mirror of
https://github.com/explosion/spaCy.git
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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
1233 lines
49 KiB
Python
1233 lines
49 KiB
Python
import math
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import sys
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from collections import Counter
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from pathlib import Path
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from typing import (
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Any,
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Dict,
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Iterable,
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List,
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Optional,
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Sequence,
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Set,
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Tuple,
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Union,
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cast,
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overload,
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)
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import numpy
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import srsly
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import typer
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from wasabi import MESSAGES, Printer, msg
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from .. import util
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from ..compat import Literal
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from ..language import Language
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from ..morphology import Morphology
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from ..pipeline import Morphologizer, SpanCategorizer, TrainablePipe
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from ..pipeline._edit_tree_internals.edit_trees import EditTrees
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from ..pipeline._parser_internals import nonproj
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from ..pipeline._parser_internals.nonproj import DELIMITER
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from ..schemas import ConfigSchemaTraining
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from ..training import Example, remove_bilu_prefix
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from ..training.initialize import get_sourced_components
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from ..util import registry, resolve_dot_names
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from ..vectors import Mode as VectorsMode
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from ._util import (
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Arg,
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Opt,
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_format_number,
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app,
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debug_cli,
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import_code,
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parse_config_overrides,
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show_validation_error,
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)
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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 new pipeline
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BLANK_MODEL_MIN_THRESHOLD = 100
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BLANK_MODEL_THRESHOLD = 2000
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# Arbitrary threshold where SpanCat performs well
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SPAN_DISTINCT_THRESHOLD = 1
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# Arbitrary threshold where SpanCat performs well
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BOUNDARY_DISTINCT_THRESHOLD = 1
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# Arbitrary threshold for filtering span lengths during reporting (percentage)
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SPAN_LENGTH_THRESHOLD_PERCENTAGE = 90
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@debug_cli.command(
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"data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
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)
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@app.command(
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"debug-data",
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context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
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hidden=True, # hide this from main CLI help but still allow it to work with warning
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)
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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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config_path: Path = Arg(..., help="Path to config file", exists=True, allow_dash=True),
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code_path: Optional[Path] = Opt(None, "--code-path", "--code", "-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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verbose: bool = Opt(False, "--verbose", "-V", help="Print additional information and explanations"),
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no_format: bool = Opt(False, "--no-format", "-NF", help="Don't pretty-print the results"),
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# fmt: on
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):
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"""
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Analyze, debug and validate your training and development data. Outputs
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useful stats, and can help you find problems like invalid entity annotations,
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cyclic dependencies, low data labels and more.
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DOCS: https://spacy.io/api/cli#debug-data
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"""
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if ctx.command.name == "debug-data":
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msg.warn(
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"The debug-data command is now available via the 'debug data' "
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"subcommand (without the hyphen). You can run python -m spacy debug "
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"--help for an overview of the other available debugging commands."
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)
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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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config_path,
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config_overrides=overrides,
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ignore_warnings=ignore_warnings,
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verbose=verbose,
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no_format=no_format,
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silent=False,
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)
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def debug_data(
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config_path: Path,
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*,
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config_overrides: Dict[str, Any] = {},
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ignore_warnings: bool = False,
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verbose: bool = False,
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no_format: bool = True,
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silent: bool = True,
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):
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msg = Printer(
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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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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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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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resume_components = [p for p in sourced_components if p not in frozen_components]
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pipeline = nlp.pipe_names
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factory_names = [nlp.get_pipe_meta(pipe).factory for pipe in nlp.pipe_names]
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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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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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nlp.initialize(lambda: train_corpus(nlp))
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msg.good("Pipeline can be initialized with data")
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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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# 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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train_dataset, factory_names, nlp, make_proj=False
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)
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gold_dev_data = _compile_gold(dev_dataset, factory_names, nlp, make_proj=True)
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train_texts = gold_train_data["texts"]
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dev_texts = gold_dev_data["texts"]
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frozen_components = T["frozen_components"]
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msg.divider("Training stats")
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msg.text(f"Language: {nlp.lang}")
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msg.text(f"Training pipeline: {', '.join(pipeline)}")
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if resume_components:
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msg.text(f"Components from other pipelines: {', '.join(resume_components)}")
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if frozen_components:
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msg.text(f"Frozen components: {', '.join(frozen_components)}")
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msg.text(f"{len(train_dataset)} training docs")
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msg.text(f"{len(dev_dataset)} evaluation docs")
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if not len(gold_dev_data):
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msg.fail("No evaluation docs")
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overlap = len(train_texts.intersection(dev_texts))
