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find-threshold: CLI command for multi-label classifier threshold tuning (#11280)
* Add foundation for find-threshold CLI functionality. * Finish first draft for find-threshold. * Add tests. * Revert adjusted import statements. * Fix mypy errors. * Fix imports. * Harmonize arguments with spacy evaluate command. * Generalize component and threshold handling. Harmonize arguments with 'spacy evaluate' CLI. * Fix Spancat test. * Add beta parameter to Scorer and PRFScore. * Make beta a component scorer setting. * Remove beta. * Update nlp.config (workaround). * Reload pipeline on threshold change. Adjust tests. Remove confection reference. * Remove assumption of component being a Pipe object or having a .cfg attribute. * Adjust test output and reference values. * Remove beta references. Delete universe.json. * Reverting unnecessary changes. Removing unused default values. Renaming variables in find-cli tests. * Update spacy/cli/find_threshold.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Remove adding labels in tests. * Remove unused error * Undo changes to PRFScorer * Change default value for n_trials. Log table iteratively. * Add warnings for pointless applications of find_threshold(). * Fix imports. * Adjust type check of TextCategorizer to exclude subclasses. * Change check of if there's only one unique value in scores. * Update spacy/cli/find_threshold.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Incorporate feedback. * Fix test issue. Update docstring. * Update docs & docstring. * Update spacy/tests/test_cli.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Add examples to docs. Rename _nlp to nlp in tests. * Update spacy/cli/find_threshold.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/cli/find_threshold.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
This commit is contained in:
Raphael Mitsch
GitHub
parent
dece775279
commit
c0fd8a2e71
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@ -27,6 +27,7 @@ from .project.dvc import project_update_dvc # noqa: F401
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from .project.push import project_push # noqa: F401
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from .project.pull import project_pull # noqa: F401
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from .project.document import project_document # noqa: F401
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from .find_threshold import find_threshold # noqa: F401
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@app.command("link", no_args_is_help=True, deprecated=True, hidden=True)
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233
spacy/cli/find_threshold.py
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233
spacy/cli/find_threshold.py
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@ -0,0 +1,233 @@
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import functools
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import operator
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from pathlib import Path
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import logging
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from typing import Optional, Tuple, Any, Dict, List
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import numpy
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import wasabi.tables
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from ..pipeline import TextCategorizer, MultiLabel_TextCategorizer
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from ..errors import Errors
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from ..training import Corpus
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from ._util import app, Arg, Opt, import_code, setup_gpu
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from .. import util
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_DEFAULTS = {
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"n_trials": 11,
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"use_gpu": -1,
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"gold_preproc": False,
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}
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@app.command(
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"find-threshold",
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context_settings={"allow_extra_args": False, "ignore_unknown_options": True},
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)
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def find_threshold_cli(
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# fmt: off
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model: str = Arg(..., help="Model name or path"),
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data_path: Path = Arg(..., help="Location of binary evaluation data in .spacy format", exists=True),
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pipe_name: str = Arg(..., help="Name of pipe to examine thresholds for"),
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threshold_key: str = Arg(..., help="Key of threshold attribute in component's configuration"),
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scores_key: str = Arg(..., help="Metric to optimize"),
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n_trials: int = Opt(_DEFAULTS["n_trials"], "--n_trials", "-n", help="Number of trials to determine optimal thresholds"),
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code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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use_gpu: int = Opt(_DEFAULTS["use_gpu"], "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
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gold_preproc: bool = Opt(_DEFAULTS["gold_preproc"], "--gold-preproc", "-G", help="Use gold preprocessing"),
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verbose: bool = Opt(False, "--silent", "-V", "-VV", help="Display more information for debugging purposes"),
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# fmt: on
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):
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"""
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Runs prediction trials for a trained model with varying tresholds to maximize
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the specified metric. The search space for the threshold is traversed linearly
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from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout`
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(the corresponding API call to `spacy.cli.find_threshold.find_threshold()`
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returns all results).
