def test_batch_with_df_transform(self):
with TestPipeline() as p:
res = (
p
| beam.Create([
Animal('Falcon', 380.0),
Animal('Falcon', 370.0),
Animal('Parrot', 24.0),
Animal('Parrot', 26.0)
])
| schemas.BatchRowsAsDataFrame()
| transforms.DataframeTransform(
lambda df: df.groupby('animal').mean(),
# TODO: Generate proxy in this case as well
proxy=schemas.generate_proxy(Animal),
include_indexes=True))
assert_that(res, equal_to([('Falcon', 375.), ('Parrot', 25.)]))
# Do the same thing, but use reset_index() to make sure 'animal' is included
with TestPipeline() as p:
with beam.dataframe.allow_non_parallel_operations():
res = (
p
| beam.Create([
Animal('Falcon', 380.0),
Animal('Falcon', 370.0),
Animal('Parrot', 24.0),
Animal('Parrot', 26.0)
])
| schemas.BatchRowsAsDataFrame()
| transforms.DataframeTransform(
lambda df: df.groupby('animal').mean().reset_index(),
# TODO: Generate proxy in this case as well
proxy=schemas.generate_proxy(Animal)))
assert_that(res, equal_to([('Falcon', 375.), ('Parrot', 25.)]))
def test_input_output_polymorphism(self):
one_series = pd.Series([1])
two_series = pd.Series([2])
three_series = pd.Series([3])
proxy = one_series[:0]
def equal_to_series(expected):
def check(actual):
actual = pd.concat(actual)
if not expected.equals(actual):
raise AssertionError('Series not equal: \n%s\n%s\n' %
(expected, actual))
return check
with beam.Pipeline() as p:
one = p | 'One' >> beam.Create([one_series])
two = p | 'Two' >> beam.Create([two_series])
assert_that(
one | 'PcollInPcollOut' >> transforms.DataframeTransform(
lambda x: 3 * x, proxy=proxy, yield_elements='pandas'),
equal_to_series(three_series),
label='CheckPcollInPcollOut')
assert_that((one, two)
| 'TupleIn' >> transforms.DataframeTransform(
lambda x, y: (x + y), (proxy, proxy),
yield_elements='pandas'),
equal_to_series(three_series),
label='CheckTupleIn')
assert_that(dict(x=one, y=two)
| 'DictIn' >> transforms.DataframeTransform(
lambda x, y: (x + y),
proxy=dict(x=proxy, y=proxy),
yield_elements='pandas'),
equal_to_series(three_series),
label='CheckDictIn')
double, triple = one | 'TupleOut' >> transforms.DataframeTransform(
lambda x: (2 * x, 3 * x), proxy, yield_elements='pandas')
assert_that(double, equal_to_series(two_series), 'CheckTupleOut0')
assert_that(triple, equal_to_series(three_series),
'CheckTupleOut1')
res = one | 'DictOut' >> transforms.DataframeTransform(
lambda x: {'res': 3 * x}, proxy, yield_elements='pandas')
assert_that(res['res'], equal_to_series(three_series),
'CheckDictOut')
def run_scenario(self, input, func):
expected = func(input)
empty = input[0:0]
input_placeholder = expressions.PlaceholderExpression(empty)
input_deferred = frame_base.DeferredFrame.wrap(input_placeholder)
actual_deferred = func(input_deferred)._expr.evaluate_at(
expressions.Session({input_placeholder: input}))
def check_correct(actual):
if actual is None:
raise AssertionError('Empty frame but expected: \n\n%s' % (expected))
sorted_actual = actual.sort_index()
sorted_expected = expected.sort_index()
if not sorted_actual.equals(sorted_expected):
raise AssertionError(
'Dataframes not equal: \n\n%s\n\n%s' %
(sorted_actual, sorted_expected))
check_correct(actual_deferred)
with beam.Pipeline() as p:
input_pcoll = p | beam.Create([input[::2], input[1::2]])
output_pcoll = input_pcoll | transforms.DataframeTransform(
func, proxy=empty)
assert_that(
output_pcoll,
lambda actual: check_correct(pd.concat(actual) if actual else None))