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if overlap:
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msg.warn(f"{overlap} training examples also in evaluation data")
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else:
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msg.good("No overlap between training and evaluation data")
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# TODO: make this feedback more fine-grained and report on updated
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# components vs. blank components
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if not resume_components and len(train_dataset) < BLANK_MODEL_THRESHOLD:
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text = f"Low number of examples to train a new pipeline ({len(train_dataset)})"
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if len(train_dataset) < BLANK_MODEL_MIN_THRESHOLD:
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msg.fail(text)
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else:
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msg.warn(text)
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msg.text(
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f"It's recommended to use at least {BLANK_MODEL_THRESHOLD} examples "
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f"(minimum {BLANK_MODEL_MIN_THRESHOLD})",
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show=verbose,
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)
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msg.divider("Vocab & Vectors")
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n_words = gold_train_data["n_words"]
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msg.info(
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f"{n_words} total word(s) in the data ({len(gold_train_data['words'])} unique)"
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)
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if gold_train_data["n_misaligned_words"] > 0:
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n_misaligned = gold_train_data["n_misaligned_words"]
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msg.warn(f"{n_misaligned} misaligned tokens in the training data")
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if gold_dev_data["n_misaligned_words"] > 0:
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n_misaligned = gold_dev_data["n_misaligned_words"]
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msg.warn(f"{n_misaligned} misaligned tokens in the dev data")
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most_common_words = gold_train_data["words"].most_common(10)
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msg.text(
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f"10 most common words: {_format_labels(most_common_words, counts=True)}",
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show=verbose,
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)
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if len(nlp.vocab.vectors):
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if nlp.vocab.vectors.mode == VectorsMode.floret:
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msg.info(
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f"floret vectors with {len(nlp.vocab.vectors)} vectors, "
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f"{nlp.vocab.vectors_length} dimensions, "
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f"{nlp.vocab.vectors.minn}-{nlp.vocab.vectors.maxn} char "
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f"n-gram subwords"
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)
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else:
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msg.info(
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f"{len(nlp.vocab.vectors)} vectors ({nlp.vocab.vectors.n_keys} "
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f"unique keys, {nlp.vocab.vectors_length} dimensions)"
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)
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n_missing_vectors = sum(gold_train_data["words_missing_vectors"].values())
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msg.warn(
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"{} words in training data without vectors ({:.0f}%)".format(
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n_missing_vectors,
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100 * (n_missing_vectors / gold_train_data["n_words"]),
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),
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)
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msg.text(
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"10 most common words without vectors: {}".format(
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_format_labels(
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gold_train_data["words_missing_vectors"].most_common(10),
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counts=True,
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)
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),
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show=verbose,
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)
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else:
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msg.info("No word vectors present in the package")
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if "spancat" in factory_names:
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model_labels_spancat = _get_labels_from_spancat(nlp)
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has_low_data_warning = False
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has_no_neg_warning = False
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msg.divider("Span Categorization")
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msg.table(model_labels_spancat, header=["Spans Key", "Labels"], divider=True)
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msg.text("Label counts in train data: ", show=verbose)
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for spans_key, data_labels in gold_train_data["spancat"].items():
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msg.text(
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f"Key: {spans_key}, {_format_labels(data_labels.items(), counts=True)}",
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show=verbose,
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)
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# Data checks: only take the spans keys in the actual spancat components
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data_labels_in_component = {
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spans_key: gold_train_data["spancat"][spans_key]
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for spans_key in model_labels_spancat.keys()
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}
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for spans_key, data_labels in data_labels_in_component.items():
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for label, count in data_labels.items():
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# Check for missing labels
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spans_key_in_model = spans_key in model_labels_spancat.keys()
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if (spans_key_in_model) and (
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label not in model_labels_spancat[spans_key]
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):
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msg.warn(
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f"Label '{label}' is not present in the model labels of key '{spans_key}'. "
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"Performance may degrade after training."
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)
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# Check for low number of examples per label
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if count <= NEW_LABEL_THRESHOLD:
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msg.warn(
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f"Low number of examples for label '{label}' in key '{spans_key}' ({count})"
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)
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has_low_data_warning = True
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# Check for negative examples
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with msg.loading("Analyzing label distribution..."):
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neg_docs = _get_examples_without_label(
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train_dataset, label, "spancat", spans_key
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)
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if neg_docs == 0:
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msg.warn(f"No examples for texts WITHOUT new label '{label}'")
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has_no_neg_warning = True
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with msg.loading("Obtaining span characteristics..."):
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span_characteristics = _get_span_characteristics(
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train_dataset, gold_train_data, spans_key
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)
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msg.info(f"Span characteristics for spans_key '{spans_key}'")
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msg.info("SD = Span Distinctiveness, BD = Boundary Distinctiveness")
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_print_span_characteristics(span_characteristics)
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_span_freqs = _get_spans_length_freq_dist(
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gold_train_data["spans_length"][spans_key]
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)
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_filtered_span_freqs = _filter_spans_length_freq_dist(
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_span_freqs, threshold=SPAN_LENGTH_THRESHOLD_PERCENTAGE
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)
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msg.info(
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f"Over {SPAN_LENGTH_THRESHOLD_PERCENTAGE}% of spans have lengths of 1 -- "
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f"{max(_filtered_span_freqs.keys())} "
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f"(min={span_characteristics['min_length']}, max={span_characteristics['max_length']}). "
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f"The most common span lengths are: {_format_freqs(_filtered_span_freqs)}. "
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"If you are using the n-gram suggester, note that omitting "
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"infrequent n-gram lengths can greatly improve speed and "
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"memory usage."
