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sklearn/model_selection/_plot.py
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Stefanie Senger
DOC/MNT Little clean up around the splitting docs and error message (#33091)
19 янв 2026, 13:39
Не верифицирован
19 янв 2026, 13:39
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# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import numpy as np from sklearn.model_selection._validation import learning_curve, validation_curve from sklearn.utils._optional_dependencies import check_matplotlib_support from sklearn.utils._plotting import _interval_max_min_ratio, _validate_score_name class _BaseCurveDisplay: def _plot_curve( self, x_data, *, ax=None, negate_score=False, score_name=None, score_type="test", std_display_style="fill_between", line_kw=None, fill_between_kw=None, errorbar_kw=None, ): check_matplotlib_support(f"{self.__class__.__name__}.plot") import matplotlib.pyplot as plt if ax is None: _, ax = plt.subplots() if negate_score: train_scores, test_scores = -self.train_scores, -self.test_scores else: train_scores, test_scores = self.train_scores, self.test_scores if std_display_style not in ("errorbar", "fill_between", None): raise ValueError( f"Unknown std_display_style: {std_display_style}. Should be one of" " 'errorbar', 'fill_between', or None." ) if score_type not in ("test", "train", "both"): raise ValueError( f"Unknown score_type: {score_type}. Should be one of 'test', " "'train', or 'both'." ) if score_type == "train": scores = {"Train": train_scores} elif score_type == "test": scores = {"Test": test_scores} else: # score_type == "both" scores = {"Train": train_scores, "Test": test_scores} if std_display_style in ("fill_between", None): # plot the mean score if line_kw is None: line_kw = {} self.lines_ = [] for line_label, score in scores.items(): self.lines_.append( *ax.plot( x_data, score.mean(axis=1), label=line_label, **line_kw, ) ) self.errorbar_ = None self.fill_between_ = None # overwritten below by fill_between if std_display_style == "errorbar": if errorbar_kw is None: errorbar_kw = {} self.errorbar_ = [] for line_label, score in scores.items(): self.errorbar_.append( ax.errorbar( x_data, score.mean(axis=1), score.std(axis=1), label=line_label, **errorbar_kw, ) ) self.lines_, self.fill_between_ = None, None elif std_display_style == "fill_between": if fill_between_kw is None: fill_between_kw = {} default_fill_between_kw = {"alpha": 0.5} fill_between_kw = {**default_fill_between_kw, **fill_between_kw} self.fill_between_ = [] for line_label, score in scores.items(): self.fill_between_.append( ax.fill_between( x_data, score.mean(axis=1) - score.std(axis=1), score.mean(axis=1) + score.std(axis=1), **fill_between_kw, ) ) score_name = self.score_name if score_name is None else score_name ax.legend() # We found that a ratio, smaller or bigger than 5, between the largest and # smallest gap of the x values is a good indicator to choose between linear # and log scale. if _interval_max_min_ratio(x_data) > 5: xscale = "symlog" if x_data.min() <= 0 else "log" else: xscale = "linear" ax.set_xscale(xscale) ax.set_ylabel(f"{score_name}") self.ax_ = ax self.figure_ = ax.figure class LearningCurveDisplay(_BaseCurveDisplay): """Learning Curve visualization. It is recommended to use :meth:`~sklearn.model_selection.LearningCurveDisplay.from_estimator` to create a :class:`~sklearn.model_selection.LearningCurveDisplay` instance. All parameters are stored as attributes. Read more in the :ref:`User Guide <visualizations>` for general information about the visualization API and :ref:`detailed documentation <learning_curve>` regarding the learning curve visualization. .. versionadded:: 1.2 Parameters ---------- train_sizes : ndarray of shape (n_unique_ticks,) Numbers of training examples that has been used to generate the learning curve. train_scores : ndarray of shape (n_ticks, n_cv_folds) Scores on training sets. test_scores : ndarray of shape (n_ticks, n_cv_folds) Scores on test set. score_name : str, default=None The name of the score used in `learning_curve`. It will override the name inferred from the `scoring` parameter. If `score` is `None`, we use `"Score"` if `negate_score` is `False` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `_` by spaces and capitalize the first letter. We remove `neg_` and replace it by `"Negative"` if `negate_score` is `False` or just remove it otherwise. Attributes ---------- ax_ : matplotlib Axes Axes with the learning curve. figure_ : matplotlib Figure Figure containing the learning curve. errorbar_ : list of matplotlib Artist or None When the `std_display_style` is `"errorbar"`, this is a list of `matplotlib.container.ErrorbarContainer` objects. If another style is used, `errorbar_` is `None`. lines_ : list of matplotlib Artist or None When the `std_display_style` is `"fill_between"`, this is a list of `matplotlib.lines.Line2D` objects corresponding to the mean train and test scores. If another style is used, `line_` is `None`. fill_between_ : list of matplotlib Artist or None When the `std_display_style` is `"fill_between"`, this is a list of `matplotlib.collections.PolyCollection` objects. If another style is used, `fill_between_` is `None`. See Also -------- sklearn.model_selection.learning_curve : Compute the learning curve. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import LearningCurveDisplay, learning_curve >>> from sklearn.tree import DecisionTreeClassifier >>> X, y = load_iris(return_X_y=True) >>> tree = DecisionTreeClassifier(random_state=0) >>> train_sizes, train_scores, test_scores = learning_curve( ... tree, X, y) >>> display = LearningCurveDisplay(train_sizes=train_sizes, ... train_scores=train_scores, test_scores=test_scores, score_name="Score") >>> display.plot() <...> >>> plt.show() """ def __init__(self, *, train_sizes, train_scores, test_scores, score_name=None): self.train_sizes = train_sizes self.train_scores = train_scores self.test_scores = test_scores self.score_name = score_name def plot( self, ax=None, *, negate_score=False, score_name=None, score_type="both", std_display_style="fill_between", line_kw=None, fill_between_kw=None, errorbar_kw=None, ): """Plot visualization. Parameters ---------- ax : matplotlib Axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. negate_score : bool, default=False Whether or not to negate the scores obtained through :func:`~sklearn.model_selection.learning_curve`. This is particularly useful when using the error denoted by `neg_*` in `scikit-learn`. score_name : str, default=None The name of the score used to decorate the y-axis of the plot. It will override the name inferred from the `scoring` parameter. If `score` is `None`, we use `"Score"` if `negate_score` is `False` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `_` by spaces and capitalize the first letter. We remove `neg_` and replace it by `"Negative"` if `negate_score` is `False` or just remove it otherwise. score_type : {"test", "train", "both"}, default="both" The type of score to plot. Can be one of `"test"`, `"train"`, or `"both"`. std_display_style : {"errorbar", "fill_between"} or None, default="fill_between" The style used to display the score standard deviation around the mean score. If None, no standard deviation representation is displayed. line_kw : dict, default=None Additional keyword arguments passed to the `plt.plot` used to draw the mean score. fill_between_kw : dict, default=None Additional keyword arguments passed to the `plt.fill_between` used to draw the score standard deviation. errorbar_kw : dict, default=None Additional keyword arguments passed to the `plt.errorbar` used to draw mean score and standard deviation score. Returns ------- display : :class:`~sklearn.model_selection.LearningCurveDisplay` Object that stores computed values. """ self._plot_curve( self.train_sizes, ax=ax, negate_score=negate_score, score_name=score_name, score_type=score_type, std_display_style=std_display_style, line_kw=line_kw, fill_between_kw=fill_between_kw, errorbar_kw=errorbar_kw, ) self.ax_.set_xlabel("Number of samples in the training set") return self @classmethod def from_estimator( cls, estimator, X, y, *, groups=None, train_sizes=np.linspace(0.1, 1.0, 5), cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=None, pre_dispatch="all", verbose=0, shuffle=False, random_state=None, error_score=np.nan, fit_params=None, ax=None, negate_score=False, score_name=None, score_type="both", std_display_style="fill_between", line_kw=None, fill_between_kw=None, errorbar_kw=None, ): """Create a learning curve display from an estimator. Read more in the :ref:`User Guide <visualizations>` for general information about the visualization API and :ref:`detailed documentation <learning_curve>` regarding the learning curve visualization. Parameters ---------- estimator : object type that implements the "fit" and "predict" methods An object of that type which is cloned for each validation. X : array-like of shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) or (n_samples, n_outputs) or None Target relative to X for classification or regression; None for unsupervised learning. groups : array-like of shape (n_samples,), default=None Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a "Group" :term:`cv` instance (e.g., :class:`GroupKFold`). train_sizes : array-like of shape (n_ticks,), \ default=np.linspace(0.1, 1.0, 5) Relative or absolute numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum size of the training set (that is determined by the selected validation method), i.e. it has to be within (0, 1]. