mirror of
https://github.com/explosion/spaCy.git
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652 lines
27 KiB
Python
652 lines
27 KiB
Python
# coding: utf8
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from __future__ import unicode_literals, division, print_function
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import plac
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import os
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from pathlib import Path
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from thinc.neural._classes.model import Model
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from timeit import default_timer as timer
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import shutil
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import srsly
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from wasabi import msg
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import contextlib
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import random
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from thinc.neural.util import require_gpu
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from .._ml import create_default_optimizer
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from ..attrs import PROB, IS_OOV, CLUSTER, LANG
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from ..gold import GoldCorpus
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from ..compat import path2str
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from .. import util
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from .. import about
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@plac.annotations(
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# fmt: off
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lang=("Model language", "positional", None, str),
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output_path=("Output directory to store model in", "positional", None, Path),
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train_path=("Location of JSON-formatted training data", "positional", None, Path),
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dev_path=("Location of JSON-formatted development data", "positional", None, Path),
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raw_text=("Path to jsonl file with unlabelled text documents.", "option", "rt", Path),
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base_model=("Name of model to update (optional)", "option", "b", str),
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pipeline=("Comma-separated names of pipeline components", "option", "p", str),
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vectors=("Model to load vectors from", "option", "v", str),
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n_iter=("Number of iterations", "option", "n", int),
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n_early_stopping=("Maximum number of training epochs without dev accuracy improvement", "option", "ne", int),
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n_examples=("Number of examples", "option", "ns", int),
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use_gpu=("Use GPU", "option", "g", int),
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version=("Model version", "option", "V", str),
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meta_path=("Optional path to meta.json to use as base.", "option", "m", Path),
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init_tok2vec=("Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.", "option", "t2v", Path),
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parser_multitasks=("Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'", "option", "pt", str),
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entity_multitasks=("Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'", "option", "et", str),
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noise_level=("Amount of corruption for data augmentation", "option", "nl", float),
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orth_variant_level=("Amount of orthography variation for data augmentation", "option", "ovl", float),
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eval_beam_widths=("Beam widths to evaluate, e.g. 4,8", "option", "bw", str),
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gold_preproc=("Use gold preprocessing", "flag", "G", bool),
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learn_tokens=("Make parser learn gold-standard tokenization", "flag", "T", bool),
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textcat_multilabel=("Textcat classes aren't mutually exclusive (multilabel)", "flag", "TML", bool),
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textcat_arch=("Textcat model architecture", "option", "ta", str),
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textcat_positive_label=("Textcat positive label for binary classes with two labels", "option", "tpl", str),
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verbose=("Display more information for debug", "flag", "VV", bool),
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debug=("Run data diagnostics before training", "flag", "D", bool),
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# fmt: on
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)
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def train(
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lang,
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output_path,
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train_path,
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dev_path,
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raw_text=None,
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base_model=None,
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pipeline="tagger,parser,ner",
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vectors=None,
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n_iter=30,
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n_early_stopping=None,
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n_examples=0,
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use_gpu=-1,
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version="0.0.0",
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meta_path=None,
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init_tok2vec=None,
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parser_multitasks="",
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entity_multitasks="",
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noise_level=0.0,
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orth_variant_level=0.0,
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eval_beam_widths="",
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gold_preproc=False,
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learn_tokens=False,
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textcat_multilabel=False,
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textcat_arch="bow",
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textcat_positive_label=None,
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verbose=False,
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debug=False,
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):
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"""
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Train or update a spaCy model. Requires data to be formatted in spaCy's
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JSON format. To convert data from other formats, use the `spacy convert`
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command.
