Package: pandas_confusion
pandas_confusionAPythonPandasimplementation ofconfusion
matrix.WORK IN PROGRESS - Use it a your own riskUsageConfusion matrixImportConfusionMatrixfrom pandas_confusion import ConfusionMatrixDefine actual values (y_actu) and predicted values (y_pred)y_actu = ['rabbit', 'cat', 'rabbit', 'rabbit', 'cat', 'dog', 'dog', 'rabbit', 'rabbit', 'cat', 'dog', 'rabbit']
y_pred = ['cat', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'cat', 'rabbit', 'rabbit']Let’s define a (non binary) confusion matrixconfusion_matrix = ConfusionMatrix(y_actu, y_pred)
print("Confusion matrix:\n%s" % confusion_matrix)You can see itPredicted  cat  dog  rabbit  __all__
Actual
cat          3    0       0        3
dog          0    1       2        3
rabbit       2    1       3        6
__all__      5    2       5       12Matplotlib plot of a confusion matrixInside a IPython notebook add this line as first cell%matplotlib inlineYou can plot confusion matrix using:import matplotlib.pyplot as plt

confusion_matrix.plot()If you are not using inline mode, you need to use to show confusion
matrix plot.plt.show()confusion_matrixMatplotlib plot of a normalized confusion matrixconfusion_matrix.plot(normalized=True)
plt.show()confusion_matrix_normBinary confusion matrixImportBinaryConfusionMatrixandBackendfrom pandas_confusion import BinaryConfusionMatrix, BackendDefine actual values (y_actu) and predicted values (y_pred)y_actu = [ True,  True, False, False, False,  True, False,  True,  True,
           False,  True, False, False, False, False, False,  True, False,
            True,  True,  True,  True, False, False, False,  True, False,
            True, False, False, False, False,  True,  True, False, False,
           False,  True,  True,  True,  True, False, False, False, False,
            True, False, False, False, False, False, False, False, False,
           False,  True,  True, False,  True, False,  True,  True,  True,
           False, False,  True, False,  True, False, False,  True, False,
           False, False, False, False, False, False, False,  True, False,
            True,  True,  True,  True, False, False,  True, False,  True,
            True, False,  True, False,  True, False, False,  True,  True,
           False, False,  True,  True, False, False, False, False, False,
           False,  True,  True, False]

y_pred = [False, False, False, False, False,  True, False, False,  True,
       False,  True, False, False, False, False, False, False, False,
        True,  True,  True,  True, False, False, False, False, False,
       False, False, False, False, False,  True, False, False, False,
       False,  True, False, False, False, False, False, False, False,
        True, False, False, False, False, False, False, False, False,
       False,  True, False, False, False, False, False, False, False,
       False, False,  True, False, False, False, False,  True, False,
       False, False, False, False, False, False, False,  True, False,
       False,  True, False, False, False, False,  True, False,  True,
        True, False, False, False,  True, False, False,  True,  True,
       False, False,  True,  True, False, False, False, False, False,
       False,  True, False, False]Let’s define a binary confusion matrixbinary_confusion_matrix = BinaryConfusionMatrix(y_actu, y_pred)
print("Binary confusion matrix:\n%s" % binary_confusion_matrix)It display as a nicely labeled Pandas DataFrameBinary confusion matrix:
Predicted  False  True  __all__
Actual
False         67     0       67
True          21    24       45
__all__       88    24      112You can get useful attributes such as True Positive (TP), True Negative
(TN) …print binary_confusion_matrix.TPMatplotlib plot of a binary confusion matrixbinary_confusion_matrix.plot()
plt.show()binary_confusion_matrixMatplotlib plot of a normalized binary confusion matrixbinary_confusion_matrix.plot(normalized=True)
plt.show()binary_confusion_matrix_normSeaborn plot of a binary confusion matrix (ToDo)from pandas_confusion import Backend
binary_confusion_matrix.plot(backend=Backend.Seaborn)Confusion matrix and class statisticsOverall statistics and class statistics of confusion matrix can be
easily displayed.y_true = [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200]
y_pred = [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200]
cm = ConfusionMatrix(y_true, y_pred)
cm.print_stats()You should get:Confusion Matrix:

Classes  100  200  500  600  __all__
Actual
100        0    0    0    0        0
200        9    6    1    0       16
500        1    1    1    0        3
600        1    0    0    0        1
__all__   11    7    2    0       20


Overall Statistics:

Accuracy: 0.35
95% CI: (0.1539092047845412, 0.59218853453282805)
No Information Rate: ToDo
P-Value [Acc > NIR]: 0.978585644357
Kappa: 0.0780141843972
Mcnemar's Test P-Value: ToDo


Class Statistics:

Classes                                 100         200         500   600
Population                               20          20          20    20
Condition positive                        0          16           3     1
Condition negative                       20           4          17    19
Test outcome positive                    11           7           2     0
Test outcome negative                     9          13          18    20
TP: True Positive                         0           6           1     0
TN: True Negative                         9           3          16    19
FP: False Positive                       11           1           1     0
FN: False Negative                        0          10           2     1
TPR: Sensivity                          NaN       0.375   0.3333333     0
TNR=SPC: Specificity                   0.45        0.75   0.9411765     1
PPV: Pos Pred Value = Precision           0   0.8571429         0.5   NaN
NPV: Neg Pred Value                       1   0.2307692   0.8888889  0.95
FPR: False-out                         0.55        0.25  0.05882353     0
FDR: False Discovery Rate                 1   0.1428571         0.5   NaN
FNR: Miss Rate                          NaN       0.625   0.6666667     1
ACC: Accuracy                          0.45        0.45        0.85  0.95
F1 score                                  0   0.5217391         0.4     0
MCC: Matthews correlation coefficient   NaN   0.1048285    0.326732   NaN
Informedness                            NaN       0.125   0.2745098     0
Markedness                                0  0.08791209   0.3888889   NaN
Prevalence                                0         0.8        0.15  0.05
LR+: Positive likelihood ratio          NaN         1.5    5.666667   NaN
LR-: Negative likelihood ratio          NaN   0.8333333   0.7083333     1
DOR: Diagnostic odds ratio              NaN         1.8           8   NaN
FOR: False omission rate                  0   0.7692308   0.1111111  0.05Statistics are also available as an OrderedDict using:cm.stats()Install$ conda install pandas scikit-learn scipy

$ pip install pandas_confusionDevelopmentYou can help to develop this library.IssuesYou can submit issues usinghttps://github.com/scls19fr/pandas_confusion/issuesCloneYou can clone repository to try to fix issues yourself using:$ git clone https://github.com/scls19fr/pandas_confusion.gitRun unit testsRun all unit tests$ nosetests -s -vRun a given test$ nosetests -s -v tests/test_pandas_confusion.py:test_pandas_confusion_normalizedInstall development version$ python setup.py installor$ sudo pip install git+git://github.com/scls19fr/pandas_confusion.gitCollaboratingFork repositoryCreate a branch which fix a given issueSubmit pull requestshttps://help.github.com/categories/collaborating/DoneContinuous integration (Travis)Convert a confusion matrix to a binary confusion matrixPython packageUnit tests (nose)Fix missing column and missing rowOverall statistics: Accuracy, 95% CI, P-Value [Acc > NIR], Kappa