Exemple #1
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    def clean(self, corpora, output_path, log=None):
        if log is None:
            log = shell.DEVNULL

        # read memory size
        mem_bytes = os.sysconf('SC_PAGE_SIZE') * os.sysconf(
            'SC_PHYS_PAGES')  # e.g. 4015976448
        mem_mb = mem_bytes / (1024.**2)  # e.g. 3.74

        extended_heap_mb = int(mem_mb * 90 / 100)

        args = [
            '-s', self._source_lang, '-t', self._target_lang, '--output',
            output_path, '--input'
        ]

        input_paths = set([corpus.get_folder() for corpus in corpora])

        for root in input_paths:
            args.append(root)

        command = mmt_javamain(self._java_mainclass,
                               args=args,
                               max_heap_mb=extended_heap_mb)
        shell.execute(command, stdout=log, stderr=log)

        return BilingualCorpus.list(output_path)
Exemple #2
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    def build(self, corpora, working_dir='.', log_file=None):
        if not os.path.isdir(working_dir):
            fileutils.makedirs(working_dir, exist_ok=True)
        if not os.path.isdir(self._model):
            fileutils.makedirs(self._model, exist_ok=True)

        merged_corpus = BilingualCorpus.make_parallel(
            'merge', working_dir, (self._source_lang, self._target_lang))

        fileutils.merge(
            [corpus.get_file(self._source_lang) for corpus in corpora],
            merged_corpus.get_file(self._source_lang))
        fileutils.merge(
            [corpus.get_file(self._target_lang) for corpus in corpora],
            merged_corpus.get_file(self._target_lang))

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'a')

            # Train model
            command = [
                self._build_bin, '-s',
                merged_corpus.get_file(self._source_lang), '-t',
                merged_corpus.get_file(self._target_lang), '-m', self._model,
                '-I', '4'
            ]
            shell.execute(command, stderr=log)
        finally:
            if log_file is not None:
                log.close()
Exemple #3
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    def build(self, corpora, working_dir='.', log=None):
        if log is None:
            log = shell.DEVNULL

        shutil.rmtree(self._model, ignore_errors=True)
        fileutils.makedirs(self._model, exist_ok=True)

        if not os.path.isdir(working_dir):
            fileutils.makedirs(working_dir, exist_ok=True)

        merged_corpus = BilingualCorpus.make_parallel(
            'merge', working_dir, (self._source_lang, self._target_lang))

        fileutils.merge(
            [corpus.get_file(self._source_lang) for corpus in corpora],
            merged_corpus.get_file(self._source_lang))
        fileutils.merge(
            [corpus.get_file(self._target_lang) for corpus in corpora],
            merged_corpus.get_file(self._target_lang))

        command = [
            self._build_bin, '-s',
            merged_corpus.get_file(self._source_lang), '-t',
            merged_corpus.get_file(self._target_lang), '-m', self._model, '-I',
            '4'
        ]
        shell.execute(command, stdout=log, stderr=log)
Exemple #4
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    def train(self, corpora, aligner, working_dir='.', log=None):
        if log is None:
            log = shell.DEVNULL

        shutil.rmtree(self._model, ignore_errors=True)
        fileutils.makedirs(self._model, exist_ok=True)

        train_corpora_path = os.path.join(working_dir, 'corpora')
        lex_model_path = os.path.join(working_dir, 'model.tlex')

        if not os.path.isdir(train_corpora_path):
            fileutils.makedirs(train_corpora_path, exist_ok=True)

        train_corpora = []  # Prepare training folder
        for corpus in corpora:
            dest_corpus = BilingualCorpus.make_parallel(corpus.name, train_corpora_path,
                                                        (self._source_lang, self._target_lang))
            source_file = corpus.get_file(self._source_lang)
            target_file = corpus.get_file(self._target_lang)

            os.symlink(source_file, dest_corpus.get_file(self._source_lang))
            os.symlink(target_file, dest_corpus.get_file(self._target_lang))

            train_corpora.append(dest_corpus)

        # Align corpora
        aligner.align(train_corpora, train_corpora_path, log=log)
        aligner.export(lex_model_path)

        # Build models
        command = [self._build_bin, '--lex', lex_model_path, '--input', train_corpora_path, '--model', self._model,
                   '-s', self._source_lang, '-t', self._target_lang, '-v', self._vb.model]
        shell.execute(command, stdout=log, stderr=log)
Exemple #5
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    def process(self, corpora, output_path, data_path=None):
        args = [
            '-s', self._source_lang, '-t', self._target_lang, '-v',
            self._vocabulary_path, '--output', output_path, '--input'
        ]

        input_paths = set([corpus.get_folder() for corpus in corpora])

        for root in input_paths:
            args.append(root)

        if data_path is not None:
            args.append('--dev')
            args.append(
                os.path.join(data_path, TrainingPreprocessor.DEV_FOLDER_NAME))
            args.append('--test')
            args.append(
                os.path.join(data_path, TrainingPreprocessor.TEST_FOLDER_NAME))

        command = mmt_javamain(self._java_mainclass, args)
        shell.execute(command,
                      stdin=shell.DEVNULL,
                      stdout=shell.DEVNULL,
                      stderr=shell.DEVNULL)

        return BilingualCorpus.splitlist(self._source_lang,
                                         self._target_lang,
                                         roots=output_path)
Exemple #6
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    def train(self, corpora, aligner, working_dir='.', log_file=None):
        if os.path.isdir(self._model) and len(os.listdir(self._model)) > 0:
            raise Exception('Model already exists at ' + self._model)

        if not os.path.isdir(self._model):
            fileutils.makedirs(self._model, exist_ok=True)

        if not os.path.isdir(working_dir):
            fileutils.makedirs(working_dir, exist_ok=True)