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"""
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if use_gpu != -1:
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require_gpu(use_gpu)
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# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
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import tqdm
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util.fix_random_seed()
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util.set_env_log(verbose)
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# Make sure all files and paths exists if they are needed
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train_path = util.ensure_path(train_path)
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dev_path = util.ensure_path(dev_path)
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meta_path = util.ensure_path(meta_path)
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output_path = util.ensure_path(output_path)
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if raw_text is not None:
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raw_text = list(srsly.read_jsonl(raw_text))
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if not train_path or not train_path.exists():
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msg.fail("Training data not found", train_path, exits=1)
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if not dev_path or not dev_path.exists():
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msg.fail("Development data not found", dev_path, exits=1)
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if meta_path is not None and not meta_path.exists():
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msg.fail("Can't find model meta.json", meta_path, exits=1)
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meta = srsly.read_json(meta_path) if meta_path else {}
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if output_path.exists() and [p for p in output_path.iterdir() if p.is_dir()]:
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msg.warn(
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"Output directory is not empty",
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"This can lead to unintended side effects when saving the model. "
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"Please use an empty directory or a different path instead. If "
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"the specified output path doesn't exist, the directory will be "
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"created for you.",
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)
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if not output_path.exists():
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output_path.mkdir()
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# Take dropout and batch size as generators of values -- dropout
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# starts high and decays sharply, to force the optimizer to explore.
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# Batch size starts at 1 and grows, so that we make updates quickly
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# at the beginning of training.
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dropout_rates = util.decaying(
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util.env_opt("dropout_from", 0.2),
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util.env_opt("dropout_to", 0.2),
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util.env_opt("dropout_decay", 0.0),
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)
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batch_sizes = util.compounding(
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util.env_opt("batch_from", 100.0),
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util.env_opt("batch_to", 1000.0),
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util.env_opt("batch_compound", 1.001),
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)
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if not eval_beam_widths:
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eval_beam_widths = [1]
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else:
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eval_beam_widths = [int(bw) for bw in eval_beam_widths.split(",")]
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if 1 not in eval_beam_widths:
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eval_beam_widths.append(1)
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eval_beam_widths.sort()
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has_beam_widths = eval_beam_widths != [1]
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# Set up the base model and pipeline. If a base model is specified, load
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# the model and make sure the pipeline matches the pipeline setting. If
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# training starts from a blank model, intitalize the language class.
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pipeline = [p.strip() for p in pipeline.split(",")]
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msg.text("Training pipeline: {}".format(pipeline))
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if base_model:
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msg.text("Starting with base model '{}'".format(base_model))
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nlp = util.load_model(base_model)
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if nlp.lang != lang:
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msg.fail(
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"Model language ('{}') doesn't match language specified as "
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"`lang` argument ('{}') ".format(nlp.lang, lang),
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exits=1,
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)
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nlp.disable_pipes([p for p in nlp.pipe_names if p not in pipeline])
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for pipe in pipeline:
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if pipe not in nlp.pipe_names:
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if pipe == "parser":
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pipe_cfg = {"learn_tokens": learn_tokens}
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elif pipe == "textcat":
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pipe_cfg = {
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"exclusive_classes": not textcat_multilabel,
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"architecture": textcat_arch,
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"positive_label": textcat_positive_label,
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}
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else:
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pipe_cfg = {}
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nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
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else:
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if pipe == "textcat":
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textcat_cfg = nlp.get_pipe("textcat").cfg
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base_cfg = {
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"exclusive_classes": textcat_cfg["exclusive_classes"],
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"architecture": textcat_cfg["architecture"],
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"positive_label": textcat_cfg["positive_label"],
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}
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pipe_cfg = {
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"exclusive_classes": not textcat_multilabel,
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"architecture": textcat_arch,
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"positive_label": textcat_positive_label,
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}
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if base_cfg != pipe_cfg:
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msg.fail(
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"The base textcat model configuration does"
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"not match the provided training options. "
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"Existing cfg: {}, provided cfg: {}".format(
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base_cfg, pipe_cfg
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),
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exits=1,
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)
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else:
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msg.text("Starting with blank model '{}'".format(lang))
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lang_cls = util.get_lang_class(lang)
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nlp = lang_cls()
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for pipe in pipeline:
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if pipe == "parser":
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pipe_cfg = {"learn_tokens": learn_tokens}
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elif pipe == "textcat":
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pipe_cfg = {
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"exclusive_classes": not textcat_multilabel,
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"architecture": textcat_arch,
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"positive_label": textcat_positive_label,
