mirror of https://github.com/explosion/spaCy.git
Fix debug data [ci skip]
This commit is contained in:
Ines Montani
parent
a2aa1f6882
commit
9bb958fd0a
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@ -8,12 +8,12 @@ import typer
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from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
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from ._util import import_code, debug_cli
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from ..training import Corpus, Example
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from ..training import Example
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from ..training.initialize import get_sourced_components
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from ..schemas import ConfigSchemaTraining
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from ..pipeline._parser_internals import nonproj
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from ..language import Language
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from ..util import registry
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from ..util import registry, resolve_dot_names
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from .. import util
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@ -37,8 +37,6 @@ BLANK_MODEL_THRESHOLD = 2000
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def debug_data_cli(
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# fmt: off
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ctx: typer.Context, # This is only used to read additional arguments
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train_path: Path = Arg(..., help="Location of JSON-formatted training data", exists=True),
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dev_path: Path = Arg(..., help="Location of JSON-formatted development data", exists=True),
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config_path: Path = Arg(..., help="Path to config file", exists=True),
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code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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ignore_warnings: bool = Opt(False, "--ignore-warnings", "-IW", help="Ignore warnings, only show stats and errors"),
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@ -62,8 +60,6 @@ def debug_data_cli(
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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debug_data(
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train_path,
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dev_path,
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config_path,
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config_overrides=overrides,
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ignore_warnings=ignore_warnings,
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@ -74,8 +70,6 @@ def debug_data_cli(
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def debug_data(
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train_path: Path,
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dev_path: Path,
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config_path: Path,
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*,
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config_overrides: Dict[str, Any] = {},
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@ -88,18 +82,11 @@ def debug_data(
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no_print=silent, pretty=not no_format, ignore_warnings=ignore_warnings
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)
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# Make sure all files and paths exists if they are needed
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if 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.exists():
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msg.fail("Development data not found", dev_path, exits=1)
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if not config_path.exists():
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msg.fail("Config file not found", config_path, exists=1)
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with show_validation_error(config_path):
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cfg = util.load_config(config_path, overrides=config_overrides)
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nlp = util.load_model_from_config(cfg)
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T = registry.resolve(
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nlp.config.interpolate()["training"], schema=ConfigSchemaTraining
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)
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config = nlp.config.interpolate()
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T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
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# Use original config here, not resolved version
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sourced_components = get_sourced_components(cfg)
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frozen_components = T["frozen_components"]
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@ -109,25 +96,15 @@ def debug_data(
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msg.divider("Data file validation")
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# Create the gold corpus to be able to better analyze 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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try:
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train_dataset = list(Corpus(train_path)(nlp))
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except ValueError as e:
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loading_train_error_message = f"Training data cannot be loaded: {e}"
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try:
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dev_dataset = list(Corpus(dev_path)(nlp))
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except ValueError as e:
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loading_dev_error_message = f"Development data cannot be loaded: {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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dot_names = [T["train_corpus"], T["dev_corpus"]]
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train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
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train_dataset = list(train_corpus(nlp))
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dev_dataset = list(dev_corpus(nlp))
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msg.good("Corpus is loadable")
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nlp.initialize(lambda: train_dataset)
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msg.good("Pipeline can be initialized with data")
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# Create all gold data here to avoid iterating over the train_dataset constantly
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gold_train_data = _compile_gold(train_dataset, factory_names, nlp, make_proj=True)
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gold_train_unpreprocessed_data = _compile_gold(
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@ -348,17 +325,11 @@ def debug_data(
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msg.divider("Part-of-speech Tagging")
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labels = [label for label in gold_train_data["tags"]]
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# TODO: does this need to be updated?
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tag_map = nlp.vocab.morphology.tag_map
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msg.info(f"{len(labels)} label(s) in data ({len(tag_map)} label(s) in tag map)")
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msg.info(f"{len(labels)} label(s) in data")
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labels_with_counts = _format_labels(
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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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if not non_tagmap:
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msg.good(f"All labels present in tag map for language '{nlp.lang}'")
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for label in non_tagmap:
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msg.fail(f"Label '{label}' not found in tag map for language '{nlp.lang}'")
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if "parser" in factory_names:
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has_low_data_warning = False
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@ -436,6 +436,7 @@ $ python -m spacy debug data [config_path] [--code] [--ignore-warnings] [--verbo
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```
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=========================== Data format validation ===========================
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✔ Corpus is loadable
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✔ Pipeline can be initialized with data
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=============================== Training stats ===============================
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Training pipeline: tagger, parser, ner
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@ -465,7 +466,7 @@ New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
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✔ No entities consisting of or starting/ending with whitespace
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=========================== Part-of-speech Tagging ===========================
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ℹ 49 labels in data (57 labels in tag map)
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ℹ 49 labels in data
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'NN' (266331), 'IN' (227365), 'DT' (185600), 'NNP' (164404), 'JJ' (119830),
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'NNS' (110957), '.' (101482), ',' (92476), 'RB' (90090), 'PRP' (90081), 'VB'
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(74538), 'VBD' (68199), 'CC' (62862), 'VBZ' (50712), 'VBP' (43420), 'VBN'
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@ -476,7 +477,6 @@ New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
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'-RRB-' (2825), '-LRB-' (2788), 'PDT' (2078), 'XX' (1316), 'RBS' (1142), 'FW'
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(794), 'NFP' (557), 'SYM' (440), 'WP$' (294), 'LS' (293), 'ADD' (191), 'AFX'
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(24)
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✔ All labels present in tag map for language 'en'
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============================= Dependency Parsing =============================
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ℹ Found 111703 sentences with an average length of 18.6 words.
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