train_pipeline.py

#!/usr/bin/python
# -*- coding: utf-8 -*-

import os
import sys
import math
import shutil
import re
from collections import defaultdict
from preferences import python_path, onmt_path, Paths, Tokenizer, __version__
from command_parser import parse_prefix, split_train_filename
import preprocessing as PP
import conllutools
import conlluplus
import base_yaml

""" ===========================================================
Training data builder and trainer for BabyLemmatizer 2

asahala 2023
https://github.com/asahala

University of Helsinki
   Origins of Emesal Project
   Centre of Excellence for Ancient Near-Eastern Empires

=========================================================== """

statistics = defaultdict(int)
counts = defaultdict(dict)
log = []

def logger(message):
    print(message)
    log.append(message)
    

def save_log(log_file):
    with open(log_file, 'w', encoding='utf-8') as f:
        for line in log:
            f.write(line + '\n')
    print(f'\n> Log saved to {log_file}')


def print_statistics():
    logger('> Training data item counts:')
    for k, v in statistics.items():
        logger(f'   {v}\t{k}')


def _rename_model(model_name, type_):
    
    def get_step(filename):
        return (''.join(c for c in filename if c.isdigit()))
        
    path = os.path.join(Paths.models, model_name, type_)
    step = sorted(get_step(x) for x in os.listdir(path)
                  if x.endswith('.pt') and x != 'model.pt')[-1]
    
    old_name = f'model_step_{int(step)}.pt'
    new_name = 'model.pt'
    os.rename(os.path.join(path, old_name), os.path.join(path, new_name))
    print(f'> Model {old_name} --> {new_name}')

    
def print_oov_rates():
    ## TODO: Rewrite this crap
    ## just do it in a single file
    for model in counts:
        stats = {}
        for data_type in ('dev', 'test'):
            for word_type in ('xlit', 'lem'):
                key = f'types-{data_type}-{word_type}'

                """ Get training and other data type counts """
                train = set(counts[model]['train'][word_type])
                this = set(counts[model][data_type][word_type])

                """ Make OOV dictionary and save it """
                out_of_vocab = this - train

                fn = os.path.join(Paths.models, model, 'lex',
                     f'{data_type}-types-oov.{word_type}')
                
                with open(fn, 'w', encoding='utf-8') as f:
                    for w in sorted(out_of_vocab):
                        freq = counts[model][data_type][word_type][w]
                        f.write(f'{w}\t{freq}\n')

                """ Count absolute and relative freqs for types """
                examples_this = len(this)
                oov_abs = len(out_of_vocab)
                oov_rel = round(100 * oov_abs / examples_this, 2)
                stats[key] = (examples_this, oov_abs, oov_rel)

                """ Count absolute and relative freqs for tokens """                
                key = f'tokens-{data_type}-{word_type}'
                train = sum(counts[model]['train'][word_type].values())
                this = sum(counts[model][data_type][word_type].values())
                examples_this = this

                oov_abs = sum(counts[model][data_type][word_type][w]
                               for w in out_of_vocab)

                oov_rel = round(100 * oov_abs / examples_this, 2)
                stats[key] = (examples_this, oov_abs, oov_rel)

        headings = ('CATEGORY', 'SIZE', 'OOV', 'OOV-%')
        logger('\n   ' + model + ' ' + '='*48)
        logger('   {: <20} {:>7} {:>7} {:>7}'.format(*headings))
        for key, values in sorted(stats.items()):
            logger('   {: <20} {:>7} {:>7} {:>7}'.format(key, *values))           
           

def make_lexicon(prefix, data_type, filename):
    """ Setup lexicon """
    ### TODO: rewrite this, uses still old conllu module
    fn = os.path.join(
        Paths.models, prefix, 'lex', f'{data_type}.all')
    fnl = os.path.join(
        Paths.models, prefix, 'lex', f'{data_type}-types.lem')
    fnx = os.path.join(
        Paths.models, prefix, 'lex', f'{data_type}-types.xlit')
    lemma_dict = defaultdict(int)
    xlit_dict = defaultdict(int)

    logger('   + Building lexicons')
    with open(fn, 'w', encoding='utf-8') as f:
        for line in conllutools.get_lexicon(filename):
            if line:
                f.write('\t'.join(line) + '\n')
                xlit, lemma, pos = line
                if xlit != conllutools.EOU[0]:
                    lemma_dict[f'{lemma} {pos}'] += 1
                    xlit_dict[xlit] += 1

