import os import tqdm from pathlib import Path from thinc.neural._classes.model import Model from timeit import default_timer as timer import shutil import srsly from wasabi import msg import contextlib import random from .._ml import create_default_optimizer from ..attrs import PROB, IS_OOV, CLUSTER, LANG from ..gold import GoldCorpus from .. import util from .. import about def train( # fmt: off lang: ("Model language", "positional", None, str), output_path: ("Output directory to store model in", "positional", None, Path), train_path: ("Location of JSON-formatted training data", "positional", None, Path), dev_path: ("Location of JSON-formatted development data", "positional", None, Path), raw_text: ("Path to jsonl file with unlabelled text documents.", "option", "rt", Path) = None, base_model: ("Name of model to update (optional)", "option", "b", str) = None, pipeline: ("Comma-separated names of pipeline components", "option", "p", str) = "tagger,parser,ner", vectors: ("Model to load vectors from", "option", "v", str) = None, n_iter: ("Number of iterations", "option", "n", int) = 30, n_early_stopping: ("Maximum number of training epochs without dev accuracy improvement", "option", "ne", int) = None, n_examples: ("Number of examples", "option", "ns", int) = 0, use_gpu: ("Use GPU", "option", "g", int) = -1, version: ("Model version", "option", "V", str) = "0.0.0", meta_path: ("Optional path to meta.json to use as base.", "option", "m", Path) = None, init_tok2vec: ("Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.", "option", "t2v", Path) = None, parser_multitasks: ("Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'", "option", "pt", str) = "", entity_multitasks: ("Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'", "option", "et", str) = "", noise_level: ("Amount of corruption for data augmentation", "option", "nl", float) = 0.0, orth_variant_level: ("Amount of orthography variation for data augmentation", "option", "ovl", float) = 0.0, eval_beam_widths: ("Beam widths to evaluate, e.g. 4,8", "option", "bw", str) = "", gold_preproc: ("Use gold preprocessing", "flag", "G", bool) = False, learn_tokens: ("Make parser learn gold-standard tokenization", "flag", "T", bool) = False, textcat_multilabel: ("Textcat classes aren't mutually exclusive (multilabel)", "flag", "TML", bool) = False, textcat_arch: ("Textcat model architecture", "option", "ta", str) = "bow", textcat_positive_label: ("Textcat positive label for binary classes with two labels", "option", "tpl", str) = None, tag_map_path: ("Location of JSON-formatted tag map", "option", "tm", Path) = None, verbose: ("Display more information for debug", "flag", "VV", bool) = False, debug: ("Run data diagnostics before training", "flag", "D", bool) = False, # fmt: on ): """ Train or update a spaCy model. Requires data to be formatted in spaCy's JSON format. To convert data from other formats, use the `spacy convert` command. """ util.fix_random_seed() util.set_env_log(verbose) # Make sure all files and paths exists if they are needed train_path = util.ensure_path(train_path) dev_path = util.ensure_path(dev_path) meta_path = util.ensure_path(meta_path) output_path = util.ensure_path(output_path) if raw_text is not None: raw_text = list(srsly.read_jsonl(raw_text)) if not train_path or not train_path.exists(): msg.fail("Training data not found", train_path, exits=1) if not dev_path or not dev_path.exists(): msg.fail("Development data not found", dev_path, exits=1) if meta_path is not None and not meta_path.exists(): msg.fail("Can't find model meta.json", meta_path, exits=1) meta = srsly.read_json(meta_path) if meta_path else {} if output_path.exists() and [p for p in output_path.iterdir() if p.is_dir()]: msg.warn( "Output directory is not empty", "This can lead to unintended side effects when saving the model. " "Please use an empty directory or a different path instead. If " "the specified output path doesn't exist, the directory will be " "created for you.", ) if not output_path.exists(): output_path.mkdir() tag_map = {} if tag_map_path is not None: tag_map = srsly.read_json(tag_map_path) # Take dropout and batch size as generators of values -- dropout # starts high and decays sharply, to force the optimizer to explore. # Batch size starts at 1 and grows, so that we make updates quickly # at the beginning of training. dropout_rates = util.decaying( util.env_opt("dropout_from", 0.2), util.env_opt("dropout_to", 0.2), util.env_opt("dropout_decay", 0.0), ) batch_sizes = util.compounding( util.env_opt("batch_from", 100.0), util.env_opt("batch_to", 1000.0), util.env_opt("batch_compound", 1.001), ) if not eval_beam_widths: eval_beam_widths = [1] else: eval_beam_widths = [int(bw) for bw in eval_beam_widths.split(",")] if 1 not in eval_beam_widths: eval_beam_widths.append(1) eval_beam_widths.sort() has_beam_widths = eval_beam_widths != [1] # Set up the base model and pipeline. If a base model is specified, load # the model and make sure the pipeline matches the pipeline setting. If # training starts from a blank model, intitalize the language class. pipeline = [p.strip() for p in pipeline.split(",")] msg.text(f"Training pipeline: {pipeline}") if base_model: msg.text(f"Starting with base model '{base_model}'") nlp = util.load_model(base_model) if nlp.lang != lang: msg.fail( f"Model language ('{nlp.lang}') doesn't match language " f"specified as `lang` argument ('{lang}') ", exits=1, ) nlp.disable_pipes([p for p in nlp.pipe_names if p not in pipeline]) for pipe in pipeline: if pipe not in nlp.pipe_names: if pipe == "parser": pipe_cfg = {"learn_tokens": learn_tokens} elif pipe == "textcat": pipe_cfg = { "exclusive_classes": not textcat_multilabel, "architecture": textcat_arch, "positive_label": textcat_positive_label, } else: pipe_cfg = {} nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg)) else: if pipe == "textcat": textcat_cfg = nlp.get_pipe("textcat").cfg base_cfg = { "exclusive_classes": textcat_cfg["exclusive_classes"], "architecture": textcat_cfg["architecture"], "positive_label": textcat_cfg["positive_label"], } pipe_cfg = { "exclusive_classes": not textcat_multilabel, "architecture": textcat_arch, "positive_label": textcat_positive_label, } if base_cfg != pipe_cfg: msg.fail( f"The base textcat model configuration does" f"not match the provided training options. " f"Existing cfg: {base_cfg}, provided cfg: {pipe_cfg}", exits=1, ) else: msg.text(f"Starting with blank model '{lang}'") lang_cls = util.get_lang_class(lang) nlp = lang_cls() for pipe in pipeline: if pipe == "parser": pipe_cfg = {"learn_tokens": learn_tokens} elif pipe == "textcat": pipe_cfg = { "exclusive_classes": not textcat_multilabel, "architecture": textcat_arch, "positive_label": textcat_positive_label, } else: pipe_cfg = {} nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg)) # Update tag map with provided mapping nlp.vocab.morphology.tag_map.update(tag_map) if vectors: msg.text(f"Loading vector from model '{vectors}'") _load_vectors(nlp, vectors) # Multitask objectives multitask_options = [("parser", parser_multitasks), ("ner", entity_multitasks)] for pipe_name, multitasks in multitask_options: if multitasks: if pipe_name not in pipeline: msg.fail( f"Can't use multitask objective without '{pipe_name}' in " f"the pipeline" ) pipe = nlp.get_pipe(pipe_name) for objective in multitasks.split(","): pipe.add_multitask_objective(objective) # Prepare training corpus msg.text(f"Counting training words (limit={n_examples})") corpus = GoldCorpus(train_path, dev_path, limit=n_examples) n_train_words = corpus.count_train() if base_model: # Start with an existing model, use default optimizer optimizer = create_default_optimizer(Model.ops) else: # Start with a blank model, call begin_training optimizer = nlp.begin_training(lambda: