Source code for yellowbrick.regressor.residuals

# yellowbrick.regressor.residuals
# Visualize the residuals between predicted and actual data for regression problems
#
# Author:   Rebecca Bilbro
# Author:   Benjamin Bengfort
# Created:  Fri Jun 03 10:30:36 2016 -0700
#
# Copyright (C) 2016 The scikit-yb developers
# For license information, see LICENSE.txt
#
# ID: residuals.py [7d3f5e6] benjamin@bengfort.com $

"""
Visualize the residuals between predicted and actual data for regression problems
"""

##########################################################################
## Imports
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import matplotlib.pyplot as plt

from scipy.stats import probplot

try:
    # Only available in Matplotlib >= 2.0.2
    from mpl_toolkits.axes_grid1 import make_axes_locatable
except ImportError:
    make_axes_locatable = None

from yellowbrick.draw import manual_legend
from yellowbrick.utils.decorators import memoized
from yellowbrick.style.palettes import LINE_COLOR
from yellowbrick.exceptions import YellowbrickValueError
from yellowbrick.regressor.base import RegressionScoreVisualizer

## Packages for export
__all__ = ["ResidualsPlot", "residuals_plot"]


##########################################################################
## Residuals Plots
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[docs]class ResidualsPlot(RegressionScoreVisualizer): """ A residual plot shows the residuals on the vertical axis and the independent variable on the horizontal axis. If the points are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a non-linear model is more appropriate. Parameters ---------- model : a Scikit-Learn regressor Should be an instance of a regressor, otherwise will raise a YellowbrickTypeError exception on instantiation. If the estimator is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by ``is_fitted``. ax : matplotlib Axes, default: None The axes to plot the figure on. If None is passed in the current axes will be used (or generated if required). hist : {True, False, None, 'density', 'frequency'}, default: True Draw a histogram showing the distribution of the residuals on the right side of the figure. Requires Matplotlib >= 2.0.2. If set to 'density', the probability density function will be plotted. If set to True or 'frequency' then the frequency will be plotted. qqplot : {True, False}, default: False Draw a Q-Q plot on the right side of the figure, comparing the quantiles of the residuals against quantiles of a standard normal distribution. Q-Q plot and histogram of residuals can not be plotted simultaneously, either `hist` or `qqplot` has to be set to False. train_color : color, default: 'b' Residuals for training data are ploted with this color but also given an opacity of 0.5 to ensure that the test data residuals are more visible. Can be any matplotlib color. test_color : color, default: 'g' Residuals for test data are plotted with this color. In order to create generalizable models, reserved test data residuals are of the most analytical interest, so these points are highlighted by having full opacity. Can be any matplotlib color. line_color : color, default: dark grey Defines the color of the zero error line, can be any matplotlib color. train_alpha : float, default: 0.75 Specify a transparency for traininig data, where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. test_alpha : float, default: 0.75 Specify a transparency for test data, where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. is_fitted : bool or str, default='auto' Specify if the wrapped estimator is already fitted. If False, the estimator will be fit when the visualizer is fit, otherwise, the estimator will not be modified. If 'auto' (default), a helper method will check if the estimator is fitted before fitting it again. kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. Attributes ---------- train_score_ : float The R^2 score that specifies the goodness of fit of the underlying regression model to the training data. test_score_ : float The R^2 score that specifies the goodness of fit of the underlying regression model to the test data. Examples -------- >>> from yellowbrick.regressor import ResidualsPlot >>> from sklearn.linear_model import Ridge >>> model = ResidualsPlot(Ridge()) >>> model.fit(X_train, y_train) >>> model.score(X_test, y_test) >>> model.show() Notes ----- ResidualsPlot is a ScoreVisualizer, meaning that it wraps a model and its primary entry point is the ``score()`` method. The residuals histogram feature requires matplotlib 2.0.2 or greater. """ def __init__( self, model, ax=None, hist=True, qqplot=False, train_color="b", test_color="g", line_color=LINE_COLOR, train_alpha=0.75, test_alpha=0.75, is_fitted="auto", **kwargs ): # Whether or not to check if the model is already fitted self.is_fitted = is_fitted # Initialize the visualizer base super(ResidualsPlot, self).