def test_is_binary_string():
assert types.is_binary(pa.binary())
assert not types.is_binary(pa.string())
assert types.is_string(pa.string())
assert types.is_unicode(pa.string())
assert not types.is_string(pa.binary())
assert types.is_fixed_size_binary(pa.binary(5))
assert not types.is_fixed_size_binary(pa.binary())
def test_is_binary_string():
assert types.is_binary(pa.binary())
assert not types.is_binary(pa.string())
assert types.is_string(pa.string())
assert types.is_unicode(pa.string())
assert not types.is_string(pa.binary())
assert types.is_fixed_size_binary(pa.binary(5))
assert not types.is_fixed_size_binary(pa.binary())
def _numpy_and_codec_from_arrow_type(field_type):
from pyarrow import types
if types.is_int8(field_type):
np_type = np.int8
elif types.is_int16(field_type):
np_type = np.int16
elif types.is_int32(field_type):
np_type = np.int32
elif types.is_int64(field_type):
np_type = np.int64
elif types.is_string(field_type):
np_type = np.unicode_
elif types.is_boolean(field_type):
np_type = np.bool_
elif types.is_float32(field_type):
np_type = np.float32
elif types.is_float64(field_type):
np_type = np.float64
elif types.is_decimal(field_type):
np_type = Decimal
elif types.is_binary(field_type):
np_type = np.string_
elif types.is_fixed_size_binary(field_type):
np_type = np.string_
elif types.is_date(field_type):
np_type = np.datetime64
elif types.is_timestamp(field_type):
np_type = np.datetime64
elif types.is_list(field_type):
np_type = _numpy_and_codec_from_arrow_type(field_type.value_type)
else:
raise ValueError('Cannot auto-create unischema due to unsupported column type {}'.format(field_type))
return np_type
def convertPyArrowTypeToGlueType(pyarrowType: pa.DataType) -> str:
if (types.is_string(pyarrowType) or types.is_unicode(pyarrowType)
or types.is_large_string(pyarrowType)
or types.is_large_unicode(pyarrowType)):
return 'string'
if (types.is_int64(pyarrowType) or types.is_uint64(pyarrowType)):
return 'bigint'
if (types.is_binary(pyarrowType)):
return 'binary'
if (types.is_boolean(pyarrowType)):
return 'boolean'
if (types.is_date(pyarrowType) or types.is_date32(pyarrowType)
or types.is_date64(pyarrowType)):
return 'date'
if (types.is_decimal(pyarrowType)):
return 'decimal(16,2)'
if (types.is_float64(pyarrowType)):
'return double'
if (types.is_float16(pyarrowType) or types.is_float32(pyarrowType)):
return 'float'
if (types.is_int16(pyarrowType) or types.is_int32(pyarrowType)
or types.is_uint16(pyarrowType) or types.is_uint32(pyarrowType)):
return 'int'
if (types.is_map(pyarrowType)):
return 'map'
if (types.is_struct(pyarrowType)):
return 'struct'
if (types.is_timestamp(pyarrowType)):
return 'timestamp'
if (types.is_union(pyarrowType)):
return 'union'
return str(pyarrowType)
def from_arrow_type(at):
""" Convert pyarrow type to Spark data type.
