def generate_src_word_accuracy_report(ref, outs, src, ref_align_file=None, out_align_files=None, acc_type='fmeas', bucket_type='freq', bucket_cutoffs=None, freq_count_file=None, freq_corpus_file=None, label_set=None, src_labels=None, title=None, case_insensitive=False): """ Generate a report for source word analysis in both plain text and graphs. Args: ref: Tokens from the reference outs: Tokens from the output file(s) src: Tokens from the source ref_align_file: Alignment file for the reference out_align_files: Alignment file for the output file acc_type: The type of accuracy to show (prec/rec/fmeas). Can also have multiple separated by '+'. bucket_type: A string specifying the way to bucket words together to calculate F-measure (freq/tag) bucket_cutoffs: The boundaries between buckets, specified as a colon-separated string. freq_corpus_file: When using "freq" as a bucketer, which corpus to use to calculate frequency. By default this uses the frequency in the reference test set, but it's often more informative se the frequency in the training set, in which case you specify the path of the target side he training corpus. freq_count_file: An alternative to freq_corpus that uses a count file in "word\tfreq" format. src_labels: either a filename of a file full of source labels, or a list of strings corresponding to `ref`. title: A string specifying the caption of the printed table case_insensitive: A boolean specifying whether to turn on the case insensitive option """ case_insensitive = True if case_insensitive == 'True' else False if not src or not ref_align_file or not out_align_files: raise ValueError("Must specify the source and the alignment files when performing source analysis.") ref_align = corpus_utils.load_tokens(ref_align_file) out_aligns = [corpus_utils.load_tokens(x) for x in arg_utils.parse_files(out_align_files)] if len(out_aligns) != len(outs): raise ValueError(f'The number of output files should be equal to the number of output alignment files.') bucketer = bucketers.create_word_bucketer_from_profile(bucket_type, bucket_cutoffs=bucket_cutoffs, freq_count_file=freq_count_file, freq_corpus_file=freq_corpus_file, freq_data=src, label_set=label_set, case_insensitive=case_insensitive) src_labels = corpus_utils.load_tokens(src_labels) if type(src_labels) == str else src_labels matches = [bucketer.calc_source_bucketed_matches(src, ref, out, ref_align, out_align, src_labels=src_labels) for out, out_align in zip(outs, out_aligns)] reporter = reporters.WordReport(bucketer=bucketer, matches=matches, acc_type=acc_type, header="Source Word Accuracy Analysis", title=title) reporter.generate_report(output_fig_file=f'src-word-acc', output_fig_format='pdf', output_directory='outputs') return reporter
def generate_word_accuracy_report(ref, outs, acc_type='fmeas', bucket_type='freq', bucket_cutoffs=None, freq_count_file=None, freq_corpus_file=None, label_set=None, ref_labels=None, out_labels=None, title=None, case_insensitive=False): """ Generate a report comparing the word accuracy in both plain text and graphs. Args: ref: Tokens from the reference outs: Tokens from the output file(s) acc_type: The type of accuracy to show (prec/rec/fmeas). Can also have multiple separated by '+'. bucket_type: A string specifying the way to bucket words together to calculate F-measure (freq/tag) bucket_cutoffs: The boundaries between buckets, specified as a colon-separated string. freq_corpus_file: When using "freq" as a bucketer, which corpus to use to calculate frequency. By default this uses the frequency in the reference test set, but it's often more informative to use the frequency in the training set, in which case you specify the path of the training corpus. freq_count_file: An alternative to freq_corpus that uses a count file in "word\tfreq" format. ref_labels: either a filename of a file full of reference labels, or a list of strings corresponding to `ref`. out_labels: output labels. must be specified if ref_labels is specified. title: A string specifying the caption of the printed table case_insensitive: A boolean specifying whether to turn on the case insensitive option """ case_insensitive = True if case_insensitive == 'True' else False if out_labels is not None: out_labels = arg_utils.parse_files(out_labels) if len(out_labels) != len(outs): raise ValueError(f'The