PyCM: Python Confusion Matrix
Overview
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
Open Hub |  |
PyPI Counter |  |
Github Stars |  |
Branch | master | dev |
CI |  |  |
Installation
⚠️ 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)
PyPI
Source code
Conda
MATLAB
- Download and install MATLAB (>=8.5, 64/32 bit)
- Download and install Python3.x (>=3.6, 64/32 bit)
- Run
pip install pycm
- Configure Python interpreter
>> pyversion PYTHON_EXECUTABLE_FULL_PATH
Usage
From vector
>>> 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
Direct CM
>>> 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
Activation threshold
threshold
is added in version 0.9
for real value prediction.
For more information visit Example3
Load from file
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 weights
sample_weight
is added in version 1.2
For more information visit Example5
Transpose
transpose
is added in version 1.2
in order to transpose input matrix (only in Direct CM
mode)
Relabel
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
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
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
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
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")
ROC curve
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
Precision-Recall curve
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
Parameter recommender
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
Compare
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'
Multilabel confusion matrix
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
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)
- List of items are available by calling
online_help()
(without argument)
- If PyCM website is not available, set
alt_link = True
(new in version 2.4
)
Screen record
Try PyCM in your browser!
PyCM can be used online in interactive Jupyter Notebooks via the Binder or Colab services! Try it out now! :


- Check
Examples
in Document
folder
Issues & bug reports
Acknowledgments
NLnet 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:
Cite
If you use PyCM in your research, we would appreciate citations to the following paper:
Haghighi, S., Jasemi, M., Hessabi, S. and Zolanvari, A., 2018. PyCM: Multiclass confusion matrix library in Python. Journal of Open Source Software, 3(25), p.729.
@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 |  |
Show your support
Star this repo
Give a ⭐️ if this project helped you!
Donate to our project
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 ;-) .

Changelog
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.
4.3 - 2025-04-04
Added
dissimilarity_matrix
method
Changed
- HTML generator engine modified
README.md
modified
- Document modified
- String templates modified
Removed
html_init
function
html_end
function
4.2 - 2025-01-14
Added
- 5 new distance/similarity
- KuhnsIII
- KuhnsIV
- KuhnsV
- KuhnsVI
- KuhnsVII
Changed
- Test system modified
- PyPI badge in
README.md
- GitHub actions are limited to the
dev
and master
branches
AUTHORS.md
updated
README.md
modified
- Document modified
4.1 - 2024-10-17
Added
- 5 new distance/similarity
- KoppenI
- KoppenII
- KuderRichardson
- KuhnsI
- KuhnsII
feature_request.yml
template
config.yml
for issue template
SECURITY.md
Changed
- Bug report template modified
thresholds_calc
function updated
__midpoint_numeric_integral__
function updated
__trapezoidal_numeric_integral__
function updated
- Diagrams updated
- Document modified
- Document build system updated
AUTHORS.md
updated
README.md
modified
- Test system modified
Python 3.12
added to test.yml
Python 3.13
added to test.yml
- Warning and error messages updated
