Datasets:

ArXiv:
data / operators.py
Elron's picture
Upload operators.py with huggingface_hub
4654b39
raw
history blame
23.8 kB
import importlib
import inspect
import uuid
from abc import abstractmethod
from copy import deepcopy
from dataclasses import field
from itertools import zip_longest
from typing import (
Any,
Callable,
Dict,
Generator,
Iterable,
List,
Optional,
Tuple,
Union,
)
from .artifact import Artifact, fetch_artifact
from .dataclass import NonPositionalField
from .dict_utils import dict_delete, dict_get, dict_set, is_subpath
from .operator import (
MultiStream,
MultiStreamOperator,
PagedStreamOperator,
SingleStreamOperator,
SingleStreamReducer,
StreamingOperator,
StreamInitializerOperator,
StreamInstanceOperator,
)
from .random_utils import random
from .stream import MultiStream, Stream
from .text_utils import nested_tuple_to_string
from .utils import flatten_dict
class FromIterables(StreamInitializerOperator):
"""
Creates a MultiStream from iterables.
Args:
iterables (Dict[str, Iterable]): A dictionary where each key-value pair represents a stream name and its corresponding iterable.
"""
def process(self, iterables: Dict[str, Iterable]) -> MultiStream:
return MultiStream.from_iterables(iterables)
class MapInstanceValues(StreamInstanceOperator):
"""A class used to map instance values into a stream.
This class is a type of StreamInstanceOperator,
it maps values of instances in a stream using predefined mappers.
Attributes:
mappers (Dict[str, Dict[str, str]]): The mappers to use for mapping instance values.
Keys are the names of the fields to be mapped, and values are dictionaries
that define the mapping from old values to new values.
strict (bool): If True, the mapping is applied strictly. That means if a value
does not exist in the mapper, it will raise a KeyError. If False, values
that are not present in the mapper are kept as they are.
"""
mappers: Dict[str, Dict[str, str]]
strict: bool = True
use_query = False
def verify(self):
# make sure the mappers are valid
for key, mapper in self.mappers.items():
assert isinstance(mapper, dict), f"Mapper for given field {key} should be a dict, got {type(mapper)}"
for k, v in mapper.items():
assert isinstance(k, str), f'Key "{k}" in mapper for field "{key}" should be a string, got {type(k)}'
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
for key, mapper in self.mappers.items():
value = dict_get(instance, key, use_dpath=self.use_query)
if value is not None:
value = str(value) # make sure the value is a string
if self.strict:
dict_set(instance, key, mapper[value], use_dpath=self.use_query)
else:
if value in mapper:
dict_set(instance, key, mapper[value], use_dpath=self.use_query)
return instance
class FlattenInstances(StreamInstanceOperator):
"""
Flattens each instance in a stream, making nested dictionary entries into top-level entries.
Args:
parent_key (str): A prefix to use for the flattened keys. Defaults to an empty string.
sep (str): The separator to use when concatenating nested keys. Defaults to "_".
"""
parent_key: str = ""
sep: str = "_"
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
return flatten_dict(instance, parent_key=self.parent_key, sep=self.sep)
class AddFields(StreamInstanceOperator):
"""
Adds specified fields to each instance in a stream.
Args:
fields (Dict[str, object]): The fields to add to each instance.
