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# coding: utf-8
import collections
from typing import Any, Callable, DefaultDict, Dict, List
"""
A couple of utilities, may be split up into separate modules.
"""
class StringPipeline:
"""
Minimalistic grouping of functions applied on an input string to produce
some cleaned or normalized output. Pipeline functions are Func[[str], str].
>>> cleanups = StringPipeline([
... str.lower,
... remove_html_tags,
... normalize_whitespace,
... normalize_ampersand,
... ])
>>> cleanups.run("<a>Input & Output</a>")
input and output
"""
def __init__(self, fs: List[Callable[[str], str]]):
self.fs = fs
def run(self, s: str) -> str:
"""
Apply all function and return result.
"""
for f in self.fs:
s = f(s)
return s
class StringAnnotator:
"""
Experimental, rationale: In some way, feature engineering; we want to
derive metrics, number from the string, do this consistently and compactly.
E.g. once we have dozens of "speaking" characteristics, a case based method
might become more readble.
if s.is_single_token and s.some_ratio > 0.4:
return MatchStatus.AMBIGIOUS
Could also subclass string and pluck more methods on it (might be even
reusable).
....
Given a string, derive a couple of metrics, based on functions. The
annotation is a dict, mapping an annotation key to a value of any type.
>>> metrics = StringAnnotator([
... has_html_tags,
... has_only_printable_characters,
... is_single_token,
... length,
... has_year_in_parentheses,
... ])
>>> metrics.run("Journal of Pataphysics 2038-2032")
{"value": "Journal of Pataphysics 2038-2032", "is_single_token": False, ... }
TODO(martin):
* SimpleNamespace, dotdict, Dataclass.
* string_utils.py or similar
* maybe adopt SpaCy or similar
"""
def __init__(self, fs: List[Callable[[str], Dict[str, Any]]]):
self.fs = fs
def run(self, s: str) -> Dict[str, Any]:
annotations: DefaultDict[str, Any] = collections.defaultdict(dict)
for f in self.fs:
result = f(s)
annotations.update(result)
return annotations
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