beam.Pipeline() as p:
result = (p | beam.Create(input_lines, reshuffle=False)
| csv_decoder.DecodeCSV(column_names=column_names))
util.assert_that(
result,
test_util.make_arrow_tables_equal_fn(self, expected_result))
def _process_column_infos(self, column_infos: List[csv_decoder.ColumnInfo]):
column_handlers = []
column_arrow_types = []
for c in column_infos:
if c.type == statistics_pb2.FeatureNameStatistics.INT:
column_handlers.append(lambda v: (int(v),))
column_arrow_types.append(pa.list_(pa.int64()))
elif c.type == statistics_pb2.FeatureNameStatistics.FLOAT:
column_handlers.append(lambda v: (float(v),))
column_arrow_types.append(pa.list_(pa.float32()))
elif c.type == statistics_pb2.FeatureNameStatistics.STRING:
column_handlers.append(lambda v: (v,))
column_arrow_types.append(pa.list_(pa.binary()))
else:
column_handlers.append(lambda _: None)
column_arrow_types.append(pa.null())
self._column_handlers = column_handlers
self._column_arrow_types = column_arrow_types
self._column_names = [c.name for c in column_infos]
def test_csv_decoder_with_unicode(self):
input_lines = [u'1,שקרכלשהו,22.34,text field']
column_names = [
'int_feature', 'unicode_feature', 'float_feature', 'str_feature'
]
expected_result = [
pa.Table.from_arrays([
pa.array([[1]], pa.list_(pa.int64())),
pa.array([[22.34]], pa.list_(pa.float32())),
pa.array([[u'שקרכלשהו'.encode('utf-8')]], pa.list_(
pa.binary())),
pa.array([[b'text field']], pa.list_(pa.binary())),
], [
'int_feature', 'float_feature', 'unicode_feature',
'str_feature'
])
]
with beam.Pipeline() as p:
result = (p | beam.Create(input_lines)
| csv_decoder.DecodeCSV(column_names=column_names))
util.assert_that(
result,
test_util.make_arrow_tables_equal_fn(self, expected_result))
value { float_list { value: [ 4.0 ] } }
}
feature {
key: "float_feature_2"
value { float_list { value: [ 5.0, 6.0 ] } }
}
feature {
key: "str_feature_1"
value { bytes_list { value: [ 'female' ] } }
}
feature {
key: "str_feature_2"
value { bytes_list { value: [ 'string', 'list' ] } }
}
}
''',
'decoded_table':
pa.Table.from_arrays([
pa.array([[0]], pa.list_(pa.int64())),
pa.array([[1, 2, 3]], pa.list_(pa.int64())),
pa.array([[4.0]], pa.list_(pa.float32())),
pa.array([[5.0, 6.0]], pa.list_(pa.float32())),
pa.array([[b'female']], pa.list_(pa.binary())),
pa.array([[b'string', b'list']], pa.list_(pa.binary()))
], [
'int_feature_1', 'int_feature_2', 'float_feature_1',
'float_feature_2', 'str_feature_1', 'str_feature_2'
])
},
]
def test_topk_uniques_combiner_with_numeric_feature(self):
# fa: 4 'a', 2 'b', 3 'c', 2 'd', 1 'e'
batches = [
pa.Table.from_arrays([
pa.array([['a', 'b', 'c', 'e'], None, ['a', 'c', 'd']]),
pa.array([[1.0, 2.0, 3.0], [4.0, 5.0], None]),
], ['fa', 'fb']),
pa.Table.from_arrays([
pa.array([['a', 'a', 'b', 'c', 'd']]),
pa.array([None], type=pa.list_(pa.float32())),
], ['fa', 'fb']),
]
expected_result = {
types.FeaturePath(['fa']):
text_format.Parse(
"""
path {
step: 'fa'
}
type: STRING
string_stats {
unique: 5
top_values {
value: 'a'
frequency: 4
}
top_values {
value: 'c'
frequency: 3
}
top_values {
value: 'd'
frequency: 2
}
top_values {
value: 'b'
frequency: 2
}
rank_histogram {
buckets {
low_rank: 0
high_rank: 0
label: "a"
sample_count: 4.0
}
buckets {
low_rank: 1
high_rank: 1
label: "c"
sample_count: 3.0
}
buckets {
low_rank: 2
high_rank: 2
label: "d"
sample_count: 2.0
}
}
}""", statistics_pb2.FeatureNameStatistics())
}
generator = (top_k_uniques_combiner_stats_generator.
