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How to use get_base_keywords method in avocado

Best Python code snippet using avocado_python

loader.py

Source:loader.py

...154            self._decorator_mapping.update(plugin.get_full_decorator_mapping())155    def get_extra_listing(self):156        for loader_plugin in self._initialized_plugins:157            loader_plugin.get_extra_listing()158    def get_base_keywords(self):159        base_path = []160        for loader_plugin in self._initialized_plugins:161            base_path += loader_plugin.get_base_keywords()162        return base_path163    def get_type_label_mapping(self):164        if self._label_mapping is None:165            raise RuntimeError(166                "LoaderProxy.discover has to be called before "167                "LoaderProxy.get_type_label_mapping"168            )169        return self._label_mapping170    def get_decorator_mapping(self):171        if self._label_mapping is None:172            raise RuntimeError(173                "LoaderProxy.discover has to be called before "174                "LoaderProxy.get_decorator_mapping"175            )...

skosmos.py

Source:skosmos.py

...69                data, key=lambda x: x.get('label', {}).get(language, x.get('label', {}).get('en', 'zzz')).lower()70            )71            cache.set(cache_key, data, CACHE_TIME)72    return data73def get_base_keywords():74    return fetch_data('http://base.uni-ak.ac.at/recherche/keywords/collection_base', source_name='base')75def get_disciplines():76    cache_key = 'get_disciplines'77    data = cache.get(cache_key, [])78    if not data:79        data = list(80            filter(81                lambda x: len(x['source'].split('/')[-1]) % 3 == 0,82                fetch_data(83                    'http://base.uni-ak.ac.at/portfolio/disciplines/oefos', fetch_children=True, source_name='voc'84                ),85            )86        )87        if data:88            cache.set(cache_key, data, CACHE_TIME)89    return data90def get_formats():91    return fetch_data('http://base.uni-ak.ac.at/portfolio/vocabulary/format_type')92def get_keywords():93    return [94        *get_base_keywords(),95        *get_disciplines(),96    ]97def get_languages():98    return fetch_data('http://base.uni-ak.ac.at/portfolio/languages/iso_639_1')99def get_languages_choices():100    language = get_language() or 'en'101    cache_key_languages = f'get_languages_{language}'102    cache_key_languages_labels = f'get_languages_labels_{language}'103    languages = cache.get(cache_key_languages, [])104    languages_labels = cache.get(cache_key_languages_labels, [])105    if not languages or not languages_labels:106        r = skosmos.top_concepts(settings.LANGUAGES_VOCID, lang=language)107        r = sorted(r, key=lambda k: k['label'].lower())108        for lang in r:...

get_keywords.py

Source:get_keywords.py

...27kb_diversity = 0.328kw_model = KeyBERT()29nltk.download('wordnet')30nltk.download('omw-1.4')31def get_base_keywords(bill: Bill):32    if bill.bill_type not in prefixes.keys():33        return []34    prefix_list = prefixes[bill.bill_type]35    number = bill.number.split(".")[-1]36    return [bill.bill_id, bill.number] + list(map(lambda x: x + number, prefix_list))37def kw_cleanup(text):38    # Remove Punctuation (Unneeded)39    # text = remove_punc(text)40    # Tokenize41    tokens = text.split(" ")42    # Remove Stopwords43    tokens = [tok for tok in tokens if tok not in stop_words]44    # Stem45    ls = WordNetLemmatizer()46    tokens = [ls.lemmatize(tok) for tok in tokens]47    # Remove bad words48    tokens = [tok for tok in tokens if tok not in nono_words]49    return ' '.join(tokens)50def keybert_extraction(summary: str):51    summary_keywords = kw_model.extract_keywords(52        docs=summary, 53        keyphrase_ngram_range=n_gram_range, 54        stop_words=stop_words, 55        top_n=top_n,56        use_mmr=use_mmr,57        diversity=kb_diversity)58    # For keyBERT, we do 1 - x[1] in the sort method since highest confidence value is best59    keywords = summary_keywords60    keywords.sort(key = lambda x: 1 - x[1])61    62    return keywords63def derive_keywords(summary: str):64    summary = summary.lower()65    summary = kw_cleanup(summary)66    keybert_keywords = keybert_extraction(summary)67    68    return list(set(keybert_keywords))69def get_keywords(bill: Bill):70    # get list of obvious keywords71    known_keywords = get_base_keywords(bill)72    # use NLP to generate other keywords73    if (bill.summary is None or len(bill.summary) > 100000) and bill.summary_short is not None:74        generated_keywords = derive_keywords(bill.summary_short.replace('\n', ''))75    elif bill.summary_short is not None and len(bill.summary) < 100000:76        generated_keywords = derive_keywords(bill.summary.replace('\n', ''))77    else:78        generated_keywords = []...

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