number of output files should be equal to the number of output labels.') bucketer = bucketers.create_word_bucketer_from_profile(bucket_type, bucket_cutoffs=bucket_cutoffs, freq_count_file=freq_count_file, freq_corpus_file=freq_corpus_file, freq_data=ref, label_set=label_set, case_insensitive=case_insensitive) ref_labels = corpus_utils.load_tokens(ref_labels) if type(ref_labels) == str else ref_labels out_labels = [corpus_utils.load_tokens(out_labels[i]) if not out_labels is None else None for i in range(len(outs))] matches = [bucketer.calc_bucketed_matches(ref, out, ref_labels=ref_labels, out_labels=out_label) for out, out_label in zip(outs, out_labels)] reporter = reporters.WordReport(bucketer=bucketer, matches=matches, acc_type=acc_type, header="Word Accuracy Analysis", title=title) reporter.generate_report(output_fig_file=f'word-acc', output_fig_format='pdf', output_directory='outputs') return reporter
def generate_src_word_accuracy_report(ref, outs, src, ref_align_file=None, acc_type='rec', bucket_type='freq', bucket_cutoffs=None, freq_count_file=None, freq_corpus_file=None, label_set=None, src_labels=None, title=None, case_insensitive=False, output_bucket_details=False, to_cache=False, cache_dicts=None): """ Generate a report for source word analysis in both plain text and graphs. Args: ref: Tokens from the reference outs: Tokens from the output file(s) src: Tokens from the source ref_align_file: Alignment file for the reference acc_type: The type of accuracy to show (prec/rec/fmeas). Can also have multiple separated by '+'. bucket_type: A string specifying the way to bucket words together to calculate F-measure (freq/tag) bucket_cutoffs: The boundaries between buckets, specified as a colon-separated string. freq_corpus_file: When using "freq" as a bucketer, which corpus to use to calculate frequency. By default this uses the frequency in the reference test set, but it's often more informative se the frequency in the training set, in which case you specify the path of the target side he training corpus. freq_count_file: An alternative to freq_corpus that uses a count file in "word\tfreq" format. src_labels: either a filename of a file full of source labels, or a list of strings corresponding to `ref`. title: A string specifying the caption of the printed table case_insensitive: A boolean specifying whether to turn on the case insensitive option output_bucket_details: A boolean specifying whether to output the number of words in each bucket to_cache: Return a list of computed statistics if True cache_dicts: A list of dictionaries that store cached statistics for each output """ # check and set parameters if type(case_insensitive) == str: case_insensitive = True if case_insensitive == 'True' else False if type(output_bucket_details) == str: output_bucket_details = True if output_bucket_details == 'True' else False if acc_type != 'rec': raise ValueError( "Source word analysis can only use recall as an accuracy type") if not src or not ref_align_file: raise ValueError( "Must specify the source and the alignment file when performing source analysis." ) if type(src_labels) == str: src_labels = corpus_utils.load_tokens(src_labels) ref_align = corpus_utils.load_alignments(ref_align_file) # compute statistics bucketer = bucketers.create_word_bucketer_from_profile( bucket_type, bucket_cutoffs=bucket_cutoffs, freq_count_file=freq_count_file, freq_corpus_file=freq_corpus_file, freq_data=src, label_set=label_set, case_insensitive=case_insensitive) cache_key_list = [ 'statistics', 'my_ref_total_list', 'my_out_totals_list', 'my_out_matches_list' ] statistics, my_ref_total_list, my_out_totals_list, my_out_matches_list = cache_utils.extract_cache_dicts( cache_dicts, cache_key_list, len(outs)) if cache_dicts is not None: my_ref_total_list = my_ref_total_list[0] my_out_totals_list = list(np.concatenate(my_out_totals_list, 1)) my_out_matches_list = list(np.concatenate(my_out_matches_list, 1)) else: statistics, my_ref_total_list, my_out_totals_list, my_out_matches_list = bucketer.calc_statistics( ref, outs, src=src, src_labels=src_labels, ref_aligns=ref_align) examples = bucketer.calc_examples(len(ref), len(outs), statistics, my_ref_total_list, my_out_matches_list) bucket_cnts, bucket_intervals = bucketer.calc_bucket_details( my_ref_total_list, my_out_totals_list, my_out_matches_list) if