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How to use _adjust_match method in localstack

Best Python code snippet using localstack_python

generic_proxy.py

Source:generic_proxy.py

...267                return source268        elif not isinstance(source, str):269            # Ignore any other types270            return source271        return self.arn_regex.sub(lambda m: self._adjust_match(m, static_partition), source)272    def _adjust_match(self, match: Match, static_partition: str = None):273        region = match.group("Region")274        partition = self._partition_lookup(region) if static_partition is None else static_partition275        service = match.group("Service")276        account_id = match.group("AccountID")277        resource_path = match.group("ResourcePath")278        return f"arn:{partition}:{service}:{region}:{account_id}:{resource_path}"279    def _partition_lookup(self, region: str):280        try:281            partition = self._get_partition_for_region(region)282        except ArnPartitionRewriteListener.InvalidRegionException:283            try:284                # If the region is not properly set (f.e. because it is set to a wildcard),285                # the partition is determined based on the default region.286                partition = self._get_partition_for_region(config.DEFAULT_REGION)...

TMMatching.py

Source:TMMatching.py

...67      else: break68    return l_best_segments69  # Estimate TER between sources segmets.70  def ter_score(self, src_x, src_y): return pyter.ter(src_x.split(), src_y.split())71  def _adjust_match(self, segment, filter, ter):72    if ter > 1:73      ter = 174    ter = (100 - (ter * 100))75    if filter:76      # Penalize match if segment doesn't match filter77      for fname, fvalues in filter.items():78        if fvalues and not set(fvalues) & set(segment[fname]):79          ter -= 580    return ter81  #Check 100% match82  def _cen_p_match(self, l_best_segments):83    #print(l_best_segments)84    segment = l_best_segments[0][0]85    match = l_best_segments[0][1]86    ter = self.ter_score(self.src_input, segment['source_text'])87    logging.info("initial ter: {}".format(ter))88    if match == 100 and 'Q' not in segment.keys() and ter == 0:  # 100% match --> Return match considering domain89        # logging.info("source: {}, tm_source: {}, tm_target: {}, match: {}".format(src_input, segment['source_text'], segment['target_text'], str(ter)))90       self.dic_match = self._create_dic_match(segment, ter)91    return self.dic_match92  def _create_dic_match(self, segment, ter):93    filter = {'domain': ['Automotive']}94    print(filter)95    self.dic_match['tm_src'] = segment['source_text']96    print(self.dic_match['tm_src'])97    self.dic_match['tm_tgt'] = segment['target_text']98    print(self.dic_match['tm_tgt'])99    self.dic_match['match'] = self._adjust_match(segment, filter, ter)100    print(self.dic_match)101    return self.dic_match102  def fuzzy_match(self, l_best_segments):103    logging.info("Improve Fuzzy match")104    fuzzy_alg = TMFuzzyMatch(self.src_input, self.src_lang, self.tgt_lang, self.dic_match, self.threshold, l_best_segments) #Class to improve fuzzy match105    self.dic_match = fuzzy_alg.process()106    return self.dic_match107  def fuzzy_match_posTagger(self, l_best_segments):108    logging.info("Improve Fuzzy match pos tagger")109    fuzzy_alg = TMFuzzyMatch_PosTagger(self.src_input, self.src_lang, self.tgt_lang, self.dic_match, self.threshold,110                             l_best_segments)  # Class to improve fuzzy match111    self.dic_match = fuzzy_alg.process()112    return self.dic_match113    # ************It is necessary check the domain***************114  #   if (improve_ter <= ter) and (115  #             improve_ter < 0.25):  # Fuzzy Match and was possible improve ter value --> Return match considering domain116  #     print("source: " + src_input + ' tm_target: ' + process_match['reg_exp']['tm_tgt_re'] + ' Match: ' + str(117  #       self._adjust_match(segment, filter, improve_ter)))118  #     break119  #   if (120  #               count == 10 or improve_ter > ter) and ter < 0.25:  # Fuzzy Match, but wasn't improved ter value --> Return elasticsearch match121  #     print("source: " + src_input + ' tm_target: ' + segment['target_text'] + ' Match: ' + str(122  #       self._adjust_match(segment, filter, ter)))123  #     todo_ok = False124  #     break125  #126  # else:127  # todo_ok = False128# ************************************************************************************129class TMFuzzyMatch(TMMatching):130  def __init__(self, src_input, src_lang, tgt_lang, dic_match, threshold, l_best_segments):131    super().__init__(src_input, src_lang, tgt_lang, dic_match, threshold)132    self.l_best_segments = l_best_segments133    #self.pipe = ['tokenizer', 'reg_exp', 'pos_tagger', 'sent_split']134    #self.processor = {'reg_exp': TMRegExpPreprocessor(),135    #                  'sent_split': 'split'}  # TMSplitPreprocessor()136  def pre_process(self, text, lang, preprocess):...

