from typing import List, Sequence, Dict, Any, Tuple, Optional from pathlib import Path from collections import Counter import sys import srsly from wasabi import Printer, MESSAGES, msg import typer from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides from ._util import import_code, debug_cli from ..training import Example from ..training.initialize import get_sourced_components from ..schemas import ConfigSchemaTraining from ..pipeline._parser_internals import nonproj from ..language import Language from ..util import registry, resolve_dot_names from .. import util # Minimum number of expected occurrences of NER label in data to train new label NEW_LABEL_THRESHOLD = 50 # Minimum number of expected occurrences of dependency labels DEP_LABEL_THRESHOLD = 20 # Minimum number of expected examples to train a new pipeline BLANK_MODEL_MIN_THRESHOLD = 100 BLANK_MODEL_THRESHOLD = 2000 @debug_cli.command( "data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True} ) @app.command( "debug-data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}, hidden=True, # hide this from main CLI help but still allow it to work with warning ) def debug_data_cli( # fmt: off ctx: typer.Context, # This is only used to read additional arguments config_path: Path = Arg(..., help="Path to config file", exists=True, allow_dash=True), code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"), ignore_warnings: bool = Opt(False, "--ignore-warnings", "-IW", help="Ignore warnings, only show stats and errors"), verbose: bool = Opt(False, "--verbose", "-V", help="Print additional information and explanations"), no_format: bool = Opt(False, "--no-format", "-NF", help="Don't pretty-print the results"), # fmt: on ): """ Analyze, debug and validate your training and development data. Outputs useful stats, and can help you find problems like invalid entity annotations, cyclic dependencies, low data labels and more. DOCS: https://nightly.spacy.io/api/cli#debug-data """ if ctx.command.name == "debug-data": msg.warn( "The debug-data command is now available via the 'debug data' " "subcommand (without the hyphen). You can run python -m spacy debug " "--help for an overview of the other available debugging commands." ) overrides = parse_config_overrides(ctx.args) import_code(code_path) debug_data( config_path, config_overrides=overrides, ignore_warnings=ignore_warnings, verbose=verbose, no_format=no_format, silent=False, ) def debug_data( config_path: Path, *, config_overrides: Dict[str, Any] = {}, ignore_warnings: bool = False, verbose: bool = False, no_format: bool = True, silent: bool = True, ): msg = Printer( no_print=silent, pretty=not no_format, ignore_warnings=ignore_warnings ) # Make sure all files and paths exists if they are needed with show_validation_error(config_path): cfg = util.load_config(config_path, overrides=config_overrides) nlp = util.load_model_from_config(cfg) config = nlp.config.interpolate() T = registry.resolve(config["training"], schema=ConfigSchemaTraining) # Use original config here, not resolved version sourced_components = get_sourced_components(cfg) frozen_components = T["frozen_components"] resume_components = [p for p in sourced_components if p not in frozen_components] pipeline = nlp.pipe_names factory_names = [nlp.get_pipe_meta(pipe).factory for pipe in nlp.pipe_names] msg.divider("Data file validation") # Create the gold corpus to be able to better analyze data dot_names = [T["train_corpus"], T["dev_corpus"]] train_corpus, dev_corpus = resolve_dot_names(config, dot_names) train_dataset = list(train_corpus(nlp)) dev_dataset = list(dev_corpus(nlp)) msg.good("Corpus is loadable") nlp.initialize(lambda: train_dataset) msg.good("Pipeline can be initialized with data") # Create all gold data