"""
from distutils.version import LooseVersion
import pyarrow as pa
import pyarrow.types as types
if types.is_boolean(at):
spark_type = BooleanType()
elif types.is_int8(at):
spark_type = ByteType()
elif types.is_int16(at):
spark_type = ShortType()
elif types.is_int32(at):
spark_type = IntegerType()
elif types.is_int64(at):
spark_type = LongType()
elif types.is_float32(at):
spark_type = FloatType()
elif types.is_float64(at):
spark_type = DoubleType()
elif types.is_decimal(at):
spark_type = DecimalType(precision=at.precision, scale=at.scale)
elif types.is_string(at):
spark_type = StringType()
elif types.is_binary(at):
spark_type = BinaryType()
elif types.is_date32(at):
spark_type = DateType()
elif types.is_timestamp(at):
spark_type = TimestampType()
elif types.is_list(at):
if types.is_timestamp(at.value_type):
raise TypeError("Unsupported type in conversion from Arrow: " + str(at))
spark_type = ArrayType(from_arrow_type(at.value_type))
elif types.is_map(at):
if LooseVersion(pa.__version__) < LooseVersion("2.0.0"):
raise TypeError("MapType is only supported with pyarrow 2.0.0 and above")
if types.is_timestamp(at.key_type) or types.is_timestamp(at.item_type):
raise TypeError("Unsupported type in conversion from Arrow: " + str(at))
spark_type = MapType(from_arrow_type(at.key_type), from_arrow_type(at.item_type))
elif types.is_struct(at):
if any(types.is_struct(field.type) for field in at):
raise TypeError("Nested StructType not supported in conversion from Arrow: " + str(at))
return StructType(
[StructField(field.name, from_arrow_type(field.type), nullable=field.nullable)
for field in at])
elif types.is_dictionary(at):
spark_type = from_arrow_type(at.value_type)
elif types.is_null(at):
spark_type = NullType()
else:
raise TypeError("Unsupported type in conversion from Arrow: " + str(at))
return spark_type
def _numpy_and_codec_from_arrow_type(field_type):
from pyarrow import types
if types.is_int8(field_type):
np_type = np.int8
codec = ScalarCodec(ByteType())
elif types.is_int16(field_type):
np_type = np.int16
codec = ScalarCodec(ShortType())
elif types.is_int32(field_type):
np_type = np.int32
codec = ScalarCodec(IntegerType())
elif types.is_int64(field_type):
np_type = np.int64
codec = ScalarCodec(LongType())
elif types.is_string(field_type):
np_type = np.unicode_
codec = ScalarCodec(StringType())
elif types.is_boolean(field_type):
np_type = np.bool_
codec = ScalarCodec(BooleanType())
elif types.is_float32(field_type):
np_type = np.float32
codec = ScalarCodec(FloatType())
elif types.is_float64(field_type):
np_type = np.float64
codec = ScalarCodec(DoubleType())
elif types.is_decimal(field_type):
np_type = Decimal
codec = ScalarCodec(DecimalType(field_type.precision,
field_type.scale))
elif types.is_binary(field_type):
codec = ScalarCodec(StringType())
np_type = np.string_
elif types.is_fixed_size_binary(field_type):
codec = ScalarCodec(StringType())
np_type = np.string_
elif types.is_date(field_type):
np_type = np.datetime64
codec = ScalarCodec(DateType())
elif types.is_timestamp(field_type):
np_type = np.datetime64
codec = ScalarCodec(TimestampType())
elif types.is_list(field_type):
_, np_type = _numpy_and_codec_from_arrow_type(field_type.value_type)
codec = None
else:
raise ValueError(
'Cannot auto-create unischema due to unsupported column type {}'.
format(field_type))
return codec, np_type
def from_arrow_type(at):
""" Convert pyarrow type to Spark data type.