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}
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else:
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pipe_cfg = {}
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nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
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if vectors:
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msg.text("Loading vector from model '{}'".format(vectors))
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_load_vectors(nlp, vectors)
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# Multitask objectives
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multitask_options = [("parser", parser_multitasks), ("ner", entity_multitasks)]
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for pipe_name, multitasks in multitask_options:
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if multitasks:
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if pipe_name not in pipeline:
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msg.fail(
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"Can't use multitask objective without '{}' in the "
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"pipeline".format(pipe_name)
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)
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pipe = nlp.get_pipe(pipe_name)
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for objective in multitasks.split(","):
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pipe.add_multitask_objective(objective)
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# Prepare training corpus
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msg.text("Counting training words (limit={})".format(n_examples))
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corpus = GoldCorpus(train_path, dev_path, limit=n_examples)
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n_train_words = corpus.count_train()
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if base_model:
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# Start with an existing model, use default optimizer
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optimizer = nlp.resume_training(device=use_gpu)
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else:
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# Start with a blank model, call begin_training
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optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
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nlp._optimizer = None
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# Load in pretrained weights
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if init_tok2vec is not None:
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components = _load_pretrained_tok2vec(nlp, init_tok2vec)
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msg.text("Loaded pretrained tok2vec for: {}".format(components))
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# Verify textcat config
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if "textcat" in pipeline:
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textcat_labels = nlp.get_pipe("textcat").cfg["labels"]
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if textcat_positive_label and textcat_positive_label not in textcat_labels:
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msg.fail(
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"The textcat_positive_label (tpl) '{}' does not match any "
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"label in the training data.".format(textcat_positive_label),
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exits=1,
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)
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if textcat_positive_label and len(textcat_labels) != 2:
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msg.fail(
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"A textcat_positive_label (tpl) '{}' was provided for training "
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"data that does not appear to be a binary classification "
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"problem with two labels.".format(textcat_positive_label),
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exits=1,
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)
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train_docs = corpus.train_docs(
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nlp,
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noise_level=noise_level,
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gold_preproc=gold_preproc,
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max_length=0,
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ignore_misaligned=True,
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)
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train_labels = set()
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if textcat_multilabel:
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multilabel_found = False
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for text, gold in train_docs:
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train_labels.update(gold.cats.keys())
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if list(gold.cats.values()).count(1.0) != 1:
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multilabel_found = True
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if not multilabel_found and not base_model:
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msg.warn(
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"The textcat training instances look like they have "
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"mutually-exclusive classes. Remove the flag "
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"'--textcat-multilabel' to train a classifier with "
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"mutually-exclusive classes."
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)
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if not textcat_multilabel:
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for text, gold in train_docs:
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train_labels.update(gold.cats.keys())
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if list(gold.cats.values()).count(1.0) != 1 and not base_model:
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msg.warn(
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"Some textcat training instances do not have exactly "
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"one positive label. Modifying training options to "
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"include the flag '--textcat-multilabel' for classes "
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"that are not mutually exclusive."
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)
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nlp.get_pipe("textcat").cfg["exclusive_classes"] = False
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textcat_multilabel = True
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break
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if base_model and set(textcat_labels) != train_labels:
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msg.fail(
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"Cannot extend textcat model using data with different "
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"labels. Base model labels: {}, training data labels: "
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"{}.".format(textcat_labels, list(train_labels)),
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exits=1,
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)
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if textcat_multilabel:
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msg.text(
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"Textcat evaluation score: ROC AUC score macro-averaged across "
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"the labels '{}'".format(", ".join(textcat_labels))
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)
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elif textcat_positive_label and len(textcat_labels) == 2:
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msg.text(
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"Textcat evaluation score: F1-score for the "
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"label '{}'".format(textcat_positive_label)
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)
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elif len(textcat_labels) > 1:
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if len(textcat_labels) == 2:
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msg.warn(
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"If the textcat component is a binary classifier with "
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"exclusive classes, provide '--textcat_positive_label' for "
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"an evaluation on the positive class."
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)
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msg.text(
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"Textcat evaluation score: F1-score macro-averaged across "
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"the labels '{}'".format(", ".join(textcat_labels))
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)
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else:
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msg.fail(
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"Unsupported textcat configuration. Use `spacy debug-data` "
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"for more information."
