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PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and accurate evaluation of a large variety of classifiers.
Fig1. ConfusionMatrix Block Diagram
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⚠️ PyCM 3.9 is the last version to support Python 3.5
⚠️ PyCM 2.4 is the last version to support Python 2.7 & Python 3.4
⚠️ Plotting capability requires Matplotlib (>= 3.0.0) or Seaborn (>= 0.9.1)
pip install pycm==4.3
pip install .
conda update conda
conda install -c sepandhaghighi pycm
Add to PATH
optionInstall pip
optionpip install pycm
>> pyversion PYTHON_EXECUTABLE_FULL_PATH
>>> from pycm import *
>>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
>>> y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
>>> cm = ConfusionMatrix(actual_vector=y_actu, predict_vector=y_pred)
>>> cm.classes
[0, 1, 2]
>>> cm.table
{0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}}
>>> cm.print_matrix()
Predict 0 1 2
Actual
0 3 0 0
1 0 1 2
2 2 1 3
>>> cm.print_normalized_matrix()
Predict 0 1 2
Actual
0 1.0 0.0 0.0
1 0.0 0.33333 0.66667
2 0.33333 0.16667 0.5
>>> cm.stat(summary=True)
Overall Statistics :
ACC Macro 0.72222
F1 Macro 0.56515
FPR Macro 0.22222
Kappa 0.35484
Overall ACC 0.58333
PPV Macro 0.56667
SOA1(Landis & Koch) Fair
TPR Macro 0.61111
Zero-one Loss 5
Class Statistics :
Classes 0 1 2
ACC(Accuracy) 0.83333 0.75 0.58333
AUC(Area under the ROC curve) 0.88889 0.61111 0.58333
AUCI(AUC value interpretation) Very Good Fair Poor
F1(F1 score - harmonic mean of precision and sensitivity) 0.75 0.4 0.54545
FN(False negative/miss/type 2 error) 0 2 3
FP(False positive/type 1 error/false alarm) 2 1 2
FPR(Fall-out or false positive rate) 0.22222 0.11111 0.33333
N(Condition negative) 9 9 6
P(Condition positive or support) 3 3 6
POP(Population) 12 12 12
PPV(Precision or positive predictive value) 0.6 0.5 0.6
TN(True negative/correct rejection) 7 8 4
TON(Test outcome negative) 7 10 7
TOP(Test outcome positive) 5 2 5
TP(True positive/hit) 3 1 3
TPR(Sensitivity, recall, hit rate, or true positive rate) 1.0 0.33333 0.5
>>> from pycm import *
>>> cm2 = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2": 2}, "Class2": {"Class1": 0, "Class2": 5}})
>>> cm2
pycm.ConfusionMatrix(classes: ['Class1', 'Class2'])
>>> cm2.classes
['Class1', 'Class2']
>>> cm2.print_matrix()
Predict Class1 Class2
Actual
Class1 1 2
Class2 0 5
>>> cm2.print_normalized_matrix()
Predict Class1 Class2
Actual
Class1 0.33333 0.66667
Class2 0.0 1.0
>>> cm2.stat(summary=True)
Overall Statistics :
ACC Macro 0.75
F1 Macro 0.66667
FPR Macro 0.33333
Kappa 0.38462
Overall ACC 0.75
PPV Macro 0.85714
SOA1(Landis & Koch) Fair
TPR Macro 0.66667
Zero-one Loss 2
Class Statistics :
Classes Class1 Class2
ACC(Accuracy) 0.75 0.75
AUC(Area under the ROC curve) 0.66667 0.66667
AUCI(AUC value interpretation) Fair Fair
F1(F1 score - harmonic mean of precision and sensitivity) 0.5 0.83333
FN(False negative/miss/type 2 error) 2 0
FP(False positive/type 1 error/false alarm) 0 2
FPR(Fall-out or false positive rate) 0.0 0.66667
N(Condition negative) 5 3
P(Condition positive or support) 3 5
POP(Population) 8 8
PPV(Precision or positive predictive value) 1.0 0.71429
TN(True negative/correct rejection) 5 1
TON(Test outcome negative) 7 1
TOP(Test outcome positive) 1 7
TP(True positive/hit) 1 5
TPR(Sensitivity, recall, hit rate, or true positive rate) 0.33333 1.0
matrix()
and normalized_matrix()
renamed to print_matrix()
and print_normalized_matrix()
in version 1.5
threshold
is added in version 0.9
for real value prediction.