def test_batching_passthrough_nested_schema(self):
with beam.Pipeline() as p:
nested_schema_pc = (p | beam.Create([
Nested(1, AnimalSpeed('Aardvark', 5))
]).with_output_types(Nested))
result = nested_schema_pc | transforms.DataframeTransform( # pylint: disable=expression-not-assigned
lambda df: df.filter(items=['animal_speed']))
assert_that(result, equal_to([(('Aardvark', 5), )]))
def test_column_manipulation(self):
def set_column(df, name, s):
df[name] = s
return df
with beam.Pipeline() as p:
_ = (self.create_animal_speed_input(p)
| transforms.DataframeTransform(lambda df: set_column(
df, 'x', df.Speed + df.Animal.str.len())))
self.assertEqual(self.fused_stages(p), 1)
def test_unbatching_series(self):
with beam.Pipeline() as p:
result = (
p
| beam.Create([(u'Falcon', 380.), (u'Falcon', 370.), (u'Parrot', 24.),
(u'Parrot', 26.)])
| beam.Map(lambda tpl: beam.Row(Animal=tpl[0], Speed=tpl[1]))
| transforms.DataframeTransform(lambda df: df.Animal))
assert_that(result, equal_to(['Falcon', 'Falcon', 'Parrot', 'Parrot']))
def test_batching_unsupported_array_schema_raises(self):
Array = typing.NamedTuple(
'Array', [('id', int), ('business_numbers', typing.Sequence[int])])
coders.registry.register_coder(Array, coders.RowCoder)
with beam.Pipeline() as p:
array_schema_pc = (p | beam.Create([Array(1, [7, 8, 9])]))
with self.assertRaisesRegex(TypeError, 'business_numbers'):
array_schema_pc | transforms.DataframeTransform( # pylint: disable=expression-not-assigned
lambda df: df.filter(items=['id']))
def test_batching_passthrough_nested_array(self):
Array = typing.NamedTuple(
'Array', [('id', int), ('business_numbers', typing.Sequence[int])])
coders.registry.register_coder(Array, coders.RowCoder)
with beam.Pipeline() as p:
array_schema_pc = (p | beam.Create([Array(1, [7, 8, 9])]))
result = array_schema_pc | transforms.DataframeTransform( # pylint: disable=expression-not-assigned
lambda df: df.filter(items=['business_numbers']))
assert_that(result, equal_to([([7, 8, 9], )]))
def test_batching_beam_row_input(self):
with beam.Pipeline() as p:
result = (
p
| beam.Create([(u'Falcon', 380.), (u'Falcon', 370.), (u'Parrot', 24.),
(u'Parrot', 26.)])
| beam.Map(lambda tpl: beam.Row(Animal=tpl[0], Speed=tpl[1]))
| transforms.DataframeTransform(
lambda df: df.groupby('Animal').mean(), include_indexes=True))
assert_that(result, equal_to([('Falcon', 375.), ('Parrot', 25.)]))
def test_batching_unsupported_nested_schema_raises(self):
Nested = typing.NamedTuple(
'Nested', [('id', int), ('animal_speed', AnimalSpeed)])
coders.registry.register_coder(Nested, coders.RowCoder)
with beam.Pipeline() as p:
nested_schema_pc = (
p | beam.Create([Nested(1, AnimalSpeed('Aardvark', 5))
]).with_output_types(Nested))
with self.assertRaisesRegex(TypeError, 'animal_speed'):
nested_schema_pc | transforms.DataframeTransform( # pylint: disable=expression-not-assigned
lambda df: df.filter(items=['id']))
def test_batching_named_tuple_input(self):
with beam.Pipeline() as p:
result = (
p | beam.Create([
AnimalSpeed('Aardvark', 5),
AnimalSpeed('Ant', 2),
AnimalSpeed('Elephant', 35),
AnimalSpeed('Zebra', 40)
]).with_output_types(AnimalSpeed)
| transforms.DataframeTransform(lambda df: df.filter(regex='Anim.*')))
assert_that(
result,
equal_to([('Aardvark', ), ('Ant', ), ('Elephant', ), ('Zebra', )]))
def test_batching_beam_row_input(self):
with beam.Pipeline() as p:
result = (
p
| beam.Create([(u'Falcon', 380.), (u'Falcon', 370.), (u'Parrot', 24.),
(u'Parrot', 26.)])