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This is applicable only for components whose predictions are influenced by
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thresholds - e.g. `textcat_multilabel` and `spancat`, but not `textcat`. Note
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that the full path to the corresponding threshold attribute in the config has to
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be provided.
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DOCS: https://spacy.io/api/cli#find-threshold
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"""
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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import_code(code_path)
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find_threshold(
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model=model,
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data_path=data_path,
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pipe_name=pipe_name,
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threshold_key=threshold_key,
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scores_key=scores_key,
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n_trials=n_trials,
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use_gpu=use_gpu,
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gold_preproc=gold_preproc,
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silent=False,
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)
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def find_threshold(
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model: str,
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data_path: Path,
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pipe_name: str,
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threshold_key: str,
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scores_key: str,
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*,
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n_trials: int = _DEFAULTS["n_trials"], # type: ignore
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use_gpu: int = _DEFAULTS["use_gpu"], # type: ignore
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gold_preproc: bool = _DEFAULTS["gold_preproc"], # type: ignore
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silent: bool = True,
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) -> Tuple[float, float, Dict[float, float]]:
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"""
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Runs prediction trials for models with varying tresholds to maximize the specified metric.
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model (Union[str, Path]): Pipeline to evaluate. Can be a package or a path to a data directory.
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data_path (Path): Path to file with DocBin with docs to use for threshold search.
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pipe_name (str): Name of pipe to examine thresholds for.
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threshold_key (str): Key of threshold attribute in component's configuration.
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scores_key (str): Name of score to metric to optimize.
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n_trials (int): Number of trials to determine optimal thresholds.
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use_gpu (int): GPU ID or -1 for CPU.
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gold_preproc (bool): Whether to use gold preprocessing. Gold preprocessing helps the annotations align to the
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tokenization, and may result in sequences of more consistent length. However, it may reduce runtime accuracy due
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to train/test skew.
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silent (bool): Whether to print non-error-related output to stdout.
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RETURNS (Tuple[float, float, Dict[float, float]]): Best found threshold, the corresponding score, scores for all
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evaluated thresholds.
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"""
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setup_gpu(use_gpu, silent=silent)
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data_path = util.ensure_path(data_path)
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if not data_path.exists():
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wasabi.msg.fail("Evaluation data not found", data_path, exits=1)
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nlp = util.load_model(model)
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if pipe_name not in nlp.component_names:
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raise AttributeError(
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Errors.E001.format(name=pipe_name, opts=nlp.component_names)
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)
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pipe = nlp.get_pipe(pipe_name)
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if not hasattr(pipe, "scorer"):
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raise AttributeError(Errors.E1045)
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if type(pipe) == TextCategorizer:
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wasabi.msg.warn(
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"The `textcat` component doesn't use a threshold as it's not applicable to the concept of "
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"exclusive classes. All thresholds will yield the same results."
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)
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if not silent:
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wasabi.msg.info(
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title=f"Optimizing for {scores_key} for component '{pipe_name}' with {n_trials} "
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f"trials."
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)
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# Load evaluation corpus.
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corpus = Corpus(data_path, gold_preproc=gold_preproc)
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dev_dataset = list(corpus(nlp))
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config_keys = threshold_key.split(".")
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def set_nested_item(
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config: Dict[str, Any], keys: List[str], value: float
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) -> Dict[str, Any]:
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"""Set item in nested dictionary. Adapted from https://stackoverflow.com/a/54138200.
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config (Dict[str, Any]): Configuration dictionary.
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keys (List[Any]): Path to value to set.
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value (float): Value to set.
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RETURNS (Dict[str, Any]): Updated dictionary.
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"""
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functools.reduce(operator.getitem, keys[:-1], config)[keys[-1]] = value
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return config
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def filter_config(
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config: Dict[str, Any], keys: List[str], full_key: str
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) -> Dict[str, Any]:
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"""Filters provided config dictionary so that only the specified keys path remains.
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config (Dict[str, Any]): Configuration dictionary.