    with open(fnl, 'w', encoding='utf-8') as fl:
        for word, freq in sorted(lemma_dict.items(),
                                 key=lambda item: item[1], reverse=True):
            fl.write(f'{word}\t{freq}\n')
            
    with open(fnx, 'w', encoding='utf-8') as fl:
        for word, freq in sorted(xlit_dict.items(),
                                 key=lambda item: item[1], reverse=True):
            fl.write(f'{word}\t{freq}\n')

    logger(f'   + {len(xlit_dict)} form types')
    logger(f'   + {len(lemma_dict)} lemma types')

    stats = {'lem': lemma_dict, 'xlit': xlit_dict}

    if not prefix in counts:
        counts[prefix] = {data_type: stats}
    else:
        counts[prefix][data_type] = stats


def _make_training_data(filename):
    """ Build training data for POS-tagger and lemmatizer.
    The data is saved to `TRAIN_PATH`. Source files must be
    in CONLL-U format and named PREFIX-SUFFIX.conllu, where
    prefix is arbitrary identifier and suffix `dev`, `test`,
    or `train` depending on which set the data belongs. """

    context = 2
    
    """ Create required folder structures for the model """
    orig_fn = os.path.split(filename)[-1]
    prefix, data_type = split_train_filename(orig_fn)
    
    logger(f'\n> Building training data from {filename}')

    """ Define model path structure """
    ## TODO: makedirs
    paths = (
        Paths.models,
        os.path.join(Paths.models, prefix),
        os.path.join(Paths.models, prefix, 'override'),
        os.path.join(Paths.models, prefix, 'tagger'),
        os.path.join(Paths.models, prefix, 'lemmatizer'),
        os.path.join(Paths.models, prefix, 'tagger', 'traindata'),
        os.path.join(Paths.models, prefix, 'lemmatizer', 'traindata'),
        os.path.join(Paths.models, prefix, 'eval'),
        os.path.join(Paths.models, prefix, 'lex'),
        os.path.join(Paths.models, prefix, 'conllu'))

    for path in paths:
        try:
            os.mkdir(path)
        except FileExistsError:
            pass

    """ Write model config file """
    with open(os.path.join(Paths.models, prefix, 'config.yaml'), 'w', encoding='utf-8') as conffile:
        conffile.write(f'## Built with version {__version__}\n')
        conffile.write(f'tokenizer: {Tokenizer.setting}\n')
    
    """ Load CoNLL-U+ file """
    this_data = conlluplus.ConlluPlus(filename)
    this_data.normalize(is_traindata=True)
    """ Fill in context information and save file to model dir """
    '''
    for src_field, tgt_field in (('form', 'formctx'), ('xpos', 'xposctx')):
        this_data.update_value(
            field = tgt_field,
            values = this_data.get_contexts(src_field, size=context))
    '''
    ## TEMPORARY: laita eri ikkuna muodolle
    #for src_field, tgt_field in ('xpos', 'xposctx'):
    this_data.update_value(
            field = 'xposctx',
            values = this_data.get_contexts('xpos', size=1))

    this_data.update_value(
            field = 'formctx',
            values = this_data.get_contexts('form', size=context))
    

    """ Create override file """
    with open(os.path.join(Paths.models, prefix, 'override', 'override.conllu'),\
              'w', encoding='utf-8') as f:
        f.write(f'## BabyLemmatizer {__version__} Override\n')
    
    """ Save this data to the model directory for reproducibility and
    ease of use """
    conllu_ext = os.path.join(Paths.models, prefix, 'conllu', f'{data_type}.conllu')
    this_data.write_file(conllu_ext)
                
    """ Generate training data """
    tagger_path = os.path.join(
        Paths.models, prefix, 'tagger', 'traindata')
    lemmatizer_path = os.path.join(
        Paths.models, prefix, 'lemmatizer', 'traindata')

    """ Define target and source files for NN-training data """
    pos_src_fn = os.path.join(tagger_path, f'{data_type}.src')
    pos_tgt_fn = os.path.join(tagger_path, f'{data_type}.tgt')
    lem_src_fn = os.path.join(lemmatizer_path, f'{data_type}.src')
    lem_tgt_fn = os.path.join(lemmatizer_path, f'{data_type}.tgt')

    logger('   + Building tagger and lemmatizer training sets')
    