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)
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msg.text(
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f"Full distribution of span lengths: {_format_freqs(_span_freqs)}",
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show=verbose,
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)
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# Add report regarding span characteristics
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if span_characteristics["avg_sd"] < SPAN_DISTINCT_THRESHOLD:
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msg.warn("Spans may not be distinct from the rest of the corpus")
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else:
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msg.good("Spans are distinct from the rest of the corpus")
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p_spans = span_characteristics["p_spans"].values()
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all_span_tokens: Counter = sum(p_spans, Counter())
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most_common_spans = [w for w, _ in all_span_tokens.most_common(10)]
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msg.text(
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"10 most common span tokens: {}".format(
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_format_labels(most_common_spans)
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),
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show=verbose,
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)
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# Add report regarding span boundary characteristics
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if span_characteristics["avg_bd"] < BOUNDARY_DISTINCT_THRESHOLD:
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msg.warn("Boundary tokens are not distinct from the rest of the corpus")
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else:
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msg.good("Boundary tokens are distinct from the rest of the corpus")
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p_bounds = span_characteristics["p_bounds"].values()
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all_span_bound_tokens: Counter = sum(p_bounds, Counter())
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most_common_bounds = [w for w, _ in all_span_bound_tokens.most_common(10)]
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msg.text(
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"10 most common span boundary tokens: {}".format(
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_format_labels(most_common_bounds)
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),
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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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f"To train a new span type, your data should include at "
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f"least {NEW_LABEL_THRESHOLD} instances of the new label",
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show=verbose,
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)
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else:
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msg.good("Good amount of examples for all labels")
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if has_no_neg_warning:
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msg.text(
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"Training data should always include examples of spans "
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"in context, as well as examples without a given span "
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"type.",
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show=verbose,
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)
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else:
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msg.good("Examples without occurrences available for all labels")
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if "ner" in factory_names:
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# Get all unique NER labels present in the data
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labels = set(
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label for label in gold_train_data["ner"] if label not in ("O", "-", None)
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)
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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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has_low_data_warning = False
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has_no_neg_warning = False
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has_ws_ents_error = False
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has_boundary_cross_ents_warning = False
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msg.divider("Named Entity Recognition")
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msg.info(f"{len(model_labels)} label(s)")
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missing_values = label_counts["-"]
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msg.text(f"{missing_values} missing value(s) (tokens with '-' label)")
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for label in labels:
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if len(label) == 0:
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msg.fail("Empty label found in train data")
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labels_with_counts = [
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(label, count)
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for label, count in label_counts.most_common()
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if label != "-"
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]
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labels_with_counts = _format_labels(labels_with_counts, counts=True)
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msg.text(f"Labels in train data: {labels_with_counts}", show=verbose)
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missing_labels = model_labels - labels
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if missing_labels:
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msg.warn(
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"Some model labels are not present in the train data. The "
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"model performance may be degraded for these labels after "
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f"training: {_format_labels(missing_labels)}."
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)
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if gold_train_data["ws_ents"]:
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msg.fail(f"{gold_train_data['ws_ents']} invalid whitespace entity spans")
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has_ws_ents_error = True
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for label in labels:
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if label_counts[label] <= NEW_LABEL_THRESHOLD:
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msg.warn(
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f"Low number of examples for label '{label}' ({label_counts[label]})"
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)
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has_low_data_warning = True
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with msg.loading("Analyzing label distribution..."):
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neg_docs = _get_examples_without_label(train_dataset, label, "ner")
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if neg_docs == 0:
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msg.warn(f"No examples for texts WITHOUT new label '{label}'")
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has_no_neg_warning = True
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if gold_train_data["boundary_cross_ents"]:
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msg.warn(
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f"{gold_train_data['boundary_cross_ents']} entity span(s) crossing sentence boundaries"
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)
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has_boundary_cross_ents_warning = True
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if not has_low_data_warning:
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msg.good("Good amount of examples for all labels")
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if not has_no_neg_warning:
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msg.good("Examples without occurrences available for all labels")
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if not has_ws_ents_error:
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msg.good("No entities consisting of or starting/ending with whitespace")
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if not has_boundary_cross_ents_warning:
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msg.good("No entities crossing sentence boundaries")
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|
if has_low_data_warning:
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msg.text(
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f"To train a new entity type, your data should include at "
|
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f"least {NEW_LABEL_THRESHOLD} instances of the new label",
|
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show=verbose,
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)
|
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if has_no_neg_warning:
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msg.text(
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"Training data should always include examples of entities "
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"in context, as well as examples without a given entity "
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"type.",
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show=verbose,
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)
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if has_ws_ents_error:
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msg.text(
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"Entity spans consisting of or starting/ending "
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"with whitespace characters are considered invalid."