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'a')

            # Prepare training folder
            for corpus in corpora:
                dest_corpus = BilingualCorpus.make_parallel(corpus.name, working_dir,
                                                            (self._source_lang, self._target_lang))
                source_file = corpus.get_file(self._source_lang)
                target_file = corpus.get_file(self._target_lang)

                os.symlink(source_file, dest_corpus.get_file(self._source_lang))
                os.symlink(target_file, dest_corpus.get_file(self._target_lang))

                aligner.align(corpus, os.path.join(working_dir, corpus.name + '.align'))

            # Build models
            command = [self._build_bin, '--input', working_dir, '--model', self._model,
                       '-s', self._source_lang, '-t', self._target_lang]
            shell.execute(command, stdout=log, stderr=log)
        finally:
            if log_file is not None:
                log.close()
Exemple #7
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    def build(self, corpora, working_dir='.', log_file=None):
        if not os.path.isdir(working_dir):
            fileutils.makedirs(working_dir, exist_ok=True)
        if not os.path.isdir(self._model):
            fileutils.makedirs(self._model, exist_ok=True)

        merged_corpus = BilingualCorpus.make_parallel('merge', working_dir, (self._source_lang, self._target_lang))

        fileutils.merge([corpus.get_file(self._source_lang) for corpus in corpora],
                        merged_corpus.get_file(self._source_lang))
        fileutils.merge([corpus.get_file(self._target_lang) for corpus in corpora],
                        merged_corpus.get_file(self._target_lang))

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'a')

            # Train model
            command = [self._build_bin,
                       '-s', merged_corpus.get_file(self._source_lang), '-t', merged_corpus.get_file(self._target_lang),
                       '-m', self._model, '-I', '4']
            shell.execute(command, stderr=log)
        finally:
            if log_file is not None:
                log.close()
Exemple #8
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Fichier : lm.py Projet : kmlx/MMT
    def train(self, corpora, lang, working_dir='.', log_file=None):
        LanguageModel.train(self, corpora, lang, working_dir, log_file)

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'w') if isinstance(log_file, str) else log_file

            # Collapse all corpora into a single text file
            merged_corpus = os.path.join(working_dir, 'merge')
            fileutils.merge([corpus.get_file(lang) for corpus in corpora], merged_corpus)

            # Create language model in ARPA format
            arpa_file = os.path.join(working_dir, 'lm.arpa')
            arpa_command = [self._lmplz_bin, '--discount_fallback', '-o', str(self._order),
                            '-S', str(self.get_mem_percent()) + '%', '-T', working_dir]
            if self._order > 2 and self.prune:
                arpa_command += ['--prune', '0', '0', '1']

            with open(merged_corpus) as stdin:
                with open(arpa_file, 'w') as stdout:
                    shell.execute(arpa_command, stdin=stdin, stdout=stdout, stderr=log)

            # Binarize ARPA file
            binarize_command = [self._bbinary_bin, arpa_file, self._model]
            shell.execute(binarize_command, stdout=log, stderr=log)
        finally:
            if log_file is not None and isinstance(log_file, str):
                log.close()
Exemple #9
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    def train(self, corpora, lang, working_dir='.', log=None):
        if log is None:
            log = shell.DEVNULL

        LanguageModel.train(self, corpora, lang, working_dir, log)

        # Collapse all corpora into a single text file
        merged_corpus = os.path.join(working_dir, 'merge')
        fileutils.merge([corpus.get_file(lang) for corpus in corpora],
                        merged_corpus)

        # Create language model in ARPA format
        arpa_file = os.path.join(working_dir, 'lm.arpa')
        arpa_command = [
            self._lmplz_bin, '--discount_fallback', '-o',
            str(self._order), '-S',
            str(self.get_mem_percent()) + '%', '-T', working_dir
        ]
        if self._order > 2 and self.prune:
            arpa_command += ['--prune', '0', '0', '1']

        with open(merged_corpus) as stdin:
            with open(arpa_file, 'w') as stdout:
                shell.execute(arpa_command,
                              stdin=stdin,
                              stdout=stdout,
                              stderr=log)

        # Binarize ARPA file
        binarize_command = [self._bbinary_bin, arpa_file, self._model]
        shell.execute(binarize_command, stdout=log, stderr=log)
Exemple #10
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    def train(self, corpora, aligner, working_dir='.', log=None):
        if log is None:
            log = shell.DEVNULL

        if os.path.isdir(self._model) and len(os.listdir(self._model)) > 0:
            raise Exception('Model already exists at ' + self._model)

        if not os.path.isdir(self._model):
            fileutils.makedirs(self._model, exist_ok=True)

        if not os.path.isdir(working_dir):
            fileutils.makedirs(working_dir, exist_ok=True)

        train_corpora = []  # Prepare training folder
        for corpus in corpora:
            dest_corpus = BilingualCorpus.make_parallel(corpus.name, working_dir,
                                                        (self._source_lang, self._target_lang))
            source_file = corpus.get_file(self._source_lang)
            target_file = corpus.get_file(self._target_lang)

            os.symlink(source_file, dest_corpus.get_file(self._source_lang))
            os.symlink(target_file, dest_corpus.get_file(self._target_lang))

            train_corpora.append(dest_corpus)

