watex.utils.plot_yb_confusion_matrix#

watex.utils.plot_yb_confusion_matrix(clf, Xt, yt, labels=None, encoder=None, savefig=None, fig_size=(6, 6), **kws)[source]#

Confusion matrix plot using the ‘yellowbrick’ package.

Creates a heatmap visualization of the sklearn.metrics.confusion_matrix(). A confusion matrix shows each combination of the true and predicted classes for a test data set.

The default color map uses a yellow/orange/red color scale. The user can choose between displaying values as the percent of true (cell value divided by sum of row) or as direct counts. If percent of true mode is selected, 100% accurate predictions are highlighted in green.

Requires a classification model.

Be sure ‘yellowbrick’ is installed before using the function, otherwise an ImportError will raise.

Parameters:

clf (classifier estimator) – A scikit-learn estimator that should be a classifier. If the model is not a classifier, an exception is raised. If the internal model is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by is_fitted.
Xt (ndarray or DataFrame of shape n x m) – A matrix of n instances with m features. Preferably, matrix represents the test data for error evaluation.
yt (ndarray or Series of length n) – An array or series of target or class values. Preferably, the array represent the test class labels data for error evaluation.
ax (matplotlib Axes, default: None) – The axes to plot the figure on. If not specified the current axes will be used (or generated if required).
sample_weight (array-like of shape = [n_samples], optional) – Passed to confusion_matrix to weight the samples.
encoder (dict or LabelEncoder, default: None) – A mapping of classes to human readable labels. Often there is a mismatch between desired class labels and those contained in the target variable passed to fit() or score(). The encoder disambiguates this mismatch ensuring that classes are labeled correctly in the visualization.
labels (list of str, default: None) – The class labels to use for the legend ordered by the index of the sorted classes discovered in the fit() method. Specifying classes in this manner is used to change the class names to a more specific format or to label encoded integer classes. Some visualizers may also use this field to filter the visualization for specific classes. For more advanced usage specify an encoder rather than class labels.
fig_size (tuple (width, height), default =(8, 6)) – the matplotlib figure size given as a tuple of width and height
savefig (str, default =None ,) – the path to save the figures. Argument is passed to matplotlib.Figure class.

Returns:

cmo – return a yellowbrick confusion matrix object instance.

Return type:

yellowbrick.classifier.confusion_matrix.ConfusionMatrix

Examples

>>> #Import the required models and fetch a an extreme gradient boosting
>>> # for instance then plot the confusion metric
>>> import matplotlib.pyplot as plt
>>> plt.style.use ('classic')
>>> from watex.datasets import fetch_data
>>> from watex.exlib.sklearn import train_test_split
>>> from watex.models import pModels
>>> from watex.utils.plotutils import plot_yb_confusion_matrix
>>> # split the  data . Note that fetch_data output X and y
>>> X, Xt, y, yt  = train_test_split (* fetch_data ('bagoue analysed'),
                                      test_size =.25  )
>>> # train the model with the best estimator
>>> pmo = pModels (model ='xgboost' )
>>> pmo.fit(X, y )
>>> print(pmo.estimator_ ) # pmo.XGB.best_estimator_
>>> #%%
>>> # Predict the score using under the hood the best estimator
>>> # for adaboost classifier
>>> ypred = pmo.predict(Xt)

>>> # now plot the score
>>> plot_yb_confusion_matrix (pmo.XGB.best_estimator_, Xt, yt  )