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https://github.com/explosion/spaCy.git
synced 2024-12-25 17:36:30 +03:00
WIP: Extending debug-data (#4114)
* Extending debug-data with dependency checks, etc. * Modify debug-data to load with GoldCorpus to iterate over .json/.jsonl files within directories * Add GoldCorpus iterator train_docs_without_preprocessing to load original train docs without shuffling and projectivizing * Report number of misaligned tokens * Add more dependency checks and messages * Update spacy/cli/debug_data.py Co-Authored-By: Ines Montani <ines@ines.io> * Fixed conflict * Move counts to _compile_gold() * Move all dependency nonproj/sent/head/cycle counting to _compile_gold() * Unclobber previous merges * Update variable names * Update more variable names, fix misspelling * Don't clobber loading error messages * Only warn about misaligned tokens if present
This commit is contained in:
adrianeboyd
Ines Montani
parent
eea7d4f4a8
commit
a58cb023d7
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@ -9,11 +9,14 @@ import srsly
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from wasabi import Printer, MESSAGES
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from ..gold import GoldCorpus, read_json_object
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from ..syntax import nonproj
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from ..util import load_model, get_lang_class
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# Minimum number of expected occurences of label in data to train new label
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# Minimum number of expected occurrences of NER label in data to train new label
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NEW_LABEL_THRESHOLD = 50
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# Minimum number of expected occurrences of dependency labels
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DEP_LABEL_THRESHOLD = 20
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# Minimum number of expected examples to train a blank model
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BLANK_MODEL_MIN_THRESHOLD = 100
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BLANK_MODEL_THRESHOLD = 2000
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@ -68,12 +71,10 @@ def debug_data(
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nlp = lang_cls()
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msg.divider("Data format validation")
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# Load the data in one – might take a while but okay in this case
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train_data = _load_file(train_path, msg)
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dev_data = _load_file(dev_path, msg)
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# Validate data format using the JSON schema
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# TODO: update once the new format is ready
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# TODO: move validation to GoldCorpus in order to be able to load from dir
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train_data_errors = [] # TODO: validate_json
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dev_data_errors = [] # TODO: validate_json
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if not train_data_errors:
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@ -88,18 +89,34 @@ def debug_data(
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sys.exit(1)
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# Create the gold corpus to be able to better analyze data
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with msg.loading("Analyzing corpus..."):
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train_data = read_json_object(train_data)
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dev_data = read_json_object(dev_data)
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corpus = GoldCorpus(train_data, dev_data)
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loading_train_error_message = ""
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loading_dev_error_message = ""
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with msg.loading("Loading corpus..."):
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corpus = GoldCorpus(train_path, dev_path)
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try:
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train_docs = list(corpus.train_docs(nlp))
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train_docs_unpreprocessed = list(corpus.train_docs_without_preprocessing(nlp))
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except ValueError as e:
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loading_train_error_message = "Training data cannot be loaded: {}".format(str(e))
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try:
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dev_docs = list(corpus.dev_docs(nlp))
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except ValueError as e:
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loading_dev_error_message = "Development data cannot be loaded: {}".format(str(e))
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if loading_train_error_message or loading_dev_error_message:
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if loading_train_error_message:
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msg.fail(loading_train_error_message)
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if loading_dev_error_message:
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msg.fail(loading_dev_error_message)
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sys.exit(1)
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msg.good("Corpus is loadable")
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# Create all gold data here to avoid iterating over the train_docs constantly
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gold_data = _compile_gold(train_docs, pipeline)
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train_texts = gold_data["texts"]
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dev_texts = set([doc.text for doc, gold in dev_docs])
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gold_train_data = _compile_gold(train_docs, pipeline)
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gold_train_unpreprocessed_data = _compile_gold(train_docs_unpreprocessed, pipeline)
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gold_dev_data = _compile_gold(dev_docs, pipeline)
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train_texts = gold_train_data["texts"]
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dev_texts = gold_dev_data["texts"]
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msg.divider("Training stats")
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msg.text("Training pipeline: {}".format(", ".join(pipeline)))
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@ -133,13 +150,21 @@ def debug_data(
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)
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msg.divider("Vocab & Vectors")
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n_words = gold_data["n_words"]
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n_words = gold_train_data["n_words"]
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msg.info(
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"{} total {} in the data ({} unique)".format(
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n_words, "word" if n_words == 1 else "words", len(gold_data["words"])
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n_words, "word" if n_words == 1 else "words", len(gold_train_data["words"])
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)
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)
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most_common_words = gold_data["words"].most_common(10)
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if gold_train_data["n_misaligned_words"] > 0:
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msg.warn(
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"{} misaligned tokens in the training data".format(gold_train_data["n_misaligned_words"])
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)
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if gold_dev_data["n_misaligned_words"] > 0:
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msg.warn(
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"{} misaligned tokens in the dev data".format(gold_dev_data["n_misaligned_words"])
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)
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most_common_words = gold_train_data["words"].most_common(10)
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msg.text(
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"10 most common words: {}".format(
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_format_labels(most_common_words, counts=True)
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@ -159,8 +184,8 @@ def debug_data(
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if "ner" in pipeline:
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# Get all unique NER labels present in the data
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labels = set(label for label in gold_data["ner"] if label not in ("O", "-"))
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label_counts = gold_data["ner"]
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labels = set(label for label in gold_train_data["ner"] if label not in ("O", "-"))
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label_counts = gold_train_data["ner"]
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model_labels = _get_labels_from_model(nlp, "ner")
