# -*- coding:utf-8 -*-
# Author: hankcs
# Date: 2019-12-31 00:22
import types
from typing import Callable, Union, Iterable, Any
from hanlp.components.lambda_wrapper import LambdaComponent
from hanlp.common.component import Component
from hanlp_common.document import Document
from hanlp.utils.component_util import load_from_meta
from hanlp_common.io import save_json, load_json
from hanlp_common.reflection import str_to_type, classpath_of
import hanlp
[docs]class Pipe(Component):
def __init__(self, component: Component, input_key: str = None, output_key: str = None, **kwargs) -> None:
super().__init__()
if not hasattr(self, 'config'):
self.config = {'classpath': classpath_of(self)}
self.output_key = output_key
self.input_key = input_key
self.component = component
self.kwargs = kwargs
self.config.update({
'component': component.config,
'input_key': self.input_key,
'output_key': self.output_key,
'kwargs': self.kwargs
})
# noinspection PyShadowingBuiltins
[docs] def predict(self, doc: Document, **kwargs) -> Document:
unpack = False
if self.input_key:
if isinstance(self.input_key, (tuple, list)):
if isinstance(self.component, LambdaComponent): # assume functions take multiple arguments
input = [doc[key] for key in self.input_key]
unpack = True
else:
input = list(list(zip(*sent)) for sent in zip(*[doc[key] for key in self.input_key]))
else:
input = doc[self.input_key]
else:
input = doc
if self.kwargs:
kwargs.update(self.kwargs)
if unpack:
kwargs['_hanlp_unpack'] = True
output = self.component(input, **kwargs)
if isinstance(output, types.GeneratorType):
output = list(output)
if self.output_key:
if not isinstance(doc, Document):
doc = Document()
if isinstance(self.output_key, tuple):
for key, value in zip(self.output_key, output):
doc[key] = value
else:
doc[self.output_key] = output
return doc
return output
def __repr__(self):
name = self.component.function.__name__ if isinstance(self.component, LambdaComponent) \
else self.component.__class__.__name__
return f'{self.input_key}->{name}->{self.output_key}'
[docs] @staticmethod
def from_config(meta: dict, **kwargs):
cls = str_to_type(meta['classpath'])
component = load_from_meta(meta['component'])
return cls(component, meta['input_key'], meta['output_key'], **meta['kwargs'])
[docs]class Pipeline(Component, list):
def __init__(self, *pipes: Pipe) -> None:
super().__init__()
if not hasattr(self, 'config'):
self.config = {'classpath': classpath_of(self)}
if pipes:
self.extend(pipes)
[docs] def append(self, component: Callable, input_key: Union[str, Iterable[str]] = None,
output_key: Union[str, Iterable[str]] = None, **kwargs):
"""
Append a pipe to the tail of this pipeline.
Args:
component: A callable function.
input_key: The input key indicating which fields will be inputted to the pipe. ``None``: inherit from
previous pipe; ``*``: use all the outputs from previous pipes wrapped in a
:class:`~hanlp_common.document.Document`.
output_key: The output key indicating where to store the outputs
**kwargs: Extra arguments passed to the ``Pipe`` constructor.
Returns:
Pipeline: A pipeline.
"""
self.insert(len(self), component, input_key, output_key, **kwargs)
return self
[docs] def insert(self, index: int, component: Callable, input_key: Union[str, Iterable[str]] = None,
output_key: Union[str, Iterable[str]] = None,
**kwargs):
"""
Args:
index: The index of the new pipe.
input_key: The input key indicating which fields will be inputted to the pipe. ``None``: inherit from
previous pipe; ``*``: use all the outputs from previous pipes wrapped in a
:class:`~hanlp_common.document.Document`.
output_key: The output key indicating where to store the outputs
**kwargs: Extra arguments passed to the ``Pipe`` constructor.
Returns:
Pipeline: A pipeline.
"""
if input_key == '*':
input_key = None
elif not input_key and len(self) and index:
input_key = self[index - 1].output_key
if not isinstance(component, Component):
component = LambdaComponent(component)
super().insert(index, Pipe(component, input_key, output_key, **kwargs))
return self
def __call__(self, doc: Union[Document, Any] = None, **kwargs) -> Document:
"""Run the pipeline as a function.
Args:
doc: A :class:`~hanlp_common.document.Document` or other data types.
**kwargs: If `doc` is set to None then create a :class:`~hanlp_common.document.Document` as the
input to the first pipe using all the parameters in ``kwargs``.
Returns:
A :class:`~hanlp_common.document.Document`.
"""
if doc is None:
doc = Document(**kwargs)
for component in self:
doc = component(doc)
return doc
[docs] def copy(self):
return self.__copy__()
def __copy__(self):
config = self.meta
return Pipeline.from_config(config)
@property
def meta(self):
return {
'classpath': classpath_of(self),
'hanlp_version': hanlp.version.__version__,
'pipes': [pipe.config for pipe in self]
}
@meta.setter
def meta(self, value):
pass
def save(self, filepath):
save_json(self.meta, filepath)
def load(self, filepath):
meta = load_json(filepath)
self.clear()
self.extend(Pipeline.from_config(meta))
[docs] @staticmethod
def from_config(meta: Union[dict, str], **kwargs):
if isinstance(meta, str):
meta = load_json(meta)
return Pipeline(*[load_from_meta(pipe) for pipe in meta['pipes']])