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keys (List[Any]): Path to value to set.
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full_key (str): Full user-specified key.
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RETURNS (Dict[str, Any]): Filtered dictionary.
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"""
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if keys[0] not in config:
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wasabi.msg.fail(
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title=f"Failed to look up `{full_key}` in config: sub-key {[keys[0]]} not found.",
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text=f"Make sure you specified {[keys[0]]} correctly. The following sub-keys are available instead: "
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f"{list(config.keys())}",
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exits=1,
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)
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return {
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keys[0]: filter_config(config[keys[0]], keys[1:], full_key)
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if len(keys) > 1
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else config[keys[0]]
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}
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# Evaluate with varying threshold values.
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scores: Dict[float, float] = {}
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config_keys_full = ["components", pipe_name, *config_keys]
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table_col_widths = (10, 10)
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thresholds = numpy.linspace(0, 1, n_trials)
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print(wasabi.tables.row(["Threshold", f"{scores_key}"], widths=table_col_widths))
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for threshold in thresholds:
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# Reload pipeline with overrides specifying the new threshold.
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nlp = util.load_model(
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model,
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config=set_nested_item(
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filter_config(
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nlp.config, config_keys_full, ".".join(config_keys_full)
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).copy(),
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config_keys_full,
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threshold,
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),
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)
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if hasattr(pipe, "cfg"):
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setattr(
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nlp.get_pipe(pipe_name),
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"cfg",
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set_nested_item(getattr(pipe, "cfg"), config_keys, threshold),
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)
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eval_scores = nlp.evaluate(dev_dataset)
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if scores_key not in eval_scores:
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wasabi.msg.fail(
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title=f"Failed to look up score `{scores_key}` in evaluation results.",
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text=f"Make sure you specified the correct value for `scores_key`. The following scores are "
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f"available: {list(eval_scores.keys())}",
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exits=1,
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)
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scores[threshold] = eval_scores[scores_key]
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if not isinstance(scores[threshold], (float, int)):
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wasabi.msg.fail(
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f"Returned score for key '{scores_key}' is not numeric. Threshold optimization only works for numeric "
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f"scores.",
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exits=1,
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)
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print(
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wasabi.row(
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[round(threshold, 3), round(scores[threshold], 3)],
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widths=table_col_widths,
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)
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)
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best_threshold = max(scores.keys(), key=(lambda key: scores[key]))
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# If all scores are identical, emit warning.
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if len(set(scores.values())) == 1:
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wasabi.msg.warn(
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title="All scores are identical. Verify that all settings are correct.",
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text=""
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if (
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not isinstance(pipe, MultiLabel_TextCategorizer)
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or scores_key in ("cats_macro_f", "cats_micro_f")
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)
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else "Use `cats_macro_f` or `cats_micro_f` when optimizing the threshold for `textcat_multilabel`.",
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)
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else:
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if not silent:
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print(
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f"\nBest threshold: {round(best_threshold, ndigits=4)} with {scores_key} value of {scores[best_threshold]}."
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)
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return best_threshold, scores[best_threshold], scores
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@ -956,6 +956,7 @@ class Errors(metaclass=ErrorsWithCodes):
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"sure it's overwritten on the subclass.")
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E1046 = ("{cls_name} is an abstract class and cannot be instantiated. If you are looking for spaCy's default "
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"knowledge base, use `InMemoryLookupKB`.")
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E1047 = ("`find_threshold()` only supports components with a `scorer` attribute.")
