# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numbers
import numpy as np
from ...utils import _safe_indexing, check_random_state
from ...utils._optional_dependencies import check_matplotlib_support
from ...utils._plotting import _validate_style_kwargs
class PredictionErrorDisplay:
"""Visualization of the prediction error of a regression model.
This tool can display "residuals vs predicted" or "actual vs predicted"
using scatter plots to qualitatively assess the behavior of a regressor,
preferably on held-out data points.
See the details in the docstrings of
:func:`~sklearn.metrics.PredictionErrorDisplay.from_estimator` or
:func:`~sklearn.metrics.PredictionErrorDisplay.from_predictions` to
create a visualizer. All parameters are stored as attributes.
For general information regarding `scikit-learn` visualization tools, read
more in the :ref:`Visualization Guide <visualizations>`.
For details regarding interpreting these plots, refer to the
:ref:`Model Evaluation Guide <visualization_regression_evaluation>`.
.. versionadded:: 1.2
Parameters
----------
y_true : ndarray of shape (n_samples,)
True values.
y_pred : ndarray of shape (n_samples,)
Prediction values.
Attributes
----------
line_ : matplotlib Artist
Optimal line representing `y_true == y_pred`. Therefore, it is a
diagonal line for `kind="predictions"` and a horizontal line for
`kind="residuals"`.
errors_lines_ : matplotlib Artist or None
Residual lines. If `with_errors=False`, then it is set to `None`.
scatter_ : matplotlib Artist
Scatter data points.
ax_ : matplotlib Axes
Axes with the different matplotlib axis.
figure_ : matplotlib Figure
Figure containing the scatter and lines.
See Also
--------
PredictionErrorDisplay.from_estimator : Prediction error visualization
given an estimator and some data.
PredictionErrorDisplay.from_predictions : Prediction error visualization
given the true and predicted targets.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from sklearn.metrics import PredictionErrorDisplay
>>> X, y = load_diabetes(return_X_y=True)
>>> ridge = Ridge().fit(X, y)
>>> y_pred = ridge.predict(X)
>>> display = PredictionErrorDisplay(y_true=y, y_pred=y_pred)
>>> display.plot()
<...>
>>> plt.show()
"""
def __init__(self, *, y_true, y_pred):
self.y_true = y_true
self.y_pred = y_pred
def plot(
self,
ax=None,
*,
kind="residual_vs_predicted",
scatter_kwargs=None,
line_kwargs=None,
):
"""Plot visualization.
Extra keyword arguments will be passed to matplotlib's ``plot``.
Parameters
----------
ax : matplotlib axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
kind : {"actual_vs_predicted", "residual_vs_predicted"}, \
default="residual_vs_predicted"
The type of plot to draw:
- "actual_vs_predicted" draws the observed values (y-axis) vs.
the predicted values (x-axis).
- "residual_vs_predicted" draws the residuals, i.e. difference
between observed and predicted values, (y-axis) vs. the predicted
values (x-axis).
scatter_kwargs : dict, default=None
Dictionary with keywords passed to the `matplotlib.pyplot.scatter`
call.
line_kwargs : dict, default=None
Dictionary with keyword passed to the `matplotlib.pyplot.plot`
call to draw the optimal line.
Returns
-------
display : :class:`~sklearn.metrics.PredictionErrorDisplay`
Object that stores computed values.
"""
check_matplotlib_support(f"{self.__class__.__name__}.plot")
expected_kind = ("actual_vs_predicted", "residual_vs_predicted")
if kind not in expected_kind:
raise ValueError(
f"`kind` must be one of {', '.join(expected_kind)}. "
f"Got {kind!r} instead."