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)
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if "textcat" in factory_names:
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msg.divider("Text Classification (Exclusive Classes)")
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labels = _get_labels_from_model(nlp, "textcat")
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msg.info(f"Text Classification: {len(labels)} label(s)")
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msg.text(f"Labels: {_format_labels(labels)}", show=verbose)
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missing_labels = labels - set(gold_train_data["cats"])
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if missing_labels:
|
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msg.warn(
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"Some model labels are not present in the train data. The "
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|
"model performance may be degraded for these labels after "
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|
f"training: {_format_labels(missing_labels)}."
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)
|
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if set(gold_train_data["cats"]) != set(gold_dev_data["cats"]):
|
|
msg.warn(
|
|
"Potential train/dev mismatch: 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 len(labels) < 2:
|
|
msg.fail(
|
|
"The model does not have enough labels. 'textcat' requires at "
|
|
"least two labels due to mutually-exclusive classes, e.g. "
|
|
"LABEL/NOT_LABEL or POSITIVE/NEGATIVE for a binary "
|
|
"classification task."
|
|
)
|
|
if (
|
|
gold_train_data["n_cats_bad_values"] > 0
|
|
or gold_dev_data["n_cats_bad_values"] > 0
|
|
):
|
|
msg.fail(
|
|
"Unsupported values for cats: the supported values are "
|
|
"1.0/True and 0.0/False."
|
|
)
|
|
if gold_train_data["n_cats_multilabel"] > 0:
|
|
# Note: you should never get here because you run into E895 on
|
|
# initialization first.
|
|
msg.fail(
|
|
"The train data contains instances without mutually-exclusive "
|
|
"classes. Use the component 'textcat_multilabel' instead of "
|
|
"'textcat'."
|
|
)
|
|
if gold_dev_data["n_cats_multilabel"] > 0:
|
|
msg.fail(
|
|
"The dev data contains instances without mutually-exclusive "
|
|
"classes. Use the component 'textcat_multilabel' instead of "
|
|
"'textcat'."
|
|
)
|
|
|
|
if "textcat_multilabel" in factory_names:
|
|
msg.divider("Text Classification (Multilabel)")
|
|
labels = _get_labels_from_model(nlp, "textcat_multilabel")
|
|
msg.info(f"Text Classification: {len(labels)} label(s)")
|
|
msg.text(f"Labels: {_format_labels(labels)}", show=verbose)
|
|
missing_labels = labels - set(gold_train_data["cats"])
|
|
if missing_labels:
|
|
msg.warn(
|
|
"Some model labels are not present in the train data. The "
|
|
"model performance may be degraded for these labels after "
|
|
f"training: {_format_labels(missing_labels)}."
|
|
)
|
|
if set(gold_train_data["cats"]) != set(gold_dev_data["cats"]):
|
|
msg.warn(
|
|
"Potential train/dev mismatch: 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_bad_values"] > 0
|
|
or gold_dev_data["n_cats_bad_values"] > 0
|
|
):
|
|
msg.fail(
|
|
"Unsupported values for cats: the supported values are "
|
|
"1.0/True and 0.0/False."
|
|
)
|
|
if gold_train_data["n_cats_multilabel"] > 0:
|
|
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 contains only instances with mutually-exclusive "
|
|
"classes."
|
|
)
|
|
else:
|
|
msg.warn(
|
|
"The train data contains only instances with "
|
|
"mutually-exclusive classes. You can potentially use the "
|
|
"component 'textcat' instead of 'textcat_multilabel'."
|
|
)
|
|
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")
|
|
label_list, counts = zip(*gold_train_data["tags"].items())
|
|
msg.info(f"{len(label_list)} label(s) in train data")
|
|
p = numpy.array(counts)
|
|
p = p / p.sum()
|
|
norm_entropy = (-p * numpy.log2(p)).sum() / numpy.log2(len(label_list))
|
|
msg.info(f"{norm_entropy} is the normalised label entropy")
|
|
model_labels = _get_labels_from_model(nlp, "tagger")
|
|
labels = set(label_list)
|
|
missing_labels = model_labels - labels
|
|
if missing_labels:
|
|
msg.warn(
|
|
"Some model labels are not present in the train data. The "
|
|
"model performance may be degraded for these labels after "
|
|
f"training: {_format_labels(missing_labels)}."
|
|
)
|
|
labels_with_counts = _format_labels(
|
|
gold_train_data["tags"].most_common(), counts=True
|
|
)
|
|
msg.text(labels_with_counts, show=verbose)
|
|
|
|
if "morphologizer" in factory_names:
|
|
msg.divider("Morphologizer (POS+Morph)")
|
|
label_list = [label for label in gold_train_data["morphs"]]
|
|
model_labels = _get_labels_from_model(nlp, "morphologizer")
|
|
msg.info(f"{len(label_list)} label(s) in train data")
|
|
labels = set(label_list)
|
|
missing_labels = model_labels - labels
|
|
if missing_labels:
|
|
msg.warn(
|
|
"Some model labels are not present in the train data. The "
|
|
"model performance may be degraded for these labels after "
|
|
f"training: {_format_labels(missing_labels)}."
