daze 0.1.1

Better multi-class confusion matrix plots for Scikit-Learn, incorporating per-class and overall evaluation measures.

Daze

^{Better multi-class confusion matrix plots for Scikit-Learn, incorporating per-class and overall evaluation measures.}

Introduction

The sklearn.metrics module allows for the plotting of a confusion matrix from a classifier (with plot_confusion_matrix), or directly from a pre-computed confusion matrix (with the internal ConfusionMatrixDisplay class).

A confusion matrix shows the discrepancy between the true labels of a dataset and the labels predicted by a classifier.

While the confusion matrix plots generated by Scikit-Learn are very informative, they omit important evaluation measures that can summarize classification performance. True positives, precision, F1 score and accuracy are example of such measures – all of which can be derived from the confusion matrix. The classification_report function in the same module provides these measures.

Daze adjusts plot_confusion_matrix to incorporate these evaluation measures directly in the confusion matrix plot, while still maintaining a very similar API to the original Scikit-Learn function.

Features

• Similar API to Scikit-Learn's plot_confusion_matrix.
• All common confusion matrix measures:
  Accuracy, TP, FP, TN, FN, TPR, FPR, TNR, FNR, Precision, Recall, F1.
• Macro & micro averaging for overall evaluation measures:
  TPR, FPR, TNR, FNR, Precision, Recall, F1.
• Supports both classifiers and pre-computed confusion matrices.

Installation

pip install daze

Documentation

The package API remains largely the same as that of sklearn.metrics.plot_confusion_matrix with a few additions and changes to the function arguments:

Click here to view the changes.

• estimator (changed): Supports the usual fitted Scikit-Learn classifier (or sklearn.pipeline.Pipeline), but also now accepts a pre-computed confusion matrix.
• X (changed): If estimator is a classifier, then X are input values as usual. If estimator is a confusion matrix, then X should be set to None.
• y_true (changed): If estimator is a classifier, then y_true are target values as usual. If estimator is a confusion matrix, then y_true should be set to None.
• normalize (added): Whether or not to normalize the plotted confusion matrix (True/False). Note that if a confusion matrix is provided, it should always be un-normalized.
• include_measures (added): Whether or not to include evaluation measures in the confusion matrix plot (True/False).
• measures (added): Collection of labels for evaluation measures to display in the plot (see documentation)
• measures_format (added): Format string for the evaluation measure values.
• include_summary (added): Whether or not to include summary measures (True/False). Note that include_measures=False overrides this setting.
• summary_type (added): The type of averaging ('micro'/'macro') used for summary measures.

Documentation for the package is available on Read The Docs.

Examples

Using a classifier object

# Load the 'iris' dataset
from sklearn import datasets
from sklearn.model_selection import train_test_split
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=1)

# Train a SVM classifier on a subset of the data
from sklearn.svm import SVC
clf = SVC(kernel='linear').fit(X_train[:10], y_train[:10])

# Plot the confusion matrix
import matplotlib.pyplot as plt
from daze import plot_confusion_matrix
plt.figure(figsize=(5.5, 5.5))
plot_confusion_matrix(clf, X_test, y_test, display_labels=iris.target_names, measures=...)
plt.show()

measures=	a, c, p, r, f1	a, tp, fp, fpr, tnr, p	a, tn, fn, tpr, fnr, r
Plot

Using a pre-computed confusion matrix

# Use the previous classifier to make predictions and create a confusion matrix
from sklearn.metrics import confusion_matrix
y_pred = clf.predict(X_test)
cm = confusion_matrix(y_test, y_pred)

# Make a plot from a pre-computed confusion matrix
plt.figure(figsize=(5.5, 5.5))
plot_confusion_matrix(cm, display_labels=iris.target_names)
plt.show()

Licensing

Daze uses Scikit-Learn source code for the majority of the ConfusionMatrixDisplay class and plot_confusion_matrix function re-implemetations, under the terms of the BSD-3-Clause license.

Click here to view the redistribution license.

BSD 3-Clause License

Copyright (c) 2007-2020 The scikit-learn developers.
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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Daze © 2021-2022, Edwin Onuonga - Released under the MIT License.
Authored and maintained by Edwin Onuonga.