| Name | pymcdm JSON |
| Version |
1.3.0
JSON |
| download |
| home_page | None |
| Summary | Python library for Multi-Criteria Decision-Making |
| upload_time | 2024-12-29 10:00:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | None |
| keywords |
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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No coveralls.
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[](https://github.com/kotbaton/pymcdm)
[](https://pypi.org/project/pymcdm/)
[](https://pymcdm.readthedocs.io/en/latest/)
[](https://opensource.org/licenses/MIT)
[](https://doi.org/10.1016/j.softx.2023.101368)
**Important:** Development process was moved from [GitLab](https://gitlab.com/shekhand/mcda) to [Github](https://github.com/kotbaton/pymcdm).
# PyMCDM
Python 3 library for solving multi-criteria decision-making (MCDM) problems.
Documentation is avaliable on [readthedocs](https://pymcdm.readthedocs.io/en/master/).
___
# Installation
You can download and install `pymcdm` library using pip:
```Bash
pip install pymcdm
```
You can run all tests with following command from the root of the project:
```Bash
python -m unittest -v
```
___
# Citing pymcdm
If usage of the pymcdm library lead to a scientific publication, please
acknowledge this fact by citing "[_Kizielewicz, B., Shekhovtsov, A.,
& Sałabun, W. (2023). pymcdm—The universal library for solving multi-criteria
decision-making problems. SoftwareX, 22, 101368._](https://doi.org/10.1016/j.softx.2023.101368)"
Or using BibTex:
```bibtex
@article{kizielewicz2023pymcdm,
title={pymcdm—The universal library for solving multi-criteria decision-making problems},
author={Kizielewicz, Bart{\l}omiej and Shekhovtsov, Andrii and Sa{\l}abun, Wojciech},
journal={SoftwareX},
volume={22},
pages={101368},
year={2023},
publisher={Elsevier}
}
```
DOI: [https://doi.org/10.1016/j.softx.2023.101368](https://doi.org/10.1016/j.softx.2023.101368)
___
# Available methods
The library contains:
* MCDA methods:
| Acronym | Method Name | Reference |
|:---------------------|-----------------------------------------------------------------------------|:--------------------------------------:|
| TOPSIS | Technique for the Order of Prioritisation by Similarity to Ideal Solution | [[1]](#c1) |
| VIKOR | VIseKriterijumska Optimizacija I Kompromisno Resenje | [[2]](#c2) |
| COPRAS | COmplex PRoportional ASsessment | [[3]](#c3) |
| PROMETHEE I & II | Preference Ranking Organization METHod for Enrichment of Evaluations I & II | [[4]](#c4) |
| COMET | Characteristic Objects Method | [[5]](#c5) |
| SPOTIS | Stable Preference Ordering Towards Ideal Solution | [[6]](#c6) |
| ARAS | Additive Ratio ASsessment | [[7]](#c7),[[8]](#c8) |
| COCOSO | COmbined COmpromise SOlution | [[9]](#c9) |
| CODAS | COmbinative Distance-based ASsessment | [[10]](#c10) |
| EDAS | Evaluation based on Distance from Average Solution | [[11]](#c11),[[12]](#c12) |
| MABAC | Multi-Attributive Border Approximation area Comparison | [[13]](#c13) |
| MAIRCA | MultiAttributive Ideal-Real Comparative Analysis | [[14]](#c14),[[15]](#c15),[[16]](#c16) |
| MARCOS | Measurement Alternatives and Ranking according to COmpromise Solution | [[17]](#c17),[[18]](#c18) |
| OCRA | Operational Competitiveness Ratings | [[19]](#c19),[[20]](#c20) |
| MOORA | Multi-Objective Optimization Method by Ratio Analysis | [[21]](#c21),[[22]](#c22) |
| RIM | Reference Ideal Method | [[48]](#c48) |
| ERVD | Election Based on relative Value Distances | [[49]](#c49) |
| PROBID | Preference Ranking On the Basis of Ideal-average Distance | [[50]](#c50) |
| WSM | Weighted Sum Model | [[51]](#c51) |
| WPM | Weighted Product Model | [[52]](#c52) |
| WASPAS | Weighted Aggregated Sum Product ASSessment | [[53]](#c53) |
| RAM | Root Assesment Method | [[62]](#c62) |
* Weighting methods:
| Acronym | Method Name | Reference |
|:--------|:------------------------------------------------------|:----------------------------------------:|