TMDbApi.py

Source:TMDbApi.py

...378        break379      # Adjust match % according to filters380      if ter >= qparams.min_match:381        self.timer.start("adjust_match")382        ter = self._adjust_match(segment, qparams.domains, ter)383        self.timer.stop("adjust_match")384        new_segments.append((segment, ter))385        match = True386      elif qparams.aut_trans and len(segments) == 1: # Mark segment as one needed to machine-translate387        match = False388        new_segments.append((segment, ter))389    new_segments.sort(key = lambda x: (x[1], x[0].tm_change_date) if (x[0].tm_change_date is not None) else (x[1], str(datetime.datetime(1970, 1, 1))), reverse = True)390    logging.info("New match segments: {}".format(new_segments))391    tm_match.timer.print()392    return new_segments, match393  def _add_segments(self, segments):394    batch_status = []395    self.timer.start("add_segments:source")396    batch_status.append(self.ml_index.add_segments(segments, 'source'))397    self.timer.stop("add_segments:source")398    self.timer.start("add_segments:target")399    batch_status.append(self.ml_index.add_segments(segments, 'target'))400    self.timer.stop("add_segments:target")401    self.timer.start("add_segments:map")402    batch_status.append(self.seg_map.add_segments(segments))403    self.timer.stop("add_segments:map")404    logging.info('Added {} segments, status: {}'.format(len(segments), batch_status))405    return batch_status406  def _find_pivot_lang(self, langs):407    langs = [l.lower() for l in langs]408    lang_graph = self.seg_map.get_lang_graph()409    path_len = shortest_path_length(lang_graph, langs[0], langs[1])410    if path_len != 2:411      return None412    # Find shortest path413    path = shortest_path(lang_graph, langs[0], langs[1])414    assert (len(path) == 3)415    # Get a pivot language and scan all pivot segments416    return path[1]417  def _src_id2tgt_id(self, src_id, source_lang, target_lang):418    # Query  mapping segment419    target_id,map_doc = self.seg_map.get(uuid.UUID(src_id), source_lang, target_lang)420    if not target_id:421      logging.warning("Can't find matching segment for {}".format(src_id))422      return None,None423    else:424      assert isinstance(target_id, uuid.UUID)425    return target_id,map_doc426  def _msrc_id2tgt_id(self, src_hits, source_lang, target_lang, return_multiple=False):427    margs = [(uuid.UUID(src_hit.meta.id), source_lang, target_lang) for src_hit in src_hits]428    return self.seg_map.mget(margs, return_multiple=return_multiple)429  def _delete(self, langs, docs, filter, force_delete):430    source_lang, target_lang = langs431    # Delete map doc, returns tuple of 2 lists: deleted source and target ids432    deleted_ids = self.seg_map.delete(langs, docs, filter, force_delete)433    logging.info("After deleting from map: {} source and {} target potential orphan segments".format(len(deleted_ids[0]), len(deleted_ids[1])))434    # Check and delete only orphans (for source and target)435    for lang,ids in zip(langs,deleted_ids):436      ids_exist = self.seg_map.mexist(source_lang, ids)437      ids_to_delete = [id for id,exists in zip(ids, ids_exist) if not exists]438      logging.info("Lang: {}, actual orphans to delete: {}".format(lang.upper(), len(ids_to_delete)))439      self.ml_index.delete(lang, ids_to_delete)440  def _doc2segment(self, md, sd=None, td=None):441    doc = md442    if sd: doc['source_pos'] = sd.get('pos')443    if td: doc['target_pos'] = td.get('pos')444    segment = TMTranslationUnit(doc)445    return segment446  def _adjust_match(self, segment, domains, match):447    if domains:448      if not set(domains) & set(getattr(segment, 'domain')):449        match -= self.QUERY_PENALIZE[0]450    if self._is_dirty(segment):451      match -= self.QUERY_PENALIZE[1]452    return match453  def _is_dirty(self, segment):454    if segment.dirty_score and segment.dirty_score >= self.DIRTY_THRESHOLD:455      return True456    return False457if __name__ == "__main__":458  logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)459  tmdb = TMDbApi()460  #tmdb.init_db()...

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