| beam.Map(lambda tpl: beam.Row(Animal=tpl[0], Speed=tpl[1]))
|
transforms.DataframeTransform(lambda df: df.groupby('Animal').mean()))
assert_that(
result,
df_equal_to(
pd.DataFrame({
'Animal': ['Falcon', 'Parrot'], 'Speed': [375., 25.]
}).set_index('Animal')))
def run_scenario(self, input, func):
expected = func(input)
empty = input[0:0]
input_placeholder = expressions.PlaceholderExpression(empty)
input_deferred = frame_base.DeferredFrame.wrap(input_placeholder)
actual_deferred = func(input_deferred)._expr.evaluate_at(
expressions.Session({input_placeholder: input}))
check_correct(expected, actual_deferred)
with beam.Pipeline() as p:
input_pcoll = p | beam.Create([input[::2], input[1::2]])
output_pcoll = input_pcoll | transforms.DataframeTransform(
func, proxy=empty, yield_elements='pandas')
assert_that(output_pcoll,
lambda actual: check_correct(expected, concat(actual)))
def test_batch_with_df_transform(self):
with TestPipeline() as p:
res = (
p
| beam.Create([
Animal('Falcon', 380.0),
Animal('Falcon', 370.0),
Animal('Parrot', 24.0),
Animal('Parrot', 26.0)
])
| schemas.BatchRowsAsDataFrame()
| transforms.DataframeTransform(
lambda df: df.groupby('animal').mean(),
# TODO: Generate proxy in this case as well
proxy=schemas.generate_proxy(Animal)))
assert_that(
res,
matches_df(
pd.DataFrame({'max_speed': [375.0, 25.0]},
index=pd.Index(data=['Falcon', 'Parrot'],
name='animal'))))
def run_scenario(self, input, func):
expected = func(input)
empty = input[0:0]
input_placeholder = expressions.PlaceholderExpression(empty)
input_deferred = frame_base.DeferredFrame.wrap(input_placeholder)
actual_deferred = func(input_deferred)._expr.evaluate_at(
expressions.Session({input_placeholder: input}))
def concat(parts):
if len(parts) > 1:
return pd.concat(parts)
elif len(parts) == 1:
return parts[0]
else:
return None
def check_correct(actual):
if actual is None:
raise AssertionError('Empty frame but expected: \n\n%s' %
(expected))
if isinstance(expected, pd.core.generic.NDFrame):
sorted_actual = actual.sort_index()
sorted_expected = expected.sort_index()
if not sorted_actual.equals(sorted_expected):
raise AssertionError('Dataframes not equal: \n\n%s\n\n%s' %
(sorted_actual, sorted_expected))
else:
if actual != expected:
raise AssertionError('Scalars not equal: %s != %s' %
(actual, expected))
check_correct(actual_deferred)
with beam.Pipeline() as p:
input_pcoll = p | beam.Create([input[::2], input[1::2]])
output_pcoll = input_pcoll | transforms.DataframeTransform(
func, proxy=empty)
assert_that(output_pcoll,
lambda actual: check_correct(concat(actual)))
def test_loc_filter(self):
with beam.Pipeline() as p:
_ = (self.create_animal_speed_input(p)
| transforms.DataframeTransform(lambda df: df[df.Speed > 10]))
self.assertEqual(self.fused_stages(p), 1)