| - | Equal/Mean weights | [[23]](#c23) |
| - | Entropy weights | [[23]](#c23), [[24]](#c24), [[25]](#c25) |
| STD | Standard Deviation weights | [[23]](#c23), [[26]](#c26) |
| MEREC | MEthod based on the Removal Effects of Criteria | [[27]](#c27) |
| CRITIC | CRiteria Importance Through Intercriteria Correlation | [[28]](#c28), [[29]](#c29) |
| CILOS | Criterion Impact LOS | [[30]](#c30) |
| IDOCRIW | Integrated Determination of Objective CRIteria Weight | [[30]](#c30) |
| - | Angular/Angle weights | [[31]](#c31) |
| - | Gini Coeficient weights | [[32]](#c32) |
| - | Statistical variance weights | [[33]](#c33) |
| AHP | Analytic Hierarchy Process | [[65]](#c65) |
| RANCOM | RANking COMparison | [[66]](#c66) |
* Normalization methods:
| Method Name | Reference |
|:---------------------------------------|:---------------------------:|
| Weitendorf’s Linear Normalization | [[34]](#c34) |
| Maximum - Linear Normalization | [[35]](#c35) |
| Sum-Based Linear Normalization | [[36]](#c36) |
| Vector Normalization | [[36]](#c36),[[37]](#c37) |
| Logarithmic Normalization | [[36]](#c36),[[37]](#c37) |
| Linear Normalization (Max-Min) | [[34]](#c34),[[38]](#c38) |
| Non-linear Normalization (Max-Min) | [[39]](#c39) |
| Enhanced Accuracy Normalization | [[40]](#c40) |
| Lai and Hwang Normalization | [[38]](#c38) |
| Zavadskas and Turskis Normalization | [[38]](#c38) |
* Correlation coefficients:
| Coefficient name | Reference |
|------------------------------------------------------|:---------------------------:|
| Spearman's rank correlation coefficient | [[41]](#c41),[[42]](#c42) |
| Pearson correlation coefficient | [[43]](#c43) |
| Weighted Spearman’s rank correlation coefficient | [[44]](#c44) |
| Rank Similarity Coefficient | [[45]](#c45) |
| Kendall rank correlation coefficient | [[46]](#c46) |
| Goodman and Kruskal's gamma | [[47]](#c47) |
| Drastic Weighted Similarity (draWS) | [[59]](#c59) |
| Weights Similarity Coefficient (WSC) | [[60]](#c60) |
| Weights Similarity Coefficient 2 (WSC2) | [[60]](#c60) |
* Helpers
| Helpers submodule | Description |
|----------------------|------------------------------------------------------------------------------------------------------------------|
| `rankdata` | Create ranking vector from the preference vector. Smaller preference values has higher positions in the ranking. |
| `rrankdata` | Alias to the `rankdata` which reverse the sorting order. |
| `correlation_matrix` | Create the correlation matrix for given coefficient from several the several rankings. |
| `normalize_matrix` | Normalize decision matrix column by column using given normalization and criteria types. |
* COMET Tools
| Class/Function | Description | Reference |
|----------------------|----------------------------------------------------------------------------------------------------|:------------:|
| `MethodExpert` | Class which allows to evaluate CO in COMET using any MCDA method. | [[56]](#c56) |
| `ManualExpert` | Class which allows to evaluate CO in COMET manually by pairwise comparisons. | [[57]](#c57) |
| `FunctionExpert` | Class which allows to evaluate CO in COMET using any expert function. | [[58]](#c58) |
| `CompromiseExpert` | Class which allows to evaluate CO in COMET using compromise between several different methods. | [[63]](#c63) |
| `TriadSupportExpert` | Class which allows to evaluate CO in COMET manually but with triads support. | [[64]](#c64) |
| `ESPExpert` | Class which allows to identify MEJ using expert-defined Expected Solution Points. | [[61]](#c61) |
| `triads_consistency` | Function to which evaluates consistency of the MEJ matrix. | [[55]](#c55) |
| `Submodel` | Class mostly for internal use in StructuralCOMET class. | [[54]](#c54) |
| `StructuralCOMET` | Class which allows to split a decision problem into submodels to be evaluated by the COMET method. | [[54]](#c54) |
___
# Usage example
Here's a small example of how use this library to solve MCDM problem.