        # Align corpora
        aligner.align(train_corpora, working_dir, log=log)

        # Build models
        command = [self._build_bin, '--input', working_dir, '--model', self._model,
                   '-s', self._source_lang, '-t', self._target_lang]
        shell.execute(command, stdout=log, stderr=log)
Exemple #11
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 def align(self, corpus, output):
     command = [
         self._align_bin, '-s',
         corpus.get_file(self._source_lang), '-t',
         corpus.get_file(self._target_lang), '-m', self._model, '-a', '1'
     ]
     with open(output, 'w') as stdout:
         shell.execute(command, stdout=stdout)
Exemple #12
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    def train(self, corpora, lang, working_dir='.', log_file=None):
        LanguageModel.train(self, corpora, lang, working_dir, log_file)

        bicorpora = []
        for corpus in corpora:
            if len(corpus.langs) > 1:
                bicorpora.append(corpus)

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'w')

            fileutils.makedirs(self._model, exist_ok=True)

            # Train static LM
            static_lm_model = os.path.join(self._model, 'background.slm')
            static_lm_wdir = os.path.join(working_dir, 'slm.temp')

            fileutils.makedirs(static_lm_wdir, exist_ok=True)

            merged_corpus = os.path.join(working_dir, 'merged_corpus')
            fileutils.merge([corpus.get_file(lang) for corpus in corpora],
                            merged_corpus)

            command = [
                self._create_slm_bin, '--discount_fallback', '-o',
                str(self._order), '--model', static_lm_model, '-S',
                str(KenLM.get_mem_percent()) + '%', '-T', static_lm_wdir
            ]
            if self._order > 2 and self.prune:
                command += ['--prune', '0', '0', '1']

            with open(merged_corpus) as stdin:
                shell.execute(command, stdin=stdin, stdout=log, stderr=log)

            # Create AdaptiveLM training folder
            alm_train_folder = os.path.join(working_dir, 'alm_train')
            fileutils.makedirs(alm_train_folder, exist_ok=True)

            for corpus in bicorpora:
                os.symlink(
                    corpus.get_file(lang),
                    os.path.join(alm_train_folder, corpus.name + '.' + lang))

            # Train adaptive LM
            adaptive_lm_model = os.path.join(self._model, 'foreground.alm')
            fileutils.makedirs(adaptive_lm_model, exist_ok=True)

            command = [
                self._create_alm_bin, '-m', adaptive_lm_model, '-i',
                alm_train_folder, '-b', '100000000'
            ]
            shell.execute(command, stdout=log, stderr=log)
        finally:
            if log_file is not None:
                log.close()
Exemple #13
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    def __process_file(self, source, dest, lang, print_tags=True, print_placeholders=False, original_spacing=False):
        command = self.__get_command(lang, print_tags, print_placeholders, original_spacing)

        if not os.path.isdir(dest.get_folder()):
            fileutils.makedirs(dest.get_folder(), exist_ok=True)

        with open(source) as input_stream:
            with open(dest.get_file(lang), 'w') as output_stream:
                shell.execute(command, stdin=input_stream, stdout=output_stream, stderr=shell.DEVNULL)
Exemple #14
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    def train(self, corpora, lang, working_dir='.', log=None):
        if log is None:
            log = shell.DEVNULL

        bicorpora = []
        for corpus in corpora:
            if len(corpus.langs) > 1:
                bicorpora.append(corpus)

        shutil.rmtree(self._model, ignore_errors=True)
        fileutils.makedirs(self._model, exist_ok=True)

        if not os.path.isdir(working_dir):
            fileutils.makedirs(working_dir, exist_ok=True)

        # Train static LM
        static_lm_model = os.path.join(self._model, 'background.slm')
        static_lm_wdir = os.path.join(working_dir, 'slm.temp')

        fileutils.makedirs(static_lm_wdir, exist_ok=True)

        merged_corpus = os.path.join(working_dir, 'merged_corpus')
        fileutils.merge([corpus.get_file(lang) for corpus in corpora],
                        merged_corpus)

        command = [
            self._create_slm_bin, '--discount-fallback', '-o',
            str(self._order), '-a',
            str(self._compression), '-q',
            str(self._quantization), '--type', 'trie', '--model',
            static_lm_model, '-T', static_lm_wdir
        ]
        if self._order > 2 and self._prune:
            command += ['--prune', '0', '1', '2']

        with open(merged_corpus) as stdin:
            shell.execute(command, stdin=stdin, stdout=log, stderr=log)

        # Create AdaptiveLM training folder
        alm_train_folder = os.path.join(working_dir, 'alm_train')
        fileutils.makedirs(alm_train_folder, exist_ok=True)

        for corpus in bicorpora:
            os.symlink(
                corpus.get_file(lang),
                os.path.join(alm_train_folder, corpus.name + '.' + lang))