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)
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# fmt: off
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row_head, output_stats = _configure_training_output(pipeline, use_gpu, has_beam_widths)
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row_widths = [len(w) for w in row_head]
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row_settings = {"widths": row_widths, "aligns": tuple(["r" for i in row_head]), "spacing": 2}
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# fmt: on
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print("")
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msg.row(row_head, **row_settings)
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msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
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try:
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iter_since_best = 0
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best_score = 0.0
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for i in range(n_iter):
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train_docs = corpus.train_docs(
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nlp,
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noise_level=noise_level,
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orth_variant_level=orth_variant_level,
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gold_preproc=gold_preproc,
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max_length=0,
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ignore_misaligned=True,
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)
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if raw_text:
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random.shuffle(raw_text)
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raw_batches = util.minibatch(
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(nlp.make_doc(rt["text"]) for rt in raw_text), size=8
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)
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words_seen = 0
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with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
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losses = {}
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for batch in util.minibatch_by_words(train_docs, size=batch_sizes):
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if not batch:
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continue
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docs, golds = zip(*batch)
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nlp.update(
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docs,
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golds,
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sgd=optimizer,
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drop=next(dropout_rates),
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losses=losses,
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)
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if raw_text:
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# If raw text is available, perform 'rehearsal' updates,
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# which use unlabelled data to reduce overfitting.
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raw_batch = list(next(raw_batches))
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nlp.rehearse(raw_batch, sgd=optimizer, losses=losses)
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if not int(os.environ.get("LOG_FRIENDLY", 0)):
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pbar.update(sum(len(doc) for doc in docs))
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words_seen += sum(len(doc) for doc in docs)
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with nlp.use_params(optimizer.averages):
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util.set_env_log(False)
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epoch_model_path = output_path / ("model%d" % i)
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nlp.to_disk(epoch_model_path)
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nlp_loaded = util.load_model_from_path(epoch_model_path)
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for beam_width in eval_beam_widths:
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for name, component in nlp_loaded.pipeline:
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if hasattr(component, "cfg"):
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component.cfg["beam_width"] = beam_width
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dev_docs = list(
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corpus.dev_docs(
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nlp_loaded,
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gold_preproc=gold_preproc,
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ignore_misaligned=True,
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)
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)
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nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
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start_time = timer()
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scorer = nlp_loaded.evaluate(dev_docs, verbose=verbose)
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end_time = timer()
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if use_gpu < 0:
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gpu_wps = None
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cpu_wps = nwords / (end_time - start_time)
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else:
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gpu_wps = nwords / (end_time - start_time)
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with Model.use_device("cpu"):
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nlp_loaded = util.load_model_from_path(epoch_model_path)
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for name, component in nlp_loaded.pipeline:
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if hasattr(component, "cfg"):
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component.cfg["beam_width"] = beam_width