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class. cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - int, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - an iterable yielding (train, test) splits as arrays of indices. For int/None inputs, if the estimator is a classifier and `y` is either binary or multiclass, :class:`~sklearn.model_selection.StratifiedKFold` is used. In all other cases, :class:`~sklearn.model_selection.KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here. scoring : str or callable, default=None The scoring method to use when calculating the learning curve. Options: - str: see :ref:`scoring_string_names` for options. - callable: a scorer callable object (e.g., function) with signature ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details. - `None`: the `estimator`'s :ref:`default evaluation criterion <scoring_api_overview>` is used. exploit_incremental_learning : bool, default=False If the estimator supports incremental learning, this will be used to speed up fitting for different training set sizes. n_jobs : int, default=None Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the different training and test sets. `None` means 1 unless in a :obj:`joblib.parallel_backend` context. `-1` means using all processors. See :term:`Glossary <n_jobs>` for more details. pre_dispatch : int or str, default='all' Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The str can be an expression like '2*n_jobs'. verbose : int, default=0 Controls the verbosity: the higher, the more messages. shuffle : bool, default=False Whether to shuffle training data before taking prefixes of it based on`train_sizes`. random_state : int, RandomState instance or None, default=None Used when `shuffle` is True. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. error_score : 'raise' or numeric, default=np.nan Value to assign to the score if an error occurs in estimator fitting. If set to 'raise', the error is raised. If a numeric value is given, FitFailedWarning is raised. fit_params : dict, default=None Parameters to pass to the fit method of the estimator. ax : matplotlib Axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. negate_score : bool, default=False Whether or not to negate the scores obtained through :func:`~sklearn.model_selection.learning_curve`. This is particularly useful when using the error denoted by `neg_*` in `scikit-learn`. score_name : str, default=None The name of the score used to decorate the y-axis of the plot. It will override the name inferred from the `scoring` parameter. If `score` is `None`, we use `"Score"` if `negate_score` is `False` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `_` by spaces and capitalize the first letter. We remove `neg_` and replace it by `"Negative"` if `negate_score` is `False` or just remove it otherwise. score_type : {"test", "train", "both"}, default="both" The type of score to plot. Can be one of `"test"`, `"train"`, or `"both"`. std_display_style : {"errorbar", "fill_between"} or None, default="fill_between" The style used to display the score standard deviation around the mean score. If `None`, no representation of the standard deviation is displayed. line_kw : dict, default=None Additional keyword arguments passed to the `plt.plot` used to draw the mean score. fill_between_kw : dict, default=None Additional keyword arguments passed to the `plt.fill_between` used to draw the score standard deviation. errorbar_kw : dict, default=None Additional keyword arguments passed to the `plt.errorbar` used to draw mean score and standard deviation score. Returns ------- display : :class:`~sklearn.model_selection.LearningCurveDisplay` Object that stores computed values. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import LearningCurveDisplay >>> from sklearn.tree import DecisionTreeClassifier >>> X, y = load_iris(return_X_y=True) >>> tree = DecisionTreeClassifier(random_state=0) >>> LearningCurveDisplay.from_estimator(tree, X, y) <...> >>> plt.show() """ check_matplotlib_support(f"{cls.__name__}.from_estimator") score_name = _validate_score_name(score_name, scoring, negate_score) train_sizes, train_scores, test_scores = learning_curve( estimator, X, y, groups=groups, train_sizes=train_sizes, cv=cv, scoring=scoring, exploit_incremental_learning=exploit_incremental_learning, n_jobs=n_jobs, pre_dispatch=pre_dispatch, verbose=verbose, shuffle=shuffle, random_state=random_state, error_score=error_score, return_times=False, params=fit_params, ) viz = cls( train_sizes=train_sizes, train_scores=train_scores, test_scores=test_scores, score_name=score_name, ) return viz.plot( ax=ax, negate_score=negate_score, score_type=score_type, std_display_style=std_display_style, line_kw=line_kw, fill_between_kw=fill_between_kw, errorbar_kw=errorbar_kw, ) class ValidationCurveDisplay(_BaseCurveDisplay): """Validation Curve visualization. It is recommended to use :meth:`~sklearn.model_selection.ValidationCurveDisplay.from_estimator` to create a :class:`~sklearn.model_selection.ValidationCurveDisplay` instance. All parameters are stored as attributes. Read more in the :ref:`User Guide <visualizations>` for general information about the visualization API and :ref:`detailed documentation <validation_curve>` regarding the validation curve visualization. .. versionadded:: 1.3 Parameters ---------- param_name : str Name of the parameter that has been varied. param_range : array-like of shape (n_ticks,) The values of the parameter that have been evaluated. train_scores : ndarray of shape (n_ticks, n_cv_folds) Scores on training sets. test_scores : ndarray of shape (n_ticks, n_cv_folds) Scores on test set. score_name : str, default=None The name of the score used in `validation_curve`. It will override the name inferred from the `scoring` parameter. If `score` is `None`, we use `"Score"` if `negate_score` is `False` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `_` by spaces and capitalize the first letter. We remove `neg_` and replace it by `"Negative"` if `negate_score` is `False` or just remove it otherwise. Attributes ---------- ax_ : matplotlib Axes Axes with the validation curve. figure_ : matplotlib Figure Figure containing the validation curve. errorbar_ : list of matplotlib Artist or None When the `std_display_style` is `"errorbar"`, this is a list of `matplotlib.container.ErrorbarContainer` objects. If another style is used, `errorbar_` is `None`. lines_ : list of matplotlib Artist or None When the `std_display_style` is `"fill_between"`, this is a list of `matplotlib.lines.Line2D` objects corresponding to the mean train and test scores. If another style is used, `line_` is `None`. fill_between_ : list of matplotlib Artist or None When the `std_display_style` is `"fill_between"`, this is a list of `matplotlib.collections.PolyCollection` objects. If another style is used, `fill_between_` is `None`. See Also -------- sklearn.model_selection.validation_curve : Compute the validation curve. Examples -------- >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import ValidationCurveDisplay, validation_curve >>> from sklearn.linear_model import LogisticRegression >>> X, y = make_classification(n_samples=1_000, random_state=0) >>> logistic_regression = LogisticRegression() >>> param_name, param_range = "C", np.logspace(-8, 3, 10) >>> train_scores, test_scores = validation_curve( ... logistic_regression, X, y, param_name=param_name, param_range=param_range ... ) >>> display = ValidationCurveDisplay( ... param_name=param_name, param_range=param_range, ... train_scores=train_scores, test_scores=test_scores, score_name="Score" ... ) >>> display.plot() <...> >>> plt.show() """ def __init__( self, *, param_name, param_range, train_scores, test_scores, score_name=None ): self.param_name = param_name self.param_range = param_range self.train_scores = train_scores self.test_scores = test_scores self.score_name = score_name def plot( self, ax=None, *, negate_score=False, score_name=None, score_type="both", std_display_style="fill_between", line_kw=None, fill_between_kw=None, errorbar_kw=None, ): """Plot visualization. Parameters ---------- ax : matplotlib Axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. negate_score : bool, default=False Whether or not to negate the scores obtained through :func:`~sklearn.model_selection.validation_curve`. This is particularly useful when using the error denoted by `neg_*` in `scikit-learn`. score_name : str, default=None The name of the score used to decorate the y-axis of the plot. It will override the name inferred from the `scoring` parameter. If `score` is `None`, we use `"Score"` if `negate_score` is `False` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `_` by spaces and capitalize the first letter. We remove `neg_` and replace it by `"Negative"` if `negate_score` is `False` or just remove it otherwise. score_type : {"test", "train", "both"}, default="both" The type of score to plot. Can be one of `"test"`, `"train"`, or `"both"`. std_display_style : {"errorbar", "fill_between"} or None, default="fill_between" The style used to display the score standard deviation around the mean score. If None, no standard deviation representation is displayed. line_kw : dict, default=None Additional keyword arguments passed to the `plt.plot` used to draw the mean score. fill_between_kw : dict, default=None Additional keyword arguments passed to the `plt.fill_between` used to draw the score standard deviation. errorbar_kw : dict, default=None Additional keyword arguments passed to the `plt.errorbar` used to draw mean score and standard deviation score. Returns ------- display : :class:`~sklearn.model_selection.ValidationCurveDisplay` Object that stores computed values. """ self._plot_curve( self.param_range, ax=ax, negate_score=negate_score, score_name=score_name, score_type=score_type, std_display_style=std_display_style, line_kw=line_kw, fill_between_kw=fill_between_kw, errorbar_kw=errorbar_kw, ) self.ax_.set_xlabel(f"{self.param_name}") return self @classmethod def from_estimator( cls, estimator, X, y, *, param_name, param_range, groups=None, cv=None, scoring=None, n_jobs=None, pre_dispatch="all", verbose=0, error_score=np.nan, fit_params=None, ax=None, negate_score=False, score_name=None, score_type="both", std_display_style="fill_between", line_kw=None, fill_between_kw=None, errorbar_kw=None, ): """Create a validation curve display from an estimator. Read more in the :ref:`User Guide <visualizations>` for general information about the visualization API and :ref:`detailed documentation <validation_curve>` regarding the validation curve visualization. Parameters ---------- estimator : object type that implements the "fit" and "predict" methods An object of that type which is cloned for