        # Train adaptive LM
        adaptive_lm_model = os.path.join(self._model, 'foreground.alm')
        fileutils.makedirs(adaptive_lm_model, exist_ok=True)

        command = [
            self._create_alm_bin, '-m', adaptive_lm_model, '-i',
            alm_train_folder, '-b', '50000000'
        ]
        shell.execute(command, stdout=log, stderr=log)
Exemple #15
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    def clean(self, source, target, input_paths, output_path):
        args = ['-s', source, '-t', target, '--output', output_path, '--input']

        for root in input_paths:
            args.append(root)

        command = mmt_javamain(self._java_mainclass, args)
        shell.execute(command, stdin=shell.DEVNULL, stdout=shell.DEVNULL, stderr=shell.DEVNULL)

        return BilingualCorpus.splitlist(source, target, roots=output_path)[0]
Exemple #16
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    def _clean_file(self, source, dest_folder, langs):
        if not os.path.isdir(dest_folder):
            fileutils.makedirs(dest_folder, exist_ok=True)

        input_folder = os.path.join(source.get_folder(), source.name)
        output_folder = os.path.join(dest_folder, source.name)

        command = ['perl', self._cleaner_script, '-ratio', str(self._ratio), input_folder, langs[0], langs[1],
                   output_folder, str(self._min), str(self._max)]
        shell.execute(command, stdout=shell.DEVNULL, stderr=shell.DEVNULL)
Exemple #17
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    def _clean_file(self, source, dest_folder, langs):
        if not os.path.isdir(dest_folder):
            fileutils.makedirs(dest_folder, exist_ok=True)

        input_folder = os.path.join(source.get_folder(), source.name)
        output_folder = os.path.join(dest_folder, source.name)

        command = ['perl', self._cleaner_script, '-ratio', str(self._ratio), input_folder, langs[0], langs[1],
                   output_folder, str(self._min), str(self._max)]
        shell.execute(command, stdout=shell.DEVNULL, stderr=shell.DEVNULL)
Exemple #18
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    def process_file(self, source, dest, lang):
        args = ['--lang', self._lang]
        if not self._print_tags:
            args.append('--no-tags')
        if self._print_placeholders:
            args.append('--print-placeholders')

        command = mmt_javamain(self._java_mainclass, args=args)

        with open(source) as input_stream:
            with open(dest.get_file(lang), 'w') as output_stream:
                shell.execute(command, stdin=input_stream, stdout=output_stream)
Exemple #19
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    def clean(self, corpora, output_path):
        args = ['-s', self._source_lang, '-t', self._target_lang, '--output', output_path, '--input']

        input_paths = set([corpus.get_folder() for corpus in corpora])

        for root in input_paths:
            args.append(root)

        command = mmt_javamain(self._java_mainclass, args)
        shell.execute(command, stdin=shell.DEVNULL, stdout=shell.DEVNULL, stderr=shell.DEVNULL)

        return BilingualCorpus.list(output_path)
Exemple #20
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    def clean(self, corpora, output_path, log=None):
        if log is None:
            log = shell.DEVNULL

        args = ['-s', self._source_lang, '-t', self._target_lang, '--output', output_path, '--input']

        input_paths = set([corpus.get_folder() for corpus in corpora])

        for root in input_paths:
            args.append(root)

        command = mmt_javamain(self._java_mainclass, args)
        shell.execute(command, stdout=log, stderr=log)

        return BilingualCorpus.list(output_path)
Exemple #21
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    def align(self, corpora, output_folder, log=None):
        if log is None:
            log = shell.DEVNULL

        root = set([corpus.get_folder() for corpus in corpora])

        if len(root) != 1:
            raise Exception('Aligner corpora must share the same folder: found  ' + str(root))

        root = root.pop()

        command = [self._align_bin, '--model', self._model,
                   '--input', root, '--output', output_folder,
                   '--source', self._source_lang, '--target', self._target_lang,
                   '--strategy', '1']
        shell.execute(command, stderr=log, stdout=log)
Exemple #22
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    def generate(self, bilingual_corpora, monolingual_corpora, output, log_file=None):
        fileutils.makedirs(self._model, exist_ok=True)

        args = ['--db', os.path.join(self._model, 'domains.db'), '-l', self._source_lang, '-c']

        source_paths = set([corpus.get_folder() for corpus in bilingual_corpora])
        for source_path in source_paths:
            args.append(source_path)

        command = cli.mmt_javamain(self._java_mainclass, args)

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'w')

            stdout, _ = shell.execute(command, stderr=log)

            domains = {}

            for domain, name in [line.rstrip('\n').split('\t', 2) for line in stdout.splitlines()]:
                domains[name] = domain

            return self._make_training_folder(bilingual_corpora, monolingual_corpora, domains, output)
        finally:
            if log_file is not None:
                log.close()
Exemple #23
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    def process(self, source, target, input_paths, output_path, data_path=None):
        args = ['-s', source, '-t', target, '--output', output_path, '--input']

        for root in input_paths:
            args.append(root)

        if data_path is not None:
            args.append('--dev')
            args.append(os.path.join(data_path, TrainingPreprocessor.DEV_FOLDER_NAME))
            args.append('--test')
            args.append(os.path.join(data_path, TrainingPreprocessor.TEST_FOLDER_NAME))

        command = mmt_javamain(self._java_mainclass, args)
        shell.execute(command, stdin=shell.DEVNULL, stdout=shell.DEVNULL, stderr=shell.DEVNULL)

        return BilingualCorpus.splitlist(source, target, roots=output_path)
Exemple #24
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    def reduce(self, corpora, output_path, word_limit, log=None):
        if log is None:
            log = shell.DEVNULL

        args = [
            '-s', self._source_lang, '-t', self._target_lang, '--words',
            str(word_limit), '--output', output_path, '--input'
        ]

        for root in set([corpus.get_folder() for corpus in corpora]):
            args.append(root)

        command = mmt_javamain(self._reduce_mainclass, args=args)
        shell.execute(command, stdout=log, stderr=log)

        return BilingualCorpus.list(output_path)
Exemple #25
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    def generate(self, bilingual_corpora, monolingual_corpora, output, log_file=None):
        fileutils.makedirs(self._model, exist_ok=True)

        args = ['--db', os.path.join(self._model, 'domains.db'), '-l', self._source_lang, '-c']

        source_paths = set([corpus.get_folder() for corpus in bilingual_corpora])
        for source_path in source_paths:
            args.append(source_path)

        command = cli.mmt_javamain(self._java_mainclass, args)

