mirror of https://github.com/chriskiehl/Gooey.git
Chris
3 years ago
7 changed files with 254 additions and 44 deletions
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1gooey/__init__.py
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0gooey/gui/components/filtering/__init__.py
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118gooey/gui/components/filtering/prefix_filter.py
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52gooey/gui/components/widgets/dropdown_filterable.py
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31gooey/tests/test_filterable_dropdown.py
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92gooey/tests/test_filtering.py
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4gooey/util/functional.py
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import re |
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import pygtrie as trie |
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from functools import reduce |
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__ALL__ = ('PrefixTokenizers', 'PrefixSearch') |
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class PrefixTokenizers: |
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# This string here is just an arbitrary long string so that |
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# re.split finds no matches and returns the entire phrase |
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ENTIRE_PHRASE = '::gooey/tokenization/entire-phrase' |
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# \s == any whitespace character |
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WORDS = r'\s' |
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@classmethod |
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def REGEX(cls, expression): |
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return expression |
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class SearchOptions: |
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def __init__(self, |
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choice_tokenizer=PrefixTokenizers.ENTIRE_PHRASE, |
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input_tokenizer=PrefixTokenizers.ENTIRE_PHRASE, |
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ignore_case=True, |
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operator='AND', |
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index_suffix= False, |
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**kwargs): |
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self.choice_tokenizer = choice_tokenizer |
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self.input_tokenizer = input_tokenizer |
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self.ignore_case = ignore_case |
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self.operator = operator |
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self.index_suffix = index_suffix |
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class PrefixSearch(object): |
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""" |
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A trie backed index for quickly finding substrings |
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in a list of options. |
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""" |
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def __init__(self, choices, options={}, *args, **kwargs): |
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self.choices = sorted(filter(None, choices)) |
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self.options: SearchOptions = SearchOptions(**options) |
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self.searchtree = self.buildSearchTrie(choices) |
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def updateChoices(self, choices): |
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self.choices = sorted(filter(None, choices)) |
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self.searchtree = trie.Trie() |
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def findMatches(self, token): |
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if not token: |
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return sorted(self.choices) |
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tokens = self.tokenizeInput(token) |
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matches = [set(flatten(self._vals(self.searchtree, prefix=t))) for t in tokens] |
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op = intersection if self.options.operator == 'AND' else union |
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return sorted(reduce(op, matches)) |
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def tokenizeInput(self, token): |
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""" |
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Cleans and tokenizes the user's input. |
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empty characters and spaces are trimmed to prevent |
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matching all paths in the index. |
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""" |
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return list(filter(None, re.split(self.options.input_tokenizer, self.clean(token)))) |
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def tokenizeChoice(self, choice): |
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""" |
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Splits the `choice` into a series of tokens based on |
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the user's criteria. |
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If suffix indexing is enabled, the individual tokens |
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are further broken down and indexed by their suffix offsets. e.g. |
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'Banana', 'anana', 'nana', 'ana' |
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""" |
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choice_ = self.clean(choice) |
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tokens = re.split(self.options.choice_tokenizer, choice_) |
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if self.options.index_suffix: |
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return [token[i:] |
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for token in tokens |
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for i in range(len(token) - 2)] |
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else: |
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return tokens |
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def clean(self, text): |
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return text.lower() if self.options.ignore_case else text |
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def buildSearchTrie(self, choices): |
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searchtrie = trie.Trie() |
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for choice in choices: |
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for token in self.tokenizeChoice(choice): |
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if not searchtrie.has_key(token): |
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searchtrie[token] = [] |
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searchtrie[token].append(choice) |
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return searchtrie |
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def _vals(self, searchtrie, **kwargs): |
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try: |
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return searchtrie.values(**kwargs) |
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except KeyError: |
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return [] |