each validation. X : array-like of shape (n_samples, n_features) Training data, where `n_samples` is the number of samples and `n_features` is the number of features. y : array-like of shape (n_samples,) or (n_samples, n_outputs) or None Target relative to X for classification or regression; None for unsupervised learning. param_name : str Name of the parameter that will be varied. param_range : array-like of shape (n_values,) The values of the parameter that will be evaluated. groups : array-like of shape (n_samples,), default=None Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a "Group" :term:`cv` instance (e.g., :class:`GroupKFold`). cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - int, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - an iterable yielding (train, test) splits as arrays of indices. For int/None inputs, if the estimator is a classifier and `y` is either binary or multiclass, :class:`~sklearn.model_selection.StratifiedKFold` is used. In all other cases, :class:`~sklearn.model_selection.KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here. scoring : str or callable, default=None Scoring method to use when computing the validation curve. Options: - str: see :ref:`scoring_string_names` for options. - callable: a scorer callable object (e.g., function) with signature ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details. - `None`: the `estimator`'s :ref:`default evaluation criterion <scoring_api_overview>` is used. n_jobs : int, default=None Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the different training and test sets. `None` means 1 unless in a :obj:`joblib.parallel_backend` context. `-1` means using all processors. See :term:`Glossary <n_jobs>` for more details. pre_dispatch : int or str, default='all' Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The str can be an expression like '2*n_jobs'. verbose : int, default=0 Controls the verbosity: the higher, the more messages. error_score : 'raise' or numeric, default=np.nan Value to assign to the score if an error occurs in estimator fitting. If set to 'raise', the error is raised. If a numeric value is given, FitFailedWarning is raised. fit_params : dict, default=None Parameters to pass to the fit method of the estimator. ax : matplotlib Axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. negate_score : bool, default=False Whether or not to negate the scores obtained through :func:`~sklearn.model_selection.validation_curve`. This is particularly useful when using the error denoted by `neg_*` in `scikit-learn`. score_name : str, default=None The name of the score used to decorate the y-axis of the plot. It will override the name inferred from the `scoring` parameter. If `score` is `None`, we use `"Score"` if `negate_score` is `False` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `_` by spaces and capitalize the first letter. We remove `neg_` and replace it by `"Negative"` if `negate_score` is `False` or just remove it otherwise. score_type : {"test", "train", "both"}, default="both" The type of score to plot. Can be one of `"test"`, `"train"`, or `"both"`. std_display_style : {"errorbar", "fill_between"} or None, default="fill_between" The style used to display the score standard deviation around the mean score. If `None`, no representation of the standard deviation is displayed. line_kw : dict, default=None Additional keyword arguments passed to the `plt.plot` used to draw the mean score. fill_between_kw : dict, default=None Additional keyword arguments passed to the `plt.fill_between` used to draw the score standard deviation. errorbar_kw : dict, default=None Additional keyword arguments passed to the `plt.errorbar` used to draw mean score and standard deviation score. Returns ------- display : :class:`~sklearn.model_selection.ValidationCurveDisplay` Object that stores computed values. Examples -------- >>> import numpy as np >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import ValidationCurveDisplay >>> from sklearn.linear_model import LogisticRegression >>> X, y = make_classification(n_samples=1_000, random_state=0) >>> logistic_regression = LogisticRegression() >>> param_name, param_range = "C", np.logspace(-8, 3, 10) >>> ValidationCurveDisplay.from_estimator( ... logistic_regression, X, y, param_name=param_name, ... param_range=param_range, ... ) <...> >>> plt.show() """ check_matplotlib_support(f"{cls.__name__}.from_estimator") score_name = _validate_score_name(score_name, scoring, negate_score) train_scores, test_scores = validation_curve( estimator, X, y, param_name=param_name, param_range=param_range, groups=groups, cv=cv, scoring=scoring, n_jobs=n_jobs, pre_dispatch=pre_dispatch, verbose=verbose, error_score=error_score, params=fit_params, ) viz = cls( param_name=param_name, param_range=np.asarray(param_range), train_scores=train_scores, test_scores=test_scores, score_name=score_name, ) return viz.plot( ax=ax, negate_score=negate_score, score_type=score_type, std_display_style=std_display_style, line_kw=line_kw, fill_between_kw=fill_between_kw, errorbar_kw=errorbar_kw, )