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'w')

            stdout, _ = shell.execute(command, stderr=log)

            domains = {}

            for domain, name in [line.rstrip('\n').split('\t', 2) for line in stdout.splitlines()]:
                domains[name] = domain

            return self._make_training_folder(bilingual_corpora, monolingual_corpora, domains, output)
        finally:
            if log_file is not None:
                log.close()
Exemple #26
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    def calculate(self, document, reference):
        script = os.path.join(cli.PYOPT_DIR, 'mmt-bleu.perl')
        command = ['perl', script, reference]

        with open(document) as input_stream:
            stdout, _ = shell.execute(command, stdin=input_stream)

        return float(stdout)
Exemple #27
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    def calculate(self, document, reference):
        script = os.path.join(cli.PYOPT_DIR, 'mmt-bleu.perl')
        command = ['perl', script, reference]

        with open(document) as input_stream:
            stdout, _ = shell.execute(command, stdin=input_stream)

        return float(stdout)
Exemple #28
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    def create_index(self, corpora, log=None):
        if log is None:
            log = shell.DEVNULL

        source_paths = set()

        for corpus in corpora:
            source_paths.add(corpus.get_folder())

        shutil.rmtree(self._index, ignore_errors=True)
        fileutils.makedirs(self._index, exist_ok=True)

        args = ['-s', self._source_lang, '-t', self._target_lang, '-i', self._index, '-c']
        for source_path in source_paths:
            args.append(source_path)

        command = mmt_javamain(self._java_mainclass, args)
        shell.execute(command, stdout=log, stderr=log)
Exemple #29
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 def _get_gpus_ram(self, gpu_ids):
     result = []
     command = ["nvidia-smi", "--query-gpu=memory.total", "--format=csv,noheader,nounits",
                "--id=%s" % ",".join(str(i) for i in gpu_ids)]
     stdout, _ = shell.execute(command)
     for line in stdout.split("\n"):
         line = line.strip()
         if line:
             result.append(int(line) * self._MB)
     return result
Exemple #30
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    def process(self, corpora, output_path, data_path=None):
        args = ['-s', self._source_lang, '-t', self._target_lang, '-v', self._vocabulary_path, '--output', output_path,
                '--input']

        input_paths = set([corpus.get_folder() for corpus in corpora])

        for root in input_paths:
            args.append(root)

        if data_path is not None:
            args.append('--dev')
            args.append(os.path.join(data_path, TrainingPreprocessor.DEV_FOLDER_NAME))
            args.append('--test')
            args.append(os.path.join(data_path, TrainingPreprocessor.TEST_FOLDER_NAME))

        command = mmt_javamain(self._java_mainclass, args)
        shell.execute(command, stdin=shell.DEVNULL, stdout=shell.DEVNULL, stderr=shell.DEVNULL)

        return BilingualCorpus.splitlist(self._source_lang, self._target_lang, roots=output_path)
Exemple #31
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    def process_file(self, input_path, output_path, lang):
        if lang == self._source_lang:
            args = ['-s', self._source_lang, '-t', self._target_lang]
        elif lang == self._target_lang:
            args = ['-s', self._target_lang, '-t', self._source_lang]
        else:
            raise ValueError('Unsupported language "%s"' % lang)

        if not self._print_tags:
            args.append('--no-tags')
        if self._print_placeholders:
            args.append('--print-placeholders')

        command = mmt_javamain(self._java_mainclass, args=args)

        with open(input_path) as input_stream:
            with open(output_path, 'w') as output_stream:
                shell.execute(command,
                              stdin=input_stream,
                              stdout=output_stream)
Exemple #32
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    def train(self, corpora, aligner, working_dir='.', log_file=None):
        if os.path.isdir(self._model) and len(os.listdir(self._model)) > 0:
            raise Exception('Model already exists at ' + self._model)

        if not os.path.isdir(self._model):
            fileutils.makedirs(self._model, exist_ok=True)

        if not os.path.isdir(working_dir):
            fileutils.makedirs(working_dir, exist_ok=True)

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'a')

            # Prepare training folder
            for corpus in corpora:
                dest_corpus = BilingualCorpus.make_parallel(
                    corpus.name, working_dir,
                    (self._source_lang, self._target_lang))
                source_file = corpus.get_file(self._source_lang)
                target_file = corpus.get_file(self._target_lang)

                os.symlink(source_file,
                           dest_corpus.get_file(self._source_lang))
                os.symlink(target_file,
                           dest_corpus.get_file(self._target_lang))

                aligner.align(
                    corpus, os.path.join(working_dir, corpus.name + '.align'))

            # Build models
            command = [
                self._build_bin, '--input', working_dir, '--model',
                self._model, '-s', self._source_lang, '-t', self._target_lang
            ]
            shell.execute(command, stdout=log, stderr=log)
        finally:
            if log_file is not None:
                log.close()
Exemple #33
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    def create_index(self, corpora, lang, log_file=None):
        source_paths = set()

        for corpus in corpora:
            source_paths.add(corpus.get_folder())

        fileutils.makedirs(self._index, exist_ok=True)

        args = ['-l', lang, '-i', self._index, '-c']
        for source_path in source_paths:
            args.append(source_path)

        command = mmt_javamain(self._java_mainclass, args)