TopKUniquesCombinerStatsGenerator(
num_top_values=4, num_rank_histogram_buckets=3))
self.assertCombinerOutputEqual(batches, generator, expected_result)
def test_batch_serialized_examples(self):
examples = [
"""
features {
feature {
key: "a"
value { float_list { value: [ 1.0, 2.0 ] } }
}
feature {
key: "b"
value { bytes_list { value: [ 'a', 'b', 'c', 'e' ] } }
}
}""",
"""
features {
feature {
key: "a"
value { float_list { value: [ 3.0, 4.0, 5.0 ] } }
}
}""",
"""
features {
feature {
key: "b"
value { bytes_list { value: [ 'd', 'e', 'f' ] } }
}
feature {
key: "d"
value { int64_list { value: [ 10, 20, 30 ] } }
}
}""",
"""
features {
feature {
key: "b"
value { bytes_list { value: [ 'a', 'b', 'c' ] } }
}
}""",
"""
features {
feature {
key: "c"
value { bytes_list { value: [ 'd', 'e', 'f' ] } }
}
}""",
]
serialized_examples = [
text_format.Merge(example_pbtxt,
tf.train.Example()).SerializeToString()
for example_pbtxt in examples
]
expected_tables = [
pa.Table.from_arrays([
pa.array([[1.0, 2.0], [3.0, 4.0, 5.0]],
type=pa.list_(pa.float32())),
pa.array([['a', 'b', 'c', 'e'], None],
type=pa.list_(pa.binary()))
], ['a', 'b']),
pa.Table.from_arrays([
pa.array([['d', 'e', 'f'], ['a', 'b', 'c']],
type=pa.list_(pa.binary())),
pa.array([[10, 20, 30], None], type=pa.list_(pa.int64()))
], ['b', 'd']),
pa.Table.from_arrays(
[pa.array([['d', 'e', 'f']], type=pa.list_(pa.binary()))],
['c']),
]
with beam.Pipeline() as p:
result = (p
| beam.Create(serialized_examples)
| batch_util.BatchSerializedExamplesToArrowTables(
desired_batch_size=2))
util.assert_that(
result,
test_util.make_arrow_tables_equal_fn(self, expected_tables))
"double_feature": np.array([2., 3., 4.], dtype=np.float64),
"bytes_feature": np.array([b"ghi"], dtype=np.object),
"unicode_feature": np.array([u"ghi"], dtype=np.object),
},
],
expected_output={
"int64_feature":
pa.array([[1, 2, 3], [4]], type=pa.list_(pa.int64())),
"uint64_feature":
pa.array([[1, 2, 3], None], type=pa.list_(pa.uint64())),
"int32_feature":
pa.array([[1, 2, 3], [4]], type=pa.list_(pa.int32())),
"uint32_feature":
pa.array([[1, 2, 3], None], type=pa.list_(pa.uint32())),
"float_feature":
pa.array([[1.], [2., 3., 4.]], type=pa.list_(pa.float32())),
"double_feature":
pa.array([[1.], [2., 3., 4.]], type=pa.list_(pa.float64())),
"bytes_feature":
pa.array([[b"abc", b"def"], [b"ghi"]],
type=pa.list_(pa.binary())),
"unicode_feature":
pa.array([[b"abc", b"def"], [b"ghi"]],
type=pa.list_(pa.string())),
}),
dict(testcase_name="mixed_unicode_and_bytes",
input_examples=[
{
"a": np.array([b"abc"], dtype=np.object),
},
{
def test_stats_pipeline_with_examples_with_no_values(self):
tables = [
pa.Table.from_arrays([
pa.array([[]], type=pa.list_(pa.float32())),
pa.array([[]], type=pa.list_(pa.binary())),
pa.array([[]], type=pa.list_(pa.int32())),
pa.array([[2]]),
], ['a', 'b', 'c', 'w']),
pa.Table.from_arrays([
pa.array([[]], type=pa.list_(pa.float32())),
pa.array([[]], type=pa.list_(pa.binary())),
pa.array([[]], type=pa.list_(pa.int32())),
pa.array([[2]]),
], ['a', 'b', 'c', 'w']),
pa.Table.from_arrays([
pa.array([[]], type=pa.list_(pa.float32())),
pa.array([[]], type=pa.list_(pa.binary())),
pa.array([[]], type=pa.list_(pa.int32())),
pa.array([[2]]),
], ['a', 'b', 'c', 'w'])
]
expected_result = text_format.Parse(
"""
datasets{
num_examples: 3
features {
path {
step: 'a'
}
type: FLOAT
num_stats {
common_stats {
num_non_missing: 3
num_values_histogram {
buckets {
sample_count: 1.5
}
buckets {
sample_count: 1.5
}
type: QUANTILES
}
weighted_common_stats {
num_non_missing: 6
}
}
}
}
features {
path {
step: 'b'
}
type: STRING
string_stats {
common_stats {
num_non_missing: 3
num_values_histogram {
buckets {
sample_count: 1.5
}
buckets {
sample_count: 1.5
}
type: QUANTILES
}
weighted_common_stats {
num_non_missing: 6
}
}
}
}
features {
path {
step: 'c'
}
type: INT
num_stats {
common_stats {
num_non_missing: 3
num_values_histogram {
buckets {
sample_count: 1.5
}
buckets {
sample_count: 1.5
}
type: QUANTILES
}
weighted_common_stats {
num_non_missing: 6
}
}
}
}
}
""", statistics_pb2.DatasetFeatureStatisticsList())
with beam.Pipeline() as p:
options = stats_options.StatsOptions(
weight_feature='w',
num_top_values=1,
num_rank_histogram_buckets=1,
num_values_histogram_buckets=2,
num_histogram_buckets=1,
num_quantiles_histogram_buckets=1,
epsilon=0.001)
result = (p | beam.Create(tables)
| stats_api.GenerateStatistics(options))
util.assert_that(
result,
test_util.make_dataset_feature_stats_list_proto_equal_fn(
self, expected_result))