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new_labels = [l for l in labels if l not in model_labels]
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existing_labels = [l for l in labels if l in model_labels]
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@ -196,8 +221,8 @@ def debug_data(
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"Existing: {}".format(_format_labels(existing_labels)), show=verbose
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)
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if gold_data["ws_ents"]:
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msg.fail("{} invalid whitespace entity spans".format(gold_data["ws_ents"]))
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if gold_train_data["ws_ents"]:
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msg.fail("{} invalid whitespace entity spans".format(gold_train_data["ws_ents"]))
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has_ws_ents_error = True
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for label in new_labels:
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@ -227,7 +252,7 @@ def debug_data(
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if has_low_data_warning:
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msg.text(
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"To train a new entity type, your data should include at "
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"least {} insteances of the new label".format(NEW_LABEL_THRESHOLD),
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"least {} instances of the new label".format(NEW_LABEL_THRESHOLD),
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show=verbose,
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)
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if has_no_neg_warning:
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@ -245,7 +270,7 @@ def debug_data(
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if "textcat" in pipeline:
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msg.divider("Text Classification")
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labels = [label for label in gold_data["textcat"]]
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labels = [label for label in gold_train_data["textcat"]]
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model_labels = _get_labels_from_model(nlp, "textcat")
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new_labels = [l for l in labels if l not in model_labels]
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existing_labels = [l for l in labels if l in model_labels]
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@ -256,7 +281,7 @@ def debug_data(
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)
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if new_labels:
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labels_with_counts = _format_labels(
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gold_data["textcat"].most_common(), counts=True
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gold_train_data["textcat"].most_common(), counts=True
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)
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msg.text("New: {}".format(labels_with_counts), show=verbose)
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if existing_labels:
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@ -266,7 +291,7 @@ def debug_data(
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if "tagger" in pipeline:
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msg.divider("Part-of-speech Tagging")
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labels = [label for label in gold_data["tags"]]
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labels = [label for label in gold_train_data["tags"]]
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tag_map = nlp.Defaults.tag_map
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msg.info(
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"{} {} in data ({} {} in tag map)".format(
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@ -277,7 +302,7 @@ def debug_data(
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)
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)
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labels_with_counts = _format_labels(
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gold_data["tags"].most_common(), counts=True
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gold_train_data["tags"].most_common(), counts=True
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)
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msg.text(labels_with_counts, show=verbose)
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non_tagmap = [l for l in labels if l not in tag_map]
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@ -292,17 +317,132 @@ def debug_data(
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if "parser" in pipeline:
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msg.divider("Dependency Parsing")
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labels = [label for label in gold_data["deps"]]
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# profile sentence length
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msg.info(
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"{} {} in data".format(
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len(labels), "label" if len(labels) == 1 else "labels"
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"Found {} sentence{} with an average length of {:.1f} words.".format(
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gold_train_data["n_sents"],
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"s" if len(train_docs) > 1 else "",
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gold_train_data["n_words"] / gold_train_data["n_sents"]
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)
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)
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# profile labels
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labels_train = [label for label in gold_train_data["deps"]]
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labels_train_unpreprocessed = [label for label in gold_train_unpreprocessed_data["deps"]]
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labels_dev = [label for label in gold_dev_data["deps"]]
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if gold_train_unpreprocessed_data["n_nonproj"] > 0:
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msg.info(
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"Found {} nonprojective train sentence{}".format(
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gold_train_unpreprocessed_data["n_nonproj"],
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"s" if gold_train_unpreprocessed_data["n_nonproj"] > 1 else ""
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)
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)
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if gold_dev_data["n_nonproj"] > 0:
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msg.info(
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"Found {} nonprojective dev sentence{}".format(
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gold_dev_data["n_nonproj"],
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"s" if gold_dev_data["n_nonproj"] > 1 else ""
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)
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)
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msg.info(
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"{} {} in train data".format(
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len(labels_train_unpreprocessed), "label" if len(labels_train) == 1 else "labels"
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)
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)
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msg.info(
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"{} {} in projectivized train data".format(
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len(labels_train), "label" if len(labels_train) == 1 else "labels"
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)
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)
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labels_with_counts = _format_labels(
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gold_data["deps"].most_common(), counts=True
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gold_train_unpreprocessed_data["deps"].most_common(), counts=True
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)
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msg.text(labels_with_counts, show=verbose)
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# rare labels in train
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for label in gold_train_unpreprocessed_data["deps"]:
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if gold_train_unpreprocessed_data["deps"][label] <= DEP_LABEL_THRESHOLD:
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msg.warn(
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"Low number of examples for label '{}' ({})".format(
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label, gold_train_unpreprocessed_data["deps"][label]
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)
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)
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has_low_data_warning = True
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# rare labels in projectivized train
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rare_projectivized_labels = []
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for label in gold_train_data["deps"]:
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if gold_train_data["deps"][label] <= DEP_LABEL_THRESHOLD and "||" in label:
