def evaluate_instance(inst, classifier, tagger): # Get the supervised POS tags... """ :param inst: :type inst: RGIgt :param classifier: MalletMaxent :param tagger: StanfordPOSTagger """ sup_gloss_tier = pos_tag_tier(inst, GLOSS_WORD_ID) # We will incrementally build up the tag sequences... sup_lang_tier = pos_tag_tier(inst, LANG_WORD_ID) sup_tags = [] prj_tags = [] cls_tags = [] # If there are no supervised tags on the gloss line, but there are on the language line... if sup_gloss_tier is None and sup_lang_tier is not None: try: add_gloss_lang_alignments(inst) project_lang_to_gloss(inst) sup_gloss_tier = pos_tag_tier(inst, GLOSS_WORD_ID) except RGXigtException: pass if sup_gloss_tier: # Do the classification classify_gloss_pos(inst, classifier) cls_tier = pos_tag_tier(inst, GLOSS_WORD_ID, tag_method=INTENT_POS_CLASS) for sup_item in sup_gloss_tier: word = xigt_find(inst, id=sup_item.alignment) if not word: continue else: word = word.value() # prj_item = xigt_find(prj_tier, alignment=sup_item.alignment) # if prj_item is None: # prj_tag = 'UNK' # else: # prj_tag = prj_item.value() cls_item = xigt_find(cls_tier, alignment=sup_item.alignment) if cls_item is None: cls_tag = 'UNK' else: cls_tag = cls_item.value() sup_tags.append(POSToken(word, label=sup_item.value())) # prj_tags.append(POSToken(word, label=prj_tag)) cls_tags.append(POSToken(word, label=cls_tag)) return sup_tags, cls_tags
def evaluate_classifier_on_instances(inst_list, classifier, feat_list, pos_class_matrix, gold_tagmap=None): """ Given a list of instances, do the evaluation on them. :param inst_list: :param classifier: :param tagger: :return: """ pd = load_posdict() if (CLASS_FEATS_DICT in feat_list) or (CLASS_FEATS_PDICT in feat_list) or (CLASS_FEATS_NDICT in feat_list) else False matches = 0 compares = 0 for inst in inst_list: sup_postier = gloss_tag_tier(inst, tag_method=INTENT_POS_MANUAL) if sup_postier is None: continue gw_tier = gloss(inst) classify_gloss_pos(inst, classifier, posdict=pd, feat_prev_gram=CLASS_FEATS_PRESW in feat_list, feat_next_gram=CLASS_FEATS_NEXSW in feat_list, feat_dict=CLASS_FEATS_DICT in feat_list, feat_prev_gram_dict=CLASS_FEATS_PDICT in feat_list, feat_next_gram_dict=CLASS_FEATS_NDICT in feat_list, feat_suffix=CLASS_FEATS_SUF in feat_list, feat_prefix=CLASS_FEATS_PRE in feat_list, feat_morph_num=CLASS_FEATS_NUMSW in feat_list, feat_has_number=CLASS_FEATS_NUM in feat_list, feat_basic=CLASS_FEATS_SW in feat_list) cls_postier = gloss_tag_tier(inst, tag_method=INTENT_POS_CLASS) for cls_tag in cls_postier: word = xigt_find(gw_tier, id=cls_tag.alignment) sup_tag = xigt_find(sup_postier, alignment=cls_tag.alignment) if sup_tag is None: continue else: sup_tag_v = sup_tag.value() if gold_tagmap is not None: sup_tag_v = gold_tagmap.get(sup_tag_v) pos_class_matrix.add(sup_tag_v, cls_tag.value()) if cls_tag.value() == sup_tag_v: matches += 1 compares += 1 return matches, compares, matches/compares*100
def evaluate_heuristic_methods_on_file(f, xc, mas, classifier_obj, tagger_obj, lang, pool=None, lock=None): EVAL_LOG.info('Evaluating heuristic methods on file "{}"'.format(os.path.basename(f))) for inst in xc: # ------------------------------------------- # Only evaluate against instances that have a gold alignment. manual = get_trans_gloss_alignment(inst, aln_method=INTENT_ALN_MANUAL) if manual is None: continue EVAL_LOG.debug('Running heuristic alignments on instance "{}"'.format(inst.id)) heur = heur_align_inst(copy_xigt(inst), lowercase=False, stem=False, tokenize=False, no_multiples=True, use_pos=False) mas.add_alignment('baseline', lang, inst.id, heur) heur = heur_align_inst(copy_xigt(inst), lowercase=True, stem=False, tokenize=False, no_multiples=True, use_pos=False) mas.add_alignment('lowercasing', lang, inst.id, heur) heur = heur_align_inst(copy_xigt(inst), lowercase=True, stem=False, tokenize=True, no_multiples=True, use_pos=False) mas.add_alignment('Tokenization', lang, inst.id, heur) heur = heur_align_inst(copy_xigt(inst), lowercase=True, stem=False, tokenize=True, no_multiples=False, use_pos=False) mas.add_alignment('Multiple Matches', lang, inst.id, heur) heur = heur_align_inst(copy_xigt(inst), lowercase=True, stem=True, tokenize=True, no_multiples=False, use_pos=False) mas.add_alignment('Morphing', lang, inst.id, heur) heur = heur_align_inst(copy_xigt(inst), lowercase=True, stem=True, tokenize=True, no_multiples=False, grams=True, use_pos=False) mas.add_alignment('Grams', lang, inst.id, heur) b = copy_xigt(inst) classify_gloss_pos(b, classifier_obj) tag_trans_pos(b, tagger_obj) heur = heur_align_inst(b, lowercase=True, stem=True, tokenize=True, no_multiples=False, grams=True, use_pos=True) mas.add_alignment('POS', lang, inst.id, heur)