)
import matplotlib.pyplot as plt
if scatter_kwargs is None:
scatter_kwargs = {}
if line_kwargs is None:
line_kwargs = {}
default_scatter_kwargs = {"color": "tab:blue", "alpha": 0.8}
default_line_kwargs = {"color": "black", "alpha": 0.7, "linestyle": "--"}
scatter_kwargs = _validate_style_kwargs(default_scatter_kwargs, scatter_kwargs)
line_kwargs = _validate_style_kwargs(default_line_kwargs, line_kwargs)
scatter_kwargs = {**default_scatter_kwargs, **scatter_kwargs}
line_kwargs = {**default_line_kwargs, **line_kwargs}
if ax is None:
_, ax = plt.subplots()
if kind == "actual_vs_predicted":
max_value = max(np.max(self.y_true), np.max(self.y_pred))
min_value = min(np.min(self.y_true), np.min(self.y_pred))
self.line_ = ax.plot(
[min_value, max_value], [min_value, max_value], **line_kwargs
)[0]
x_data, y_data = self.y_pred, self.y_true
xlabel, ylabel = "Predicted values", "Actual values"
self.scatter_ = ax.scatter(x_data, y_data, **scatter_kwargs)
# force to have a squared axis
ax.set_aspect("equal", adjustable="datalim")
ax.set_xticks(np.linspace(min_value, max_value, num=5))
ax.set_yticks(np.linspace(min_value, max_value, num=5))
else: # kind == "residual_vs_predicted"
self.line_ = ax.plot(
[np.min(self.y_pred), np.max(self.y_pred)],
[0, 0],
**line_kwargs,
)[0]
self.scatter_ = ax.scatter(
self.y_pred, self.y_true - self.y_pred, **scatter_kwargs
)
xlabel, ylabel = "Predicted values", "Residuals (actual - predicted)"
ax.set(xlabel=xlabel, ylabel=ylabel)
self.ax_ = ax
self.figure_ = ax.figure
return self
@classmethod
def from_estimator(
cls,
estimator,
X,
y,
*,
kind="residual_vs_predicted",
subsample=1_000,
random_state=None,
ax=None,
scatter_kwargs=None,
line_kwargs=None,
):
"""Plot the prediction error given a regressor and some data.
For general information regarding `scikit-learn` visualization tools,
read more in the :ref:`Visualization Guide <visualizations>`.
For details regarding interpreting these plots, refer to the
:ref:`Model Evaluation Guide <visualization_regression_evaluation>`.
.. versionadded:: 1.2
Parameters
----------
estimator : estimator instance
Fitted regressor or a fitted :class:`~sklearn.pipeline.Pipeline`
in which the last estimator is a regressor.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input values.
y : array-like of shape (n_samples,)
Target values.
kind : {"actual_vs_predicted", "residual_vs_predicted"}, \
default="residual_vs_predicted"
The type of plot to draw:
- "actual_vs_predicted" draws the observed values (y-axis) vs.
the predicted values (x-axis).
- "residual_vs_predicted" draws the residuals, i.e. difference
between observed and predicted values, (y-axis) vs. the predicted
values (x-axis).
subsample : float, int or None, default=1_000
Sampling the samples to be shown on the scatter plot. If `float`,
it should be between 0 and 1 and represents the proportion of the
original dataset. If `int`, it represents the number of samples
display on the scatter plot. If `None`, no subsampling will be
applied. by default, 1000 samples or less will be displayed.
random_state : int or RandomState, default=None
Controls the randomness when `subsample` is not `None`.
See :term:`Glossary <random_state>` for details.
ax : matplotlib axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
scatter_kwargs : dict, default=None
Dictionary with keywords passed to the `matplotlib.pyplot.scatter`
call.
line_kwargs : dict, default=None
Dictionary with keyword passed to the `matplotlib.pyplot.plot`
call to draw the optimal line.
Returns
-------
display : :class:`~sklearn.metrics.PredictionErrorDisplay`
Object that stores the computed values.
See Also
--------
PredictionErrorDisplay : Prediction error visualization for regression.