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# Deprecated model shortcuts, only used in errors and warnings
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@ -1,7 +1,7 @@
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from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast
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from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any
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from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
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from thinc.api import Optimizer
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from thinc.types import Ragged, Ints2d, Floats2d, Ints1d
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from thinc.types import Ragged, Ints2d, Floats2d
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import numpy
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@ -1,9 +1,10 @@
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import os
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import math
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from collections import Counter
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from typing import Tuple, List, Dict, Any
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import pkg_resources
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from random import sample
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from typing import Counter
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import numpy
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import pytest
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import srsly
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from click import NoSuchOption
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@ -28,11 +29,12 @@ from spacy.cli.package import get_third_party_dependencies
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from spacy.cli.package import _is_permitted_package_name
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from spacy.cli.project.run import _check_requirements
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from spacy.cli.validate import get_model_pkgs
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from spacy.cli.find_threshold import find_threshold
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from spacy.lang.en import English
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from spacy.lang.nl import Dutch
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from spacy.language import Language
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from spacy.schemas import ProjectConfigSchema, RecommendationSchema, validate
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from spacy.tokens import Doc
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from spacy.tokens import Doc, DocBin
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from spacy.tokens.span import Span
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from spacy.training import Example, docs_to_json, offsets_to_biluo_tags
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from spacy.training.converters import conll_ner_to_docs, conllu_to_docs
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@ -859,6 +861,122 @@ def test_span_length_freq_dist_output_must_be_correct():
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assert list(span_freqs.keys()) == [3, 1, 4, 5, 2]
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def test_cli_find_threshold(capsys):
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thresholds = numpy.linspace(0, 1, 10)
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def make_examples(nlp: Language) -> List[Example]:
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docs: List[Example] = []
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for t in [
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(
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"I am angry and confused in the Bank of America.",
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{
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"cats": {"ANGRY": 1.0, "CONFUSED": 1.0, "HAPPY": 0.0},
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"spans": {"sc": [(31, 46, "ORG")]},
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},
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),
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(
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"I am confused but happy in New York.",
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{
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"cats": {"ANGRY": 0.0, "CONFUSED": 1.0, "HAPPY": 1.0},
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"spans": {"sc": [(27, 35, "GPE")]},
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},
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),
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]:
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doc = nlp.make_doc(t[0])
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docs.append(Example.from_dict(doc, t[1]))
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return docs
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def init_nlp(
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components: Tuple[Tuple[str, Dict[str, Any]], ...] = ()
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) -> Tuple[Language, List[Example]]:
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new_nlp = English()
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new_nlp.add_pipe( # type: ignore
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factory_name="textcat_multilabel",
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name="tc_multi",
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config={"threshold": 0.9},
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)
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# Append additional components to pipeline.
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for cfn, comp_config in components:
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new_nlp.add_pipe(cfn, config=comp_config)
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new_examples = make_examples(new_nlp)
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new_nlp.initialize(get_examples=lambda: new_examples)
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for i in range(5):
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new_nlp.update(new_examples)
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return new_nlp, new_examples
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with make_tempdir() as docs_dir:
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# Check whether find_threshold() identifies lowest threshold above 0 as (first) ideal threshold, as this matches
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# the current model behavior with the examples above. This can break once the model behavior changes and serves
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# mostly as a smoke test.
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nlp, examples = init_nlp()
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DocBin(docs=[example.reference for example in examples]).to_disk(
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docs_dir / "docs.spacy"
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)
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with make_tempdir() as nlp_dir:
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nlp.to_disk(nlp_dir)
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res = find_threshold(
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model=nlp_dir,
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data_path=docs_dir / "docs.spacy",
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pipe_name="tc_multi",
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threshold_key="threshold",
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scores_key="cats_macro_f",
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silent=True,
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)
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assert res[0] != thresholds[0]
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assert thresholds[0] < res[0] < thresholds[9]
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assert res[1] == 1.0
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assert res[2][1.0] == 0.0
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# Test with spancat.
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nlp, _ = init_nlp((("spancat", {}),))
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with make_tempdir() as nlp_dir:
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nlp.to_disk(nlp_dir)
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res = find_threshold(
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model=nlp_dir,
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data_path=docs_dir / "docs.spacy",
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pipe_name="spancat",
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threshold_key="threshold",
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scores_key="spans_sc_f",
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silent=True,
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)
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assert res[0] != thresholds[0]
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assert thresholds[0] < res[0] < thresholds[8]
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assert res[1] >= 0.6
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assert res[2][1.0] == 0.0
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# Having multiple textcat_multilabel components should work, since the name has to be specified.