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
4.0 - 2023-06-07
Added
pycmMultiLabelError
class
MultiLabelCM
class
get_cm_by_class
method
get_cm_by_sample
method
__mlcm_vector_handler__
function
__mlcm_assign_classes__
function
__mlcm_vectors_filter__
function
__set_to_multihot__
function
deprecated
function
Changed
- Document modified
README.md
modified
- Example-4 modified
- Test system modified
- Python 3.5 support dropped
3.9 - 2023-05-01
Added
OVERALL_PARAMS
dictionary
__imbalancement_handler__
function
vector_serializer
function
- NPV micro/macro
log_loss
method
- 23 new distance/similarity
- Dennis
- Digby
- Dispersion
- Doolittle
- Eyraud
- Fager & McGowan
- Faith
- Fleiss-Levin-Paik
- Forbes I
- Forbes II
- Fossum
- Gilbert & Wells
- Goodall
- Goodman & Kruskal's Lambda
- Goodman & Kruskal Lambda-r
- Guttman's Lambda A
- Guttman's Lambda B
- Hamann
- Harris & Lahey
- Hawkins & Dotson
- Kendall's Tau
- Kent & Foster I
- Kent & Foster II
Changed
metrics_off
parameter added to ConfusionMatrix __init__
method
CLASS_PARAMS
changed to a dictionary
- Code style modified
sort
parameter added to relabel
method
- Document modified
CONTRIBUTING.md
updated
codecov
removed from dev-requirements.txt
- Test system modified
3.8 - 2023-02-01
Added
distance
method
__contains__
method
__getitem__
method
- Goodman-Kruskal's Lambda A benchmark
- Goodman-Kruskal's Lambda B benchmark
- Krippendorff's Alpha benchmark
- Pearson's C benchmark
- 30 new distance/similarity
- AMPLE
- Anderberg's D
- Andres & Marzo's Delta
- Baroni-Urbani & Buser I
- Baroni-Urbani & Buser II
- Batagelj & Bren
- Baulieu I
- Baulieu II
- Baulieu III
- Baulieu IV
- Baulieu V
- Baulieu VI
- Baulieu VII
- Baulieu VIII
- Baulieu IX
- Baulieu X
- Baulieu XI
- Baulieu XII
- Baulieu XIII
- Baulieu XIV
- Baulieu XV
- Benini I
- Benini II
- Canberra
- Clement
- Consonni & Todeschini I
- Consonni & Todeschini II
- Consonni & Todeschini III
- Consonni & Todeschini IV
- Consonni & Todeschini V
Changed
relabel
method sort bug fixed
README.md
modified
Compare
overall benchmarks default weights updated
- Document modified
- Test system modified
3.7 - 2022-12-15
Added
Curve
class
ROCCurve
class
PRCurve
class
pycmCurveError
class
Changed
CONTRIBUTING.md
updated
matrix_params_calc
function optimized
README.md
modified
- Document modified
- Test system modified
Python 3.11
added to test.yml
3.6 - 2022-08-17
Added
- Hamming distance
- Braun-Blanquet similarity
Changed
classes
parameter added to matrix_params_from_table
function
- Matrices with
numpy.integer
elements are now accepted
- Arrays added to
matrix
parameter accepting formats
- Website changed to http://www.pycm.io
- Document modified
README.md
modified
3.5 - 2022-04-27
Added
- Anaconda workflow
- Custom iterating setting
- Custom casting setting
Changed
plot
method updated
class_statistics
function modified
overall_statistics
function modified
BCD_calc
function modified
CONTRIBUTING.md
updated
CODE_OF_CONDUCT.md
updated
- Document modified
3.4 - 2022-01-26
Added
- Colab badge
- Discord badge
brier_score
method
Changed
J (Jaccard index)
section in Document.ipynb
updated
save_obj
method updated
Python 3.10
added to test.yml
- Example-3 updated
- Docstrings of the functions updated
CONTRIBUTING.md
updated
3.3 - 2021-10-27
Added
__compare_weight_handler__
function
Changed
is_imbalanced
parameter added to ConfusionMatrix __init__
method
class_benchmark_weight
and overall_benchmark_weight
parameters added to Compare __init__
method
statistic_recommend
function modified
- Compare
weight
parameter renamed to class_weight
- Document modified
- License updated
AUTHORS.md
updated
README.md
modified
- Block diagrams updated
3.2 - 2021-08-11
Added
Changed
classes
