def test_insert_explicit_nulls(self):
TestSchema = Unischema('TestSchema', [
UnischemaField('nullable', np.int32, (), ScalarCodec(StringType()), True),
UnischemaField('not_nullable', np.int32, (), ScalarCodec(ShortType()), False),
])
# Insert_explicit_nulls to leave the dictionary as is.
row_dict = {'nullable': 0, 'not_nullable': 1}
insert_explicit_nulls(TestSchema, row_dict)
self.assertEqual(len(row_dict), 2)
self.assertEqual(row_dict['nullable'], 0)
self.assertEqual(row_dict['not_nullable'], 1)
# Insert_explicit_nulls to leave the dictionary as is.
row_dict = {'nullable': None, 'not_nullable': 1}
insert_explicit_nulls(TestSchema, row_dict)
self.assertEqual(len(row_dict), 2)
self.assertEqual(row_dict['nullable'], None)
self.assertEqual(row_dict['not_nullable'], 1)
# We are missing a nullable field here. insert_explicit_nulls should add a None entry.
row_dict = {'not_nullable': 1}
insert_explicit_nulls(TestSchema, row_dict)
self.assertEqual(len(row_dict), 2)
self.assertEqual(row_dict['nullable'], None)
self.assertEqual(row_dict['not_nullable'], 1)
# We are missing a not_nullable field here. Should raise an ValueError.
row_dict = {'nullable': 0}
with self.assertRaises(ValueError):
insert_explicit_nulls(TestSchema, row_dict)
def test_serialize_filesystem_factory(tmpdir):
SimpleSchema = Unischema('SimpleSchema', [
UnischemaField('id', np.int32, (), ScalarCodec(IntegerType()), False),
UnischemaField('foo', np.int32, (), ScalarCodec(IntegerType()), False),
])
class BogusFS(pyarrow.LocalFileSystem):
def __getstate__(self):
raise RuntimeError("can not serialize")
rows_count = 10
output_url = "file://{0}/fs_factory_test".format(tmpdir)
rowgroup_size_mb = 256
spark = SparkSession.builder.config('spark.driver.memory',
'2g').master('local[2]').getOrCreate()
sc = spark.sparkContext
with materialize_dataset(spark,
output_url,
SimpleSchema,
rowgroup_size_mb,
filesystem_factory=BogusFS):
rows_rdd = sc.parallelize(range(rows_count))\
.map(lambda x: {'id': x, 'foo': x})\
.map(lambda x: dict_to_spark_row(SimpleSchema, x))
spark.createDataFrame(rows_rdd, SimpleSchema.as_spark_schema()) \
.write \
.parquet(output_url)
def get_petastorm_column(df_column):
column_type = df_column.type
column_name = df_column.name
column_is_nullable = df_column.is_nullable
column_array_dimensions = df_column.array_dimensions
# Reference:
# https://github.com/uber/petastorm/blob/master/petastorm/
# tests/test_common.py
petastorm_column = None
if column_type == ColumnType.INTEGER:
petastorm_column = UnischemaField(column_name, np.int32, (),
ScalarCodec(IntegerType()),
column_is_nullable)
elif column_type == ColumnType.FLOAT:
petastorm_column = UnischemaField(column_name, np.float64, (),
ScalarCodec(FloatType()),
column_is_nullable)
elif column_type == ColumnType.TEXT:
petastorm_column = UnischemaField(column_name, np.str_, (),
ScalarCodec(StringType()),
column_is_nullable)
elif column_type == ColumnType.NDARRAY:
petastorm_column = UnischemaField(column_name, np.uint8,
column_array_dimensions,
NdarrayCodec(),
column_is_nullable)
else:
LoggingManager().log("Invalid column type: " + str(column_type),
LoggingLevel.ERROR)
return petastorm_column
def test_encode_scalar_float():
codec = ScalarCodec(FloatType())
expected = np.random.random(()).astype(np.float64)
field = UnischemaField(name='field_float', numpy_dtype=np.float32, shape=(), codec=codec, nullable=False)
encoded = codec.encode(field, expected)
assert isinstance(encoded, float)
assert expected == encoded
def test_create_schema_view_using_unischema_fields():
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
])
view = TestSchema.create_schema_view([TestSchema.int_field])
assert set(view.fields.keys()) == {'int_field'}
def test_make_named_tuple():
TestSchema = Unischema('TestSchema', [
UnischemaField('string_scalar', np.string_,
(), ScalarCodec(StringType()), True),
UnischemaField('int32_scalar', np.int32,
(), ScalarCodec(ShortType()), False),
UnischemaField('uint8_scalar', np.uint8,
(), ScalarCodec(ShortType()), False),
UnischemaField('int32_matrix', np.float32,
(10, 20, 3), NdarrayCodec(), True),
UnischemaField('decimal_scalar', Decimal,
(10, 20, 3), ScalarCodec(DecimalType(10, 9)), False),
])
TestSchema.make_namedtuple(string_scalar='abc',
int32_scalar=10,
uint8_scalar=20,
int32_matrix=np.int32((10, 20, 3)),
decimal_scalar=Decimal(123) / Decimal(10))
TestSchema.make_namedtuple(string_scalar=None,
int32_scalar=10,
uint8_scalar=20,
int32_matrix=None,
decimal_scalar=Decimal(123) / Decimal(10))
def test_create_schema_view_no_field_matches_regex():
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
])
view = TestSchema.create_schema_view(['bogus'])
assert not view.fields
def test_insert_explicit_nulls():
TestSchema = Unischema('TestSchema', [
UnischemaField('nullable', np.int32, (), ScalarCodec(StringType()), True),
UnischemaField('not_nullable', np.int32, (), ScalarCodec(ShortType()), False),
])