"""
fields: Dict[str, object]
use_query: bool = False
use_deepcopy: bool = False
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
if self.use_query:
for key, value in self.fields.items():
if self.use_deepcopy:
value = deepcopy(value)
dict_set(instance, key, value, use_dpath=self.use_query)
else:
if self.use_deepcopy:
self.fields = deepcopy(self.fields)
instance.update(self.fields)
return instance
class FieldOperator(StreamInstanceOperator):
"""
A general stream that processes the values of a field (or multiple ones
Args:
field (Optional[str]): The field to process, if only a single one is passed Defaults to None
to_field (Optional[str]): Field name to save, if only one field is to be saved, if None is passed the operator would happen in-place and replace "field" Defaults to None
field_to_field (Optional[Union[List[Tuple[str, str]], Dict[str, str]]]): Mapping from fields to process to their names after this process, duplicates are allowed. Defaults to None
process_every_value (bool): Processes the values in a list instead of the list as a value, similar to *var. Defaults to False
use_query (bool): Whether to use dpath style queries. Defaults to False
"""
field: Optional[str] = None
to_field: Optional[str] = None
field_to_field: Optional[Union[List[Tuple[str, str]], Dict[str, str]]] = None
process_every_value: bool = False
use_query: bool = False
def verify(self):
super().verify()
assert self.field is not None or self.field_to_field is not None, "Must supply a field to work on"
assert (
self.to_field is None or self.field_to_field is None
), f"Can not apply operator to create both on {self.to_field} and on the mapping from fields to fields {self.field_to_field}"
assert (
self.field is None or self.field_to_field is None
), f"Can not apply operator both on {self.field} and on the mapping from fields to fields {self.field_to_field}"
assert self._field_to_field, f"the from and to fields must be defined got: {self._field_to_field}"
@abstractmethod
def process_value(self, value: Any) -> Any:
pass
def prepare(self):
if self.to_field is None:
self.to_field = self.field
if self.field_to_field is None:
self._field_to_field = [(self.field, self.to_field)]
else:
try:
self._field_to_field = [(k, v) for k, v in self.field_to_field.items()]
except AttributeError:
self._field_to_field = self.field_to_field
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
for from_field, to_field in self._field_to_field:
try:
old_value = dict_get(instance, from_field, use_dpath=self.use_query)
except TypeError as e:
raise TypeError(f"Failed to get {from_field} from {instance}")
if self.process_every_value:
new_value = [self.process_value(value) for value in old_value]
else:
new_value = self.process_value(old_value)
if self.use_query and is_subpath(from_field, to_field):
dict_delete(instance, from_field)
dict_set(instance, to_field, new_value, use_dpath=self.use_query, not_exist_ok=True)
return instance
class RenameFields(FieldOperator):
"""
Renames fields
"""
def process_value(self, value: Any) -> Any:
return value
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
res = super().process(instance=instance, stream_name=stream_name)
vals = [x[1] for x in self._field_to_field]
for key, _ in self._field_to_field:
if self.use_query and "/" in key:
continue
if key not in vals:
res.pop(key)
return res
class AddConstant(FieldOperator):
"""
Adds a number, similar to field + add
Args:
add (float): sum to add
"""
add: float
def process_value(self, value: Any) -> Any:
return value + self.add
class ShuffleFieldValues(FieldOperator):
"""
Shuffles an iterable value
"""
def process_value(self, value: Any) -> Any:
res = list(value)
random.shuffle(res)
return res
class JoinStr(FieldOperator):
"""
Joins a list of strings (contents of a field), similar to str.join()
Args:
separator (str): text to put between values
"""
separator: str = ","
def process_value(self, value: Any) -> Any:
return self.separator.join(str(x) for x in value)
class Apply(StreamInstanceOperator):
__allow_unexpected_arguments__ = True
function: Callable = NonPositionalField(required=True)
to_field: str = NonPositionalField(required=True)
def function_to_str(self, function: Callable) -> str:
return function.__qualname__
def str_to_function(self, function_str: str) -> Callable:
splitted = function_str.split(".", 1)
if len(splitted) == 1:
return getattr(__builtins__, function_str)
else:
module_name, function_name = splitted
if module_name in globals():
obj = globals()[module_name]
else:
obj = importlib.import_module(module_name)
for part in function_name.split("."):
obj = getattr(obj, part)
return obj
def prepare(self):
super().prepare()
if isinstance(self.function, str):
self.function = self.str_to_function(self.function)
def get_init_dict(self):
result = super().get_init_dict()
result["function"] = self.function_to_str(self.function)
return result
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
argv = [instance[arg] for arg in self._argv]
kwargs = {key: instance[val] for key, val in self._kwargs}
result = self.function(*argv, **kwargs)
instance[self.to_field] = result
return instance
class ListFieldValues(StreamInstanceOperator):
"""
Concatanates values of multiple fields into a list to list(fields)
"""
fields: str
to_field: str
use_query: bool = False
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
values = []
for field in self.fields:
values.append(dict_get(instance, field, use_dpath=self.use_query))
instance[self.to_field] = values
return instance
class ZipFieldValues(StreamInstanceOperator):
"""
Zips values of multiple fields similar to list(zip(*fields))
"""
fields: str
to_field: str
longest: bool = False
use_query: bool = False
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
values = []
for field in self.fields:
values.append(dict_get(instance, field, use_dpath=self.use_query))
if self.longest:
zipped = zip_longest(*values)
else:
zipped = zip(*values)
instance[self.to_field] = list(zipped)
return instance
class IndexOf(StreamInstanceOperator):
"""
Finds the location of one value in another (iterable) value similar to to_field=search_in.index(index_of)
"""
search_in: str
index_of: str
to_field: str
use_query: bool = False
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
lst = dict_get(instance, self.search_in, use_dpath=self.use_query)
item = dict_get(instance, self.index_of, use_dpath=self.use_query)
instance[self.to_field] = lst.index(item)
return instance
class TakeByField(StreamInstanceOperator):
"""
Takes value from one field based on another field similar to field[index]
"""
field: str
index: str
to_field: str = None
use_query: bool = False
def prepare(self):
if self.to_field is None:
self.to_field = self.field
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
value = dict_get(instance, self.field, use_dpath=self.use_query)
index_value = dict_get(instance, self.index, use_dpath=self.use_query)
instance[self.to_field] = value[index_value]
return instance
class CopyFields(FieldOperator):
"""
Copies specified fields from one field to another.