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dev_docs = list(
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corpus.dev_docs(
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nlp_loaded,
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gold_preproc=gold_preproc,
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ignore_misaligned=True,
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)
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)
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start_time = timer()
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scorer = nlp_loaded.evaluate(dev_docs, verbose=verbose)
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end_time = timer()
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cpu_wps = nwords / (end_time - start_time)
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acc_loc = output_path / ("model%d" % i) / "accuracy.json"
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srsly.write_json(acc_loc, scorer.scores)
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# Update model meta.json
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meta["lang"] = nlp.lang
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meta["pipeline"] = nlp.pipe_names
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meta["spacy_version"] = ">=%s" % about.__version__
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if beam_width == 1:
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meta["speed"] = {
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"nwords": nwords,
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"cpu": cpu_wps,
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"gpu": gpu_wps,
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}
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meta["accuracy"] = scorer.scores
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else:
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meta.setdefault("beam_accuracy", {})
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meta.setdefault("beam_speed", {})
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meta["beam_accuracy"][beam_width] = scorer.scores
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meta["beam_speed"][beam_width] = {
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"nwords": nwords,
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"cpu": cpu_wps,
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"gpu": gpu_wps,
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}
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meta["vectors"] = {
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"width": nlp.vocab.vectors_length,
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"vectors": len(nlp.vocab.vectors),
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"keys": nlp.vocab.vectors.n_keys,
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"name": nlp.vocab.vectors.name,
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}
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meta.setdefault("name", "model%d" % i)
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meta.setdefault("version", version)
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meta["labels"] = nlp.meta["labels"]
|
|
meta_loc = output_path / ("model%d" % i) / "meta.json"
|
|
srsly.write_json(meta_loc, meta)
|
|
util.set_env_log(verbose)
|
|
|
|
progress = _get_progress(
|
|
i,
|
|
losses,
|
|
scorer.scores,
|
|
output_stats,
|
|
beam_width=beam_width if has_beam_widths else None,
|
|
cpu_wps=cpu_wps,
|
|
gpu_wps=gpu_wps,
|
|
)
|
|
if i == 0 and "textcat" in pipeline:
|
|
textcats_per_cat = scorer.scores.get("textcats_per_cat", {})
|
|
for cat, cat_score in textcats_per_cat.items():
|
|
if cat_score.get("roc_auc_score", 0) < 0:
|
|
msg.warn(
|
|
"Textcat ROC AUC score is undefined due to "
|
|
"only one value in label '{}'.".format(cat)
|
|
)
|
|
msg.row(progress, **row_settings)
|
|
# Early stopping
|
|
if n_early_stopping is not None:
|
|
current_score = _score_for_model(meta)
|
|
if current_score < best_score:
|
|
iter_since_best += 1
|
|
else:
|
|
iter_since_best = 0
|
|
best_score = current_score
|
|
if iter_since_best >= n_early_stopping:
|
|
msg.text(
|
|
"Early stopping, best iteration "
|
|
"is: {}".format(i - iter_since_best)
|
|
)
|
|
msg.text(
|
|
"Best score = {}; Final iteration "
|
|
"score = {}".format(best_score, current_score)
|
|
)
|
|
break
|
|
finally:
|
|
with nlp.use_params(optimizer.averages):
|
|
final_model_path = output_path / "model-final"
|
|
nlp.to_disk(final_model_path)
|
|
msg.good("Saved model to output directory", final_model_path)
|
|
with msg.loading("Creating best model..."):
|
|
best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
|
|
msg.good("Created best model", best_model_path)
|
|
|
|
|
|
def _score_for_model(meta):
|
|
""" Returns mean score between tasks in pipeline that can be used for early stopping. """
|
|
mean_acc = list()
|
|
pipes = meta["pipeline"]
|
|
acc = meta["accuracy"]
|
|
if "tagger" in pipes:
|
|
mean_acc.append(acc["tags_acc"])
|
|
if "parser" in pipes:
|
|
mean_acc.append((acc["uas"] + acc["las"]) / 2)
|
|
if "ner" in pipes:
|
|
mean_acc.append((acc["ents_p"] + acc["ents_r"] + acc["ents_f"]) / 3)
|
|
if "textcat" in pipes:
|
|
mean_acc.append(acc["textcat_score"])
|
|
return sum(mean_acc) / len(mean_acc)
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def _create_progress_bar(total):
|
|
# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
|
|
import tqdm
|
|
|
|
if int(os.environ.get("LOG_FRIENDLY", 0)):
|
|
yield
|
|
else:
|
|
pbar = tqdm.tqdm(total=total, leave=False)
|
|
yield pbar
|
|
|
|
|
|
def _load_vectors(nlp, vectors):
|
|
util.load_model(vectors, vocab=nlp.vocab)
|
|
for lex in nlp.vocab:
|
|
values = {}
|
|
for attr, func in nlp.vocab.lex_attr_getters.items():
|
|
# These attrs are expected to be set by data. Others should
|
|
# be set by calling the language functions.
|
|
if attr not in (CLUSTER, PROB, IS_OOV, LANG):
|
|
values[lex.vocab.strings[attr]] = func(lex.orth_)
|
|
lex.set_attrs(**values)
|
|
lex.is_oov = False
|
|
|
|
|
|
def _load_pretrained_tok2vec(nlp, loc):
|
|
"""Load pretrained weights for the 'token-to-vector' part of the component
|
|
models, which is typically a CNN. See 'spacy pretrain'. Experimental.