For more information visit Example3
file
is added in version 0.9.5
in order to load saved confusion matrix with .obj
format generated by save_obj
method.
For more information visit Example4
sample_weight
is added in version 1.2
For more information visit Example5
transpose
is added in version 1.2
in order to transpose input matrix (only in Direct CM
mode)
relabel
method is added in version 1.5
in order to change ConfusionMatrix classnames.
>>> cm.relabel(mapping={0: "L1", 1: "L2", 2: "L3"})
>>> cm
pycm.ConfusionMatrix(classes: ['L1', 'L2', 'L3'])
position
method is added in version 2.8
in order to find the indexes of observations in predict_vector
which made TP, TN, FP, FN.
>>> cm.position()
{0: {'FN': [], 'FP': [0, 7], 'TP': [1, 4, 9], 'TN': [2, 3, 5, 6, 8, 10, 11]}, 1: {'FN': [5, 10], 'FP': [3], 'TP': [6], 'TN': [0, 1, 2, 4, 7, 8, 9, 11]}, 2: {'FN': [0, 3, 7], 'FP': [5, 10], 'TP': [2, 8, 11], 'TN': [1, 4, 6, 9]}}
to_array
method is added in version 2.9
in order to returns the confusion matrix in the form of a NumPy array. This can be helpful to apply different operations over the confusion matrix for different purposes such as aggregation, normalization, and combination.
>>> cm.to_array()
array([[3, 0, 0],
[0, 1, 2],
[2, 1, 3]])
>>> cm.to_array(normalized=True)
array([[1. , 0. , 0. ],
[0. , 0.33333, 0.66667],
[0.33333, 0.16667, 0.5 ]])
>>> cm.to_array(normalized=True, one_vs_all=True, class_name="L1")
array([[1. , 0. ],
[0.22222, 0.77778]])
combine
method is added in version 3.0
in order to merge two confusion matrices. This option will be useful in mini-batch learning.
>>> cm_combined = cm2.combine(cm3)
>>> cm_combined.print_matrix()
Predict Class1 Class2
Actual
Class1 2 4
Class2 0 10
plot
method is added in version 3.0
in order to plot a confusion matrix using Matplotlib or Seaborn.
>>> cm.plot()
>>> from matplotlib import pyplot as plt
>>> cm.plot(cmap=plt.cm.Greens, number_label=True, plot_lib="matplotlib")
>>> cm.plot(cmap=plt.cm.Reds, normalized=True, number_label=True, plot_lib="seaborn")
ROCCurve
, added in version 3.7
, is devised to compute the Receiver Operating Characteristic (ROC) or simply ROC curve. In ROC curves, the Y axis represents the True Positive Rate, and the X axis represents the False Positive Rate. Thus, the ideal point is located at the top left of the curve, and a larger area under the curve represents better performance. ROC curve is a graphical representation of binary classifiers' performance. In PyCM, ROCCurve
binarizes the output based on the "One vs. Rest" strategy to provide an extension of ROC for multi-class classifiers. Getting the actual labels vector, the target probability estimates of the positive classes, and the list of ordered labels of classes, this method is able to compute and plot TPR-FPR pairs for different discrimination thresholds and compute the area under the ROC curve.