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rare_projectivized_labels.append("{}: {}".format(label, str(gold_train_data["deps"][label])))
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if len(rare_projectivized_labels) > 0:
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msg.warn(
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"Low number of examples for {} label{} in the "
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"projectivized dependency trees used for training. You may "
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"want to projectivize labels such as punct before "
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"training in order to improve parser performance.".format(
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len(rare_projectivized_labels),
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"s" if len(rare_projectivized_labels) > 1 else "")
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)
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msg.warn(
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"Projectivized labels with low numbers of examples: "
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"{}".format("\n".join(rare_projectivized_labels)),
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show=verbose
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)
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has_low_data_warning = True
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# labels only in train
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if set(labels_train) - set(labels_dev):
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msg.warn(
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"The following labels were found only in the train data: "
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"{}".format(", ".join(set(labels_train) - set(labels_dev))),
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show=verbose
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)
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# labels only in dev
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if set(labels_dev) - set(labels_train):
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msg.warn(
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"The following labels were found only in the dev data: " +
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", ".join(set(labels_dev) - set(labels_train)),
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show=verbose
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)
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if has_low_data_warning:
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msg.text(
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"To train a parser, your data should include at "
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"least {} instances of each label.".format(DEP_LABEL_THRESHOLD),
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show=verbose,
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)
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# multiple root labels
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if len(gold_train_unpreprocessed_data["roots"]) > 1:
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msg.warn(
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"Multiple root labels ({}) ".format(", ".join(gold_train_unpreprocessed_data["roots"])) +
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"found in training data. spaCy's parser uses a single root "
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"label ROOT so this distinction will not be available."
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)
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# these should not happen, but just in case
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if gold_train_data["n_nonproj"] > 0:
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msg.fail(
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"Found {} nonprojective projectivized train sentence{}".format(
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gold_train_data["n_nonproj"],
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"s" if gold_train_data["n_nonproj"] > 1 else ""
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)
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)
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if gold_train_data["n_cycles"] > 0:
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msg.fail(
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"Found {} projectivized train sentence{} with cycles".format(
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gold_train_data["n_cycles"],
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"s" if gold_train_data["n_cycles"] > 1 else ""
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)
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)
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msg.divider("Summary")
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good_counts = msg.counts[MESSAGES.GOOD]
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warn_counts = msg.counts[MESSAGES.WARN]
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@ -350,16 +490,25 @@ def _compile_gold(train_docs, pipeline):
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"tags": Counter(),
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"deps": Counter(),
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"words": Counter(),
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"roots": Counter(),
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"ws_ents": 0,
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"n_words": 0,
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"n_misaligned_words": 0,
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"n_sents": 0,
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"n_nonproj": 0,
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"n_cycles": 0,
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"texts": set(),
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}
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for doc, gold in train_docs:
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data["words"].update(gold.words)
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data["n_words"] += len(gold.words)
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valid_words = [x for x in gold.words if x is not None]
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data["words"].update(valid_words)
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data["n_words"] += len(valid_words)
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data["n_misaligned_words"] += len(gold.words) - len(valid_words)
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data["texts"].add(doc.text)
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if "ner" in pipeline:
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for i, label in enumerate(gold.ner):
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if label is None:
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continue
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if label.startswith(("B-", "U-", "L-")) and doc[i].is_space:
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# "Illegal" whitespace entity
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data["ws_ents"] += 1
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@ -371,9 +520,17 @@ def _compile_gold(train_docs, pipeline):
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if "textcat" in pipeline:
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data["cats"].update(gold.cats)
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if "tagger" in pipeline:
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data["tags"].update(gold.tags)
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data["tags"].update([x for x in gold.tags if x is not None])
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if "parser" in pipeline:
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data["deps"].update(gold.labels)
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data["deps"].update([x for x in gold.labels if x is not None])
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for i, (dep, head) in enumerate(zip(gold.labels, gold.heads)):
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if head == i:
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data["roots"].update([dep])
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data["n_sents"] += 1
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if nonproj.is_nonproj_tree(gold.heads):
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data["n_nonproj"] += 1
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if nonproj.contains_cycle(gold.heads):
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data["n_cycles"] += 1
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return data
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@ -216,6 +216,10 @@ class GoldCorpus(object):
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make_projective=True)
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yield from gold_docs
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def train_docs_without_preprocessing(self, nlp, gold_preproc=False):
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gold_docs = self.iter_gold_docs(nlp, self.train_tuples, gold_preproc=gold_preproc)
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yield from gold_docs
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def dev_docs(self, nlp, gold_preproc=False):
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gold_docs = self.iter_gold_docs(nlp, self.dev_tuples, gold_preproc=gold_preproc)
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yield from gold_docs
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Block a user