|
|
)
|
|
labels_with_counts = _format_labels(
|
|
gold_train_data["morphs"].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 DELIMITER 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"
|
|
)
|
|
|
|
if "trainable_lemmatizer" in factory_names:
|
|
msg.divider("Trainable Lemmatizer")
|
|
trees_train: Set[str] = gold_train_data["lemmatizer_trees"]
|
|
trees_dev: Set[str] = gold_dev_data["lemmatizer_trees"]
|
|
# This is necessary context when someone is attempting to interpret whether the
|
|
# number of trees exclusively in the dev set is meaningful.
|
|
msg.info(f"{len(trees_train)} lemmatizer trees generated from training data")
|
|
msg.info(f"{len(trees_dev)} lemmatizer trees generated from dev data")
|
|
dev_not_train = trees_dev - trees_train
|
|
|
|
if len(dev_not_train) != 0:
|
|
pct = len(dev_not_train) / len(trees_dev)
|
|
msg.info(
|
|
f"{len(dev_not_train)} lemmatizer trees ({pct*100:.1f}% of dev trees)"
|
|
" were found exclusively in the dev data."
|
|
)
|
|
else:
|
|
# Would we ever expect this case? It seems like it would be pretty rare,
|
|
# and we might actually want a warning?
|
|
msg.info("All trees in dev data present in training data.")
|
|
|
|
if gold_train_data["n_low_cardinality_lemmas"] > 0:
|
|
n = gold_train_data["n_low_cardinality_lemmas"]
|
|
msg.warn(f"{n} training docs with 0 or 1 unique lemmas.")
|
|
|
|
if gold_dev_data["n_low_cardinality_lemmas"] > 0:
|
|
n = gold_dev_data["n_low_cardinality_lemmas"]
|
|
msg.warn(f"{n} dev docs with 0 or 1 unique lemmas.")
|
|
|
|
if gold_train_data["no_lemma_annotations"] > 0:
|
|
n = gold_train_data["no_lemma_annotations"]
|
|
msg.warn(f"{n} training docs with no lemma annotations.")
|
|
else:
|
|
msg.good("All training docs have lemma annotations.")
|
|
|
|
if gold_dev_data["no_lemma_annotations"] > 0:
|
|
n = gold_dev_data["no_lemma_annotations"]
|
|
msg.warn(f"{n} dev docs with no lemma annotations.")
|
|
else:
|
|
msg.good("All dev docs have lemma annotations.")
|
|
|
|
if gold_train_data["partial_lemma_annotations"] > 0:
|
|
n = gold_train_data["partial_lemma_annotations"]
|
|
msg.info(f"{n} training docs with partial lemma annotations.")
|
|
else:
|
|
msg.good("All training docs have complete lemma annotations.")
|
|
|
|
if gold_dev_data["partial_lemma_annotations"] > 0:
|
|
n = gold_dev_data["partial_lemma_annotations"]
|
|
msg.info(f"{n} dev docs with partial lemma annotations.")
|
|
else:
|
|
msg.good("All dev docs have complete lemma annotations.")
|
|
|
|
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: Dict[str, Any] = {
|
|
"ner": Counter(),
|
|
"cats": Counter(),
|
|
"tags": Counter(),
|
|
"morphs": Counter(),
|
|
"deps": Counter(),
|
|
"words": Counter(),
|
|
"roots": Counter(),
|
|
"spancat": dict(),
|
|
"spans_length": dict(),
|
|
"spans_per_type": dict(),
|
|
"sb_per_type": dict(),
|
|
"ws_ents": 0,
|
|
"boundary_cross_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,
|
|
"n_cats_bad_values": 0,
|
|
"texts": set(),
|
|
"lemmatizer_trees": set(),
|
|
"no_lemma_annotations": 0,
|
|
"partial_lemma_annotations": 0,
|
|
"n_low_cardinality_lemmas": 0,
|
|
}
|
|
if "trainable_lemmatizer" in factory_names:
|
|
trees = EditTrees(nlp.vocab.strings)
|
|
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:
|
|
sent_starts = eg.get_aligned_sent_starts()
|
|
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-")):
|
|
combined_label = remove_bilu_prefix(label)
|
|
data["ner"][combined_label] += 1
|
|
if sent_starts[i] and label.startswith(("I-", "L-")):
|
|
data["boundary_cross_ents"] += 1
|
|
elif label == "-":
|
|
data["ner"]["-"] += 1
|
|
if "spancat" in factory_names:
|
|
for spans_key in list(eg.reference.spans.keys()):
|
|
# Obtain the span frequency
|
|
if spans_key not in data["spancat"]:
|
|
data["spancat"][spans_key] = Counter()
|
|
for i, span in enumerate(eg.reference.spans[spans_key]):
|
|
if span.label_ is None:
|
|
continue
|
|
else:
|
|