>>> crv = ROCCurve(actual_vector=np.array([1, 1, 2, 2]), probs=np.array([[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]]), classes=[2, 1])
>>> crv.thresholds
[0.1, 0.2, 0.35, 0.4, 0.6, 0.65, 0.8, 0.9]
>>> auc_trp = crv.area()
>>> auc_trp[1]
0.75
>>> auc_trp[2]
0.75
PRCurve
, added in version 3.7
, is devised to compute the Precision-Recall curve in which the Y axis represents the Precision, and the X axis represents the Recall of a classifier. Thus, the ideal point is located at the top right of the curve, and a larger area under the curve represents better performance. Precision-Recall curve is a graphical representation of binary classifiers' performance. In PyCM, PRCurve
binarizes the output based on the "One vs. Rest" strategy to provide an extension of this curve for multi-class classifiers. Getting the actual labels vector, the target probability estimates of the positive classes, and the list of ordered labels of classes, this method is able to compute and plot Precision-Recall pairs for different discrimination thresholds and compute the area under the curve.
>>> crv = PRCurve(actual_vector=np.array([1, 1, 2, 2]), probs=np.array([[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]]), classes=[2, 1])
>>> crv.thresholds
[0.1, 0.2, 0.35, 0.4, 0.6, 0.65, 0.8, 0.9]
>>> auc_trp = crv.area()
>>> auc_trp[1]
0.29166666666666663
>>> auc_trp[2]
0.29166666666666663
This option has been added in version 1.9
to recommend the most related parameters considering the characteristics of the input dataset.
The suggested parameters are selected according to some characteristics of the input such as being balance/imbalance and binary/multi-class.
All suggestions can be categorized into three main groups: imbalanced dataset, binary classification for a balanced dataset, and multi-class classification for a balanced dataset.
The recommendation lists have been gathered according to the respective paper of each parameter and the capabilities which had been claimed by the paper.
>>> cm.imbalance
False
>>> cm.binary
False
>>> cm.recommended_list
['MCC', 'TPR Micro', 'ACC', 'PPV Macro', 'BCD', 'Overall MCC', 'Hamming Loss', 'TPR Macro', 'Zero-one Loss', 'ERR', 'PPV Micro', 'Overall ACC']
is_imbalanced
parameter has been added in version 3.3
, so the user can indicate whether the concerned dataset is imbalanced or not. As long as the user does not provide any information in this regard, the automatic detection algorithm will be used.
>>> cm = ConfusionMatrix(y_actu, y_pred, is_imbalanced=True)
>>> cm.imbalance
True
>>> cm = ConfusionMatrix(y_actu, y_pred, is_imbalanced=False)
>>> cm.imbalance
False
In version 2.0
, a method for comparing several confusion matrices is introduced. This option is a combination of several overall and class-based benchmarks. Each of the benchmarks evaluates the performance of the classification algorithm from good to poor and give them a numeric score. The score of good and poor performances are 1 and 0, respectively.
After that, two scores are calculated for each confusion matrices, overall and class-based. The overall score is the average of the score of seven overall benchmarks which are Landis & Koch, Cramer, Matthews, Goodman-Kruskal's Lambda A, Goodman-Kruskal's Lambda B, Krippendorff's Alpha, and Pearson's C. In the same manner, the class-based score is the average of the score of six class-based benchmarks which are Positive Likelihood Ratio Interpretation, Negative Likelihood Ratio Interpretation, Discriminant Power Interpretation, AUC value Interpretation, Matthews Correlation Coefficient Interpretation and Yule's Q Interpretation. It should be noticed that if one of the benchmarks returns none for one of the classes, that benchmarks will be eliminated in total averaging. If the user sets weights for the classes, the averaging over the value of class-based benchmark scores will transform to a weighted average.
If the user sets the value of by_class
boolean input True
, the best confusion matrix is the one with the maximum class-based score. Otherwise, if a confusion matrix obtains the maximum of both overall and class-based scores, that will be reported as the best confusion matrix, but in any other case, the compared object doesn’t select the best confusion matrix.