parameter added to matrix_params_calc
function
classes
parameter added to __obj_vector_handler__
function
classes
parameter added to ConfusionMatrix __init__
method
name
parameter removed from html_init
function
shortener
parameter added to html_table
function
shortener
parameter added to save_html
method
- Document modified
- HTML report modified
3.1 - 2021-03-11
Added
requirements-splitter.py
sensitivity_index
method
Changed
- Test system modified
overall_statistics
function modified
- HTML report modified
- Document modified
- References format updated
CONTRIBUTING.md
updated
3.0 - 2020-10-26
Added
plot_test.py
axes_gen
function
add_number_label
function
plot
method
combine
method
matrix_combine
function
Changed
- Document modified
README.md
modified
- Example-2 deprecated
- Example-7 deprecated
- Error messages modified
2.9 - 2020-09-23
Added
notebook_check.py
to_array
method
__copy__
method
copy
method
Changed
average
method refactored
2.8 - 2020-07-09
Added
label_map
attribute
positions
attribute
position
method
- Krippendorff's Alpha
- Aickin's Alpha
weighted_alpha
method
Changed
- Single class bug fixed
CLASS_NUMBER_ERROR
error type changed to pycmMatrixError
relabel
method bug fixed
- Document modified
README.md
modified
2.7 - 2020-05-11
Added
average
method
weighted_average
method
weighted_kappa
method
pycmAverageError
class
- Bangdiwala's B
- MATLAB examples
- Github action
Changed
- Document modified
README.md
modified
relabel
method bug fixed
sparse_table_print
function bug fixed
matrix_check
function bug fixed
- Minor bug in
Compare
class fixed
- Class names mismatch bug fixed
2.6 - 2020-03-25
Added
custom_rounder
function
complement
function
sparse_matrix
attribute
sparse_normalized_matrix
attribute
- Net benefit (NB)
- Yule's Q interpretation (QI)
- Adjusted Rand index (ARI)
- TNR micro/macro
- FPR micro/macro
- FNR micro/macro
Changed
sparse
parameter added to print_matrix
,print_normalized_matrix
and save_stat
methods
header
parameter added to save_csv
method
- Handler functions moved to
pycm_handler.py
- Error objects moved to
pycm_error.py
- Verified tests references updated
- Verified tests moved to
verified_test.py
- Test system modified
CONTRIBUTING.md
updated
- Namespace optimized
README.md
modified
- Document modified
print_normalized_matrix
method modified
normalized_table_calc
function modified
setup.py
modified
- summary mode updated
- Dockerfile updated
Python 3.8
added to .travis.yaml
and appveyor.yml
Removed
2.5 - 2019-10-16
Added
__version__
variable
- Individual classification success index (ICSI)
- Classification success index (CSI)
- Example-8 (Confidence interval)
install.sh
autopep8.sh
- Dockerfile
CI
method (supported statistics : ACC
,AUC
,Overall ACC
,Kappa
,TPR
,TNR
,PPV
,NPV
,PLR
,NLR
,PRE
)
Changed
test.sh
moved to .travis
folder
- Python 3.4 support dropped
- Python 2.7 support dropped
AUTHORS.md
updated
save_stat
,save_csv
and save_html
methods Non-ASCII character bug fixed
- Mixed type input vectors bug fixed
CONTRIBUTING.md
updated
- Example-3 updated
README.md
modified
- Document modified
CI
attribute renamed to CI95
kappa_se_calc
function renamed to kappa_SE_calc
se_calc
function modified and renamed to SE_calc
- CI/SE functions moved to
pycm_ci.py
- Minor bug in
save_html
method fixed
2.4 - 2019-07-31
Added
- Tversky index (TI)
- Area under the PR curve (AUPR)
FUNDING.yml
Changed
AUC_calc
function modified
- Document modified
summary
parameter added to save_html
,save_stat
,save_csv
and stat
methods
sample_weight
bug in numpy
array format fixed
- Inputs manipulation bug fixed
- Test system modified
- Warning system modified
alt_link
parameter added to save_html
method and online_help
function
Compare
class tests moved to compare_test.py