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'w')

            shell.execute(command, stdout=log, stderr=log)
        finally:
            if log_file is not None:
                log.close()
Exemple #34
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    def _tune_run(self, decoder, corpora, lr, epochs, output_file, reference_file):
        with open(output_file, 'wb') as output:
            for source, target in corpora:
                if lr == 0.:
                    suggestions = None
                else:
                    suggestions = [Suggestion(source, target, 1.)]
                translation = decoder.translate(self.source_lang, self.target_lang, source,
                                                suggestions=suggestions, tuning_epochs=epochs, tuning_learning_rate=lr)

                output.write(translation.text.encode('utf-8'))
                output.write('\n')

        command = ['perl', self._bleu_script, reference_file]
        with open(output_file) as input_stream:
            stdout, _ = shell.execute(command, stdin=input_stream)

        return float(stdout) * 100
Exemple #35
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    def generate(self,
                 bilingual_corpora,
                 monolingual_corpora,
                 output,
                 log=None):
        if log is None:
            log = shell.DEVNULL

        fileutils.makedirs(self._model, exist_ok=True)

        args = [
            '--db',
            os.path.join(self._model, 'domains.db'), '-s', self._source_lang,
            '-t', self._target_lang, '-c'
        ]

        source_paths = set(
            [corpus.get_folder() for corpus in bilingual_corpora])
        for source_path in source_paths:
            args.append(source_path)

        command = cli.mmt_javamain(self._java_mainclass, args)
        stdout, _ = shell.execute(command, stderr=log)

        domains = {}

        for domain, name in [
                line.rstrip('\n').split('\t', 2)
                for line in stdout.splitlines()
        ]:
            domains[name] = domain

        bilingual_corpora = [
            corpus.symlink(output, name=domains[corpus.name])
            for corpus in bilingual_corpora
        ]
        monolingual_corpora = [
            corpus.symlink(output) for corpus in monolingual_corpora
        ]

        return bilingual_corpora, monolingual_corpora
Exemple #36
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    def tune(self, corpora=None, debug=False, context_enabled=True, random_seeds=False, max_iterations=25):
        if corpora is None:
            corpora = BilingualCorpus.list(os.path.join(self.engine.data_path, TrainingPreprocessor.DEV_FOLDER_NAME))

        if len(corpora) == 0:
            raise IllegalArgumentException('empty corpora')

        if not self.is_running():
            raise IllegalStateException('No MMT Server running, start the engine first')

        tokenizer = Tokenizer()

        target_lang = self.engine.target_lang
        source_lang = self.engine.source_lang

        source_corpora = [BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(), [source_lang])
                          for corpus in corpora]
        reference_corpora = [BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(), [target_lang])
                             for corpus in corpora]

        cmdlogger = _tuning_logger(4)
        cmdlogger.start(self, corpora)

        working_dir = self.engine.get_tempdir('tuning')
        mert_wd = os.path.join(working_dir, 'mert')

        try:
            # Tokenization
            tokenized_output = os.path.join(working_dir, 'reference_corpora')
            fileutils.makedirs(tokenized_output, exist_ok=True)

            with cmdlogger.step('Corpora tokenization') as _:
                reference_corpora = tokenizer.process_corpora(reference_corpora, tokenized_output)

            # Create merged corpus
            with cmdlogger.step('Merging corpus') as _:
                # source
                source_merged_corpus = os.path.join(working_dir, 'corpus.' + source_lang)

                with open(source_merged_corpus, 'wb') as out:
                    for corpus in source_corpora:
                        out.write(corpus.get_file(source_lang) + '\n')

                # target
                target_merged_corpus = os.path.join(working_dir, 'corpus.' + target_lang)
                fileutils.merge([corpus.get_file(target_lang) for corpus in reference_corpora], target_merged_corpus)

            # Run MERT algorithm
            with cmdlogger.step('Tuning') as _:
                # Start MERT
                decoder_flags = ['--port', str(self.api.port)]

                if not context_enabled:
                    decoder_flags.append('--skip-context-analysis')
                    decoder_flags.append('1')

                fileutils.makedirs(mert_wd, exist_ok=True)

                with tempfile.NamedTemporaryFile() as runtime_moses_ini:
                    command = [self._mert_script, source_merged_corpus, target_merged_corpus,
                               self._mert_i_script, runtime_moses_ini.name, '--threads',
                               str(multiprocessing.cpu_count()), '--mertdir', cli.BIN_DIR,
                               '--mertargs', '\'--binary --sctype BLEU\'', '--working-dir', mert_wd, '--nbest', '100',
                               '--decoder-flags', '"' + ' '.join(decoder_flags) + '"', '--nonorm', '--closest',
                               '--no-filter-phrase-table']

                    if not random_seeds:
                        command.append('--predictable-seeds')
                    if max_iterations > 0:
                        command.append('--maximum-iterations={num}'.format(num=max_iterations))

                    with open(self.engine.get_logfile('mert'), 'wb') as log:
                        shell.execute(' '.join(command), stdout=log, stderr=log)

            # Read optimized configuration
            with cmdlogger.step('Applying changes') as _:
                bleu_score = 0
                weights = {}
                found_weights = False

                with open(os.path.join(mert_wd, 'moses.ini')) as moses_ini:
                    for line in moses_ini:
                        line = line.strip()

                        if len(line) == 0:
                            continue
                        elif found_weights:
                            tokens = line.split()
                            weights[tokens[0].rstrip('=')] = [float(val) for val in tokens[1:]]
                        elif line.startswith('# BLEU'):
                            bleu_score = float(line.split()[2])
                        elif line == '[weight]':
                            found_weights = True