# Insert_explicit_nulls to leave the dictionary as is.
row_dict = {'nullable': 0, 'not_nullable': 1}
insert_explicit_nulls(TestSchema, row_dict)
assert len(row_dict) == 2
assert row_dict['nullable'] == 0
assert row_dict['not_nullable'] == 1
# Insert_explicit_nulls to leave the dictionary as is.
row_dict = {'nullable': None, 'not_nullable': 1}
insert_explicit_nulls(TestSchema, row_dict)
assert len(row_dict) == 2
assert row_dict['nullable'] is None
assert row_dict['not_nullable'] == 1
# We are missing a nullable field here. insert_explicit_nulls should add a None entry.
row_dict = {'not_nullable': 1}
insert_explicit_nulls(TestSchema, row_dict)
assert len(row_dict) == 2
assert row_dict['nullable'] is None
assert row_dict['not_nullable'] == 1
# We are missing a not_nullable field here. Should raise an ValueError.
row_dict = {'nullable': 0}
with pytest.raises(ValueError):
insert_explicit_nulls(TestSchema, row_dict)
def test_encode_scalar_string():
codec = ScalarCodec(StringType())
expected = 'surprise'
field = UnischemaField(name='field_string', numpy_dtype=np.unicode_, shape=(), codec=codec, nullable=False)
encoded = codec.encode(field, expected)
assert isinstance(encoded, str)
assert expected == encoded
def test_predicate_on_dataset(tmpdir):
TestSchema = Unischema('TestSchema', [
UnischemaField('id', np.int32, (), ScalarCodec(IntegerType()), False),
UnischemaField('test_field', np.int32, (), ScalarCodec(IntegerType()), False),
])
def test_row_generator(x):
"""Returns a single entry in the generated dataset."""
return {'id': x,
'test_field': x * x}
blocklet_size_mb = 256
dataset_url = "file://{0}/partitioned_test_dataset".format(tmpdir)
spark = SparkSession.builder.config('spark.driver.memory', '2g').master('local[2]').getOrCreate()
sc = spark.sparkContext
rows_count = 10
with materialize_dataset_carbon(spark, dataset_url, TestSchema, blocklet_size_mb):
rows_rdd = sc.parallelize(range(rows_count)) \
.map(test_row_generator) \
.map(lambda x: dict_to_spark_row(TestSchema, x))
spark.createDataFrame(rows_rdd, TestSchema.as_spark_schema()) \
.write \
.save(path=dataset_url, format='carbon')
with make_carbon_reader(dataset_url, predicate=in_lambda(['id'], lambda x: x == 3)) as reader:
assert next(reader).id == 3
with make_carbon_reader(dataset_url, predicate=in_lambda(['id'], lambda x: x == '3')) as reader:
with pytest.raises(StopIteration):
# Predicate should have selected none, so a StopIteration should be raised.
next(reader)
def test_create_schema_view_using_regex_and_unischema_fields(self):
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
UnischemaField('other_string_field', np.string_, (), ScalarCodec(StringType()), False),
])
view = TestSchema.create_schema_view(['int.*$', TestSchema.string_field])
self.assertEqual(set(view.fields.keys()), {'int_field', 'string_field'})
def test_create_schema_view_using_invalid_type():
""" Exercises code paths unischema.create_schema_view ValueError, and unischema.__str__."""