def test_inst_pos_heur(self): inst = copy_xigt(self.inst) print(classify_gloss_pos(inst)) print(tag_trans_pos(inst)) print(heur_align_inst(inst, use_pos=True))
def enrich(**kwargs): global classifier if ARG_OUTFILE not in kwargs: ENRICH_LOG.critical("No output file specified.") sys.exit() # ============================================================================= # Set up the alternate classifier path... # ============================================================================= class_path = kwargs.get('class_path') #=========================================================================== # Set up the different arguments... #=========================================================================== inpath = kwargs.get(ARG_INFILE) parse_args = kwargs.get(PARSE_VAR, []) pos_args = kwargs.get(POS_VAR, []) aln_args = kwargs.get(ALN_VAR, []) max_parse_length = kwargs.get('max_parse_length', 10) if not (parse_args or pos_args or aln_args): ENRICH_LOG.warning("No enrichment specified. Basic processing only will be performed.") #=========================================================================== # Sanity check the arguments. #=========================================================================== # Check that alignment is asked for if projection is asked for. if (ARG_POS_PROJ in pos_args or ARG_PARSE_PROJ in parse_args) and (not aln_args): ENRICH_LOG.warn("You have asked for projection methods but have not requested " + \ "alignments to be generated. Projection may fail if alignment not already present in file.") ENRICH_LOG.log(1000, 'Loading input file...') with open(inpath, 'r', encoding='utf-8') as in_f: corp = xigtxml.load(in_f, mode=INCREMENTAL) # ------------------------------------------- # Initialize the English tagger if: # A) "proj" option is selected for pos. # B) "trans" option is given for pos. # C) "heurpos" option is given for alignment. # ------------------------------------------- s = None if ARG_POS_PROJ in pos_args or ARG_POS_TRANS in pos_args or ARG_ALN_HEURPOS in aln_args: ENRICH_LOG.log(1000, 'Initializing tagger...') tagger = c.getpath('stanford_tagger_trans') try: s = StanfordPOSTagger(tagger) except TaggerError as te: ENRICH_LOG.critical(te) sys.exit(2) # ------------------------------------------- # Initialize the parser if: # A) "trans" option is given for parse # B) "proj" option is given for parse. # ------------------------------------------- if ARG_PARSE_TRANS in parse_args or ARG_PARSE_PROJ in parse_args: ENRICH_LOG.log(1000, "Intializing English parser...") sp = stanford_parser.StanfordParser() # ------------------------------------------- # Initialize the classifier if: # A) "class" option is given for pos # B) "heurpos" option is given for alignment. # ------------------------------------------- m = None if ARG_POS_CLASS in pos_args or ARG_ALN_HEURPOS in aln_args: ENRICH_LOG.log(1000, "Initializing gloss-line classifier...") p = load_posdict() m = mallet_maxent.MalletMaxent(classifier) # -- 1b) Giza Gloss to Translation alignment -------------------------------------- if ARG_ALN_GIZA in aln_args or ARG_ALN_GIZAHEUR in aln_args: ENRICH_LOG.log(1000, 'Aligning gloss and translation lines using mgiza++...') try: if ARG_ALN_GIZAHEUR in aln_args: giza_align_t_g(corp, resume=True, use_heur=True, symmetric=kwargs.get(ALN_SYM_VAR, SYMMETRIC_INTERSECT)) if ARG_ALN_GIZA in aln_args: giza_align_t_g(corp, resume=True, use_heur=False, symmetric=kwargs.get(ALN_SYM_VAR, SYMMETRIC_INTERSECT)) except GizaAlignmentException as gae: gl = logging.getLogger('giza') gl.critical(str(gae)) raise gae # ------------------------------------------- # Begin iterating through the corpus # ------------------------------------------- for inst in corp: feedback_string = 'Instance {:15s}: {{:20s}}{{}}'.format(inst.id) reasons = [] inst_status = None def fail(reason): nonlocal inst_status, reasons if reason not in reasons: reasons.append(reason) inst_status = 'WARN' def success(): nonlocal inst_status inst_status = 