For more examples with explanation see [examples](https://gitlab.com/shekhand/mcda/-/blob/master/examples/examples.ipynb).
```python
import numpy as np
from pymcdm.methods import TOPSIS
from pymcdm.helpers import rrankdata
# Define decision matrix (3 criteria, 4 alternative)
alts = np.array([
[4, 4, 0.2],
[1, 5, 0.5],
[3, 2, 0.3],
[4, 3, 0.5]
])
# Define criteria weights (should sum up to 1)
weights = np.array([0.3, 0.5, 0.2])
# Define criteria types (1 for profit, -1 for cost)
types = np.array([1, -1, 1])
# Create object of the method
# Note, that default normalization method for TOPSIS is minmax
topsis = TOPSIS()
# Determine preferences and ranking for alternatives
pref = topsis(alts, weights, types)
ranking = rrankdata(pref)
for r, p in zip(ranking, pref):
print(r, p)
```
And the output of this example (numbers are rounded):
```bash
3.0 0.469
4.0 0.255
1.0 0.765
2.0 0.747
```
If you want to inspect computation process in details, you can add the following code to the above example:
```python
results = topsis(alts, weights, types, verbose=True)
print(results)
```
This will produce output that contains formatted decision matrix, intermediate results, preference values and ranking.
---
# References
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"description": "[](https://github.com/kotbaton/pymcdm)\n[](https://pypi.org/project/pymcdm/)\n[](https://pymcdm.readthedocs.io/en/latest/)\n[](https://opensource.org/licenses/MIT)\n[](https://doi.org/10.1016/j.softx.2023.101368)\n\n**Important:** Development process was moved from [GitLab](https://gitlab.com/shekhand/mcda) to [Github](https://github.com/kotbaton/pymcdm).\n\n# PyMCDM\n\nPython 3 library for solving multi-criteria decision-making (MCDM) problems.\n\nDocumentation is avaliable on [readthedocs](https://pymcdm.readthedocs.io/en/master/).\n\n___\n\n# Installation\n\nYou can download and install `pymcdm` library using pip:\n\n```Bash\npip install pymcdm\n```\n\nYou can run all tests with following command from the root of the project:\n\n```Bash\npython -m unittest -v\n```\n\n___\n\n# Citing pymcdm\n\nIf usage of the pymcdm library lead to a scientific publication, please \nacknowledge this fact by citing \"[_Kizielewicz, B., Shekhovtsov, A., \n& Sa\u0142abun, W. (2023). pymcdm\u2014The universal library for solving multi-criteria \ndecision-making problems. SoftwareX, 22, 101368._](https://doi.org/10.1016/j.softx.2023.101368)\"\n\nOr using BibTex:\n```bibtex\n@article{kizielewicz2023pymcdm,\n title={pymcdm\u2014The universal library for solving multi-criteria decision-making problems},\n author={Kizielewicz, Bart{\\l}omiej and Shekhovtsov, Andrii and Sa{\\l}abun, Wojciech},\n journal={SoftwareX},\n volume={22},\n pages={101368},\n year={2023},\n publisher={Elsevier}\n}\n```\n\nDOI: [https://doi.org/10.1016/j.softx.2023.101368](https://doi.org/10.1016/j.softx.2023.101368)\n\n___\n\n# Available methods\n\nThe library contains:\n\n* MCDA methods:\n\n| Acronym \t | Method Name | Reference |\n|:---------------------|-----------------------------------------------------------------------------|:--------------------------------------:|\n| TOPSIS \t | Technique for the Order of Prioritisation by Similarity to Ideal Solution | [[1]](#c1) |\n| VIKOR \t | VIseKriterijumska Optimizacija I Kompromisno Resenje | [[2]](#c2) |\n| COPRAS \t | COmplex PRoportional ASsessment | [[3]](#c3) |\n| PROMETHEE I & II \t | Preference Ranking Organization METHod for Enrichment of Evaluations I & II | [[4]](#c4) |\n| COMET \t | Characteristic Objects Method | [[5]](#c5) |\n| SPOTIS \t | Stable Preference Ordering Towards Ideal Solution | [[6]](#c6) |\n| ARAS \t | Additive Ratio ASsessment | [[7]](#c7),[[8]](#c8) |\n| COCOSO \t | COmbined COmpromise SOlution | [[9]](#c9) |\n| CODAS \t | COmbinative Distance-based ASsessment | [[10]](#c10) |\n| EDAS \t | Evaluation based on Distance from Average Solution | [[11]](#c11),[[12]](#c12) |\n| MABAC \t | Multi-Attributive Border Approximation area Comparison | [[13]](#c13) |\n| MAIRCA \t | MultiAttributive Ideal-Real Comparative Analysis | [[14]](#c14),[[15]](#c15),[[16]](#c16) |\n| MARCOS \t | Measurement Alternatives and Ranking according to COmpromise Solution | [[17]](#c17),[[18]](#c18) |\n| OCRA \t | Operational Competitiveness Ratings | [[19]](#c19),[[20]](#c20) |\n| MOORA \t | Multi-Objective Optimization Method by Ratio Analysis | [[21]](#c21),[[22]](#c22) |\n| RIM \t | Reference Ideal Method | [[48]](#c48) |\n| ERVD \t | Election Based on relative Value Distances | [[49]](#c49) |\n| PROBID | Preference Ranking On the Basis of Ideal-average Distance | [[50]](#c50) |\n| WSM | Weighted Sum Model | [[51]](#c51) |\n| WPM | Weighted Product Model | [[52]](#c52) |\n| WASPAS | Weighted Aggregated Sum Product ASSessment | [[53]](#c53) |\n| RAM \t | Root Assesment Method | [[62]](#c62) |\n\n* Weighting methods:\n\n| Acronym | Method Name | Reference |\n|:--------|:------------------------------------------------------|:----------------------------------------:|\n| - | Equal/Mean weights | [[23]](#c23) |\n| - | Entropy weights | [[23]](#c23), [[24]](#c24), [[25]](#c25) |\n| STD | Standard Deviation weights | [[23]](#c23), [[26]](#c26) |\n| MEREC | MEthod based on the Removal Effects of Criteria | [[27]](#c27) |\n| CRITIC | CRiteria Importance Through Intercriteria Correlation | [[28]](#c28), [[29]](#c29) |\n| CILOS | Criterion Impact LOS | [[30]](#c30) |\n| IDOCRIW | Integrated Determination of Objective CRIteria Weight | [[30]](#c30) |\n| - | Angular/Angle weights | [[31]](#c31) |\n| - | Gini Coeficient weights | [[32]](#c32) |\n| - | Statistical variance weights | [[33]](#c33) |\n| AHP | Analytic Hierarchy Process | [[65]](#c65) |\n| RANCOM | RANking COMparison | [[66]](#c66) |\n\n* Normalization methods:\n\n| Method Name \t | Reference \t |\n|:---------------------------------------|:---------------------------:|\n| Weitendorf\u2019s Linear Normalization \t | [[34]](#c34) \t |\n| Maximum - Linear Normalization \t | [[35]](#c35) \t |\n| Sum-Based Linear Normalization \t | [[36]](#c36) \t |\n| Vector Normalization \t | [[36]](#c36),[[37]](#c37) \t |\n| Logarithmic Normalization \t | [[36]](#c36),[[37]](#c37) \t |\n| Linear Normalization (Max-Min) \t | [[34]](#c34),[[38]](#c38) \t |\n| Non-linear Normalization (Max-Min) \t | [[39]](#c39) \t |\n| Enhanced Accuracy Normalization \t | [[40]](#c40) \t |\n| Lai and Hwang Normalization | [[38]](#c38) |\n| Zavadskas and Turskis Normalization | [[38]](#c38) |\n\n* Correlation coefficients:\n\n| Coefficient name \t | Reference \t |\n|------------------------------------------------------|:---------------------------:|\n| Spearman's rank correlation coefficient \t | [[41]](#c41),[[42]](#c42) \t |\n| Pearson correlation coefficient \t | [[43]](#c43) \t |\n| Weighted Spearman\u2019s rank correlation coefficient \t | [[44]](#c44) \t |\n| Rank Similarity Coefficient \t | [[45]](#c45) \t |\n| Kendall rank correlation coefficient \t | [[46]](#c46) \t |\n| Goodman and Kruskal's gamma \t | [[47]](#c47) \t |\n| Drastic Weighted Similarity (draWS) | [[59]](#c59) \t |\n| Weights Similarity Coefficient (WSC) | [[60]](#c60) \t |\n| Weights Similarity Coefficient 2 (WSC2) | [[60]](#c60) \t |\n\n* Helpers\n\n| Helpers submodule | Description |\n|----------------------|------------------------------------------------------------------------------------------------------------------|\n| `rankdata` | Create ranking vector from the preference vector. Smaller preference values has higher positions in the ranking. |\n| `rrankdata` | Alias to the `rankdata` which reverse the sorting order. |\n| `correlation_matrix` | Create the correlation matrix for given coefficient from several the several rankings. |\n| `normalize_matrix` | Normalize decision matrix column by column using given normalization and criteria types. |\n\n* COMET Tools\n\n| Class/Function | Description | Reference |\n|----------------------|----------------------------------------------------------------------------------------------------|:------------:|\n| `MethodExpert` | Class which allows to evaluate CO in COMET using any MCDA method. | [[56]](#c56) |\n| `ManualExpert` | Class which allows to evaluate CO in COMET manually by pairwise comparisons. | [[57]](#c57) |\n| `FunctionExpert` | Class which allows to evaluate CO in COMET using any expert function. | [[58]](#c58) |\n| `CompromiseExpert` | Class which allows to evaluate CO in COMET using compromise between several different methods. | [[63]](#c63) |\n| `TriadSupportExpert` | Class which allows to evaluate CO in COMET manually but with triads support. | [[64]](#c64) |\n| `ESPExpert` | Class which allows to identify MEJ using expert-defined Expected Solution Points. | [[61]](#c61) |\n| `triads_consistency` | Function to which evaluates consistency of the MEJ matrix. | [[55]](#c55) |\n| `Submodel` | Class mostly for internal use in StructuralCOMET class. | [[54]](#c54) |\n| `StructuralCOMET` | Class which allows to split a decision problem into submodels to be evaluated by the COMET method. | [[54]](#c54) |\n\n___\n# Usage example\n\nHere's a small example of how use this library to solve MCDM problem.\nFor more examples with explanation see [examples](https://gitlab.com/shekhand/mcda/-/blob/master/examples/examples.ipynb).\n\n```python\nimport numpy as np\nfrom pymcdm.methods import TOPSIS\nfrom pymcdm.helpers import rrankdata\n\n# Define decision matrix (3 criteria, 4 alternative)\nalts = np.array([\n [4, 4, 0.2],\n [1, 5, 0.5],\n [3, 2, 0.3],\n [4, 3, 0.5]\n])\n\n# Define criteria weights (should sum up to 1)\nweights = np.array([0.3, 0.5, 0.2])\n\n# Define criteria types (1 for profit, -1 for cost)\ntypes = np.array([1, -1, 1])\n\n# Create object of the method\n# Note, that default normalization method for TOPSIS is minmax\ntopsis = TOPSIS()\n\n# Determine preferences and ranking for alternatives\npref = topsis(alts, weights, types)\nranking = rrankdata(pref)\n\nfor r, p in zip(ranking, pref):\n print(r, p)\n```\n\nAnd the output of this example (numbers are rounded):\n\n```bash\n3.0 0.469\n4.0 0.255\n1.0 0.765\n2.0 0.747\n```\n\nIf you want to inspect computation process in details, you can add the following code to the above example:\n\n```python\nresults = topsis(alts, weights, types, verbose=True)\nprint(results)\n```\n\nThis will produce output that contains formatted decision matrix, intermediate results, preference values and ranking.\n\n---\n# References\n\n<a name=\"c1\">[1]</a> Hwang, C. 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