"""
import pyarrow.types as types
if types.is_boolean(at):
spark_type = BooleanType()
elif types.is_int8(at):
spark_type = ByteType()
elif types.is_int16(at):
spark_type = ShortType()
elif types.is_int32(at):
spark_type = IntegerType()
elif types.is_int64(at):
spark_type = LongType()
elif types.is_float32(at):
spark_type = FloatType()
elif types.is_float64(at):
spark_type = DoubleType()
elif types.is_decimal(at):
spark_type = DecimalType(precision=at.precision, scale=at.scale)
elif types.is_string(at):
spark_type = StringType()
elif types.is_binary(at):
spark_type = BinaryType()
elif types.is_date32(at):
spark_type = DateType()
elif types.is_timestamp(at):
spark_type = TimestampType()
elif types.is_list(at):
if types.is_timestamp(at.value_type):
raise TypeError("Unsupported type in conversion from Arrow: " +
str(at))
spark_type = ArrayType(from_arrow_type(at.value_type))
elif types.is_struct(at):
if any(types.is_struct(field.type) for field in at):
raise TypeError(
"Nested StructType not supported in conversion from Arrow: " +
str(at))
return StructType([
StructField(field.name,
from_arrow_type(field.type),
nullable=field.nullable) for field in at
])
elif types.is_dictionary(at):
spark_type = from_arrow_type(at.value_type)
else:
raise TypeError("Unsupported type in conversion from Arrow: " +
str(at))
return spark_type
def _cast(self, val: Any, dtype: pa.DataType) -> Any:
"""Fix columns with mixed/serialized dtypes"""
if not val:
return None
if is_string(dtype):
casted = str(val)
elif is_floating(dtype):
casted = self._cast_float(val, dtype)
elif is_temporal(dtype):
casted = self._cast_temporal(val, dtype)
else:
casted = val
return casted
def _getType(value):
if patypes.is_float_value(value):
dtype = DoubleType() # VectorDataType.DOUBLE
ptype = float
elif patypes.is_integer_value(value):
dtype = IntegerType() # VectorDataType.INTEGER
ptype = int
elif patypes.is_string(value):
dtype = StringType() # VectorDataType.STRING
ptype = str
else:
# maybe not the best default choice, but...
print("Unrecognized datatype {}, attempting to use Double".format(
type(value)))
dtype = DoubleType() # VectorDataType.DOUBLE
ptype = float
return dtype, ptype
def _getVectorLengthAndType(self, name, row):
dtype = VectorDataType.DOUBLE
try:
v0 = row[name]
if isinstance(v0, (DenseVector, SparseVector)):
num_elements = len(v0)
value = v0[0]
if patypes.is_float_value(value):
dtype = VectorDataType.DOUBLE # DoubleType()
elif patypes.is_integer_value(value):
dtype = VectorDataType.INTEGER # IntegerType()
elif patypes.is_string(value):
dtype = VectorDataType.STRING # StringType()
else:
# maybe not the best default choice, but...
print("Unrecognized datatype {}, attempting to use Double".
format(type(value)))
dtype = VectorDataType.DOUBLE # DoubleType()
except Exception as exc:
print("Skipping VectorUDT `{}` due to error:\n{}".format(
name, exc))
return num_elements, dtype
def to_legate_dtype(dtype):
if type(dtype) == str:
if dtype not in _DTYPE_MAPPING:
raise ValueError(f"invalid dtype {dtype}")
return _DTYPE_MAPPING[dtype]
elif isinstance(dtype, np.dtype):
if dtype.name not in _DTYPE_MAPPING:
raise ValueError(f"unsupported dtype {dtype}")
return _DTYPE_MAPPING[dtype.name]
elif isinstance(dtype, pa.DataType):
if pyarrow_dtype.is_string(dtype):
return string
else:
return to_legate_dtype(dtype.to_pandas_dtype())
elif pandas_dtype.is_bool_dtype(dtype):
return bool
elif pandas_dtype.is_string_dtype(dtype):
return string
else:
try:
return to_legate_dtype(np.dtype(dtype))
except TypeError:
raise TypeError("Unsupported dtype: %s " % str(dtype))
def is_possible_feature(arrow_type: DataType) -> bool:
"""Check if data type is possibly an ML feature."""
return is_boolean(arrow_type) or is_string(arrow_type) or is_num(
arrow_type) # noqa: E501
def is_possible_cat(arrow_type: DataType, /) -> bool:
"""Check if data type is possibly categorical."""
return (is_boolean(arrow_type) or is_string(arrow_type)
or is_num(arrow_type))
def is_possible_cat(arrow_type):
return is_boolean(arrow_type) or is_string(arrow_type) or is_num(
arrow_type)