    """ Build training data """
    with open(pos_src_fn, 'w', encoding='utf-8') as pos_src,\
         open(pos_tgt_fn, 'w', encoding='utf-8') as pos_tgt,\
         open(lem_src_fn, 'w', encoding='utf-8') as lem_src,\
         open(lem_tgt_fn, 'w', encoding='utf-8') as lem_tgt:

        fields = ('form', 'lemma', 'xpos', 'formctx', 'xposctx')
        for data in this_data.get_contents(*fields):
            form, lemma, xpos, formctx, xposctx = data
            pos_src.write(PP.make_tagger_src(formctx, context=context) + '\n')
            pos_tgt.write(xpos + '\n')
            lem_src.write(PP.make_lem_src(form, xposctx) + '\n')
            lem_tgt.write(PP.get_chars_lemma(lemma) + '\n')
            statistics[filename] += 1
    
    """ Build YAML-definitions for models. The network architecture
    and its parameters follow (Kanerva, Ginter & Salakoski 2020),
    i.e. TurkuNLP's Universal Lemmatizer where BabyLemmatizer 1.0 was
    based on. Also build override lexicons for future use.

    BabyLemmatizer uses significantly lower number of training steps
    as it seems to improve OOV lemmatization. """

    if data_type == 'train':

        """ Setup neural net """
        examples = statistics[filename]
        steps_per_epoch = int(math.ceil(int(examples) / 64))

        ## Tagger setup (tmp)
        total_steps = int(examples * 0.20)
        start_decay = int(math.ceil(total_steps / 2))

        hyper_tagger = base_yaml.set_hyper(
            examples,
            steps_per_epoch,
            total_steps,
            start_decay)

        ## Lemmatizer setup (tmp)
        total_steps = int(examples * 0.15)
        start_decay = int(math.ceil(total_steps / 2))        
        
        hyper_lemmatizer = base_yaml.set_hyper(
            examples,
            steps_per_epoch,
            total_steps,
            start_decay)
        
        base_yaml.make_lemmatizer_yaml(
            prefix, hyper_lemmatizer)

        base_yaml.make_tagger_yaml(
            prefix, hyper_tagger)

    make_lexicon(prefix, data_type, filename)


def build_train_data(*models):
    """ Build train data from CoNLL-U files in the given
    folder.

    :param models         arbitrary number of model names that
                          correspond to file prefixes in the conllu path
    :param conllu_path    location of CoNLL-U files

    :type models          str
    :type models          str                        """
    
    filelist = [x for x in os.listdir(Paths.conllu)
                if x.endswith('.conllu') and x.startswith(tuple(models))]

    if not filelist:
        print(f'\n> Path "{Path.conllu}" does not contain'\
              ' files with given prefix')
    
    for filename in sorted(filelist):
        _make_training_data(os.path.join(Paths.conllu, filename))
        
    print_statistics()
    print_oov_rates()

    """ Get model prefix by removing digits """
    if len(models) > 1:
        prefix = ''.join(c for c in models[0] if not c.isdigit())
    else:
        prefix = models[0]
        
    save_log(f'build-log-{prefix}.txt')


def train_model(*models, cpu=False):
    """ Run this method to train the models; this simply calls OpenNMT
    from the command line with required parameters to train basic
    models for raw tagging and lemmatization.

    :param models         arbitrary number of model names that
                          correspond to file prefixes in the conllu path

    :type models          str """

    if cpu:
        gpu = ''
    else:
        gpu = '-gpu_ranks 0 -world_size 1'

        
    for model in sorted(models):
        if model not in os.listdir(Paths.models):            
            print(f'> Run build_training_data({model}) before training.')
            sys.exit(0)

        ## TODO: use os.path.join instead of string formatting
        model_path = os.path.join(Paths.models, model)

        for yaml in (x for x in os.listdir(model_path) if x.endswith('.yaml')):
            os.system(f'{python_path}python {onmt_path}build_vocab.py '\
                      f'-config {model_path}/{yaml} -n_sample -1 '\
                      f'-num_threads 2')
            os.system(f'{python_path}python {onmt_path}train.py '\
                      f'-config {model_path}/{yaml} {gpu}')
            pass
        _rename_model(model, 'lemmatizer')
        _rename_model(model, 'tagger')


if __name__ == "__main__":
    prefix = 'urartian0'
    models = parse_prefix(prefix)
    build_train_data(*models)
    #train_model('a', 'b')#*models)
    pass