__init__(model, ax=ax, **kwargs) # TODO: allow more scatter plot arguments for train and test points # See #475 (RE: ScatterPlotMixin) self.colors = { "train_point": train_color, "test_point": test_color, "line": line_color, } self.hist = hist if self.hist not in {True, "density", "frequency", None, False}: raise YellowbrickValueError( "'{}' is an invalid argument for hist, use None, True, " "False, 'density', or 'frequency'".format(hist) ) self.qqplot = qqplot if self.qqplot not in {True, False}: raise YellowbrickValueError( "'{}' is an invalid argument for qqplot, use True, " " or False".format(hist) ) if self.hist in {True, "density", "frequency"} and self.qqplot in {True}: raise YellowbrickValueError( "Set either hist or qqplot to False, can not plot " "both of them simultaneously." ) if self.hist in {True, "density", "frequency"}: self.hax # If hist is True, test the version availability if self.qqplot in {True}: self.qqax # If qqplot is True, test the version availability # Store labels and colors for the legend ordered by call self._labels, self._colors = [], [] self.alphas = {"train_point": train_alpha, "test_point": test_alpha} @memoized def hax(self): """ Returns the histogram axes, creating it only on demand. """ if make_axes_locatable is None: raise YellowbrickValueError( ( "residuals histogram requires matplotlib 2.0.2 or greater " "please upgrade matplotlib or set hist=False on the visualizer" ) ) divider = make_axes_locatable(self.ax) hax = divider.append_axes("right", size=1, pad=0.1, sharey=self.ax) hax.yaxis.tick_right() hax.grid(False, axis="x") return hax @memoized def qqax(self): """ Returns the Q-Q plot axes, creating it only on demand. """ if make_axes_locatable is None: raise YellowbrickValueError( ( "residuals histogram requires matplotlib 2.0.2 or greater " "please upgrade matplotlib or set qqplot=False on the visualizer" ) ) divider = make_axes_locatable(self.ax) qqax = divider.append_axes("right", size=2, pad=0.25, sharey=self.ax) qqax.yaxis.tick_right() return qqax
[docs] def fit(self, X, y, **kwargs): """ Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features y : ndarray or Series of length n An array or series of target values kwargs: keyword arguments passed to Scikit-Learn API. Returns ------- self : ResidualsPlot The visualizer instance """ # fit the underlying model to the data super(ResidualsPlot, self).fit(X, y, **kwargs) self.score(X, y, train=True) return self
[docs] def score(self, X, y=None, train=False, **kwargs): """ Generates predicted target values using the Scikit-Learn estimator. Parameters ---------- X : array-like X (also X_test) are the dependent variables of test set to predict y : array-like y (also y_test) is the independent actual variables to score against train : boolean If False, `score` assumes that the residual points being plotted are from the test data; if True, `score` assumes the residuals are the train data. Returns ------- score : float The score of the underlying estimator, usually the R-squared score for regression estimators. """ # Do not call super in order to differentiate train and test scores. score = self.estimator.score(X, y, **kwargs) if train: self.train_score_ = score else: self.test_score_ = score y_pred = self.predict(X) residuals = y_pred - y self.draw(y_pred, residuals, train=train) return score
[docs] def draw(self, y_pred, residuals, train=False, **kwargs): """ Draw the residuals against the predicted value for the specified split. It is best to draw the training split first, then the test split so that the test split (usually smaller) is above the training split; particularly if the histogram is turned on. Parameters ---------- y_pred : ndarray or Series of length n An array or series of predicted target values residuals : ndarray or Series of length n An array or series of the difference between the predicted and the target values train : boolean, default: False If False, `draw` assumes that the residual points being plotted are from the test data; if True, `draw` assumes the residuals are the train data. Returns ------- ax : matplotlib Axes The axis with the plotted figure """ if train: color = self.colors["train_point"] label = "Train $R^2 = {:0.3f}$".format(self.train_score_) alpha = self.alphas["train_point"] else: color = self.colors["test_point"] label = "Test $R^2 = {:0.3f}$".format(self.test_score_) alpha = self.alphas["test_point"] # Update the legend information self._labels.append(label) self._colors.append(color) # Draw the residuals scatter plot self.ax.scatter(y_pred, residuals, c=color, alpha=alpha, label=label) # Add residuals histogram if self.hist in {True, "frequency"}: self.hax.hist(residuals, bins=50, orientation="horizontal", color=color) elif self.hist == "density": self.hax.hist( residuals, bins=50, orientation="horizontal", density=True, color=color ) # Add residuals histogram if self.qqplot in {True}: osm, osr = probplot(residuals, dist="norm", fit=False) self.qqax.scatter(osm, osr, c=color, alpha=alpha, label=label) # Ensure the current axes is always the main residuals axes plt.sca(self.ax) return self.ax