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def intersection(a, b): |
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return a.intersection(b) |
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def union(a, b): |
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return a.union(b) |
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def flatten(xs): |
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return [item for x in xs for item in x] |
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@ -0,0 +1,92 @@ |
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import unittest |
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from gooey import PrefixTokenizers |
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from gui.components.filtering.prefix_filter import SearchOptions, PrefixSearch |
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from collections import namedtuple |
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TestData = namedtuple('TestData', [ |
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'options', |
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'input_string', |
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'expected_results', |
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]) |
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Places = namedtuple('Places', [ |
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'kabul', |
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'tirana', |
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'kyoto', |
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'tokyo' |
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]) |
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class TestPrefixFilter(unittest.TestCase): |
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def setUp(self): |
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self.testdata = Places( |
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'Afghanistan Kabul', |
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'Albania Tirana', |
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'Japan Kyoto', |
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'Japan Tokyo' |
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) |
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def test_prefix_searching(self): |
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p = self.testdata |
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cases = [ |
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TestData({'ignore_case': True}, 'a', [p.kabul, p.tirana]), |
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TestData({'ignore_case': True}, 'A', [p.kabul, p.tirana]), |
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TestData({'ignore_case': False}, 'a', []), |
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TestData({'ignore_case': False}, 'A', [p.kabul, p.tirana]), |
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# when using the phrase tokenizer, the search input must |
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# match starting from the beginning. So we find Afghanistan |
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TestData({'choice_tokenizer': PrefixTokenizers.ENTIRE_PHRASE}, 'Afghan', [p.kabul]), |
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# but we cannot look up Kyoto because the phrase begins with "Japan" |
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TestData({'choice_tokenizer': PrefixTokenizers.ENTIRE_PHRASE}, 'Kyoto', []), |
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# So if we start with "Japan K" it'll be returned |
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TestData({'choice_tokenizer': PrefixTokenizers.ENTIRE_PHRASE}, 'Japan K', [p.kyoto]), |
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# word tokenizer will split on all whitespace and index |
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# each choice one for each UNIQUE word |
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# so passing in 'a' will match "Af" and "Al" as usual |
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TestData({'choice_tokenizer': PrefixTokenizers.WORDS}, 'a', [p.kabul, p.tirana]), |
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# but now we can also find Kyoto without prefixing "japan" as we'd |
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# need to do with the phrase tokenizer |
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TestData({'choice_tokenizer': PrefixTokenizers.WORDS}, 'kyo', [p.kyoto]), |
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# if we tokenize the input, we're perform two searches against the index |
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# The default operator is AND, which means all the words in your search |
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# input must match the choice for it to count as as a hit. |
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# In this example, we index the choices under PHRASE, but set the input |
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# tokenizer to WORDS. Our input 'Japan K' gets tokenized to ['Japan', 'K'] |
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# There is no phrase which starts with Both "Japan" and "K" so we get no |
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# matches returned |
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TestData({'choice_tokenizer': PrefixTokenizers.ENTIRE_PHRASE, |
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'input_tokenizer': PrefixTokenizers.WORDS}, 'Japan K', []), |
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# Tokenize the choices by WORDS means we can now filter on both words |
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TestData({'choice_tokenizer': PrefixTokenizers.WORDS, |
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'input_tokenizer': PrefixTokenizers.WORDS}, 'Jap K', [p.kyoto]), |
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# the default AND behavior can be swapped to OR to facilitate matching across |
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# different records in the index. |
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TestData({'choice_tokenizer': PrefixTokenizers.WORDS, |
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'input_tokenizer': PrefixTokenizers.WORDS, |
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'operator': 'OR'}, 'Kyo Tok', [p.kyoto, p.tokyo]), |
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# Turning on Suffix indexing allow matching anywhere within a word. |
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# Now 'kyo' will match both the beginning 'Kyoto' and substring 'ToKYO' |
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TestData({'choice_tokenizer': PrefixTokenizers.WORDS, |
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'input_tokenizer': PrefixTokenizers.WORDS, |
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'index_suffix': True}, 'kyo ', [p.kyoto, p.tokyo]), |
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TestData({'choice_tokenizer': PrefixTokenizers.WORDS, |
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'input_tokenizer': PrefixTokenizers.WORDS, |
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'index_suffix': True}, 'j kyo ', [p.kyoto, p.tokyo]), |
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] |
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for case in cases: |
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with self.subTest(case): |
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searcher = PrefixSearch(self.testdata, case.options) |
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result = searcher.findMatches(case.input_string) |
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self.assertEqual(result, case.expected_results) |
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