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How to use resolve_from_str method in Pytest
Best Python code snippet using pytest
resolvers.py
Source:resolvers.py 
1"""2Resolução dos algoritmos chamados nas queries.3"""4import logging5import itertools6import spacy7from difflib import get_close_matches as closest_token8from nltk import sent_tokenize, word_tokenize9from lisa_processing.util.nlp import (stemming, text_classifier,10 get_word_offense_level, remove_stopwords,11 remove_punctuations, get_offense_level,12 get_tokens_pol, is_stopword,13 get_word_polarity, detailed_stopword_removal)14from lisa_processing.util.normalizer import Normalizer15from lisa_processing.util.tools import (get_entity_description,16 get_pos_tag_description)17logger = logging.getLogger('lisa')18logger.info('Loading Spacy...')19SPACY = spacy.load('pt')20logger.info('Done!')21class Resolver:22 """23 Classe que contém métodos resolutivos par ao processamento dos algoritmos24 utilizados pela API.25 """26 @staticmethod27 def resolve_lemming(input_data):28 """29 Resolução do processamento de lemming.30 return : <list>31 """32 normalizer = Normalizer()33 lemma_from_list = lambda texts: SPACY(normalizer.list_to_string(texts))34 execute = {35 str: SPACY,36 list: lemma_from_list37 }38 tokens = execute.get(type(input_data))(input_data)39 return [token.lemma_ for token in tokens]40 @staticmethod41 def resolve_stemming(input_data):42 """43 Resolução do processamento de stemming44 return : <list>45 """46 normalizer = Normalizer()47 stem_from_list = lambda text_list: stemming(48 Resolver.resolve_tokenize(normalizer.list_to_string(text_list))49 )50 stem_from_str = lambda text: stemming(51 Resolver.resolve_tokenize(text)52 )53 execute = {54 str: stem_from_str,55 list: stem_from_list56 }57 return execute.get(type(input_data))(input_data)58 @staticmethod59 def resolve_dependency_parse(input_data):60 """61 Resolução do processamento de Dependency Parsing62 param : input_data : <str> ou <list>63 return <list> de <dict>64 """65 normalizer = Normalizer()66 resolve_from_list = lambda text_list: SPACY(67 normalizer.list_to_string(text_list)68 )69 execute = {70 str: SPACY,71 list: resolve_from_list72 }73 tokens = execute.get(type(input_data))(input_data)74 result = []75 for token in tokens:76 result.append({77 'element': token.text,78 'children': [str(child) for child in token.children],79 'ancestors': [str(anc) for anc in token.ancestors]80 })81 return result82 @staticmethod83 def resolve_lexical_text_classifier(input_data):84 """85 Resolve a análise de sentimentos de um texto utilizando o86 algoritmo léxico.87 param : text : <str>88 return : <float>89 """90 normalizer = Normalizer()91 classify_from_list = lambda tokens: text_classifier(92 normalizer.list_to_string(tokens)93 )94 execute = {95 str: text_classifier,96 list: classify_from_list97 }98 return execute.get(type(input_data))(input_data)99 @staticmethod100 def resolve_sentence_segmentation(input_data):101 """102 Resolve a fragmentação de sentenças a partir de:103 - uma lista de textos <list>; ou104 - um texto puro (<str>)105 return : <list> : Lista de sentenças (<str>)106 """107 list_sent_tokenize = lambda sent_list: list(108 itertools.chain(*[sent_tokenize(sent) for sent in sent_list])109 )110 execute = {111 str: sent_tokenize,112 list: list_sent_tokenize113 }114 return execute.get(type(input_data))(input_data)115 @staticmethod116 def resolve_tokenize(input_data):117 """118 Resolve a atomização de palávras a partir de:119 - uma lista de sentenças <list>; ou120 - um texto puro (<str>)121 return : <list> : Lista de tokens (<str>)122 """123 list_tokenize = lambda texts: list(124 itertools.chain(*[word_tokenize(text) for text in texts])125 )126 execute = {127 str: word_tokenize,128 list: list_tokenize129 }130 return execute.get(type(input_data))(input_data)131 @staticmethod132 def resolve_remove_stopwords(input_data):133 """134 Resolve a remoção de palávras vazias a partir de:135 - uma lista de sentenças <list>; ou136 - um texto puro (<str>)137 return : <list> : Lista de tokens com significado semântico (<str>)138 """139 normalizer = Normalizer()140 remove_sw_from_str = lambda text: remove_stopwords(141 Resolver.resolve_tokenize(text)142 )143 remove_sw_from_list = lambda text_list: remove_stopwords(144 Resolver.resolve_tokenize(normalizer.list_to_string(text_list))145 )146 execute = {147 str: remove_sw_from_str,148 list: remove_sw_from_list149 }150 return execute.get(type(input_data))(input_data)151 @staticmethod152 def resolve_remove_puncts(input_data):153 """154 Resolve o processamento para remocão de pontuações a partir de:155 - uma lista de sentenças <list>; ou156 - um texto puro (<str>)157 return : <list> : Lista de tokens (<str>)158 """159 normalizer = Normalizer()160 remove_puncts_from_str = lambda text: remove_punctuations(161 Resolver.resolve_tokenize(text)162 )163 remove_puncts_from_list = lambda text_list: remove_punctuations(164 Resolver.resolve_tokenize(normalizer.list_to_string(text_list))165 )166 execute = {167 str: remove_puncts_from_str,168 list: remove_puncts_from_list169 }170 return execute.get(type(input_data))(input_data)171 @staticmethod172 def resolve_word_offense(input_data):173 """174 Resolve o processamento de identificação de palávras ofensivas.175 param : input_data : <str> or <list>176 return : <list> : Lista de <dict>177 """178 normalizer = Normalizer()179 resolve_from_list = lambda text_list: Resolver.resolve_tokenize(180 normalizer.list_to_string(text_list)181 )182 resolve_from_str = lambda text: Resolver.resolve_tokenize(text)183 execute = {184 str: resolve_from_str,185 list: resolve_from_list186 }187 tokens = execute.get(type(input_data))(input_data)188 pairs = get_word_offense_level(tokens)189 output = []190 for pair in pairs:191 # o processamento retorna o radical, ams queremos ot ermo completo192 token = closest_token(pair[0], tokens)193 # se não houver use o própprio radical194 full_token = token or pair195 output.append({196 'token': full_token[0],197 'is_offensive': bool(pair[1]),198 'value': pair[1]199 })200 return output201 @staticmethod202 def resolve_text_offense(input_data):203 """204 Resolve o processamento de classificação de um texto como ofensivo.205 param : input_data : <str> ou <list>206 return <dict>207 """208 normalizer = Normalizer()209 resolve_from_list = lambda token_list: get_offense_level(210 normalizer.list_to_string(token_list)211 )212 execute = {213 list: resolve_from_list,214 str: get_offense_level215 }216 is_offensive, average = execute.get(type(input_data))(input_data)217 return {'is_offensive': is_offensive, 'average': average}218 @staticmethod219 def resolve_word_polarity(input_data):220 """221 Resolve o processamento de polarização de palávras.222 param : input_data : <str> ou <list>223 return : <list> de <dict>224 """225 resolve_from_string = lambda text: get_tokens_pol(226 Resolver.resolve_tokenize(text)227 )228 execute = {229 str: resolve_from_string,230 list: get_tokens_pol231 }232 output = execute.get(type(input_data))(input_data)233 return output234 @staticmethod235 def resolve_named_entity(input_data):236 """237 Resolve o processamento de entidades nomeadas.238 param : input_data : <list> ou <str>239 return : <list> de <dict>240 """241 normalizer = Normalizer()242 resolve_from_string = lambda text: [{243 'token': ent.text,244 'entity': ent.label_,245 'description': get_entity_description(ent.label_)246 } for ent in SPACY(text).ents]247 resolve_from_list = lambda text_list: resolve_from_string(248 normalizer.list_to_string(text_list)249 )250 execute = {251 str: resolve_from_string,252 list: resolve_from_list253 }254 return execute.get(type(input_data))(input_data)255 @staticmethod256 def resolve_part_of_speech(input_data):257 """258 Resolve a marcação POS na entrada fornecida.259 param : input_data : <list> ou <str>260 return : <list> de <dict>261 """262 normalizer = Normalizer()263 resolve_from_string = lambda text: [{264 'token': token.text,265 'tag': token.pos_,266 'description': get_pos_tag_description(token.pos_)267 } for token in SPACY(text)]268 resolve_from_list = lambda text_list: resolve_from_string(269 normalizer.list_to_string(text_list)270 )271 execute = {272 str: resolve_from_string,273 list: resolve_from_list274 }275 return execute.get(type(input_data))(input_data)276 @staticmethod277 def resolve_token_inspection(input_data):278 """279 Resolução da inspeção de tokens.280 param : input_data : <list> ou <str>281 return : <list> de <dict>282 """283 normalizer = Normalizer()284 resolve_from_string = lambda text: [{285 'token': token.text,286 'is_alpha': token.is_alpha,287 'is_ascii': token.is_ascii,288 