output_bucket_details else (None, None) if to_cache: cache_dict = cache_utils.return_cache_dict(cache_key_list, [ statistics, [my_ref_total_list], [my_out_totals_list], [my_out_matches_list] ]) return cache_dict # generate reports reporter = reporters.WordReport(bucketer=bucketer, statistics=statistics, examples=examples, bucket_cnts=bucket_cnts, bucket_intervals=bucket_intervals, src_sents=src, ref_sents=ref, ref_aligns=ref_align, out_sents=outs, src_labels=src_labels, acc_type=acc_type, header="Source Word Accuracy Analysis", title=title) reporter.generate_report(output_fig_file=f'src-word-acc', output_fig_format='pdf', output_directory='outputs') return reporter
def generate_word_accuracy_report(ref, outs, src=None, acc_type='fmeas', bucket_type='freq', bucket_cutoffs=None, freq_count_file=None, freq_corpus_file=None, label_set=None, ref_labels=None, out_labels=None, title=None, case_insensitive=False, to_cache=False, cache_dicts=None): """ Generate a report comparing the word accuracy in both plain text and graphs. Args: ref: Tokens from the reference outs: Tokens from the output file(s) src: Tokens from the source acc_type: The type of accuracy to show (prec/rec/fmeas). Can also have multiple separated by '+'. bucket_type: A string specifying the way to bucket words together to calculate F-measure (freq/tag) bucket_cutoffs: The boundaries between buckets, specified as a colon-separated string. freq_corpus_file: When using "freq" as a bucketer, which corpus to use to calculate frequency. By default this uses the frequency in the reference test set, but it's often more informative to use the frequency in the training set, in which case you specify the path of the training corpus. freq_count_file: An alternative to freq_corpus that uses a count file in "word\tfreq" format. ref_labels: either a filename of a file full of reference labels, or a list of strings corresponding to `ref`. out_labels: output labels. must be specified if ref_labels is specified. title: A string specifying the caption of the printed table case_insensitive: A boolean specifying whether to turn on the case insensitive option to_cache: Return a list of computed statistics if True cache_dicts: A list of dictionaries that store cached statistics for each output """ # check and set parameters if type(case_insensitive) == str: case_insensitive = True if case_insensitive == 'True' else False if type(ref_labels) == str: ref_labels = corpus_utils.load_tokens(ref_labels) if out_labels is not None: out_label_files = arg_utils.parse_files(out_labels) out_labels = [corpus_utils.load_tokens(x) for x in out_label_files] if len(out_labels) != len(outs): raise ValueError( f'The number of output files should be equal to the number of output labels.' ) for i, (o, ol) in enumerate(zip(outs, out_labels)): if len(o) != len(ol): raise ValueError( f'The labels in {out_label_files[i]} do not match the length of the output file {outs[i]}.' ) # compute statistics bucketer = bucketers.create_word_bucketer_from_profile( bucket_type, bucket_cutoffs=bucket_cutoffs, freq_count_file=freq_count_file, freq_corpus_file=freq_corpus_file, freq_data=ref, label_set=label_set, case_insensitive=case_insensitive) cache_key_list = ['statistics', 'my_ref_total_list', 'my_out_matches_list'] statistics, my_ref_total_list, my_out_matches_list = cache_utils.extract_cache_dicts( cache_dicts, cache_key_list, len(outs)) if cache_dicts is None: statistics, my_ref_total_list, my_out_matches_list = bucketer.calc_statistics( ref, outs, ref_labels=ref_labels, out_labels=out_labels) else: my_ref_total_list = my_ref_total_list[0] my_out_matches_list = list(np.concatenate(my_out_matches_list, 1)) examples = bucketer.calc_examples(len(ref), len(outs), statistics, my_ref_total_list, my_out_matches_list) if to_cache: cache_dict = cache_utils.return_cache_dict( cache_key_list, [statistics, [my_ref_total_list], [my_out_matches_list]]) return cache_dict # generate reports reporter = reporters.WordReport(bucketer=bucketer, statistics=statistics, examples=examples, src_sents=src, ref_sents=ref, ref_labels=ref_labels, out_sents=outs, out_labels=out_labels, acc_type=acc_type, header="Word Accuracy Analysis", title=title) reporter.generate_report(output_fig_file=f'word-acc', output_fig_format='pdf', output_directory='outputs') return reporter