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nlp, _ = init_nlp((("textcat_multilabel", {}),))
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with make_tempdir() as nlp_dir:
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nlp.to_disk(nlp_dir)
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assert find_threshold(
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model=nlp_dir,
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data_path=docs_dir / "docs.spacy",
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pipe_name="tc_multi",
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threshold_key="threshold",
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scores_key="cats_macro_f",
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silent=True,
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)
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# Specifying the name of an non-existing pipe should fail.
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nlp, _ = init_nlp()
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with make_tempdir() as nlp_dir:
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nlp.to_disk(nlp_dir)
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with pytest.raises(AttributeError):
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find_threshold(
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model=nlp_dir,
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data_path=docs_dir / "docs.spacy",
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pipe_name="_",
|
||||
threshold_key="threshold",
|
||||
scores_key="cats_macro_f",
|
||||
silent=True,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"reqs,output",
|
||||
[
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ menu:
|
|||
- ['train', 'train']
|
||||
- ['pretrain', 'pretrain']
|
||||
- ['evaluate', 'evaluate']
|
||||
- ['find-threshold', 'find-threshold']
|
||||
- ['assemble', 'assemble']
|
||||
- ['package', 'package']
|
||||
- ['project', 'project']
|
||||
|
|
@ -1161,6 +1162,46 @@ $ python -m spacy evaluate [model] [data_path] [--output] [--code] [--gold-prepr
|
|||
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
|
||||
| **CREATES** | Training results and optional metrics and visualizations. |
|
||||
|
||||
## find-threshold {#find-threshold new="3.5" tag="command"}
|
||||
|
||||
Runs prediction trials for a trained model with varying tresholds to maximize
|
||||
the specified metric. The search space for the threshold is traversed linearly
|
||||
from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout`
|
||||
(the corresponding API call to `spacy.cli.find_threshold.find_threshold()`
|
||||
returns all results).
|
||||
|
||||
This is applicable only for components whose predictions are influenced by
|
||||
thresholds - e.g. `textcat_multilabel` and `spancat`, but not `textcat`. Note
|
||||
that the full path to the corresponding threshold attribute in the config has to
|
||||
be provided.
|
||||
|
||||
> #### Examples
|
||||
>
|
||||
> ```cli
|
||||
> # For textcat_multilabel:
|
||||
> $ python -m spacy find-threshold my_nlp data.spacy textcat_multilabel threshold cats_macro_f
|
||||
> ```
|
||||
>
|
||||
> ```cli
|
||||
> # For spancat:
|
||||
> $ python -m spacy find-threshold my_nlp data.spacy spancat threshold spans_sc_f
|
||||
> ```
|
||||
|
||||
|
||||
| Name | Description |
|
||||
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `model` | Pipeline to evaluate. Can be a package or a path to a data directory. ~~str (positional)~~ |
|
||||
| `data_path` | Path to file with DocBin with docs to use for threshold search. ~~Path (positional)~~ |
|
||||
| `pipe_name` | Name of pipe to examine thresholds for. ~~str (positional)~~ |
|
||||
| `threshold_key` | Key of threshold attribute in component's configuration. ~~str (positional)~~ |
|
||||
| `scores_key` | Name of score to metric to optimize. ~~str (positional)~~ |
|
||||
| `--n_trials`, `-n` | Number of trials to determine optimal thresholds. ~~int (option)~~ |
|
||||
| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
|
||||
| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
|
||||
| `--gold-preproc`, `-G` | Use gold preprocessing. ~~bool (flag)~~ |
|
||||
| `--silent`, `-V`, `-VV` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
|
||||
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
|
||||
|
||||
## assemble {#assemble tag="command"}
|
||||
|
||||
Assemble a pipeline from a config file without additional training. Expects a
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user