watex.utils.plot_confusion_matrices#

watex.utils.plot_confusion_matrices(clfs, Xt, yt, annot=True, pkg=None, normalize='true', sample_weight=None, encoder=None, fig_size=(22, 6), savefig=None, subplot_kws=None, **scorer_kws)[source]#

Plot inline multiple model confusion matrices using either the sckitlearn or ‘yellowbrick’

Parameters

  • clfs (list of classifier estimators) – A scikit-learn estimator that should be a classifier. If the model is not a classifier, an exception is raised. Note that the classifier must be fitted beforehand.

  • Xt (ndarray or DataFrame of shape (M X N)) – A matrix of n instances with m features. Preferably, matrix represents the test data for error evaluation.

  • yt (ndarray of shape (M, ) or Series oF length (M, )) – An array or series of target or class values. Preferably, the array represent the test class labels data for error evaluation.

  • pkg (str, optional , default ='sklearn') – the library to handle the plot. It could be ‘yellowbrick’. The basic confusion matrix is handled by the scikit-learn package.

  • normalize ({'true', 'pred', 'all'}, default=None) – Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population. If None, confusion matrix will not be normalized.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

  • 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.

annot: bool, default=True

Annotate the number of samples (right or wrong prediction ) in the plot. Set False to mute the display.

fig_sizetuple (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.

Examples

>>> 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.premodels import p
>>> from watex.utils.plotutils import plot_confusion_matrices
>>> # 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  )
>>> # compose the models
>>> # from RBF, and poly
>>> models =[ p.SVM.rbf.best_estimator_,
         p.LogisticRegression.best_estimator_,
         p.RandomForest.best_estimator_
         ]
>>> models
[SVC(C=2.0, coef0=0, degree=1, gamma=0.125), LogisticRegression(),
 RandomForestClassifier(criterion='entropy', max_depth=16, n_estimators=350)]
>>> # now fit all estimators
>>> fitted_models = [model.fit(X, y) for model in models ]
>>> plot_confusion_matrices(fitted_models , Xt, yt)