Args:
field_to_field (Union[List[List], Dict[str, str]]): A list of lists, where each sublist contains the source field and the destination field, or a dictionary mapping source fields to destination fields.
use_dpath (bool): Whether to use dpath for accessing fields. Defaults to False.
"""
def process_value(self, value: Any) -> Any:
return value
class AddID(StreamInstanceOperator):
id_field_name: str = "id"
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
instance[self.id_field_name] = str(uuid.uuid4()).replace("-", "")
return instance
class CastFields(StreamInstanceOperator):
"""
Casts specified fields to specified types.
Args:
types (Dict[str, str]): A dictionary mapping fields to their new types.
nested (bool): Whether to cast nested fields. Defaults to False.
fields (Dict[str, str]): A dictionary mapping fields to their new types.
defaults (Dict[str, object]): A dictionary mapping types to their default values for cases of casting failure.
"""
types = {
"int": int,
"float": float,
"str": str,
"bool": bool,
}
fields: Dict[str, str] = field(default_factory=dict)
failure_defaults: Dict[str, object] = field(default_factory=dict)
use_nested_query: bool = False
cast_multiple: bool = False
def _cast_single(self, value, type, field):
try:
return self.types[type](value)
except:
if field not in self.failure_defaults:
raise ValueError(
f'Failed to cast field "{field}" with value {value} to type "{type}", and no default value is provided.'
)
return self.failure_defaults[field]
def _cast_multiple(self, values, type, field):
values = [self._cast_single(value, type, field) for value in values]
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
for field, type in self.fields.items():
value = dict_get(instance, field, use_dpath=self.use_nested_query)
if self.cast_multiple:
casted_value = self._cast_multiple(value, type, field)
else:
casted_value = self._cast_single(value, type, field)
dict_set(instance, field, casted_value, use_dpath=self.use_nested_query)
return instance
def recursive_divide(instance, divisor, strict=False):
if isinstance(instance, dict):
for key, value in instance.items():
instance[key] = recursive_divide(value, divisor, strict=strict)
elif isinstance(instance, list):
for i, value in enumerate(instance):
instance[i] = recursive_divide(value, divisor, strict=strict)
elif isinstance(instance, float):
instance /= divisor
elif strict:
raise ValueError(f"Cannot divide instance of type {type(instance)}")
return instance
class DivideAllFieldsBy(StreamInstanceOperator):
divisor: float = 1.0
strict: bool = False
recursive: bool = True
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
return recursive_divide(instance, self.divisor, strict=self.strict)
class ArtifactFetcherMixin:
"""
Provides a way to fetch and cache artifacts in the system.
Args:
cache (Dict[str, Artifact]): A cache for storing fetched artifacts.
"""
cache: Dict[str, Artifact] = {}
@classmethod
def get_artifact(cls, artifact_identifier: str) -> Artifact:
if artifact_identifier not in cls.cache:
artifact, artifactory = fetch_artifact(artifact_identifier)
cls.cache[artifact_identifier] = artifact
return cls.cache[artifact_identifier]
class ApplyValueOperatorsField(StreamInstanceOperator, ArtifactFetcherMixin):
"""
Applies value operators to each instance in a stream based on specified fields.
Args:
value_field (str): The field containing the value to be operated on.
operators_field (str): The field containing the operators to be applied.
default_operators (List[str]): A list of default operators to be used if no operators are found in the instance.
"""
value_field: str
operators_field: str
default_operators: List[str] = None
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
operator_names = instance.get(self.operators_field)
if operator_names is None:
assert (
self.default_operators is not None
), f"No operators found in {self.field} field and no default operators provided"
operator_names = self.default_operators
if isinstance(operator_names, str):
operator_names = [operator_names]
for name in operator_names:
operator = self.get_artifact(name)
instance = operator(instance, self.value_field)
return instance
class FilterByValues(SingleStreamOperator):
"""
Filters a stream, yielding only instances that match specified values.
Args:
values (Dict[str, Any]): The values that instances should match to be included in the output.
