import numpy as np
from . import learning_curve
from ..utils import check_matplotlib_support
class LearningCurveDisplay:
"""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>`.
.. 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 be used to
decorate the y-axis. If `None`, the generic name `"Score"` will be
used.
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="test",
log_scale=False,
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. If
`None`, the generic name "Score" will be used.
score_type : {"test", "train", "both"}, default="test"
The type of score to plot. Can be one of `"test"`, `"train"`, or
`"both"`.
log_scale : bool, default=False
Whether or not to use a logarithmic scale for the x-axis.
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.
"""
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 = {"Training metric": train_scores}
elif score_type == "test":
scores = {"Testing metric": test_scores}
else: # score_type == "both"
scores = {"Training metric": train_scores, "Testing metric": 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(
self.train_sizes,
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(
self.train_sizes,
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(
self.train_sizes,
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()
if log_scale:
ax.set_xscale("log")
ax.set_xlabel("Number of samples in the training set")
ax.set_ylabel(f"{score_name}")
self.ax_ = ax
self.figure_ = ax.figure
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="test",
log_scale=False,
std_display_style="fill_between",
line_kw=None,
fill_between_kw=None,
errorbar_kw=None,
):
"""Create a learning curve display from an estimator.
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_selectionKFold` 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
A string (see :ref:`scoring_parameter`) or
a scorer callable object / function with signature
`scorer(estimator, X, y)` (see :ref:`scoring`).
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.
If `None`, the generic `"Score"` name will be used.
score_type : {"test", "train", "both"}, default="test"
The type of score to plot. Can be one of `"test"`, `"train"`, or
`"both"`.
log_scale : bool, default=False
Whether or not to use a logarithmic scale for the x-axis.
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 = "Score" if score_name is None else score_name
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,
fit_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,
log_scale=log_scale,
std_display_style=std_display_style,
line_kw=line_kw,
fill_between_kw=fill_between_kw,
errorbar_kw=errorbar_kw,
)