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
])
with pytest.raises(ValueError, match='must be either a string'):
TestSchema.create_schema_view([42])
def test_create_schema_view_fails_validate():
""" Exercises code paths unischema.create_schema_view ValueError, and unischema.__str__."""
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
])
with pytest.raises(ValueError, match='does not belong to the schema'):
TestSchema.create_schema_view([UnischemaField('id', np.int64, (), ScalarCodec(LongType()), False)])
def test_dict_to_spark_row_field_validation_scalar_nullable():
"""Test various validations done on data types when converting a dictionary to a spark row"""
TestSchema = Unischema('TestSchema', [
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), True),
UnischemaField('nullable_implicitly_set', np.string_, (), ScalarCodec(StringType()), True),
])
assert isinstance(dict_to_spark_row(TestSchema, {'string_field': None}), Row)
def test_create_schema_view_using_regex_and_unischema_fields_with_duplicates():
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
UnischemaField('other_string_field', np.string_, (), ScalarCodec(StringType()), False),
])
view = TestSchema.create_schema_view(['int.*$', TestSchema.int_field])
assert set(view.fields.keys()) == {'int_field'}
def test_create_schema_view_fails_validate(self):
""" Exercises code paths unischema.create_schema_view ValueError, and unischema.__str__."""
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
])
with self.assertRaises(ValueError) as ex:
TestSchema.create_schema_view([UnischemaField('id', np.int64, (), ScalarCodec(LongType()), False)])
self.assertTrue('does not belong to the schema' in str(ex.exception))
def test_bad_unischema_field_shape():
codec = ScalarCodec(IntegerType())
field = UnischemaField(name='field_int',
numpy_dtype=np.int32,
shape=(1, ),
codec=codec,
nullable=False)
with pytest.raises(ValueError, match='must be an empty tuple'):
codec.encode(field, np.int32(1))
def test_bad_encoded_data_shape():
codec = ScalarCodec(IntegerType())
field = UnischemaField(name='field_int',
numpy_dtype=np.int32,
shape=(),
codec=codec,
nullable=False)
with pytest.raises(TypeError):
codec.decode(field, codec.encode(field, np.asarray([10, 10])))
def test_encode_non_scalar_type_is_passed(non_scalar_value):
codec = ScalarCodec(FloatType())
field = UnischemaField(name='field_float',
numpy_dtype=np.float32,
shape=(),
codec=codec,
nullable=False)
with pytest.raises(TypeError, match='Expected a scalar'):
codec.encode(field, non_scalar_value)
def test_create_schema_view_using_invalid_type(self):
""" Exercises code paths unischema.create_schema_view ValueError, and unischema.__str__."""
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
])
with self.assertRaises(ValueError) as ex:
TestSchema.create_schema_view([42])
self.assertTrue('must be either a string' in str(ex.exception))
def test_encode_scalar_bool():
codec = ScalarCodec(BooleanType())
field = UnischemaField(name='field_bool', numpy_dtype=np.bool, shape=(), codec=codec, nullable=False)
encoded = codec.encode(field, np.bool_(True))
assert isinstance(codec.encode(field, encoded), bool)
assert encoded
encoded = codec.encode(field, np.bool_(False))
assert not encoded
def test_scalar_codec_decimal():
codec = ScalarCodec(DecimalType(4, 3))
field = UnischemaField(name='field_decimal',
numpy_dtype=Decimal,
shape=(),
codec=codec,
nullable=False)
value = Decimal('123.4567')
assert codec.decode(field, codec.encode(field, value)) == value
def test_encode_scalar_int():
codec = ScalarCodec(IntegerType())
field = UnischemaField(name='field_int',
numpy_dtype=np.int32,
shape=(),
codec=codec,
nullable=False)
encoded = codec.encode(field, np.int32(42))
assert isinstance(encoded, int)
assert 42 == encoded
def test_numeric_types(spark_numpy_types):
spark_type, numpy_type = spark_numpy_types
codec = ScalarCodec(spark_type())
field = UnischemaField(name='field_int', numpy_dtype=numpy_type, shape=(), codec=codec, nullable=False)
min_val, max_val = np.iinfo(numpy_type).min, np.iinfo(numpy_type).max
assert codec.decode(field, codec.encode(field, numpy_type(min_val))) == min_val
assert codec.decode(field, codec.encode(field, numpy_type(max_val))) == max_val
def test_fields(self):
"""Try using 'fields' getter"""
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
])
self.assertEqual(len(TestSchema.fields), 2)
self.assertEqual(TestSchema.fields['int_field'].name, 'int_field')
self.assertEqual(TestSchema.fields['string_field'].name, 'string_field')
def test_fields():
"""Try using 'fields' getter"""
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
])
assert len(TestSchema.fields) == 2
assert TestSchema.fields['int_field'].name == 'int_field'
assert TestSchema.fields['string_field'].name == 'string_field'
def main(train: str, test: str, target_train: str, target_test: str):
# initialise logger
logger = logging.getLogger(__file__)
logger.addHandler(logging.StreamHandler())
logger.setLevel('INFO')
logger.info('Initialising local spark')
spark = init_local_spark()
logger.info('Preparing schema')
# petastorm schema
schema = Unischema('data_schema', [
UnischemaField('time_window', np.str,
(), ScalarCodec(StringType()), False),
UnischemaField('src_ip', np.str, (), ScalarCodec(StringType()), False),
UnischemaField('feature', np.float32,
(1, 69), CompressedNdarrayCodec(), False),
UnischemaField('label', np.str, (), ScalarCodec(StringType()), True),
])
# processing train
logger.info('Processing train parquet files')
logger.info('Read parquet')
train_feature_df = spark.read.parquet(train)
logger.info('Composing features...')