>>> cm2 = ConfusionMatrix(matrix={0: {0: 2, 1: 50, 2: 6}, 1: {0: 5, 1: 50, 2: 3}, 2: {0: 1, 1: 7, 2: 50}})
>>> cm3 = ConfusionMatrix(matrix={0: {0: 50, 1: 2, 2: 6}, 1: {0: 50, 1: 5, 2: 3}, 2: {0: 1, 1: 55, 2: 2}})
>>> cp = Compare({"cm2": cm2, "cm3": cm3})
>>> print(cp)
Best : cm2
Rank Name Class-Score Overall-Score
1 cm2 0.50278 0.58095
2 cm3 0.33611 0.52857
>>> cp.best
pycm.ConfusionMatrix(classes: [0, 1, 2])
>>> cp.sorted
['cm2', 'cm3']
>>> cp.best_name
'cm2'
From version 4.0
, MultiLabelCM
has been added to calculate class-wise or sample-wise multilabel confusion matrices. In class-wise mode, confusion matrices are calculated for each class, and in sample-wise mode, they are generated per sample. All generated confusion matrices are binarized with a one-vs-rest transformation.
>>> mlcm = MultiLabelCM(actual_vector=[{"cat", "bird"}, {"dog"}], predict_vector=[{"cat"}, {"dog", "bird"}], classes=["cat", "dog", "bird"])
>>> mlcm.actual_vector_multihot
[[1, 0, 1], [0, 1, 0]]
>>> mlcm.predict_vector_multihot
[[1, 0, 0], [0, 1, 1]]
>>> mlcm.get_cm_by_class("cat").print_matrix()
Predict 0 1
Actual
0 1 0
1 0 1
>>> mlcm.get_cm_by_sample(0).print_matrix()
Predict 0 1
Actual
0 1 0
1 1 1
online_help
function is added in version 1.1
in order to open each statistics definition in web browser
>>> from pycm import online_help
>>> online_help("J")
>>> online_help("SOA1(Landis & Koch)")
>>> online_help(2)
online_help()
(without argument)alt_link = True
(new in version 2.4
)PyCM can be used online in interactive Jupyter Notebooks via the Binder or Colab services! Try it out now! :
Examples
in Document
folderNLnet foundation has supported the PyCM project from version 3.6 to 4.0 through the NGI Assure Fund. This fund is set up by NLnet foundation with funding from the European Commission's Next Generation Internet program, administered by DG Communications Networks, Content, and Technology under grant agreement No 957073.
Python Software Foundation (PSF) grants PyCM library partially for version 3.7. PSF is the organization behind Python. Their mission is to promote, protect, and advance the Python programming language and to support and facilitate the growth of a diverse and international community of Python programmers.
Some parts of the infrastructure for this project are supported by:
If you use PyCM in your research, we would appreciate citations to the following paper:
@article{Haghighi2018,
doi = {10.21105/joss.00729},
url = {https://doi.org/10.21105/joss.00729},
year = {2018},
month = {may},
publisher = {The Open Journal},
volume = {3},
number = {25},
pages = {729},
author = {Sepand Haghighi and Masoomeh Jasemi and Shaahin Hessabi and Alireza Zolanvari},
title = {{PyCM}: Multiclass confusion matrix library in Python},
journal = {Journal of Open Source Software}
}
Download PyCM.bib
JOSS | |
Zenodo |
Give a ⭐️ if this project helped you!
If you do like our project and we hope that you do, can you please support us? Our project is not and is never going to be working for profit. We need the money just so we can continue doing what we do ;-) .
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog and this project adheres to Semantic Versioning.
