spaCy/spacy/train.py

# coding: utf8
from __future__ import absolute_import, unicode_literals

import random
import tqdm

from thinc.neural.optimizers import Adam
from thinc.neural.ops import NumpyOps, CupyOps

from .gold import GoldParse, merge_sents
from .scorer import Scorer


class Trainer(object):
    """
    Manage training of an NLP pipeline.
    """
    def __init__(self, nlp, gold_tuples):
        self.nlp = nlp
        self.gold_tuples = gold_tuples
        self.nr_epoch = 0

    def epochs(self, nr_epoch, augment_data=None, gold_preproc=False):
        cached_golds = {}
        def _epoch(indices):
            for i in tqdm.tqdm(indices):
                raw_text, paragraph_tuples = self.gold_tuples[i]
                if gold_preproc:
                    raw_text = None
                else:
                    paragraph_tuples = merge_sents(paragraph_tuples)
                if augment_data is None:
                    docs = self.make_docs(raw_text, paragraph_tuples)
                    if i in cached_golds:
                        golds = cached_golds[i]
                    else:
                        golds = self.make_golds(docs, paragraph_tuples)
                else:
                    raw_text, paragraph_tuples = augment_data(raw_text, paragraph_tuples)
                    docs = self.make_docs(raw_text, paragraph_tuples)
                    golds = self.make_golds(docs, paragraph_tuples)
                for doc, gold in zip(docs, golds):
                    yield doc, gold

        indices = list(range(len(self.gold_tuples)))
        for itn in range(nr_epoch):
            random.shuffle(indices)
            yield _epoch(indices)
            self.nr_epoch += 1

    def update(self, docs, golds, drop=0.):
        for process in self.nlp.pipeline:
            if hasattr(process, 'update'):
                loss = process.update(doc, gold, sgd=self.sgd, drop=drop,
                                      itn=self.nr_epoch)
                self.sgd.finish_update()
            else:
                process(doc)
        return doc

    def evaluate(self, dev_sents, gold_preproc=False):
        scorer = Scorer()
        for raw_text, paragraph_tuples in dev_sents:
            if gold_preproc:
                raw_text = None
            else:
                paragraph_tuples = merge_sents(paragraph_tuples)
            docs = self.make_docs(raw_text, paragraph_tuples)
            golds = self.make_golds(docs, paragraph_tuples)
            for doc, gold in zip(docs, golds):
                for process in self.nlp.pipeline:
                    process(doc)
                scorer.score(doc, gold)
        return scorer

    def make_docs(self, raw_text, paragraph_tuples):
        if raw_text is not None:
            return [self.nlp.tokenizer(raw_text)]
        else:
            return [self.nlp.tokenizer.tokens_from_list(sent_tuples[0][1])
                    for sent_tuples in paragraph_tuples]

    def make_golds(self, docs, paragraph_tuples):
        if len(docs) == 1:
            return [GoldParse.from_annot_tuples(docs[0], sent_tuples[0])
                    for sent_tuples in paragraph_tuples]
        else:
            return [GoldParse.from_annot_tuples(doc, sent_tuples[0])
                    for doc, sent_tuples in zip(docs, paragraph_tuples)]