                _ = self.api.update_features(weights)

            cmdlogger.completed(bleu_score)
        finally:
            if not debug:
                self.engine.clear_tempdir()
Exemple #37
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    def align(self, corpus, langs, model_dir, working_dir='.', log_file=None):
        WordAligner.align(self, corpus, langs, working_dir, log_file)

        l1 = langs[0]
        l2 = langs[1]
        corpus_name = 'corpus'
        langs_suffix = l1 + '-' + l2

        fwd_file = os.path.join(working_dir, corpus_name + '.' + langs_suffix + '.fwd')
        bwd_file = os.path.join(working_dir, corpus_name + '.' + langs_suffix + '.bwd')
        bal_file = os.path.join(working_dir, corpus_name + '.' + langs_suffix + '.bal')
        aligned_file_path = os.path.join(working_dir, corpus_name + '.' + langs_suffix + '.aligned')

        corpus_l1 = corpus.get_file(l1)
        corpus_l2 = corpus.get_file(l2)

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'a')

            with open(corpus_l1) as source_corpus, \
                    open(corpus_l2) as target_corpus, \
                    open(aligned_file_path, 'w') as aligned_file:
                for x, y in zip(source_corpus, target_corpus):
                    aligned_file.write(x.strip() + ' ||| ' + y.strip() + '\n')

            cpus = multiprocessing.cpu_count()

            # Create forward model
            fwd_model = os.path.join(model_dir, 'model.align.fwd')
            command = [self._align_bin, '-d', '-v', '-o', '-n', str(cpus), '-B', '-p', fwd_model, '-i',
                       aligned_file_path]
            shell.execute(command, stderr=log)

            # Compute forward alignments
            command = [self._align_bin, '-d', '-v', '-o', '-n', str(cpus), '-B', '-f', fwd_model, '-i',
                       aligned_file_path]
            with open(fwd_file, 'w') as stdout:
                shell.execute(command, stdout=stdout, stderr=log)

            # Create backward model
            bwd_model = os.path.join(model_dir, 'model.align.bwd')
            command = [self._align_bin, '-d', '-v', '-o', '-n', str(cpus), '-B', '-p', bwd_model, '-r', '-i',
                       aligned_file_path]
            shell.execute(command, stderr=log)

            # Compute backward alignments
            command = [self._align_bin, '-d', '-v', '-o', '-n', str(cpus), '-B', '-f', bwd_model, '-r', '-i',
                       aligned_file_path]
            with open(bwd_file, 'w') as stdout:
                shell.execute(command, stdout=stdout, stderr=log)

        finally:
            if log_file is not None:
                log.close()

        encoder = _FastAlignBALEncoder(corpus, langs, fwd_file, bwd_file)
        encoder.encode(bal_file)

        return bal_file
Exemple #38
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    def train(self, corpora, aligner, working_dir='.', log_file=None):
        if os.path.isdir(self._model) and len(os.listdir(self._model)) > 0:
            raise Exception('Model already exists at ' + self._model)

        if not os.path.isdir(self._model):
            fileutils.makedirs(self._model, exist_ok=True)

        if not os.path.isdir(working_dir):
            fileutils.makedirs(working_dir, exist_ok=True)

        l1 = self._source_lang
        l2 = self._target_lang
        langs = (l1, l2)
        langs_suffix = l1 + '-' + l2

        mct_base = self._get_model_basename()
        dmp_file = mct_base + '.dmp'
        mam_file = mct_base + '.' + langs_suffix + '.mam'
        lex_file = mct_base + '.' + langs_suffix + '.lex'

        log = shell.DEVNULL

        try:
            if log_file is not None:
                log = open(log_file, 'a')

            # Clean corpus for training
            clean_output = os.path.join(working_dir, 'clean_corpora')
            fileutils.makedirs(clean_output, exist_ok=True)
            corpora = self._cleaner.clean(corpora, clean_output, (self._source_lang, self._target_lang))

            # Create merged corpus and domains list file (dmp)
            merged_corpus = BilingualCorpus.make_parallel(os.path.basename(mct_base), working_dir, langs)

            fileutils.merge([corpus.get_file(l1) for corpus in corpora], merged_corpus.get_file(l1))
            fileutils.merge([corpus.get_file(l2) for corpus in corpora], merged_corpus.get_file(l2))
            with open(dmp_file, 'w') as dmp:
                for corpus in corpora:
                    dmp.write(str(corpus.name) + ' ' + str(corpus.count_lines()) + '\n')

            # Create alignments in 'bal' file and symmetrize
            bal_file = aligner.align(merged_corpus, langs, self._model, working_dir, log_file)

            symal_file = os.path.join(working_dir, 'alignments.' + langs_suffix + '.symal')
            symal_command = [self._symal_bin, '-a=g', '-d=yes', '-f=yes', '-b=yes']
            with open(bal_file) as stdin:
                with open(symal_file, 'w') as stdout:
                    shell.execute(symal_command, stdin=stdin, stdout=stdout, stderr=log)

            # Execute mtt-build
            mttbuild_command = self._get_mttbuild_command(mct_base, dmp_file, l1)
            with open(merged_corpus.get_file(l1)) as stdin:
                shell.execute(mttbuild_command, stdin=stdin, stdout=log, stderr=log)

            mttbuild_command = self._get_mttbuild_command(mct_base, dmp_file, l2)
            with open(merged_corpus.get_file(l2)) as stdin:
                shell.execute(mttbuild_command, stdin=stdin, stdout=log, stderr=log)