'is_currency': token.is_currency,289 'is_digit': token.is_digit,290 'is_punct': token.is_punct,291 'is_space': token.is_space,292 'is_stop': is_stopword(token.text),293 'lemma': token.lemma_,294 'pos_tag': get_pos_tag_description(token.pos_),295 'vector': token.vector,296 'polarity': get_word_polarity(token.text),297 'is_offensive': get_offense_level(token.text)[0],298 'root': stemming([token.text])[0]299 } for token in SPACY(text)]300 resolve_from_list = lambda text_list: resolve_from_string(301 normalizer.list_to_string(text_list)302 )303 execute = {304 str: resolve_from_string,305 list: resolve_from_list306 }307 return execute.get(type(input_data))(input_data)308 @staticmethod309 def resolve_datailed_stopword_removal(input_data):310 """311 Resolve a remoção detalhada de palávras vazias a partir de:312 - uma lista de sentenças <list>; ou313 - um texto puro (<str>)314 return : <dict> : Dicionário contendo detalhes da operação315 """316 normalizer = Normalizer()317 remove_sw_from_str = lambda text: detailed_stopword_removal(318 Resolver.resolve_tokenize(text)319 )320 remove_sw_from_list = lambda text_list: detailed_stopword_removal(321 Resolver.resolve_tokenize(normalizer.list_to_string(text_list))322 )323 execute = {324 str: remove_sw_from_str,325 list: remove_sw_from_list326 }327 return execute.get(type(input_data))(input_data) 328 @staticmethod329 def resolve_similarity(first, second):330 """331 Resolve a comparação de similaridade entre332 dois termos.333 """334 first = SPACY(first)335 second = SPACY(second)336 return first.similarity(second)337 @staticmethod338 def resolve_sentiment_batch_extraction(input_data):339 """340 Resolve a extração de sentimentos em lote.341 param : input_data : <list>342 return : <dict>343 """344 positive_sentiments = []345 negative_sentiments = []346 neutral_sentiments = []347 for data in input_data:348 extraction = {'text': data, 'sentiment': text_classifier(data)}349 if extraction['sentiment'] > 0:350 positive_sentiments.append(extraction)351 elif extraction['sentiment'] < 0:352 negative_sentiments.append(extraction)353 else:354 neutral_sentiments.append(extraction)355 # total de amostras avaliadas356 count = len(positive_sentiments) + \357 len(negative_sentiments) + \358 len(neutral_sentiments)359 # Calcula o sentimento total360 positives = [data.get('sentiment', 0) for data in positive_sentiments]361 negatives = [data.get('sentiment', 0) for data in negative_sentiments]362 # Neutros são sempre 0 então somamos apenas positivos e negativos363 total_sentiment = sum(positives + negatives)364 # A média de sentimento é a razão do total pelo número de possibilidades365 mean_sentiment = total_sentiment / count366 return {367 'count': count,368 'positive_sentiments': positive_sentiments,369 'negative_sentiments': negative_sentiments,370 'neutral_sentiments': neutral_sentiments,371 'total_sentiment': total_sentiment,372 'mean_sentiment': mean_sentiment373 }374 @staticmethod375 def resolve_char_count(input_data):376 """377 Resolve a conbtagem de caracteres.378 param : input_data : <str>379 return : <list>380 """...
cacheprovider.py
Source:cacheprovider.py
...35 cachedir.mkdir()36 return cls(cachedir, config)37 @staticmethod38 def cache_dir_from_config(config):39 return resolve_from_str(config.getini("cache_dir"), config.rootdir)40 def warn(self, fmt, **args):41 from _pytest.warnings import _issue_config_warning42 from _pytest.warning_types import PytestWarning43 _issue_config_warning(44 PytestWarning(fmt.format(**args) if args else fmt), self._config45 )46 def makedir(self, name):47 """ return a directory path object with the given name. If the48 directory does not yet exist, it will be created. You can use it49 to manage files likes e. g. store/retrieve database50 dumps across test sessions.51 :param name: must be a string not containing a ``/`` separator.52 Make sure the name contains your plugin or application53 identifiers to prevent clashes with other cache users....
paths.py
Source:paths.py
1from .compat import Path2from os.path import expanduser, expandvars, isabs3def resolve_from_str(input, root):4 assert not isinstance(input, Path), "would break on py2"5 root = Path(root)6 input = expanduser(input)7 input = expandvars(input)8 if isabs(input):9 return Path(input)10 else:...
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