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