data["spancat"][spans_key][span.label_] += 1
|
|
|
|
# Obtain the span length
|
|
if spans_key not in data["spans_length"]:
|
|
data["spans_length"][spans_key] = dict()
|
|
for span in gold.spans[spans_key]:
|
|
if span.label_ is None:
|
|
continue
|
|
if span.label_ not in data["spans_length"][spans_key]:
|
|
data["spans_length"][spans_key][span.label_] = []
|
|
data["spans_length"][spans_key][span.label_].append(len(span))
|
|
|
|
# Obtain spans per span type
|
|
if spans_key not in data["spans_per_type"]:
|
|
data["spans_per_type"][spans_key] = dict()
|
|
for span in gold.spans[spans_key]:
|
|
if span.label_ not in data["spans_per_type"][spans_key]:
|
|
data["spans_per_type"][spans_key][span.label_] = []
|
|
data["spans_per_type"][spans_key][span.label_].append(span)
|
|
|
|
# Obtain boundary tokens per span type
|
|
window_size = 1
|
|
if spans_key not in data["sb_per_type"]:
|
|
data["sb_per_type"][spans_key] = dict()
|
|
for span in gold.spans[spans_key]:
|
|
if span.label_ not in data["sb_per_type"][spans_key]:
|
|
# Creating a data structure that holds the start and
|
|
# end tokens for each span type
|
|
data["sb_per_type"][spans_key][span.label_] = {
|
|
"start": [],
|
|
"end": [],
|
|
}
|
|
for offset in range(window_size):
|
|
sb_start_idx = span.start - (offset + 1)
|
|
if sb_start_idx >= 0:
|
|
data["sb_per_type"][spans_key][span.label_]["start"].append(
|
|
gold[sb_start_idx : sb_start_idx + 1]
|
|
)
|
|
|
|
sb_end_idx = span.end + (offset + 1)
|
|
if sb_end_idx <= len(gold):
|
|
data["sb_per_type"][spans_key][span.label_]["end"].append(
|
|
gold[sb_end_idx - 1 : sb_end_idx]
|
|
)
|
|
|
|
if "textcat" in factory_names or "textcat_multilabel" in factory_names:
|
|
data["cats"].update(gold.cats)
|
|
if any(val not in (0, 1) for val in gold.cats.values()):
|
|
data["n_cats_bad_values"] += 1
|
|
if list(gold.cats.values()).count(1) != 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 "morphologizer" in factory_names:
|
|
pos_tags = eg.get_aligned("POS", as_string=True)
|
|
morphs = eg.get_aligned("MORPH", as_string=True)
|
|
for pos, morph in zip(pos_tags, morphs):
|
|
# POS may align (same value for multiple tokens) when morph
|
|
# doesn't, so if either is misaligned (None), treat the
|
|
# annotation as missing so that truths doesn't end up with an
|
|
# unknown morph+POS combination
|
|
if pos is None or morph is None:
|
|
pass
|
|
# If both are unset, the annotation is missing (empty morph
|
|
# converted from int is "_" rather than "")
|
|
elif pos == "" and morph == "":
|
|
pass
|
|
# Otherwise, generate the combined label
|
|
else:
|
|
label_dict = Morphology.feats_to_dict(morph)
|
|
if pos:
|
|
label_dict[Morphologizer.POS_FEAT] = pos
|
|
label = eg.reference.vocab.strings[
|
|
eg.reference.vocab.morphology.add(label_dict)
|
|
]
|
|
data["morphs"].update([label])
|
|
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
|
|
if "trainable_lemmatizer" in factory_names:
|
|
# from EditTreeLemmatizer._labels_from_data
|
|
if all(token.lemma == 0 for token in gold):
|
|
data["no_lemma_annotations"] += 1
|
|
continue
|
|
if any(token.lemma == 0 for token in gold):
|
|
data["partial_lemma_annotations"] += 1
|
|
lemma_set = set()
|
|
for token in gold:
|
|
if token.lemma != 0:
|
|
lemma_set.add(token.lemma)
|
|
tree_id = trees.add(token.text, token.lemma_)
|
|
tree_str = trees.tree_to_str(tree_id)
|
|
data["lemmatizer_trees"].add(tree_str)
|
|
# We want to identify cases where lemmas aren't assigned
|
|
# or are all assigned the same value, as this would indicate
|
|
# an issue since we're expecting a large set of lemmas
|
|
if len(lemma_set) < 2 and len(gold) > 1:
|
|
data["n_low_cardinality_lemmas"] += 1
|
|
return data
|
|
|
|
|
|
@overload
|
|
def _format_labels(labels: Iterable[str], counts: Literal[False] = False) -> str:
|
|
...
|
|
|
|
|
|
@overload
|
|
def _format_labels(
|
|
labels: Iterable[Tuple[str, int]],
|
|
counts: Literal[True],
|
|
) -> str:
|
|
...