|
|
"""
|
|
with loc.open("rb") as file_:
|
|
weights_data = file_.read()
|
|
loaded = []
|
|
for name, component in nlp.pipeline:
|
|
if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
|
|
component.tok2vec.from_bytes(weights_data)
|
|
loaded.append(name)
|
|
return loaded
|
|
|
|
|
|
def _collate_best_model(meta, output_path, components):
|
|
bests = {}
|
|
for component in components:
|
|
bests[component] = _find_best(output_path, component)
|
|
best_dest = output_path / "model-best"
|
|
shutil.copytree(path2str(output_path / "model-final"), path2str(best_dest))
|
|
for component, best_component_src in bests.items():
|
|
shutil.rmtree(path2str(best_dest / component))
|
|
shutil.copytree(
|
|
path2str(best_component_src / component), path2str(best_dest / component)
|
|
)
|
|
accs = srsly.read_json(best_component_src / "accuracy.json")
|
|
for metric in _get_metrics(component):
|
|
meta["accuracy"][metric] = accs[metric]
|
|
srsly.write_json(best_dest / "meta.json", meta)
|
|
return best_dest
|
|
|
|
|
|
def _find_best(experiment_dir, component):
|
|
accuracies = []
|
|
for epoch_model in experiment_dir.iterdir():
|
|
if epoch_model.is_dir() and epoch_model.parts[-1] != "model-final":
|
|
accs = srsly.read_json(epoch_model / "accuracy.json")
|
|
scores = [accs.get(metric, 0.0) for metric in _get_metrics(component)]
|
|
accuracies.append((scores, epoch_model))
|
|
if accuracies:
|
|
return max(accuracies)[1]
|
|
else:
|
|
return None
|
|
|
|
|
|
def _get_metrics(component):
|
|
if component == "parser":
|
|
return ("las", "uas", "token_acc")
|
|
elif component == "tagger":
|
|
return ("tags_acc",)
|
|
elif component == "ner":
|
|
return ("ents_f", "ents_p", "ents_r")
|
|
return ("token_acc",)
|
|
|
|
|
|
def _configure_training_output(pipeline, use_gpu, has_beam_widths):
|
|
row_head = ["Itn"]
|
|
output_stats = []
|
|
for pipe in pipeline:
|
|
if pipe == "tagger":
|
|
row_head.extend(["Tag Loss ", " Tag % "])
|
|
output_stats.extend(["tag_loss", "tags_acc"])
|
|
elif pipe == "parser":
|
|
row_head.extend(["Dep Loss ", " UAS ", " LAS "])
|
|
output_stats.extend(["dep_loss", "uas", "las"])
|
|
elif pipe == "ner":
|
|
row_head.extend(["NER Loss ", "NER P ", "NER R ", "NER F "])
|
|
output_stats.extend(["ner_loss", "ents_p", "ents_r", "ents_f"])
|
|
elif pipe == "textcat":
|
|
row_head.extend(["Textcat Loss", "Textcat"])
|
|
output_stats.extend(["textcat_loss", "textcat_score"])
|
|
row_head.extend(["Token %", "CPU WPS"])
|
|
output_stats.extend(["token_acc", "cpu_wps"])
|
|
|
|
if use_gpu >= 0:
|
|
row_head.extend(["GPU WPS"])
|
|
output_stats.extend(["gpu_wps"])
|
|
|
|
if has_beam_widths:
|
|
row_head.insert(1, "Beam W.")
|
|
return row_head, output_stats
|
|
|
|
|
|
def _get_progress(
|
|
itn, losses, dev_scores, output_stats, beam_width=None, cpu_wps=0.0, gpu_wps=0.0
|
|
):
|
|
scores = {}
|
|
for stat in output_stats:
|
|
scores[stat] = 0.0
|
|
scores["dep_loss"] = losses.get("parser", 0.0)
|
|
scores["ner_loss"] = losses.get("ner", 0.0)
|
|
scores["tag_loss"] = losses.get("tagger", 0.0)
|
|
scores["textcat_loss"] = losses.get("textcat", 0.0)
|
|
scores["cpu_wps"] = cpu_wps
|
|
scores["gpu_wps"] = gpu_wps or 0.0
|
|
scores.update(dev_scores)
|
|
formatted_scores = []
|
|
for stat in output_stats:
|
|
format_spec = "{:.3f}"
|
|
if stat.endswith("_wps"):
|
|
format_spec = "{:.0f}"
|
|
formatted_scores.append(format_spec.format(scores[stat]))
|
|
result = [itn + 1]
|
|
result.extend(formatted_scores)
|
|
if beam_width is not None:
|
|
result.insert(1, beam_width)
|
|
return result
|