train_input = FeatureComposer(spark, train_feature_df).transform(
remove_malicious=True, remove_null_label=True)
logger.info('Changing schema...')
train_input = change_df_schema(spark, schema, train_input)
logger.info('Persisting...')
save_parquet_for_petastorm_parquet(spark, train_input, target_train,
schema)
logger.info('Train input done')
# processing test
logger.info('Processing test parquet files')
logger.info('Read parquet')
test_feature_df = spark.read.parquet(test)
logger.info('Composing features...')
test_input = FeatureComposer(spark, test_feature_df).transform(
remove_malicious=False, remove_null_label=True)
logger.info('Changing schema...')
test_input = change_df_schema(spark, schema, test_input)
logger.info('Persisting...')
save_parquet_for_petastorm_parquet(spark, test_input, target_test, schema)
logger.info('Test input done')
def test_as_spark_schema(self):
"""Try using 'as_spark_schema' function"""
TestSchema = Unischema('TestSchema', [
UnischemaField('int_field', np.int8, (), ScalarCodec(IntegerType()), False),
UnischemaField('string_field', np.string_, (), ScalarCodec(StringType()), False),
])
spark_schema = TestSchema.as_spark_schema()
self.assertEqual(spark_schema.fields[0].name, 'int_field')
self.assertEqual(spark_schema.fields[1].name, 'string_field')
self.assertEqual(TestSchema.fields['int_field'].name, 'int_field')
self.assertEqual(TestSchema.fields['string_field'].name, 'string_field')
def test_invalid_schema_field(synthetic_dataset):
# Let's assume we are selecting columns using a schema which is different from the one
# stored in the dataset. Would expect to get a reasonable error message
BogusSchema = Unischema('BogusSchema', [
UnischemaField('partition_key', np.string_, (), ScalarCodec(StringType()), False),
UnischemaField('id', np.int64, (), ScalarCodec(LongType()), False),
UnischemaField('bogus_key', np.int32, (), ScalarCodec(ShortType()), False)])
expected_values = {'bogus_key': 11, 'id': 1}
with pytest.raises(ValueError) as e:
Reader(synthetic_dataset.url, schema_fields=BogusSchema.fields.values(), shuffle_options=ShuffleOptions(False),
predicate=EqualPredicate(expected_values), reader_pool=ThreadPool(1))
assert 'bogus_key' in str(e)
def from_arrow_schema(cls, parquet_dataset):
"""
Convert an apache arrow schema into a unischema object. This is useful for datasets of only scalars
which need no special encoding/decoding. If there is an unsupported type in the arrow schema, it will
throw an exception.
:param arrow_schema: :class:`pyarrow.lib.Schema`
:return: A :class:`Unischema` object.
"""
meta = parquet_dataset.pieces[0].get_metadata(parquet_dataset.fs.open)
arrow_schema = meta.schema.to_arrow_schema()
unischema_fields = []
for partition_name in parquet_dataset.partitions.partition_names:
unischema_fields.append(
UnischemaField(partition_name, np.str_, (),
ScalarCodec(StringType()), False))
for column_name in arrow_schema.names:
arrow_field = arrow_schema.field_by_name(column_name)
field_type = arrow_field.type
codec, np_type = _numpy_and_codec_from_arrow_type(field_type)
unischema_fields.append(
UnischemaField(column_name, np_type, (), codec,
arrow_field.nullable))
return Unischema('inferred_schema', unischema_fields)