here to avoid iterating over the train_dataset constantly gold_train_data = _compile_gold(train_dataset, factory_names, nlp, make_proj=True) gold_train_unpreprocessed_data = _compile_gold( train_dataset, factory_names, nlp, make_proj=False ) gold_dev_data = _compile_gold(dev_dataset, factory_names, nlp, make_proj=True) train_texts = gold_train_data["texts"] dev_texts = gold_dev_data["texts"] frozen_components = T["frozen_components"] msg.divider("Training stats") msg.text(f"Language: {nlp.lang}") msg.text(f"Training pipeline: {', '.join(pipeline)}") if resume_components: msg.text(f"Components from other pipelines: {', '.join(resume_components)}") if frozen_components: msg.text(f"Frozen components: {', '.join(frozen_components)}") msg.text(f"{len(train_dataset)} training docs") msg.text(f"{len(dev_dataset)} evaluation docs") if not len(gold_dev_data): msg.fail("No evaluation docs") overlap = len(train_texts.intersection(dev_texts)) if overlap: msg.warn(f"{overlap} training examples also in evaluation data") else: msg.good("No overlap between training and evaluation data") # TODO: make this feedback more fine-grained and report on updated # components vs. blank components if not resume_components and len(train_dataset) < BLANK_MODEL_THRESHOLD: text = f"Low number of examples to train a new pipeline ({len(train_dataset)})" if len(train_dataset) < BLANK_MODEL_MIN_THRESHOLD: msg.fail(text) else: msg.warn(text) msg.text( f"It's recommended to use at least {BLANK_MODEL_THRESHOLD} examples " f"(minimum {BLANK_MODEL_MIN_THRESHOLD})", show=verbose, ) msg.divider("Vocab & Vectors") n_words = gold_train_data["n_words"] msg.info( f"{n_words} total word(s) in the data ({len(gold_train_data['words'])} unique)" ) if gold_train_data["n_misaligned_words"] > 0: n_misaligned = gold_train_data["n_misaligned_words"] msg.warn(f"{n_misaligned} misaligned tokens in the training data") if gold_dev_data["n_misaligned_words"] > 0: n_misaligned = gold_dev_data["n_misaligned_words"] msg.warn(f"{n_misaligned} misaligned tokens in the dev data") most_common_words = gold_train_data["words"].most_common(10) msg.text( f"10 most common words: {_format_labels(most_common_words, counts=True)}", show=verbose, ) if len(nlp.vocab.vectors): msg.info( f"{len(nlp.vocab.vectors)} vectors ({nlp.vocab.vectors.n_keys} " f"unique keys, {nlp.vocab.vectors_length} dimensions)" ) n_missing_vectors = sum(gold_train_data["words_missing_vectors"].values()) msg.warn( "{} words in training data without vectors ({:0.2f}%)".format( n_missing_vectors, n_missing_vectors / gold_train_data["n_words"] ), ) msg.text( "10 most common words without vectors: {}".format( _format_labels( gold_train_data["words_missing_vectors"].most_common(10), counts=True, ) ), show=verbose, ) else: msg.info("No word vectors present in the package") if "ner" in factory_names: # Get all unique NER labels present in the data labels = set( label for label in gold_train_data["ner"] if label not in ("O", "-", None) ) label_counts = gold_train_data["ner"] model_labels = _get_labels_from_model(nlp, "ner") new_labels = [l for l in labels if l not in model_labels] existing_labels = [l for l in labels if l in model_labels] has_low_data_warning = False has_no_neg_warning = False has_ws_ents_error = False has_punct_ents_warning = False msg.divider("Named Entity Recognition") msg.info( f"{len(new_labels)} new label(s), {len(existing_labels)} existing label(s)" ) missing_values = label_counts["-"] msg.text(f"{missing_values} missing value(s) (tokens with '-' label)") for label in new_labels: if len(label) == 0: msg.fail("Empty label found in new labels") if new_labels: labels_with_counts = [ (label, count) for label, count in label_counts.most_common() if label != "-" ] labels_with_counts = _format_labels(labels_with_counts, counts=True) msg.text(f"New: {labels_with_counts}", show=verbose) if existing_labels: msg.text(f"Existing: {_format_labels(existing_labels)}", show=verbose) if gold_train_data["ws_ents"]: msg.fail(f"{gold_train_data['ws_ents']} invalid whitespace entity spans") has_ws_ents_error = True if gold_train_data["punct_ents"]: msg.warn(f"{gold_train_data['punct_ents']} entity span(s) with punctuation") has_punct_ents_warning = True for label in new_labels: if label_counts[label] <= NEW_LABEL_THRESHOLD: msg.warn( f"Low number of examples for new label '{label}' ({label_counts[label]})" ) has_low_data_warning = True with msg.loading("Analyzing label distribution..."): neg_docs = _get_examples_without_label(train_dataset, label) if neg_docs == 0: msg.warn(f"No examples for texts WITHOUT new label '{label}'") has_no_neg_warning = True if not has_low_data_warning: msg.good("Good amount of examples for all labels") if not has_no_neg_warning: msg.good("Examples without occurrences available for all labels") if not has_ws_ents_error: msg.good("No entities consisting of or starting/ending with whitespace") if not has_punct_ents_warning: msg.good("No entities consisting of or starting/ending with punctuation") if has_low_data_warning: msg.text( f"To train a new entity type, your data should include at " f"least {NEW_LABEL_THRESHOLD} instances of the new label", show=verbose, ) if has_no_neg_warning: msg.text( "Training data should always include examples of entities " "in context, as well as examples without a given entity " "type.", show=verbose, ) if has_ws_ents_error: msg.text( "As of spaCy v2.1.0, entity spans consisting of or starting/ending " "with whitespace characters are considered invalid." ) if has_punct_ents_warning: msg.text( "Entity spans consisting of or starting/ending " "with punctuation can not be trained with a noise level > 0." ) if "textcat" in factory_names: msg.divider("Text Classification") labels = [label for label in gold_train_data["cats"]] model_labels = _get_labels_from_model(nlp, "textcat") new_labels = [l for l in labels if l not in model_labels] existing_labels = [l for l in labels if l in model_labels] msg.info( f"Text Classification: {len(new_labels)} new label(s), " f"{len(existing_labels)} existing label(s)" ) if new_labels: labels_with_counts = _format_labels( gold_train_data["cats"].most_common(), counts=True ) msg.text(f"New: {labels_with_counts}", show=verbose) if existing_labels: msg.text(f"Existing: {_format_labels(existing_labels)}", show=verbose) if set(gold_train_data["cats"]) != set(gold_dev_data["cats"]): msg.fail( f"The train and dev labels are not the same. " f"Train labels: {_format_labels(gold_train_data['cats'])}. " f"Dev labels: {_format_labels(gold_dev_data['cats'])}." ) if gold_train_data["n_cats_multilabel"] > 0: msg.info( "The train data contains instances without " "mutually-exclusive classes. Use '--textcat-multilabel' " "when training." ) if gold_dev_data["n_cats_multilabel"] == 0: msg.warn( "Potential train/dev mismatch: the train data contains " "instances without mutually-exclusive classes while the " "dev data does not." ) else: msg.info( "The train data contains only instances with " "mutually-exclusive classes." ) if gold_dev_data["n_cats_multilabel"] > 0: msg.fail( "Train/dev mismatch: the dev data contains instances " "without mutually-exclusive classes while the train data " "contains only instances with mutually-exclusive classes." ) if "tagger" in factory_names: msg.divider("Part-of-speech Tagging") labels = [label for label in gold_train_data["tags"]] # TODO: does this need to be updated? msg.info(f"{len(labels)} label(s) in data") labels_with_counts = _format_labels( gold_train_data["tags"].most_common(), counts=True ) msg.text(labels_with_counts, show=verbose) if "parser" in factory_names: has_low_data_warning = False msg.divider("Dependency Parsing") # profile sentence length msg.info( f"Found {gold_train_data['n_sents']} sentence(s) with an average " f"length of {gold_train_data['n_words'] / gold_train_data['n_sents']:.1f} words." ) # check for documents with multiple sentences sents_per_doc = gold_train_data["n_sents"] / len(gold_train_data["texts"]) if sents_per_doc < 1.1: msg.warn( f"The training data contains {sents_per_doc:.2f} sentences per " f"document. When there are very few documents containing more " f"than one sentence, the parser will not learn how to segment " f"longer texts into sentences." ) # profile labels labels_train = [label for label in gold_train_data["deps"]] labels_train_unpreprocessed = [ label for label in gold_train_unpreprocessed_data["deps"] ] labels_dev = [label for label in gold_dev_data["deps"]] if gold_train_unpreprocessed_data["n_nonproj"] > 0: n_nonproj = gold_train_unpreprocessed_data["n_nonproj"] msg.info(f"Found {n_nonproj} nonprojective train sentence(s)") if gold_dev_data["n_nonproj"] > 0: n_nonproj = gold_dev_data["n_nonproj"] msg.info(f"Found {n_nonproj} nonprojective dev sentence(s)") msg.info(f"{len(labels_train_unpreprocessed)} label(s) in train data") msg.info(f"{len(labels_train)} label(s) in projectivized train data") labels_with_counts = _format_labels( gold_train_unpreprocessed_data["deps"].most_common(), counts=True ) msg.text(labels_with_counts, show=verbose) # rare labels in train for label in gold_train_unpreprocessed_data["deps"]: if gold_train_unpreprocessed_data["deps"][label] <= DEP_LABEL_THRESHOLD: msg.warn( f"Low number of examples for label '{label}' " f"({gold_train_unpreprocessed_data['deps'][label]})" ) has_low_data_warning = True # rare labels in projectivized train rare_projectivized_labels = [] for label in gold_train_data["deps"]: if gold_train_data["deps"][label] <= DEP_LABEL_THRESHOLD and "||" in label: rare_projectivized_labels.append( f"{label}: {gold_train_data['deps'][label]}" ) if len(rare_projectivized_labels) > 0: msg.warn( f"Low number of examples for {len(rare_projectivized_labels)} " "label(s) in the projectivized dependency trees used for " "training. You may want to projectivize labels such as punct " "before training in order to improve parser performance." ) msg.warn( f"Projectivized labels with low numbers of examples: ", ", ".join(rare_projectivized_labels), show=verbose, ) has_low_data_warning = True # labels only in train if set(labels_train) - set(labels_dev): msg.warn( "The following labels were found only in the train data:", ", ".join(set(labels_train) - set(labels_dev)), show=verbose, ) # labels only in dev if set(labels_dev) - set(labels_train): msg.warn( "The following labels were found only in the dev data:", ", ".join(set(labels_dev) - set(labels_train)), show=verbose, ) if has_low_data_warning: msg.text( f"To train a parser, your data should include at " f"least {DEP_LABEL_THRESHOLD} instances of each label.", show=verbose, ) # multiple root labels if len(gold_train_unpreprocessed_data["roots"]) > 1: msg.warn( f"Multiple root labels " f"({', '.join(gold_train_unpreprocessed_data['roots'])}) " f"found in training data. spaCy's parser uses a single root " f"label ROOT so this distinction will not be available." ) # these should not happen, but just in case if gold_train_data["n_nonproj"] > 0: msg.fail( f"Found {gold_train_data['n_nonproj']} nonprojective " f"projectivized train sentence(s)" ) if gold_train_data["n_cycles"] > 0: msg.fail( f"Found {gold_train_data['n_cycles']} projectivized train sentence(s) with cycles" ) msg.divider("Summary") good_counts = msg.counts[MESSAGES.GOOD] warn_counts = msg.counts[MESSAGES.WARN] fail_counts = msg.counts[MESSAGES.FAIL] if good_counts: msg.good(f"{good_counts} {'check' if good_counts == 1 else 'checks'} passed") if warn_counts: msg.warn(f"{warn_counts} {'warning' if warn_counts == 1 else 'warnings'}") if fail_counts: msg.fail(f"{fail_counts} {'error' if fail_counts == 1 else 'errors'}") sys.exit(1) def _load_file(file_path: Path, msg: Printer) -> None: file_name = file_path.parts[-1] if file_path.suffix == ".json": with msg.loading(f"Loading {file_name}..."): data = srsly.read_json(file_path) msg.good(f"Loaded {file_name}") return data elif file_path.suffix == ".jsonl": with msg.loading(f"Loading {file_name}..."): data = srsly.read_jsonl(file_path) msg.good(f"Loaded {file_name}") return data msg.fail( f"Can't load file extension {file_path.suffix}", "Expected .json or .jsonl", exits=1, ) def _compile_gold( examples: Sequence[Example], factory_names: List[str], nlp: Language, make_proj: bool, ) -> Dict[str, Any]: data = { "ner": Counter(), "cats": Counter(), "tags": Counter(), "deps": Counter(), "words": Counter(), "roots": Counter(), "ws_ents": 0, "punct_ents": 0, "n_words": 0, "n_misaligned_words": 0, "words_missing_vectors": Counter(), "n_sents": 0, "n_nonproj": 0, "n_cycles": 0, "n_cats_multilabel": 0, "texts": set(), } for eg in examples: gold = eg.reference doc = eg.predicted valid_words = [x.text for x in gold] data["words"].update(valid_words) data["n_words"] += len(valid_words) align = eg.alignment for token in doc: if token.orth_.isspace(): continue if align.x2y.lengths[token.i] != 1: data["n_misaligned_words"] += 1 data["texts"].add(doc.text) if len(nlp.vocab.vectors): for word in [t.text for t in doc]: if nlp.vocab.strings[word] not in nlp.vocab.vectors: data["words_missing_vectors"].update([word]) if "ner" in factory_names: for i, label in enumerate(eg.get_aligned_ner()): if label is None: continue if label.startswith(("B-", "U-", "L-")) and doc[i].is_space: # "Illegal" whitespace entity data["ws_ents"] += 1 if label.startswith(("B-", "U-", "L-")) and doc[i].text in [ ".", "'", "!", "?", ",", ]: # punctuation entity: could be replaced by whitespace when training with noise, # so add a warning to alert the user to this unexpected side effect. data["punct_ents"] += 1 if label.startswith(("B-", "U-")): combined_label = label.split("-")[1] data["ner"][combined_label] += 1 elif label == "-": data["ner"]["-"] += 1 if "textcat" in factory_names: data["cats"].update(gold.cats) if list(gold.cats.values()).count(1.0) != 1: data["n_cats_multilabel"] += 1 if "tagger" in factory_names: tags = eg.get_aligned("TAG", as_string=True) data["tags"].update([x for x in tags if x is not None]) if "parser" in factory_names: aligned_heads, aligned_deps = eg.get_aligned_parse(projectivize=make_proj) data["deps"].update([x for x in aligned_deps if x is not None]) for i, (dep, head) in enumerate(zip(aligned_deps, aligned_heads)): if head == i: data["roots"].update([dep]) data["n_sents"] += 1 if nonproj.is_nonproj_tree(aligned_heads): data["n_nonproj"] += 1 if nonproj.contains_cycle(aligned_heads): data["n_cycles"] += 1 return data def _format_labels(labels: List[Tuple[str, int]], counts: bool = False) -> str: if counts: return ", ".join([f"'{l}' ({c})" for l, c in labels]) return ", ".join([f"'{l}'" for l in labels]) def _get_examples_without_label(data: Sequence[Example], label: str) -> int: count = 0 for eg in data: labels = [ label.split("-")[1] for label in eg.get_aligned_ner() if label not in ("O", "-", None) ] if label not in labels: count += 1 return count def _get_labels_from_model(nlp: Language, pipe_name: str) -> Sequence[str]: if pipe_name not in nlp.pipe_names: return set() pipe = nlp.get_pipe(pipe_name) return pipe.labels