- Warning tests moved to
warning_test.py
2.3 - 2019-06-27
Added
- Adjusted F-score (AGF)
- Overlap coefficient (OC)
- Otsuka-Ochiai coefficient (OOC)
Changed
save_stat
and save_vector
parameters added to save_obj
method
- Document modified
README.md
modified
- Parameters recommendation for imbalance dataset modified
- Minor bug in
Compare
class fixed
pycm_help
function modified
- Benchmarks color modified
2.2 - 2019-05-30
Added
- Negative likelihood ratio interpretation (NLRI)
- Cramer's benchmark (SOA5)
- Matthews correlation coefficient interpretation (MCCI)
- Matthews's benchmark (SOA6)
- F1 macro
- F1 micro
- Accuracy macro
Changed
Compare
class score calculation modified
- Parameters recommendation for multi-class dataset modified
- Parameters recommendation for imbalance dataset modified
README.md
modified
- Document modified
- Logo updated
2.1 - 2019-05-06
Added
- Adjusted geometric mean (AGM)
- Yule's Q (Q)
Compare
class and parameters recommendation system block diagrams
Changed
- Document links bug fixed
- Document modified
2.0 - 2019-04-15
Added
- G-Mean (GM)
- Index of balanced accuracy (IBA)
- Optimized precision (OP)
- Pearson's C (C)
Compare
class
- Parameters recommendation warning
ConfusionMatrix
equal method
Changed
- Document modified
stat_print
function bug fixed
table_print
function bug fixed
Beta
parameter renamed to beta
(F_calc
function & F_beta
method)
- Parameters recommendation for imbalance dataset modified
normalize
parameter added to save_html
method
pycm_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 modified
- Docstrings modified
1.9 - 2019-02-25
Added
- Automatic/Manual (AM)
- Bray-Curtis dissimilarity (BCD)
CODE_OF_CONDUCT.md
ISSUE_TEMPLATE.md
PULL_REQUEST_TEMPLATE.md
CONTRIBUTING.md
- X11 color names support for
save_html
method
- Parameters recommendation system
- Warning message for high dimension matrix print
- Interactive notebooks section (binder)
Changed
save_matrix
and normalize
parameters added to save_csv
method
README.md
modified
- Document modified
ConfusionMatrix.__init__
optimized
- Document and examples output files moved to different folders
- Test system modified
relabel
method bug fixed
1.8 - 2019-01-05
Added
- Lift score (LS)
version_check.py
Changed
color
parameter added to save_html
method
- Error messages modified
- Document modified
- Website changed to http://www.pycm.ir
- Interpretation functions moved to
pycm_interpret.py
- Utility functions moved to
pycm_util.py
- Unnecessary
else
and elif
removed
==
changed to is
1.7 - 2018-12-18
Added
- Gini index (GI)
- Example-7
pycm_profile.py
Changed
class_name
parameter added to stat
,save_stat
,save_csv
and save_html
methods
overall_param
and class_param
parameters empty list bug fixed
matrix_params_calc
, matrix_params_from_table
and vector_filter
functions optimized
overall_MCC_calc
, CEN_misclassification_calc
and convex_combination
functions optimized
- Document modified
1.6 - 2018-12-06
Added
- AUC value interpretation (AUCI)
- Example-6
- Anaconda cloud package
Changed
overall_param
and class_param
parameters added to stat
,save_stat
and save_html
methods
class_param
parameter added to save_csv
method
_
removed from overall statistics names
README.md
modified
- Document modified
1.5 - 2018-11-26
Added
- Relative classifier information (RCI)
- Discriminator power (DP)
- Youden's index (Y)
- Discriminant power interpretation (DPI)
- Positive likelihood ratio interpretation (PLRI)
__len__
method
relabel
method
__class_stat_init__
function
__overall_stat_init__
function
matrix
attribute as dict
normalized_matrix
attribute as dict
normalized_table
attribute as dict
Changed
README.md
modified
- Document modified
LR+
renamed to PLR
LR-