            # Create 'mam' file
            mam_command = [self._symal2mam_bin, mam_file]
            with open(symal_file) as stdin:
                shell.execute(mam_command, stdin=stdin, stdout=log, stderr=log)

            # Create 'lex' file
            lex_command = [self._mmlexbuild_bin, mct_base + '.', l1, l2, '-o', lex_file]
            shell.execute(lex_command, stdout=log, stderr=log)
        finally:
            if log_file is not None:
                log.close()
Exemple #39
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    def tune(self,
             corpora=None,
             debug=False,
             context_enabled=True,
             random_seeds=False,
             max_iterations=25,
             early_stopping_value=None):
        if corpora is None:
            corpora = BilingualCorpus.list(
                os.path.join(self.engine.data_path,
                             TrainingPreprocessor.DEV_FOLDER_NAME))

        target_lang = self.engine.target_lang
        source_lang = self.engine.source_lang

        corpora = [
            corpus for corpus in corpora
            if source_lang in corpus.langs and target_lang in corpus.langs
        ]
        if len(corpora) == 0:
            raise IllegalArgumentException(
                'No %s > %s corpora found into specified path' %
                (source_lang, target_lang))

        source_corpora = [
            BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(),
                                          [source_lang]) for corpus in corpora
        ]
        reference_corpora = [
            BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(),
                                          [target_lang]) for corpus in corpora
        ]

        cmdlogger = _tuning_logger(4)
        cmdlogger.start(self, corpora)

        working_dir = self.engine.get_tempdir('tuning')
        mert_wd = os.path.join(working_dir, 'mert')

        try:
            # Tokenization
            tokenizer = Tokenizer(target_lang)
            tokenized_output = os.path.join(working_dir, 'reference_corpora')
            fileutils.makedirs(tokenized_output, exist_ok=True)

            with cmdlogger.step('Corpora tokenization') as _:
                reference_corpora = tokenizer.process_corpora(
                    reference_corpora, tokenized_output)

            # Create merged corpus
            with cmdlogger.step('Merging corpus') as _:
                # source
                source_merged_corpus = os.path.join(working_dir,
                                                    'corpus.' + source_lang)

                with open(source_merged_corpus, 'wb') as out:
                    for corpus in source_corpora:
                        out.write(corpus.get_file(source_lang) + '\n')

                # target
                target_merged_corpus = os.path.join(working_dir,
                                                    'corpus.' + target_lang)
                fileutils.merge([
                    corpus.get_file(target_lang)
                    for corpus in reference_corpora
                ], target_merged_corpus)

            # Run MERT algorithm
            with cmdlogger.step('Tuning') as _:
                # Start MERT
                decoder_flags = ['--port', str(self.api.port)]

                if self.api.root is not None:
                    decoder_flags += ['--root', self.api.root]

                if not context_enabled:
                    decoder_flags.append('--skip-context-analysis')
                    decoder_flags.append('1')

                fileutils.makedirs(mert_wd, exist_ok=True)

                with tempfile.NamedTemporaryFile() as runtime_moses_ini:
                    command = [
                        self._mert_script, source_merged_corpus,
                        target_merged_corpus, self._mert_i_script,
                        runtime_moses_ini.name, '--threads',
                        str(multiprocessing.cpu_count()), '--mertdir',
                        cli.BIN_DIR, '--mertargs',
                        '\'--binary --sctype BLEU\'', '--working-dir', mert_wd,
                        '--nbest', '100', '--decoder-flags',
                        '"' + ' '.join(decoder_flags) + '"', '--nonorm',
                        '--closest', '--no-filter-phrase-table'
                    ]

                    if early_stopping_value is not None:
                        command += [
                            '--bleuscorer', self._scorer_script,
                            '--bleuscorer-flags "-nt" --early-stopping-value %d'
                            % early_stopping_value
                        ]

                    if not random_seeds:
                        command.append('--predictable-seeds')
                    if max_iterations > 0:
                        command.append('--maximum-iterations={num}'.format(
                            num=max_iterations))

                    with open(self.engine.get_logfile('mert'), 'wb') as log:
                        shell.execute(' '.join(command),
                                      stdout=log,
                                      stderr=log)

            # Read optimized configuration
            with cmdlogger.step('Applying changes') as _:
                bleu_score = 0
                weights = {}
                found_weights = False

                with open(os.path.join(mert_wd, 'moses.ini')) as moses_ini:
                    for line in moses_ini:
                        line = line.strip()

                        if len(line) == 0:
                            continue
                        elif found_weights:
                            tokens = line.split()
                            weights[tokens[0].rstrip('=')] = [
                                float(val) for val in tokens[1:]
                            ]
                        elif line.startswith('# BLEU'):
                            bleu_score = float(line.split()[2])
                        elif line == '[weight]':
                            found_weights = True

                _ = self.api.update_features(weights)

            cmdlogger.completed(bleu_score)
        finally:
            if not debug:
                self.engine.clear_tempdir("tuning")
Exemple #40
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 def align(self, corpus, output):
     command = [self._align_bin, '-s', corpus.get_file(self._source_lang), '-t', corpus.get_file(self._target_lang),
                '-m', self._model, '-a', '1']
     with open(output, 'w') as stdout:
         shell.execute(command, stdout=stdout)
Exemple #41
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    def export(self, path, log=None):
        if log is None:
            log = shell.DEVNULL

        command = [self._export_bin, '--model', self._model, '--output', path]
        shell.execute(command, stderr=log, stdout=log)