|
|
|
|
|
|
def _format_labels(
|
|
labels: Union[Iterable[str], Iterable[Tuple[str, int]]],
|
|
counts: bool = False,
|
|
) -> str:
|
|
if counts:
|
|
return ", ".join(
|
|
[f"'{l}' ({c})" for l, c in cast(Iterable[Tuple[str, int]], labels)]
|
|
)
|
|
return ", ".join([f"'{l}'" for l in cast(Iterable[str], labels)])
|
|
|
|
|
|
def _format_freqs(freqs: Dict[int, float], sort: bool = True) -> str:
|
|
if sort:
|
|
freqs = dict(sorted(freqs.items()))
|
|
|
|
_freqs = [(str(k), v) for k, v in freqs.items()]
|
|
return ", ".join(
|
|
[f"{l} ({c}%)" for l, c in cast(Iterable[Tuple[str, float]], _freqs)]
|
|
)
|
|
|
|
|
|
def _get_examples_without_label(
|
|
data: Sequence[Example],
|
|
label: str,
|
|
component: Literal["ner", "spancat"] = "ner",
|
|
spans_key: Optional[str] = "sc",
|
|
) -> int:
|
|
count = 0
|
|
for eg in data:
|
|
if component == "ner":
|
|
labels = [
|
|
remove_bilu_prefix(label)
|
|
for label in eg.get_aligned_ner()
|
|
if label not in ("O", "-", None)
|
|
]
|
|
|
|
if component == "spancat":
|
|
labels = (
|
|
[span.label_ for span in eg.reference.spans[spans_key]]
|
|
if spans_key in eg.reference.spans
|
|
else []
|
|
)
|
|
|
|
if label not in labels:
|
|
count += 1
|
|
return count
|
|
|
|
|
|
def _get_labels_from_model(nlp: Language, factory_name: str) -> Set[str]:
|
|
pipe_names = [
|
|
pipe_name
|
|
for pipe_name in nlp.pipe_names
|
|
if nlp.get_pipe_meta(pipe_name).factory == factory_name
|
|
]
|
|
labels: Set[str] = set()
|
|
for pipe_name in pipe_names:
|
|
pipe = nlp.get_pipe(pipe_name)
|
|
assert isinstance(pipe, TrainablePipe)
|
|
labels.update(pipe.labels)
|
|
return labels
|
|
|
|
|
|
def _get_labels_from_spancat(nlp: Language) -> Dict[str, Set[str]]:
|
|
pipe_names = [
|
|
pipe_name
|
|
for pipe_name in nlp.pipe_names
|
|
if nlp.get_pipe_meta(pipe_name).factory == "spancat"
|
|
]
|
|
labels: Dict[str, Set[str]] = {}
|
|
for pipe_name in pipe_names:
|
|
pipe = nlp.get_pipe(pipe_name)
|
|
assert isinstance(pipe, SpanCategorizer)
|
|
if pipe.key not in labels:
|
|
labels[pipe.key] = set()
|
|
labels[pipe.key].update(pipe.labels)
|
|
return labels
|
|
|
|
|
|
def _gmean(l: List) -> float:
|
|
"""Compute geometric mean of a list"""
|
|
return math.exp(math.fsum(math.log(i) for i in l) / len(l))
|
|
|
|
|
|
def _wgt_average(metric: Dict[str, float], frequencies: Counter) -> float:
|
|
total = sum(value * frequencies[span_type] for span_type, value in metric.items())
|
|
return total / sum(frequencies.values())
|
|
|
|
|
|
def _get_distribution(docs, normalize: bool = True) -> Counter:
|
|
"""Get the frequency distribution given a set of Docs"""
|
|
word_counts: Counter = Counter()
|
|
for doc in docs:
|
|
for token in doc:
|
|
# Normalize the text
|
|
t = token.text.lower().replace("``", '"').replace("''", '"')
|
|
word_counts[t] += 1
|
|
if normalize:
|
|
total = sum(word_counts.values(), 0.0)
|
|
word_counts = Counter({k: v / total for k, v in word_counts.items()})
|
|
return word_counts
|
|
|
|
|
|
def _get_kl_divergence(p: Counter, q: Counter) -> float:
|
|
"""Compute the Kullback-Leibler divergence from two frequency distributions"""
|
|
total = 0.0
|
|
for word, p_word in p.items():
|
|
total += p_word * math.log(p_word / q[word])
|
|
return total
|
|
|
|
|
|
def _format_span_row(span_data: List[Dict], labels: List[str]) -> List[Any]:
|
|
"""Compile into one list for easier reporting"""
|
|
d = {
|
|
label: [label] + list(_format_number(d[label]) for d in span_data)
|
|
for label in labels
|
|
}
|
|
return list(d.values())
|
|
|
|
|
|
def _get_span_characteristics(
|
|
examples: List[Example], compiled_gold: Dict[str, Any], spans_key: str
|
|
) -> Dict[str, Any]:
|
|
"""Obtain all span characteristics"""
|
|
data_labels = compiled_gold["spancat"][spans_key]
|
|
# Get lengths
|
|
span_length = {
|
|
label: _gmean(l)
|
|
for label, l in compiled_gold["spans_length"][spans_key].items()
|
|
}
|
|
spans_per_type = {
|
|
label: len(spans)
|
|
