dazu/components/component.py
import logging
from typing import Any, Dict, Hashable, List, Optional, Text
from dazu.config import DazuConfig, override_defaults
from dazu.typing import Message, Module, TrainingData
from dazu.typing.model import Metadata
logger = logging.getLogger(__name__)
class UnsupportedLanguageError(Exception):
"""Raised when a component is created but the language is not supported.
:param component: component name
:param language: language that component doesn't support
"""
def __init__(self, component: Text, language: Text) -> None:
self.component = component
self.language = language
super().__init__(component, language)
def __str__(self) -> Text:
return (
f"component '{self.component}' does not support language '{self.language}'."
)
# Component class inspired by RasaHQ/rasa
class Component(Module):
"""A component is a message processing unit in a pipeline.
Components are collected sequentially in a pipeline. Each component
is called one after another. This holds for
initialization, training, persisting and loading the components.
If a component comes first in a pipeline, its
methods will be called first.
E.g. to process an incoming message, the ``process`` method of
each component will be called. During the processing
(as well as the training, persisting and initialization)
components can pass information to other components.
The information is passed to other components by providing
attributes to the so called pipeline context. The
pipeline context contains all the information of the previous
components a component can use to do its own
processing. For example, a featurizer component can provide
features that are used by another component down
the pipeline to do intent classification."""
# Defines what attributes the pipeline component will
# provide when called. The listed attributes
# should be set by the component on the message object
# during test and train, e.g.
# ```message.set("entities", [...])```
provides = []
# Which attributes on a message are required by this
# component. E.g. if requires contains "tokens", than a
# previous component in the pipeline needs to have "tokens"
# within the above described `provides` property.
# Use `any_of("option_1", "option_2")` to define that either
# "option_1" or "option_2" needs to be present in the
# provided properties from the previous components.
requires = []
# Defines the default configuration parameters of a component
# these values can be overwritten in the pipeline configuration
# of the model. The component should choose sensible defaults
# and should be able to create reasonable results with the defaults.
defaults = {}
# Defines what language(s) this component can handle.
# This attribute is designed for instance method: `can_handle_language`.
# Default value is None which means it can handle all languages.
# This is an important feature for backwards compatibility of components.
language_list = None
def __init__(self, component_config: Optional[Dict[Text, Any]] = None) -> None:
if not component_config:
component_config = {}
# makes sure the name of the configuration is part of the config
# this is important for e.g. persistence
component_config["name"] = type(self).name()
self.component_config = override_defaults(self.defaults, component_config)
self.partial_processing_pipeline = None
self.partial_processing_context = None
@classmethod
def required_packages(cls) -> List[Text]:
"""Specify which python packages need to be installed to use this
component, e.g. ``["spacy"]``. More specifically, these should be
importable python package names e.g. `sklearn` and not package
names in the dependencies sense e.g. `scikit-learn`
This list of requirements allows us to fail early during training
if a required package is not installed."""
return []
@classmethod
def load(
cls,
component_config: Dict[Text, Any],
model_dir: Optional[Text] = None,
model_metadata: Optional["Metadata"] = None,
cached_component: Optional["Component"] = None,
**kwargs: Any,
) -> "Component":
"""Load this component from file.
After a component has been trained, it will be persisted by
calling `persist`. When the pipeline gets loaded again,
this component needs to be able to restore itself.
Components can rely on any context attributes that are
created by :meth:`components.Component.create`
calls to components previous
to this one."""
if cached_component:
return cached_component
else:
return cls(component_config)
@classmethod
def create(
cls, component_config: Dict[Text, Any], config: DazuConfig
) -> "Component":
"""Creates this component (e.g. before a training is started).
Method can access all configuration parameters."""
# Check language supporting
language = config.language
if not cls.can_handle_language(language):
# check failed
raise UnsupportedLanguageError(cls.name, language)
return cls(component_config)
def provide_context(self) -> Optional[Dict[Text, Any]]:
"""Initialize this component for a new pipeline
This function will be called before the training
is started and before the first message is processed using
the interpreter. The component gets the opportunity to
add information to the context that is passed through
the pipeline during training and message parsing. Most
components do not need to implement this method.
It's mostly used to initialize framework environments
like MITIE and spacy
(e.g. loading word vectors for the pipeline)."""
def train(
self, training_data: TrainingData, cfg: DazuConfig, **kwargs: Any
) -> None:
"""Train this component.
This is the components chance to train itself provided
with the training data. The component can rely on
any context attribute to be present, that gets created
by a call to :meth:`rasa.nlu.components.Component.create`
of ANY component and
on any context attributes created by a call to
:meth:`rasa.nlu.components.Component.train`
of components previous to this one."""
def process(self, message: Message, **kwargs: Any) -> None:
"""Process an incoming message.
This is the components chance to process an incoming
message. The component can rely on
any context attribute to be present, that gets created
by a call to :meth:`rasa.nlu.components.Component.create`
of ANY component and
on any context attributes created by a call to
:meth:`rasa.nlu.components.Component.process`
of components previous to this one."""
def persist(self, file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]:
"""Persist this component to disk for future loading."""
@classmethod
def cache_key(
cls, component_meta: Dict[Text, Any], model_metadata: "Metadata"
) -> Optional[Text]:
"""This key is used to cache components.
If a component is unique to a model it should return None.
Otherwise, an instantiation of the
component will be reused for all models where the
metadata creates the same key."""
return None
def __getstate__(self) -> Any:
d = self.__dict__.copy()
# these properties should not be pickled
if "partial_processing_context" in d:
del d["partial_processing_context"]
if "partial_processing_pipeline" in d:
del d["partial_processing_pipeline"]
return d
def __eq__(self, other) -> bool:
return self.__dict__ == other.__dict__
def prepare_partial_processing(
self, pipeline: List["Component"], context: Dict[Text, Any]
) -> None:
"""Sets the pipeline and context used for partial processing.
The pipeline should be a list of components that are
previous to this one in the pipeline and
have already finished their training (and can therefore
be safely used to process messages)."""
self.partial_processing_pipeline = pipeline
self.partial_processing_context = context
def partially_process(self, message: Message) -> Message:
"""Allows the component to process messages during
training (e.g. external training data).
The passed message will be processed by all components
previous to this one in the pipeline."""
if self.partial_processing_context is not None:
for component in self.partial_processing_pipeline:
component.process(message, **self.partial_processing_context)
else:
logger.info("Failed to run partial processing due to missing pipeline.")
return message
@classmethod
def can_handle_language(cls, language: Hashable) -> bool:
"""Check if component supports a specific language.
This method can be overwritten when needed. (e.g. dynamically
determine which language is supported.)"""
# if language_list is set to `None` it means: support all languages
if language is None or cls.language_list is None:
return True
return language in cls.language_list