'OK' # ------------------------------------------- # Define the reasons for failure # ------------------------------------------- F_GLOSS_LINE = "NOGLOSS" F_LANG_LINE = "NOLANG" F_TRANS_LINE = "NOTRANS" F_BAD_LINES = "BADLINES" F_L_G_ALN = "L_G_ALIGN" F_T_G_ALN = "G_T_ALIGN" F_NO_TRANS_POS="NO_POS_TRANS" F_PROJECTION = "PROJECTION" F_UNKNOWN = "UNKNOWN" F_PARSELEN = "OVER_MAX_LENGTH" try: # ------------------------------------------- # Get the different lines # ------------------------------------------- def tryline(func): nonlocal inst try: return func(inst) except NoNormLineException as nnle: return None gl = tryline(gloss_line) tls = tryline(trans_lines) lls = tryline(lang_lines) has_gl = gl is not None has_tl = tls is not None has_ll = lls is not None has_all = lambda: (has_gl and has_tl and has_ll) # ------------------------------------------- # Translation Line # ------------------------------------------- if has_tl: if ARG_POS_PROJ in pos_args or ARG_POS_TRANS in pos_args or ARG_ALN_HEURPOS in aln_args: try: tag_trans_pos(inst, s) except CriticalTaggerError as cte: ENRICH_LOG.critical(str(cte)) sys.exit(2) if ARG_PARSE_PROJ in parse_args or ARG_PARSE_TRANS in parse_args: if len(trans(inst)) <= max_parse_length: parse_translation_line(inst, sp, pt=True, dt=True) else: fail(F_PARSELEN) # 4) POS tag the gloss line -------------------------------------------- if has_gl: if ARG_POS_CLASS in pos_args or ARG_ALN_HEURPOS in aln_args: classify_gloss_pos(inst, m, posdict=p) # ------------------------------------------- # Try getting alignments. # ------------------------------------------- if has_gl and has_ll: try: add_gloss_lang_alignments(inst) except GlossLangAlignException as glae: fail(F_L_G_ALN) if has_gl and has_tl: if ARG_ALN_HEURPOS in aln_args: heur_align_inst(inst, use_pos=True) if ARG_ALN_HEUR in aln_args: heur_align_inst(inst, use_pos=False) # ------------------------------------------- # Now, do the necessary projection tasks. # ------------------------------------------- # Project the classifier tags... if has_ll and has_gl and ARG_POS_CLASS in pos_args: try: project_gloss_pos_to_lang(inst, tag_method=INTENT_POS_CLASS) except GlossLangAlignException: fail(F_L_G_ALN) # ------------------------------------------- # Do the trans-to-lang projection... # ------------------------------------------- if has_all(): proj_aln_method = ALN_ARG_MAP[kwargs.get('proj_aln', ARG_ALN_ANY)] aln = get_trans_gloss_alignment(inst, aln_method=proj_aln_method) if not aln or len(aln) == 0: fail(F_T_G_ALN) else: # ------------------------------------------- # POS Projection # ------------------------------------------- if ARG_POS_PROJ in pos_args: trans_tags = trans_tag_tier(inst) if not trans_tags: fail(F_NO_TRANS_POS) else: project_trans_pos_to_gloss(inst) try: project_gloss_pos_to_lang(inst, tag_method=INTENT_POS_PROJ) except GlossLangAlignException as glae: fail(F_L_G_ALN) # ------------------------------------------- # Parse projection # ------------------------------------------- if ARG_PARSE_PROJ in parse_args: try: project_pt_tier(inst, proj_aln_method=proj_aln_method) except PhraseStructureProjectionException as pspe: fail(F_PROJECTION) except NoAlignmentProvidedError as nape: fail(F_T_G_ALN) try: project_ds_tier(inst, proj_aln_method=proj_aln_method) except ProjectionException as pe: fail(F_PROJECTION) except NoAlignmentProvidedError as nape: fail(F_T_G_ALN) # Sort the tiers... ---------------------------------------------------- inst.sort_tiers() except Exception as e: # ENRICH_LOG.warn("Unknown Error occurred processing instance {}".format(inst.id)) ENRICH_LOG.debug(e) # raise(e) fail(F_UNKNOWN) if not reasons: success() ENRICH_LOG.info(feedback_string.format(inst_status, ','.join(reasons))) ENRICH_LOG.log(1000, 'Writing output file...') if hasattr(kwargs.get(ARG_OUTFILE), 'write'): xigtxml.dump(kwargs.get(ARG_OUTFILE), corp) else: xigtxml.dump(writefile(kwargs.get(ARG_OUTFILE)), corp) ENRICH_LOG.log(1000, 'Done.') ENRICH_LOG.log(1000, "{} instances written.".format(len(corp)))
def test_classify_pos_tags(self): tags = classify_gloss_pos(self.igt, MalletMaxent(), posdict=load_posdict()) self.assertEqual(tags, self.tags)
def broken_german_test(self): xc = xc_load(os.path.join(testfile_dir, 'xigt/broken-german-instance.xml')) inst = xc[0] self.assertIsNotNone(classify_gloss_pos(inst))