for label, spans in compiled_gold["spans_per_type"][spans_key].items()
|
|
}
|
|
min_lengths = [min(l) for l in compiled_gold["spans_length"][spans_key].values()]
|
|
max_lengths = [max(l) for l in compiled_gold["spans_length"][spans_key].values()]
|
|
|
|
# Get relevant distributions: corpus, spans, span boundaries
|
|
p_corpus = _get_distribution([eg.reference for eg in examples], normalize=True)
|
|
p_spans = {
|
|
label: _get_distribution(spans, normalize=True)
|
|
for label, spans in compiled_gold["spans_per_type"][spans_key].items()
|
|
}
|
|
p_bounds = {
|
|
label: _get_distribution(sb["start"] + sb["end"], normalize=True)
|
|
for label, sb in compiled_gold["sb_per_type"][spans_key].items()
|
|
}
|
|
|
|
# Compute for actual span characteristics
|
|
span_distinctiveness = {
|
|
label: _get_kl_divergence(freq_dist, p_corpus)
|
|
for label, freq_dist in p_spans.items()
|
|
}
|
|
sb_distinctiveness = {
|
|
label: _get_kl_divergence(freq_dist, p_corpus)
|
|
for label, freq_dist in p_bounds.items()
|
|
}
|
|
|
|
return {
|
|
"sd": span_distinctiveness,
|
|
"bd": sb_distinctiveness,
|
|
"spans_per_type": spans_per_type,
|
|
"lengths": span_length,
|
|
"min_length": min(min_lengths),
|
|
"max_length": max(max_lengths),
|
|
"avg_sd": _wgt_average(span_distinctiveness, data_labels),
|
|
"avg_bd": _wgt_average(sb_distinctiveness, data_labels),
|
|
"avg_length": _wgt_average(span_length, data_labels),
|
|
"labels": list(data_labels.keys()),
|
|
"p_spans": p_spans,
|
|
"p_bounds": p_bounds,
|
|
}
|
|
|
|
|
|
def _print_span_characteristics(span_characteristics: Dict[str, Any]):
|
|
"""Print all span characteristics into a table"""
|
|
headers = ("Span Type", "Length", "SD", "BD", "N")
|
|
# Wasabi has this at 30 by default, but we might have some long labels
|
|
max_col = max(30, max(len(label) for label in span_characteristics["labels"]))
|
|
# Prepare table data with all span characteristics
|
|
table_data = [
|
|
span_characteristics["lengths"],
|
|
span_characteristics["sd"],
|
|
span_characteristics["bd"],
|
|
span_characteristics["spans_per_type"],
|
|
]
|
|
table = _format_span_row(
|
|
span_data=table_data, labels=span_characteristics["labels"]
|
|
)
|
|
# Prepare table footer with weighted averages
|
|
footer_data = [
|
|
span_characteristics["avg_length"],
|
|
span_characteristics["avg_sd"],
|
|
span_characteristics["avg_bd"],
|
|
]
|
|
|
|
footer = (
|
|
["Wgt. Average"] + ["{:.2f}".format(round(f, 2)) for f in footer_data] + ["-"]
|
|
)
|
|
msg.table(
|
|
table,
|
|
footer=footer,
|
|
header=headers,
|
|
divider=True,
|
|
aligns=["l"] + ["r"] * (len(footer_data) + 1),
|
|
max_col=max_col,
|
|
)
|
|
|
|
|
|
def _get_spans_length_freq_dist(
|
|
length_dict: Dict, threshold=SPAN_LENGTH_THRESHOLD_PERCENTAGE
|
|
) -> Dict[int, float]:
|
|
"""Get frequency distribution of spans length under a certain threshold"""
|
|
all_span_lengths = []
|
|
for _, lengths in length_dict.items():
|
|
all_span_lengths.extend(lengths)
|
|
|
|
freq_dist: Counter = Counter()
|
|
for i in all_span_lengths:
|
|
if freq_dist.get(i):
|
|
freq_dist[i] += 1
|
|
else:
|
|
freq_dist[i] = 1
|
|
|
|
# We will be working with percentages instead of raw counts
|
|
freq_dist_percentage = {}
|
|
for span_length, count in freq_dist.most_common():
|
|
percentage = (count / len(all_span_lengths)) * 100.0
|
|
percentage = round(percentage, 2)
|
|
freq_dist_percentage[span_length] = percentage
|
|
|
|
return freq_dist_percentage
|
|
|
|
|
|
def _filter_spans_length_freq_dist(
|
|
freq_dist: Dict[int, float], threshold: int
|
|
) -> Dict[int, float]:
|
|
"""Filter frequency distribution with respect to a threshold
|
|
|
|
We're going to filter all the span lengths that fall
|
|
around a percentage threshold when summed.
|
|
"""
|
|
total = 0.0
|
|
filtered_freq_dist = {}
|
|
for span_length, dist in freq_dist.items():
|
|
if total >= threshold:
|
|
break
|
|
else:
|
|
filtered_freq_dist[span_length] = dist
|
|
total += dist
|
|
return filtered_freq_dist
|