]jr9PSSKrSSKrSSKJrJr SSKJr SSKJ r "SS5r g)N)_safe_indexingcheck_random_state)check_matplotlib_support)_validate_style_kwargscz\rSrSrSrSrS SSSS.Sjjr\SSSSSSS .S j5r\SSSSSSS .S j5r S r g)PredictionErrorDisplay aVisualization 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 `. For details regarding interpreting these plots, refer to the :ref:`Model Evaluation Guide `. .. 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() cXlX lgNy_truey_pred)selfr rs ]/home/mande/repo/quber/.venv/lib/python3.13/site-packages/sklearn/metrics/_plot/regression.py__init__PredictionErrorDisplay.__init__Qs   Nresidual_vs_predicted)kindscatter_kwargs line_kwargscB[URRS35 SnX%;a"[SSR U5SU<S35eSSKJn Uc0nUc0nS S S .nS S SS.n[Xs5n[X5n0UEUEn0UEUEnUcUR5upUS:XGa:[[R"UR5[R"UR55n [[R"UR5[R"UR55n UR"X/X/40UD6SUlURURpSupUR "X40UD6UlUR%SSS9 UR'[R("XSS95 UR+[R("XSS95 OUR"[R"UR5[R"UR5/SS/40UD6SUlUR "URURUR- 40UD6UlSupUR-XS9 XlUR0UlU$)aPlot visualization. Extra keyword arguments will be passed to matplotlib's ``plot``. Parameters ---------- ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. kind : {"actual_vs_predicted", "residual_vs_predicted"}, default="residual_vs_predicted" The type of plot to draw: - "actual_vs_predicted" draws the observed values (y-axis) vs. the predicted values (x-axis). - "residual_vs_predicted" draws the residuals, i.e. difference between observed and predicted values, (y-axis) vs. the predicted values (x-axis). scatter_kwargs : dict, default=None Dictionary with keywords passed to the `matplotlib.pyplot.scatter` call. line_kwargs : dict, default=None Dictionary with keyword passed to the `matplotlib.pyplot.plot` call to draw the optimal line. Returns ------- display : :class:`~sklearn.metrics.PredictionErrorDisplay` Object that stores computed values. z.plot)actual_vs_predictedrz`kind` must be one of z, z. Got z instead.rNztab:blueg?)coloralphablackgffffff?z--)rr linestyler)Predicted valuesz Actual valuesequaldatalim) adjustable)num)rzResiduals (actual - predicted))xlabelylabel)r __class____name__ ValueErrorjoinmatplotlib.pyplotpyplotrsubplotsmaxnpr rminplotline_scatterscatter_ set_aspect set_xtickslinspace set_ytickssetax_figurefigure_)raxrrr expected_kindpltdefault_scatter_kwargsdefault_line_kwargs_ max_value min_valuex_datay_datar$r%s rr0PredictionErrorDisplay.plotUslT !DNN$;$;#BMDJ"[[$++F@NFJJvHHDM MM'iM 8 MM"++iB C MM"++iB C $bffT[[&9:A DJ JJ T[[4;;6:HDMRNF f,yy  ri)r subsample random_stater<rrc [URS35 URU5n URUU UUUUUU S9$)a[ 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 `. For details regarding interpreting these plots, refer to the :ref:`Model Evaluation Guide `. .. 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 ` 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() z.from_estimator)r rrrGrHr<rr)rr'predictfrom_predictions) cls estimatorXyrrGrHr<rrrs rfrom_estimator%PredictionErrorDisplay.from_estimatorsXt !CLL>!AB""1%##%)#$  rc[URS35 [U5n[U5n [ U[ R 5(aUS::a[SUS35eOG[ U[ R5(a(US::dUS:a[SUS35e[X-5nUb=XI:a8UR[R"U 5US9n [XSS 9n[X*SS 9nU"UUS 9n U RUUUUS 9$) a 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 `. For details regarding interpreting these plots, refer to the :ref:`Model Evaluation Guide `. .. 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 ` 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() z.from_predictionsrzWhen an integer, subsample=z should be positive.z!When a floating-point, subsample=z should be in the (0, 1) range.)size)axisr )r<rrr)rr'rlen isinstancenumbersIntegralr(Realintchoicer.arangerr0) rLr rrrGrHr<rr n_samplesindicesvizs rrK'PredictionErrorDisplay.from_predictions&s(l !CLL>1B!CD),7 K i!1!1 2 2A~ 1)Y)OG#F!rms"<I:P P r