dissimilarity_matrix
methodREADME.md
modifiedhtml_init
functionhtml_end
functionREADME.md
dev
and master
branchesAUTHORS.md
updatedREADME.md
modifiedfeature_request.yml
templateconfig.yml
for issue templateSECURITY.md
thresholds_calc
function updated__midpoint_numeric_integral__
function updated__trapezoidal_numeric_integral__
function updatedAUTHORS.md
updatedREADME.md
modifiedPython 3.12
added to test.yml
Python 3.13
added to test.yml
pycm_util.py
renamed to utils.py
pycm_test.py
renamed to basic_test.py
pycm_profile.py
renamed to profile.py
pycm_param.py
renamed to params.py
pycm_overall_func.py
renamed to overall_funcs.py
pycm_output.py
renamed to output.py
pycm_obj.py
renamed to cm.py
pycm_multilabel_cm.py
renamed to multilabel_cm.py
pycm_interpret.py
renamed to interpret.py
pycm_handler.py
renamed to handlers.py
pycm_error.py
renamed to errors.py
pycm_distance.py
renamed to distance.py
pycm_curve.py
renamed to curve.py
pycm_compare.py
renamed to compare.py
pycm_class_func.py
renamed to class_funcs.py
pycm_ci.py
renamed to ci.py
pycmMultiLabelError
classMultiLabelCM
classget_cm_by_class
methodget_cm_by_sample
method__mlcm_vector_handler__
function__mlcm_assign_classes__
function__mlcm_vectors_filter__
function__set_to_multihot__
functiondeprecated
functionREADME.md
modifiedOVERALL_PARAMS
dictionary__imbalancement_handler__
functionvector_serializer
functionlog_loss
methodmetrics_off
parameter added to ConfusionMatrix __init__
methodCLASS_PARAMS
changed to a dictionarysort
parameter added to relabel
methodCONTRIBUTING.md
updatedcodecov
removed from dev-requirements.txt
distance
method__contains__
method__getitem__
methodrelabel
method sort bug fixedREADME.md
modifiedCompare
overall benchmarks default weights updatedCurve
classROCCurve
classPRCurve
classpycmCurveError
classCONTRIBUTING.md
updatedmatrix_params_calc
function optimizedREADME.md
modifiedPython 3.11
added to test.yml
classes
parameter added to matrix_params_from_table
functionnumpy.integer
elements are now acceptedmatrix
parameter accepting formatsREADME.md
modifiedplot
method updatedclass_statistics
function modifiedoverall_statistics
function modifiedBCD_calc
function modifiedCONTRIBUTING.md
updatedCODE_OF_CONDUCT.md
updatedbrier_score
methodJ (Jaccard index)
section in Document.ipynb
updatedsave_obj
method updatedPython 3.10
added to test.yml
CONTRIBUTING.md
updated__compare_weight_handler__
functionis_imbalanced
parameter added to ConfusionMatrix __init__
methodclass_benchmark_weight
and overall_benchmark_weight
parameters added to Compare __init__
methodstatistic_recommend
function modifiedweight
parameter renamed to class_weight
AUTHORS.md
updatedREADME.md
modifiedclasses_filter
functionclasses
parameter added to matrix_params_calc
functionclasses
parameter added to __obj_vector_handler__
functionclasses
parameter added to ConfusionMatrix __init__
methodname
parameter removed from html_init
functionshortener
parameter added to html_table
functionshortener
parameter added to save_html
methodrequirements-splitter.py
sensitivity_index
methodoverall_statistics
function modifiedCONTRIBUTING.md
updatedplot_test.py
axes_gen
functionadd_number_label
functionplot
methodcombine
methodmatrix_combine
functionREADME.md
modifiednotebook_check.py
to_array
method__copy__
methodcopy
methodaverage
method refactoredlabel_map
attributepositions
attributeposition
methodweighted_alpha
methodCLASS_NUMBER_ERROR
error type changed to pycmMatrixError
relabel
method bug fixedREADME.md
modifiedaverage
methodweighted_average
methodweighted_kappa
methodpycmAverageError
classREADME.md
modifiedrelabel
method bug fixedsparse_table_print
function bug fixedmatrix_check
function bug fixedCompare
class fixedcustom_rounder
functioncomplement
functionsparse_matrix
attributesparse_normalized_matrix
attributesparse
parameter added to print_matrix
,print_normalized_matrix
and save_stat
methodsheader
