Source code for textblob.base

# -*- coding: utf-8 -*-
"""Abstract base classes for models (taggers, noun phrase extractors, etc.)
which define the interface for descendant classes.

.. versionchanged:: 0.7.0
    All base classes are defined in the same module, ``textblob.base``.
"""
from __future__ import absolute_import
from abc import ABCMeta, abstractmethod

import nltk

from textblob.compat import with_metaclass

##### POS TAGGERS #####

[docs]class BaseTagger(with_metaclass(ABCMeta)): """Abstract tagger class from which all taggers inherit from. All descendants must implement a ``tag()`` method. """ @abstractmethod
[docs] def tag(self, text, tokenize=True): """Return a list of tuples of the form (word, tag) for a given set of text. """ return
##### NOUN PHRASE EXTRACTORS #####
[docs]class BaseNPExtractor(with_metaclass(ABCMeta)): """Abstract base class from which all NPExtractor classes inherit. Descendant classes must implement an ``extract(text)`` method that returns a list of noun phrases as strings. """ @abstractmethod
[docs] def extract(self, text): """Return a list of noun phrases (strings) for a body of text.""" return
##### TOKENIZERS #####
[docs]class BaseTokenizer(with_metaclass(ABCMeta), nltk.tokenize.api.TokenizerI): """Abstract base class from which all Tokenizer classes inherit. Descendant classes must implement a ``tokenize(text)`` method that returns a list of noun phrases as strings. """ @abstractmethod
[docs] def tokenize(self, text): """Return a list of tokens (strings) for a body of text. :rtype: list """ return
[docs] def itokenize(self, text, *args, **kwargs): """Return a generator that generates tokens "on-demand". .. versionadded:: 0.6.0 :rtype: generator """ return (t for t in self.tokenize(text, *args, **kwargs))
##### SENTIMENT ANALYZERS #### DISCRETE = 'ds' CONTINUOUS = 'co'
[docs]class BaseSentimentAnalyzer(with_metaclass(ABCMeta)): """Abstract base class from which all sentiment analyzers inherit. Should implement an ``analyze(text)`` method which returns either the results of analysis. """ kind = DISCRETE def __init__(self): self._trained = False def train(self): # Train me self._trained = True @abstractmethod
[docs] def analyze(self, text): """Return the result of of analysis. Typically returns either a tuple, float, or dictionary. """ # Lazily train the classifier if not self._trained: self.train() # Analyze text return None
##### PARSERS #####
[docs]class BaseParser(with_metaclass(ABCMeta)): """Abstract parser class from which all parsers inherit from. All descendants must implement a ``parse()`` method. """ @abstractmethod
[docs] def parse(self, text): """Parses the text.""" return