mirror of
https://github.com/explosion/spaCy.git
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511 lines
20 KiB
Python
511 lines
20 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 Printer
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import contextlib
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import random
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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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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=(
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"Path to jsonl file with unlabelled text documents.",
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"option",
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"rt",
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Path,
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),
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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=(
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"Maximum number of training epochs without dev accuracy improvement",
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"option",
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"ne",
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int,
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),
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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=(
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"Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.",
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"option",
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"t2v",
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Path,
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),
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parser_multitasks=(
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"Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'",
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"option",
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"pt",
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str,
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),
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entity_multitasks=(
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"Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'",
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"option",
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"et",
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str,
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),
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noise_level=("Amount of corruption for data augmentation", "option", "nl", 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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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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)
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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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eval_beam_widths="",
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gold_preproc=False,
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learn_tokens=False,
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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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# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
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import tqdm
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msg = Printer()
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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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other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipeline]
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nlp.disable_pipes(*other_pipes)
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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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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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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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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 = create_default_optimizer(Model.ops)
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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 pre-trained 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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# fmt: off
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row_head = ["Itn", "Dep Loss", "NER Loss", "UAS", "NER P", "NER R", "NER F", "Tag %", "Token %", "CPU WPS", "GPU WPS"]
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row_widths = [3, 10, 10, 7, 7, 7, 7, 7, 7, 7, 7]
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if has_beam_widths:
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row_head.insert(1, "Beam W.")
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row_widths.insert(1, 7)
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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, noise_level=noise_level, gold_preproc=gold_preproc, max_length=0
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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(nlp_loaded, gold_preproc=gold_preproc)
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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, debug)
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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(nlp_loaded, gold_preproc=gold_preproc)
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)
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start_time = timer()
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scorer = nlp_loaded.evaluate(dev_docs)
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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_loc = output_path / ("model%d" % i) / "meta.json"
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srsly.write_json(meta_loc, meta)
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util.set_env_log(verbose)
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progress = _get_progress(
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i,
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losses,
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scorer.scores,
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beam_width=beam_width if has_beam_widths else None,
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cpu_wps=cpu_wps,
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gpu_wps=gpu_wps,
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)
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msg.row(progress, **row_settings)
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# Early stopping
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if n_early_stopping is not None:
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current_score = _score_for_model(meta)
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if current_score < best_score:
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iter_since_best += 1
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else:
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iter_since_best = 0
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best_score = current_score
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if iter_since_best >= n_early_stopping:
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msg.text(
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"Early stopping, best iteration "
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"is: {}".format(i - iter_since_best)
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)
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msg.text(
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"Best score = {}; Final iteration "
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"score = {}".format(best_score, current_score)
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)
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break
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finally:
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with nlp.use_params(optimizer.averages):
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final_model_path = output_path / "model-final"
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nlp.to_disk(final_model_path)
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msg.good("Saved model to output directory", final_model_path)
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with msg.loading("Creating best model..."):
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best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
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msg.good("Created best model", best_model_path)
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def _score_for_model(meta):
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""" Returns mean score between tasks in pipeline that can be used for early stopping. """
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mean_acc = list()
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pipes = meta["pipeline"]
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acc = meta["accuracy"]
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if "tagger" in pipes:
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mean_acc.append(acc["tags_acc"])
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if "parser" in pipes:
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mean_acc.append((acc["uas"] + acc["las"]) / 2)
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if "ner" in pipes:
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mean_acc.append((acc["ents_p"] + acc["ents_r"] + acc["ents_f"]) / 3)
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return sum(mean_acc) / len(mean_acc)
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@contextlib.contextmanager
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def _create_progress_bar(total):
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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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if int(os.environ.get("LOG_FRIENDLY", 0)):
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yield
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else:
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pbar = tqdm.tqdm(total=total, leave=False)
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yield pbar
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def _load_vectors(nlp, vectors):
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util.load_model(vectors, vocab=nlp.vocab)
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for lex in nlp.vocab:
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values = {}
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for attr, func in nlp.vocab.lex_attr_getters.items():
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# These attrs are expected to be set by data. Others should
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# be set by calling the language functions.
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if attr not in (CLUSTER, PROB, IS_OOV, LANG):
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values[lex.vocab.strings[attr]] = func(lex.orth_)
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lex.set_attrs(**values)
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lex.is_oov = False
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def _load_pretrained_tok2vec(nlp, loc):
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"""Load pre-trained weights for the 'token-to-vector' part of the component
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models, which is typically a CNN. See 'spacy pretrain'. Experimental.
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"""
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with loc.open("rb") as file_:
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weights_data = file_.read()
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loaded = []
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for name, component in nlp.pipeline:
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if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
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component.tok2vec.from_bytes(weights_data)
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loaded.append(name)
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return loaded
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def _collate_best_model(meta, output_path, components):
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bests = {}
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for component in components:
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bests[component] = _find_best(output_path, component)
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best_dest = output_path / "model-best"
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shutil.copytree(path2str(output_path / "model-final"), path2str(best_dest))
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for component, best_component_src in bests.items():
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shutil.rmtree(path2str(best_dest / component))
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shutil.copytree(
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path2str(best_component_src / component), path2str(best_dest / component)
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)
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accs = srsly.read_json(best_component_src / "accuracy.json")
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for metric in _get_metrics(component):
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meta["accuracy"][metric] = accs[metric]
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srsly.write_json(best_dest / "meta.json", meta)
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return best_dest
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def _find_best(experiment_dir, component):
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accuracies = []
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for epoch_model in experiment_dir.iterdir():
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if epoch_model.is_dir() and epoch_model.parts[-1] != "model-final":
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accs = srsly.read_json(epoch_model / "accuracy.json")
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scores = [accs.get(metric, 0.0) for metric in _get_metrics(component)]
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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 _get_progress(itn, losses, dev_scores, beam_width=None, cpu_wps=0.0, gpu_wps=0.0):
|
|
scores = {}
|
|
for col in [
|
|
"dep_loss",
|
|
"tag_loss",
|
|
"uas",
|
|
"tags_acc",
|
|
"token_acc",
|
|
"ents_p",
|
|
"ents_r",
|
|
"ents_f",
|
|
"cpu_wps",
|
|
"gpu_wps",
|
|
]:
|
|
scores[col] = 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.update(dev_scores)
|
|
scores["cpu_wps"] = cpu_wps
|
|
scores["gpu_wps"] = gpu_wps or 0.0
|
|
result = [
|
|
itn,
|
|
"{:.3f}".format(scores["dep_loss"]),
|
|
"{:.3f}".format(scores["ner_loss"]),
|
|
"{:.3f}".format(scores["uas"]),
|
|
"{:.3f}".format(scores["ents_p"]),
|
|
"{:.3f}".format(scores["ents_r"]),
|
|
"{:.3f}".format(scores["ents_f"]),
|
|
"{:.3f}".format(scores["tags_acc"]),
|
|
"{:.3f}".format(scores["token_acc"]),
|
|
"{:.0f}".format(scores["cpu_wps"]),
|
|
"{:.0f}".format(scores["gpu_wps"]),
|
|
]
|
|
if beam_width is not None:
|
|
result.insert(1, beam_width)
|
|
return result
|