"""
values: Dict[str, Any]
def process(self, stream: Stream, stream_name: str = None) -> Generator:
for instance in stream:
if all(instance[key] == value for key, value in self.values.items()):
yield instance
class Unique(SingleStreamReducer):
"""
Reduces a stream to unique instances based on specified fields.
Args:
fields (List[str]): The fields that should be unique in each instance.
"""
fields: List[str] = field(default_factory=list)
@staticmethod
def to_tuple(instance: dict, fields: List[str]) -> tuple:
result = []
for field in fields:
value = instance[field]
if isinstance(value, list):
value = tuple(value)
result.append(value)
return tuple(result)
def process(self, stream: Stream) -> Stream:
seen = set()
for instance in stream:
values = self.to_tuple(instance, self.fields)
if values not in seen:
seen.add(values)
return list(seen)
class SplitByValue(MultiStreamOperator):
"""
Splits a MultiStream into multiple streams based on unique values in specified fields.
Args:
fields (List[str]): The fields to use when splitting the MultiStream.
"""
fields: List[str] = field(default_factory=list)
def process(self, multi_stream: MultiStream) -> MultiStream:
uniques = Unique(fields=self.fields)(multi_stream)
result = {}
for stream_name, stream in multi_stream.items():
stream_unique_values = uniques[stream_name]
for unique_values in stream_unique_values:
filtering_values = {field: value for field, value in zip(self.fields, unique_values)}
filtered_streams = FilterByValues(values=filtering_values)._process_single_stream(stream)
filtered_stream_name = stream_name + "_" + nested_tuple_to_string(unique_values)
result[filtered_stream_name] = filtered_streams
return MultiStream(result)
class ApplyStreamOperatorsField(SingleStreamOperator, ArtifactFetcherMixin):
"""
Applies stream operators to a stream based on specified fields in each instance.
Args:
field (str): The field containing the operators to be applied.
reversed (bool): Whether to apply the operators in reverse order.
"""
field: str
reversed: bool = False
def process(self, stream: Stream, stream_name: str = None) -> Generator:
first_instance = stream.peak()
operators = first_instance.get(self.field, [])
if isinstance(operators, str):
operators = [operators]
if self.reversed:
operators = list(reversed(operators))
for operator_name in operators:
operator = self.get_artifact(operator_name)
assert isinstance(operator, StreamingOperator), f"Operator {operator_name} must be a SingleStreamOperator"
stream = operator(MultiStream({"tmp": stream}))["tmp"]
yield from stream
class AddFieldNamePrefix(StreamInstanceOperator):
"""
Adds a prefix to each field name in each instance of a stream.
Args:
prefix_dict (Dict[str, str]): A dictionary mapping stream names to prefixes.
"""
prefix_dict: Dict[str, str]
def prepare(self):
return super().prepare()
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
return {self.prefix_dict[stream_name] + key: value for key, value in instance.items()}
class MergeStreams(MultiStreamOperator):
"""
Merges multiple streams into a single stream.
Args:
new_stream_name (str): The name of the new stream resulting from the merge.
add_origin_stream_name (bool): Whether to add the origin stream name to each instance.
origin_stream_name_field_name (str): The field name for the origin stream name.
"""
new_stream_name: str = "all"
add_origin_stream_name: bool = True
origin_stream_name_field_name: str = "origin"
def merge(self, multi_stream):
for stream_name, stream in multi_stream.items():
for instance in stream:
if self.add_origin_stream_name:
instance[self.origin_stream_name_field_name] = stream_name
yield instance
def process(self, multi_stream: MultiStream) -> MultiStream:
return MultiStream({self.new_stream_name: Stream(self.merge, gen_kwargs={"multi_stream": multi_stream})})
class Shuffle(PagedStreamOperator):
"""
Shuffles the order of instances in each page of a stream.
Args:
page_size (int): The size of each page in the stream. Defaults to 1000.
"""
def process(self, page: List[Dict], stream_name: str = None) -> Generator:
random.shuffle(page)
yield from page
class EncodeLabels(StreamInstanceOperator):
"""
Encode labels of specified fields together a into integers.
Args:
fields (List[str]): The fields to encode together.
"""
fields: List[str]
def _process_multi_stream(self, multi_stream: MultiStream) -> MultiStream:
self.encoder = {}
return super()._process_multi_stream(multi_stream)
def process(self, instance: Dict[str, Any], stream_name: str = None) -> Dict[str, Any]:
for field in self.fields:
values = dict_get(instance, field, use_dpath=True)
if not isinstance(values, list):
values = [values]
for value in values:
if value not in self.encoder:
self.encoder[value] = len(self.encoder)
new_values = [self.encoder[value] for value in values]
dict_set(instance, field, new_values, use_dpath=True, set_multiple=True)
return instance