PredictionErrorDisplay.from_predictions : Prediction error visualization
given the true and predicted targets.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from sklearn.metrics import PredictionErrorDisplay
>>> X, y = load_diabetes(return_X_y=True)
>>> ridge = Ridge().fit(X, y)
>>> disp = PredictionErrorDisplay.from_estimator(ridge, X, y)
>>> plt.show()
"""
check_matplotlib_support(f"{cls.__name__}.from_estimator")
y_pred = estimator.predict(X)
return cls.from_predictions(
y_true=y,
y_pred=y_pred,
kind=kind,
subsample=subsample,
random_state=random_state,
ax=ax,
scatter_kwargs=scatter_kwargs,
line_kwargs=line_kwargs,
)
@classmethod
def from_predictions(
cls,
y_true,
y_pred,
*,
kind="residual_vs_predicted",
subsample=1_000,
random_state=None,
ax=None,
scatter_kwargs=None,
line_kwargs=None,
):
"""Plot the prediction error given the true and predicted targets.
For general information regarding `scikit-learn` visualization tools,
read more in the :ref:`Visualization Guide <visualizations>`.
For details regarding interpreting these plots, refer to the
:ref:`Model Evaluation Guide <visualization_regression_evaluation>`.
.. versionadded:: 1.2
Parameters
----------
y_true : array-like of shape (n_samples,)
True target values.
y_pred : array-like of shape (n_samples,)
Predicted target values.
kind : {"actual_vs_predicted", "residual_vs_predicted"}, \
default="residual_vs_predicted"
The type of plot to draw:
- "actual_vs_predicted" draws the observed values (y-axis) vs.
the predicted values (x-axis).
- "residual_vs_predicted" draws the residuals, i.e. difference
between observed and predicted values, (y-axis) vs. the predicted
values (x-axis).
subsample : float, int or None, default=1_000
Sampling the samples to be shown on the scatter plot. If `float`,
it should be between 0 and 1 and represents the proportion of the
original dataset. If `int`, it represents the number of samples
display on the scatter plot. If `None`, no subsampling will be
applied. by default, 1000 samples or less will be displayed.
random_state : int or RandomState, default=None
Controls the randomness when `subsample` is not `None`.
See :term:`Glossary <random_state>` for details.
ax : matplotlib axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
scatter_kwargs : dict, default=None
Dictionary with keywords passed to the `matplotlib.pyplot.scatter`
call.
line_kwargs : dict, default=None
Dictionary with keyword passed to the `matplotlib.pyplot.plot`
call to draw the optimal line.
Returns
-------
display : :class:`~sklearn.metrics.PredictionErrorDisplay`
Object that stores the computed values.
See Also
--------
PredictionErrorDisplay : Prediction error visualization for regression.
PredictionErrorDisplay.from_estimator : Prediction error visualization
given an estimator and some data.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from sklearn.metrics import PredictionErrorDisplay
>>> X, y = load_diabetes(return_X_y=True)
>>> ridge = Ridge().fit(X, y)
>>> y_pred = ridge.predict(X)
>>> disp = PredictionErrorDisplay.from_predictions(y_true=y, y_pred=y_pred)
>>> plt.show()
"""
check_matplotlib_support(f"{cls.__name__}.from_predictions")
random_state = check_random_state(random_state)
n_samples = len(y_true)
if isinstance(subsample, numbers.Integral):
if subsample <= 0:
raise ValueError(
f"When an integer, subsample={subsample} should be positive."
)
elif isinstance(subsample, numbers.Real):
if subsample <= 0 or subsample >= 1:
raise ValueError(
f"When a floating-point, subsample={subsample} should"
" be in the (0, 1) range."
)
subsample = int(n_samples * subsample)
if subsample is not None and subsample < n_samples:
indices = random_state.choice(np.arange(n_samples), size=subsample)
y_true = _safe_indexing(y_true, indices, axis=0)
y_pred = _safe_indexing(y_pred, indices, axis=0)
viz = cls(
y_true=y_true,
y_pred=y_pred,
)
return viz.plot(
ax=ax,
kind=kind,
scatter_kwargs=scatter_kwargs,
line_kwargs=line_kwargs,
)