parameter added to save_csv
methodpycm_handler.py
pycm_error.py
verified_test.py
CONTRIBUTING.md
updatedREADME.md
modifiedprint_normalized_matrix
method modifiednormalized_table_calc
function modifiedsetup.py
modifiedPython 3.8
added to .travis.yaml
and appveyor.yml
PC_PI_calc
function__version__
variableinstall.sh
autopep8.sh
CI
method (supported statistics : ACC
,AUC
,Overall ACC
,Kappa
,TPR
,TNR
,PPV
,NPV
,PLR
,NLR
,PRE
)test.sh
moved to .travis
folderAUTHORS.md
updatedsave_stat
,save_csv
and save_html
methods Non-ASCII character bug fixedCONTRIBUTING.md
updatedREADME.md
modifiedCI
attribute renamed to CI95
kappa_se_calc
function renamed to kappa_SE_calc
se_calc
function modified and renamed to SE_calc
pycm_ci.py
save_html
method fixedFUNDING.yml
AUC_calc
function modifiedsummary
parameter added to save_html
,save_stat
,save_csv
and stat
methodssample_weight
bug in numpy
array format fixedalt_link
parameter added to save_html
method and online_help
functionCompare
class tests moved to compare_test.py
warning_test.py
save_stat
and save_vector
parameters added to save_obj
methodREADME.md
modifiedCompare
class fixedpycm_help
function modifiedCompare
class score calculation modifiedREADME.md
modifiedCompare
class and parameters recommendation system block diagramsCompare
classConfusionMatrix
equal methodstat_print
function bug fixedtable_print
function bug fixedBeta
parameter renamed to beta
(F_calc
function & F_beta
method)normalize
parameter added to save_html
methodpycm_func.py
splitted into pycm_class_func.py
and pycm_overall_func.py
vector_filter
,vector_check
,class_check
and matrix_check
functions moved to pycm_util.py
RACC_calc
and RACCU_calc
functions exception handler modifiedCODE_OF_CONDUCT.md
ISSUE_TEMPLATE.md
PULL_REQUEST_TEMPLATE.md
CONTRIBUTING.md
save_html
methodsave_matrix
and normalize
parameters added to save_csv
methodREADME.md
modifiedConfusionMatrix.__init__
optimizedrelabel
method bug fixedversion_check.py
color
parameter added to save_html
methodpycm_interpret.py
pycm_util.py
else
and elif
removed==
changed to is
pycm_profile.py
class_name
parameter added to stat
,save_stat
,save_csv
and save_html
methodsoverall_param
and class_param
parameters empty list bug fixedmatrix_params_calc
, matrix_params_from_table
and vector_filter
functions optimizedoverall_MCC_calc
, CEN_misclassification_calc
and convex_combination
functions optimizedoverall_param
and class_param
parameters added to stat
,save_stat
and save_html
methodsclass_param
parameter added to save_csv
method_
removed from overall statistics namesREADME.md
modified__len__
methodrelabel
method__class_stat_init__
function__overall_stat_init__
functionmatrix
attribute as dictnormalized_matrix
attribute as dictnormalized_table
attribute as dictREADME.md
modifiedLR+
renamed to PLR
LR-
renamed to NLR
normalized_matrix
method renamed to print_normalized_matrix
matrix
method renamed to print_matrix
entropy_calc
fixedcross_entropy_calc
fixedconditional_entropy_calc
fixedprint_table
bug for large numbers fixedsave_obj
fixedtranspose
bug in save_obj
fixedPython 3.7
added to .travis.yaml
and appveyor.yml
one_vs_all
dev-requirements.txt
README.md
modifiedsave_stat
modifiedrequirements.txt
modifiedREADME.md
modifiedsample_weight
transpose
README.md
modifiedOSX
env added to .travis.yml
online_help
functionREADME.md
modifiedhtml_table
function modifiedtable_print
function modifiednormalized_table_print
function modifiedREADME.md
modifiedREADME.md
modifiedREADME.md
modifiedsetup.py
modifiedREADME.md
modifiedREADME.md
modifiedpycmVectorError
classpycmMatrixError
classnumpy
arrayspycmError
classdigit
parameter to ConfusionMatrix
objectoverall_stat
statistic_result
to class_stat
params
to stat
README.md
FAQs
Multi-class confusion matrix library in Python
We found that pycm demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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