corpus.train_examples, device=use_gpu) nlp._optimizer = None # Load in pretrained weights if init_tok2vec is not None: components = _load_pretrained_tok2vec(nlp, init_tok2vec) msg.text(f"Loaded pretrained tok2vec for: {components}") # Verify textcat config if "textcat" in pipeline: textcat_labels = nlp.get_pipe("textcat").cfg["labels"] if textcat_positive_label and textcat_positive_label not in textcat_labels: msg.fail( f"The textcat_positive_label (tpl) '{textcat_positive_label}' " f"does not match any label in the training data.", exits=1, ) if textcat_positive_label and len(textcat_labels) != 2: msg.fail( "A textcat_positive_label (tpl) '{textcat_positive_label}' was " "provided for training data that does not appear to be a " "binary classification problem with two labels.", exits=1, ) train_data = corpus.train_data( nlp, noise_level=noise_level, gold_preproc=gold_preproc, max_length=0, ignore_misaligned=True, ) train_labels = set() if textcat_multilabel: multilabel_found = False for ex in train_data: train_labels.update(ex.gold.cats.keys()) if list(ex.gold.cats.values()).count(1.0) != 1: multilabel_found = True if not multilabel_found and not base_model: msg.warn( "The textcat training instances look like they have " "mutually-exclusive classes. Remove the flag " "'--textcat-multilabel' to train a classifier with " "mutually-exclusive classes." ) if not textcat_multilabel: for ex in train_data: train_labels.update(ex.gold.cats.keys()) if list(ex.gold.cats.values()).count(1.0) != 1 and not base_model: msg.warn( "Some textcat training instances do not have exactly " "one positive label. Modifying training options to " "include the flag '--textcat-multilabel' for classes " "that are not mutually exclusive." ) nlp.get_pipe("textcat").cfg["exclusive_classes"] = False textcat_multilabel = True break if base_model and set(textcat_labels) != train_labels: msg.fail( f"Cannot extend textcat model using data with different " f"labels. Base model labels: {textcat_labels}, training data " f"labels: {list(train_labels)}", exits=1, ) if textcat_multilabel: msg.text( f"Textcat evaluation score: ROC AUC score macro-averaged across " f"the labels '{', '.join(textcat_labels)}'" ) elif textcat_positive_label and len(textcat_labels) == 2: msg.text( f"Textcat evaluation score: F1-score for the " f"label '{textcat_positive_label}'" ) elif len(textcat_labels) > 1: if len(textcat_labels) == 2: msg.warn( "If the textcat component is a binary classifier with " "exclusive classes, provide '--textcat_positive_label' for " "an evaluation on the positive class." ) msg.text( f"Textcat evaluation score: F1-score macro-averaged across " f"the labels '{', '.join(textcat_labels)}'" ) else: msg.fail( "Unsupported textcat configuration. Use `spacy debug-data` " "for more information." ) # fmt: off row_head, output_stats = _configure_training_output(pipeline, use_gpu, has_beam_widths) row_widths = [len(w) for w in row_head] row_settings = {"widths": row_widths, "aligns": tuple(["r" for i in row_head]), "spacing": 2} # fmt: on print("") msg.row(row_head, **row_settings) msg.row(["-" * width for width in row_settings["widths"]], **row_settings) try: iter_since_best = 0 best_score = 0.0 for i in range(n_iter): train_data = corpus.train_dataset( nlp, noise_level=noise_level, orth_variant_level=orth_variant_level, gold_preproc=gold_preproc, max_length=0, ignore_misaligned=True, ) if raw_text: random.shuffle(raw_text) raw_batches = util.minibatch( (nlp.make_doc(rt["text"]) for rt in raw_text), size=8 ) words_seen = 0 with tqdm.tqdm(total=n_train_words, leave=False) as pbar: losses = {} for batch in util.minibatch_by_words(train_data, size=batch_sizes): if not batch: continue nlp.update( batch, sgd=optimizer, drop=next(dropout_rates), losses=losses, ) if raw_text: # If raw text is available, perform 'rehearsal' updates, # which use unlabelled data to reduce overfitting. raw_batch = list(next(raw_batches)) nlp.rehearse(raw_batch, sgd=optimizer, losses=losses) docs = [ex.doc for ex in batch] if not int(os.environ.get("LOG_FRIENDLY", 0)): pbar.update(sum(len(doc) for doc in docs)) words_seen += sum(len(doc) for doc in docs) with nlp.use_params(optimizer.averages): util.set_env_log(False) epoch_model_path = output_path / f"model{i}" nlp.to_disk(epoch_model_path) nlp_loaded = util.load_model_from_path(epoch_model_path) for beam_width in eval_beam_widths: for name, component in nlp_loaded.pipeline: if hasattr(component, "cfg"): component.cfg["beam_width"] = beam_width dev_dataset = list( corpus.dev_dataset( nlp_loaded, gold_preproc=gold_preproc, ignore_misaligned=True, ) ) nwords = sum(len(ex.doc) for ex in dev_dataset) start_time = timer() scorer = nlp_loaded.evaluate(dev_dataset, verbose=verbose) end_time = timer() if use_gpu < 0: gpu_wps = None cpu_wps = nwords / (end_time - start_time) else: gpu_wps = nwords / (end_time - start_time) with Model.use_device("cpu"): nlp_loaded = util.load_model_from_path(epoch_model_path) for name, component in nlp_loaded.pipeline: if hasattr(component, "cfg"): component.cfg["beam_width"] = beam_width dev_dataset = list( corpus.dev_dataset( nlp_loaded, gold_preproc=gold_preproc, ignore_misaligned=True, ) ) start_time = timer() scorer = nlp_loaded.evaluate(dev_dataset, verbose=verbose) end_time = timer() cpu_wps = nwords / (end_time - start_time) acc_loc = output_path / f"model{i}" / "accuracy.json" srsly.write_json(acc_loc, scorer.scores) # Update model meta.json meta["lang"] = nlp.lang meta["pipeline"] = nlp.pipe_names meta["spacy_version"] = f">={about.__version__}" if beam_width == 1: meta["speed"] = { "nwords": nwords, "cpu": cpu_wps, "gpu": gpu_wps, } meta["accuracy"] = scorer.scores else: meta.setdefault("beam_accuracy", {}) meta.setdefault("beam_speed", {}) meta["beam_accuracy"][beam_width] = scorer.scores meta["beam_speed"][beam_width] = { "nwords": nwords, "cpu": cpu_wps, "gpu": gpu_wps, } meta["vectors"] = { "width": nlp.vocab.vectors_length, "vectors": len(nlp.vocab.vectors), "keys": nlp.vocab.vectors.n_keys, "name": nlp.vocab.vectors.name, } meta.setdefault("name", f"model{i}") meta.setdefault("version", version) meta["labels"] = nlp.meta["labels"] meta_loc = output_path / f"model{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( f"Textcat ROC AUC score is undefined due to " f"only one value in label '{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( f"Early stopping, best iteration is: {i - iter_since_best}" ) msg.text( f"Best score = {best_score}; Final iteration 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): 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(str(output_path / "model-final"), str(best_dest)) for component, best_component_src in bests.items(): shutil.rmtree(str(best_dest / component)) shutil.copytree( str(best_component_src / component), str(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", "sent_f") elif component == "tagger": return ("tags_acc",) elif component == "ner": return ("ents_f", "ents_p", "ents_r") elif component == "sentrec": return ("sent_p", "sent_r", "sent_f",) 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 ", "Sent P", "Sent R", "Sent F"]) output_stats.extend(["dep_loss", "uas", "las", "sent_p", "sent_r", "sent_f"]) 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"]) elif pipe == "sentrec": row_head.extend(["Sentrec Loss", "Sent P", "Sent R", "Sent F"]) output_stats.extend(["sentrec_loss", "sent_p", "sent_r", "sent_f"]) 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.") # remove duplicates row_head_dict = {k: 1 for k in row_head} output_stats_dict = {k: 1 for k in output_stats} return row_head_dict.keys(), output_stats_dict.keys() 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["sentrec_loss"] = losses.get("sentrec", 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