[docs] def finalize(self, **kwargs): """ Prepares the plot for rendering by adding a title, legend, and axis labels. Also draws a line at the zero residuals to show the baseline. Parameters ---------- kwargs: generic keyword arguments. Notes ----- Generally this method is called from show and not directly by the user. """ # Add the title to the plot self.set_title("Residuals for {} Model".format(self.name)) # Set the legend with full opacity patches using manual legend manual_legend(self, self._labels, self._colors, loc="best", frameon=True) # Create a full line across the figure at zero error. self.ax.axhline(y=0, c=self.colors["line"]) # Set the axes labels self.ax.set_ylabel("Residuals") self.ax.set_xlabel("Predicted Value") # Finalize the histogram axes if self.hist: self.hax.axhline(y=0, c=self.colors["line"]) self.hax.set_xlabel("Distribution") # Finalize the histogram axes if self.qqplot: self.qqax.set_title("Q-Q plot") self.qqax.set_xlabel("Theoretical quantiles") self.qqax.set_ylabel("Observed quantiles")
########################################################################## ## Quick Method ##########################################################################
[docs]def residuals_plot( model, X_train, y_train, X_test=None, y_test=None, ax=None, hist=True, qqplot=False, train_color="b", test_color="g", line_color=LINE_COLOR, train_alpha=0.75, test_alpha=0.75, is_fitted="auto", show=True, **kwargs ): """ResidualsPlot quick method: A residual plot shows the residuals on the vertical axis and the independent variable on the horizontal axis. If the points are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a non-linear model is more appropriate. Parameters ---------- model : a Scikit-Learn regressor Should be an instance of a regressor, otherwise will raise a YellowbrickTypeError exception on instantiation. If the estimator is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by ``is_fitted``. X_train : ndarray or DataFrame of shape n x m A feature array of n instances with m features the model is trained on. Used to fit the visualizer and also to score the visualizer if test splits are not directly specified. y_train : ndarray or Series of length n An array or series of target or class values. Used to fit the visualizer and also to score the visualizer if test splits are not specified. X_test : ndarray or DataFrame of shape n x m, default: None An optional feature array of n instances with m features that the model is scored on if specified, using X_train as the training data. y_test : ndarray or Series of length n, default: None An optional array or series of target or class values that serve as actual labels for X_test for scoring purposes. ax : matplotlib Axes, default: None The axes to plot the figure on. If None is passed in the current axes will be used (or generated if required). hist : {True, False, None, 'density', 'frequency'}, default: True Draw a histogram showing the distribution of the residuals on the right side of the figure. Requires Matplotlib >= 2.0.2. If set to 'density', the probability density function will be plotted. If set to True or 'frequency' then the frequency will be plotted. qqplot : {True, False}, default: False Draw a Q-Q plot on the right side of the figure, comparing the quantiles of the residuals against quantiles of a standard normal distribution. Q-Q plot and histogram of residuals can not be plotted simultaneously, either `hist` or `qqplot` has to be set to False. train_color : color, default: 'b' Residuals for training data are ploted with this color but also given an opacity of 0.5 to ensure that the test data residuals are more visible. Can be any matplotlib color. test_color : color, default: 'g' Residuals for test data are plotted with this color. In order to create generalizable models, reserved test data residuals are of the most analytical interest, so these points are highlighted by having full opacity. Can be any matplotlib color. line_color : color, default: dark grey Defines the color of the zero error line, can be any matplotlib color. train_alpha : float, default: 0.75 Specify a transparency for traininig data, where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. test_alpha : float, default: 0.75 Specify a transparency for test data, where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. is_fitted : bool or str, default='auto' Specify if the wrapped estimator is already fitted. If False, the estimator will be fit when the visualizer is fit, otherwise, the estimator will not be modified. If 'auto' (default), a helper method will check if the estimator is fitted before fitting it again. show: bool, default: True If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply calls ``finalize()`` kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. Returns ------- viz : ResidualsPlot Returns the fitted ResidualsPlot that created the figure. """ # Instantiate the visualizer viz = ResidualsPlot( model=model, ax=ax, hist=hist, qqplot=qqplot, train_color=train_color, test_color=test_color, line_color=line_color, train_alpha=train_alpha, test_alpha=test_alpha, is_fitted=is_fitted, **kwargs ) # Fit the visualizer viz.fit(X_train, y_train) # Score the visualizer if X_test is not None and y_test is not None: viz.score(X_test, y_test) elif X_test is not None or y_test is not None: raise YellowbrickValueError( "both X_test and y_test are required if one is specified" ) else: viz.score(X_train, y_train) # Draw the final visualization if show: viz.show() else: viz.finalize() # Return the visualizer return viz