renamed to NLR
normalized_matrix
method renamed to print_normalized_matrix
matrix
method renamed to print_matrix
entropy_calc
fixed
cross_entropy_calc
fixed
conditional_entropy_calc
fixed
print_table
bug for large numbers fixed
- JSON key bug in
save_obj
fixed
transpose
bug in save_obj
fixed
Python 3.7
added to .travis.yaml
and appveyor.yml
1.4 - 2018-11-12
Added
- Area under curve (AUC)
- AUNU
- AUNP
- Class balance accuracy (CBA)
- Global performance index (RR)
- Overall MCC
- Distance index (dInd)
- Similarity index (sInd)
one_vs_all
dev-requirements.txt
Changed
README.md
modified
- Document modified
save_stat
modified
requirements.txt
modified
1.3 - 2018-10-10
Added
- Confusion entropy (CEN)
- Overall confusion entropy (Overall CEN)
- Modified confusion entropy (MCEN)
- Overall modified confusion entropy (Overall MCEN)
- Information score (IS)
Changed
1.2 - 2018-10-01
Added
- No information rate (NIR)
- P-Value
sample_weight
transpose
Changed
README.md
modified
- Key error in some parameters fixed
OSX
env added to .travis.yml
1.1 - 2018-09-08
Added
- Zero-one loss
- Support
online_help
function
Changed
README.md
modified
html_table
function modified
table_print
function modified
normalized_table_print
function modified
1.0 - 2018-08-30
Added
Changed
0.9.5 - 2018-07-08
Added
- Obj load
- Obj save
- Example-4
Changed
README.md
modified
- Block diagram updated
0.9 - 2018-06-28
Added
- Activation threshold
- Example-3
- Jaccard index
- Overall Jaccard index
Changed
README.md
modified
setup.py
modified
0.8.6 - 2018-05-31
Added
- Example section in document
- Python 2.7 CI
- JOSS paper pdf
Changed
- Cite section
- ConfusionMatrix docstring
- round function changed to numpy.around
README.md
modified
0.8.5 - 2018-05-21
Added
- Example-1 (Comparison of three different classifiers)
- Example-2 (How to plot via matplotlib)
- JOSS paper
- ConfusionMatrix docstring
Changed
- Table size in HTML report
- Test system
README.md
modified
0.8.1 - 2018-03-22
Added
- Goodman and Kruskal's lambda B
- Goodman and Kruskal's lambda A
- Cross entropy
- Conditional entropy
- Joint entropy
- Reference entropy
- Response entropy
- Kullback-Liebler divergence
- Direct ConfusionMatrix
- Kappa unbiased
- Kappa no prevalence
- Random accuracy unbiased
pycmVectorError
class
pycmMatrixError
class
- Mutual information
- Support
numpy
arrays
Changed
Removed
0.7 - 2018-02-26
Added
- Cramer's V
- 95% confidence interval
- Chi-Squared
- Phi-Squared
- Chi-Squared DF
- Standard error
- Kappa standard error
- Kappa 95% confidence interval
- Cicchetti benchmark
Changed
- Overall statistics color in HTML report
- Parameters description link in HTML report
0.6 - 2018-02-21
Added
- CSV report
- Changelog
- Output files
digit
parameter to ConfusionMatrix
object
Changed
- Confusion matrix color in HTML report
- Parameters description link in HTML report
- Capitalize descriptions
0.5 - 2018-02-17
Added
- Scott's pi
- Gwet's AC1
- Bennett S score
- HTML report
0.4 - 2018-02-05
Added
- TPR micro/macro
- PPV micro/macro
- Overall RACC
- Error rate (ERR)
- FBeta score
- F0.5
- F2
- Fleiss benchmark
- Altman benchmark
- Output file(.pycm)
Changed
- Class with zero item
- Normalized matrix
Removed
- Kappa and SOA for each class
0.3 - 2018-01-27
Added
- Kappa
- Random accuracy
- Landis and Koch benchmark
overall_stat
0.2 - 2018-01-24
Added
- Population
- Condition positive
- Condition negative
- Test outcome positive
- Test outcome negative
- Prevalence
- G-measure
- Matrix method
- Normalized matrix method
- Params method
Changed
statistic_result
to class_stat
params
to stat
0.1 - 2018-01-22
Added
- ACC
- BM
- DOR
- F1-Score
- FDR
- FNR
- FOR
- FPR
- LR+
- LR-
- MCC
- MK
- NPV
- PPV
- TNR
- TPR
- documents and
README.md