pycm
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Published
20 hours ago •
sepandhaghighi
sepandhaghighi
Multi-class confusion matrix library in Python
Table of contents
- Overview
- Installation
- Usage
- Document
- Try PyCM in Your Browser
- Issues & Bug Reports
- Todo
- Outputs
- Dependencies
- Contribution
- Acknowledgments
- References
- Cite
- Authors
- License
- Show Your Support
- Changelog
- Code of Conduct
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
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Installation
⚠️ 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)
Source code
- Download Version 3.6 or Latest Source
- Run
pip install -r requirements.txtorpip3 install -r requirements.txt(Need root access) - Run
python3 setup.py installorpython setup.py install(Need root access)
PyPI
- Check Python Packaging User Guide
- Run
pip install pycm==3.6orpip3 install pycm==3.6(Need root access)
Conda
- Check Conda Managing Package
- Update Conda using
conda update conda(Need root access) - Run
conda install -c sepandhaghighi pycm(Need root access)
Easy install
- Run
easy_install --upgrade pycm(Need root access)
MATLAB
- Download and install MATLAB (>=8.5, 64/32 bit)
- Download and install Python3.x (>=3.5, 64/32 bit)
- [x] Select
Add to PATHoption - [x] Select
Install pipoption
- [x] Select
- Run
pip install pycmorpip3 install pycm(Need root access) - Configure Python interpreter
>> pyversion PYTHON_EXECUTABLE_FULL_PATH
- Visit MATLAB Examples
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()andnormalized_matrix()renamed toprint_matrix()andprint_normalized_matrix()inversion 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.
```pycon
>>> cm.plot()
>>> from matplotlib import pyplot as plt
>>> cm.plot(cmap=plt.cm.Greens,number_label=True,plot_lib="matplotlib")
```
<img src="https://github.com/sepandhaghighi/pycm/raw/master/Otherfiles/plot2.png">
```pycon
>>> cm.plot(cmap=plt.cm.Reds,normalized=True,number_label=True,plot_lib="seaborn")
```
<img src="https://github.com/sepandhaghighi/pycm/raw/master/Otherfiles/plot3.png">
### Online help
`online_help` function is added in `version 1.1` in order to open each statistics definition in web browser
```pycon
>>> 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 inversion 2.4)
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 six overall benchmarks which are Landis & Koch, Fleiss, Altman, Cicchetti, Cramer, and Matthews. 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.
```pycon
>>> 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 0.425
2 cm3 0.33611 0.33056
>>> cp.best
pycm.ConfusionMatrix(classes: [0, 1, 2])
>>> cp.sorted
['cm2', 'cm3']
>>> cp.best_name
'cm2'
```
### Acceptable data types
**ConfusionMatrix**
1. `actual_vector` : python `list` or numpy `array` of any stringable objects
2. `predict_vector` : python `list` or numpy `array` of any stringable objects
3. `matrix` : `dict`
4. `digit`: `int`
5. `threshold` : `FunctionType (function or lambda)`
6. `file` : `File object`
7. `sample_weight` : python `list` or numpy `array` of numbers
8. `transpose` : `bool`
9. `classes` : python `list`
10. `is_imbalanced` : `bool`
* Run `help(ConfusionMatrix)` for `ConfusionMatrix` object details
**Compare**
1. `cm_dict` : python `dict` of `ConfusionMatrix` object (`str` : `ConfusionMatrix`)
2. `by_class` : `bool`
3. `class_weight` : python `dict` of class weights (`class_name` : `float`)
4. `class_benchmark_weight`: python `dict` of class benchmark weights (`class_benchmark_name` : `float`)
5. `overall_benchmark_weight`: python `dict` of overall benchmark weights (`overall_benchmark_name` : `float`)
6. `digit`: `int`
* Run `help(Compare)` for `Compare` object details
For more information visit [here](https://github.com/sepandhaghighi/pycm/tree/master/Document "Document")
<div align="center">
<a href="https://asciinema.org/a/171863" target="_blank"><img src="https://asciinema.org/a/171863.png" /></a>
