Metadata-Version: 2.4
Name: pymcdm
Version: 1.4.0
Summary: Python library for Multi-Criteria Decision-Making
Author-email: Andrii Shekhovtsov <andrii-shekhovtsov@zut.edu.pl>, Bartłomiej Kizielewicz <bartlomiej-kizielewicz@zut.edu.pl>
License-Expression: MIT
Project-URL: Homepage, https://github.com/kotbaton/pymcdm
Project-URL: Issues, https://github.com/kotbaton/pymcdm/issues
Project-URL: Documentation, https://pymcdm.readthedocs.io/
Project-URL: Changelog, https://github.com/kotbaton/pymcdm/blob/master/CHANGELOG.md
Keywords: MCDM,Multi-Criteria Decision-Making,Decision Analysis,Operations Research
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: tabulate
Provides-Extra: docs
Requires-Dist: sphinx-autoapi; extra == "docs"
Requires-Dist: nbsphinx; extra == "docs"
Requires-Dist: IPython; extra == "docs"
Requires-Dist: myst_parser; extra == "docs"
Requires-Dist: sphinx-rtd-theme; extra == "docs"
Requires-Dist: recommonmark; extra == "docs"
Requires-Dist: pandoc; extra == "docs"
Dynamic: license-file

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**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)              |
| RAFSI                 | Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval |              [[69]](#c69)              |
| LoPM                	 | Limits on Property Method                                                                            |              [[67]](#c67)              |
| LMAW                  | Logarithmic Methodology of Additive Weights                                                          |              [[70]](#c70)               | 
| AROMAN                | Alternative Ranking Order Method Accounting for Two-Step Normalization)                              |              [[72]](#c72)               | 

* MCDA Methods' extensions and variations:

| Acronym       	|    Reference         	    |
|:-----------------|:-------------------------:|
| Balanced SPOTIS	|   [[68]](#c68)       	    |

* 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)               |
| LOPCOW  | LOgarithmic Percentage Change-driven Objective Weighting  |               [[71]](#c71)               |
| -       | 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)       	     |

* Distance metrics:

| Metric Name                    	 |  Reference           |
|-----------------------------------|:--------------------:|
| Kemeny distance              	 |    [[73]](#73) 	    |
| Frobenius distance              	 |    [[74]](#74) 	    |
| draWS distance              	     |    [[75]](#75) 	    |

* 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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