</div>
## Try PyCM in your browser!
PyCM can be used online in interactive Jupyter Notebooks via the Binder or Colab services! Try it out now! :
[](https://mybinder.org/v2/gh/sepandhaghighi/pycm/master)
[](https://colab.research.google.com/github/sepandhaghighi/pycm/blob/master)
* Check `Examples` in `Document` folder
## Issues & bug reports
1. Fill an issue and describe it. We'll check it ASAP!
- Please complete the issue template
2. Discord : [https://discord.com/invite/zqpU2b3J3f](https://discord.com/invite/zqpU2b3J3f)
3. Website : [https://www.pycm.io](https://www.pycm.io)
4. Mailing List : [https://mail.python.org/mailman3/lists/pycm.python.org/](https://mail.python.org/mailman3/lists/pycm.python.org/)
5. Email : [[email protected]](mailto:[email protected] "[email protected]")
## Outputs
1. [HTML](http://www.pycm.io/test.html)
2. [CSV](https://github.com/sepandhaghighi/pycm/blob/master/Otherfiles/test.csv)
3. [PyCM](https://github.com/sepandhaghighi/pycm/blob/master/Otherfiles/test.pycm)
4. [OBJ](https://github.com/sepandhaghighi/pycm/blob/master/Otherfiles/test.obj)
5. [COMP](https://github.com/sepandhaghighi/pycm/blob/master/Otherfiles/test.comp)
## Dependencies
<table>
<tr>
<td align="center">master</td>
<td align="center">dev</td>
</tr>
<tr>
<td align="center"><a href="https://requires.io/github/sepandhaghighi/pycm/requirements/?branch=master"><img src="https://requires.io/github/sepandhaghighi/pycm/requirements.svg?branch=master" alt="Requirements Status" /></a></td>
<td align="center"><a href="https://requires.io/github/sepandhaghighi/pycm/requirements/?branch=dev"><img src="https://requires.io/github/sepandhaghighi/pycm/requirements.svg?branch=dev" alt="Requirements Status" /></a></td>
</tr>
</table>
## Acknowledgments
[NLnet foundation](https://nlnet.nl) has supported the PyCM project from version **3.6** to **4.0** through the [NGI Assure](https://nlnet.nl/assure) Fund. This fund is set up by [NLnet foundation](https://nlnet.nl) with funding from the European Commission's [Next Generation Internet program](https://ngi.eu), administered by DG Communications Networks, Content, and Technology under grant agreement [**No 957073**](https://nlnet.nl/project/PyCM/).
<a href="https://nlnet.nl"><img src="https://github.com/sepandhaghighi/pycm/raw/master/Otherfiles/NlNet.svg" height="50px" alt="NLnet foundation"></a> <a href="https://nlnet.nl/assure"><img src="https://github.com/sepandhaghighi/pycm/raw/master/Otherfiles/NGIAssure.svg" height="50px" alt="NGI Assure"></a>
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## Cite
If you use PyCM in your research, we would appreciate citations to the following paper :
<pre>
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.
</pre>
<pre>
@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}
}
</pre>
Download [PyCM.bib](http://www.pycm.io/PYCM.bib)
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<tr>
<td align="center">JOSS</td>
<td align="center"><a href="https://doi.org/10.21105/joss.00729"><img src="http://joss.theoj.org/papers/10.21105/joss.00729/status.svg"></a></td>
</tr>
<tr>
<td align="center">Zenodo</td>
<td align="center"><a href="https://doi.org/10.5281/zenodo.1157173"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.1157173.svg" alt="DOI"></a></td>
</tr>
<tr>
<td align="center">Researchgate</td>
<td align="center"><a href="https://www.researchgate.net/project/PYCM-python-confusion-matrix"><img src="https://img.shields.io/badge/Researchgate-PyCM-yellow.svg"></a></td>
</tr>
</table>
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Multi-class confusion matrix library in Python