Metadata-Version: 2.1
Name: scikit-mcda
Version: 0.21.4
Summary: Library for Multi-criteria Decision Aid Methods
Home-page: https://gitlab.com/cybercrafter/scikit-mcda
Author: Antonio Horta
Author-email: ajhorta@cybercrafter.com.br
License: Apache License 2.0
Description: scikit-mcda
        ===========
        
        It is a python library made to provide multi-criteria decision aid for developers and operacional researchers.
        
        Scikit-mcda provides an easy way to apply several popular decision-making methods. It can be used as part of your development or for analytical experiments using notebooks like Jupyter, colab or kaggle. The package is available on the Pypi allowing installation by *pip install scikit-mcda* command.
        
        :Some methods available:
          DMUU:
            laplace, hurwicz, maximax, maximin, minimax-regret ...
          MCDA:
            Weighted Sum Model (WSM), Weighted Product Model (WPM) , Weighted Aggregated Sum Product Assessment (WASPAS), TOPSIS ...
          Definition of criteria weights:
            Manually, Entropy, Ranking Methods ... 
          Nomalization:
            Z score, MinMax, Logistic, Max, Sum and RootSumSquared
        
        
        
        Scikit-mcda is free to use for personal, commercial and academic projects, always respecting the terms of the Apache 2.0 License. Do not forget to refer to this Library when it is used in your experiments, lectures, presentations, classes and research papers. The reference must follow this citation format.
        
        .. code-block::
        
          (HORTA, 2021)
        
          HORTA, Antonio (2021). Scikit-mcda: The Python library for multi-criteria decision aid. 
          Version 0.21.3. [opensource], 17 jan. 2021. Available in: https://gitlab.com/cybercrafter/scikit-mcda. 
          Acessed in: 17 jan. 2021.
        
        It's a project made by Cybercrafter® <ajhorta@cybercrafter.com.br>
        
        Module for Decision-making Under Uncertainty (DMUU)
        ---------------------------------------------------
        
        **DMUU**: Class Module for Decision-making Under Uncertainty
        
        **Attributes:**
        
        .. code-block:: python
          
          df_original = DataFrame
          df_calc = DataFrame
          decision = {"alternative":,
                      "index":,
                      "value": ,
                      "criteria": ,
                      "result": ,
                      "type_dm": "DMUU",
                      "hurwicz_coeficient":}
        
        **Criteria Methods**:
        
        - maximax()
        - maximin()
        - laplace()
        - minimax_regret()
        - hurwicz(coef)
        
        **Properties**
        
        - pretty_original(tablefmt='psql')
        - pretty_calc(tablefmt='psql')
        - pretty_decision(tablefmt='psql')
        
        tablefmt: "psql" or "latex" or "html" 
        
        **Methods**:
        
        - dataframe(alt_data, alt_labels=[], state_labels=[])
        - decision_making(dmuu_criteria_list=[])
        
        Quick Start for DMUU
        --------------------
        
        .. code-block:: python
          
          from scikitmcda.dmuu import DMUU
        
          # Defining labels for Alternatives and States")
          
          dmuu = DMUU()
        
          dmuu.dataframe([[5000, 2000, 100],
                          [50, 50, 500]],
                          ["ALT_A", "ALT_B"],
                          ["STATE A", "STATE B", "STATE C"]
                          )
        
          print(dmuu.pretty_original())
          +----+----------------+-----------+-----------+-----------+
          |    | alternatives   |   STATE A |   STATE B |   STATE C |
          |----+----------------+-----------+-----------+-----------|
          |  0 | ALT_A          |      5000 |      2000 |       100 |
          |  1 | ALT_B          |        50 |        50 |       500 |
          +----+----------------+-----------+-----------+-----------+
        
          
          # Specifying the criteria method
          
          dmuu.minimax_regret()
        
          print(dmuu.pretty_calc())
          +----+----------------+-----------+-----------+-----------+------------------+
          |    | alternatives   |   STATE A |   STATE B |   STATE C | minimax-regret   |
          |----+----------------+-----------+-----------+-----------+------------------|
          |  0 | ALT_A          |      5000 |      2000 |       100 | (400, 1)         |
          |  1 | ALT_B          |        50 |        50 |       500 | (4950, 0)        |
          +----+----------------+-----------+-----------+-----------+------------------+
        
          print(dmuu.pretty_decision())
          +---------------+---------+---------+----------------+-------------------------------+-----------+----------------------+
          | alternative   |   index |   value | criteria       | result                        | type_dm   | hurwicz_coeficient   |
          |---------------+---------+---------+----------------+-------------------------------+-----------+----------------------|
          | ALT_A         |       0 |     400 | minimax-regret | {'ALT_A': 400, 'ALT_B': 4950} | DMUU      |                      |
          +---------------+---------+---------+----------------+-------------------------------+-----------+----------------------+
        
          # Many crietria methods
        
          dmuu.decision_making([dmuu.maximax(), dmuu.maximin(), dmuu.hurwicz(0.8), dmuu.minimax_regret()])
        
          print(dmuu.pretty_calc())
          +----+----------------+-----------+-----------+-----------+------------------+-----------+-----------+------------------+
          |    | alternatives   |   STATE A |   STATE B |   STATE C | minimax-regret   | maximax   | maximin   | hurwicz          |
          |----+----------------+-----------+-----------+-----------+------------------+-----------+-----------+------------------|
          |  0 | ALT_A          |      5000 |      2000 |       100 | (400, 1)         | (5000, 1) | (100, 1)  | (4020.0, 1, 0.8) |
          |  1 | ALT_B          |        50 |        50 |       500 | (4950, 0)        | (500, 0)  | (50, 0)   | (410.0, 0, 0.8)  |
          +----+----------------+-----------+-----------+-----------+------------------+-----------+-----------+------------------+
        
          print(dmuu.pretty_decision())
          +---------------+---------+---------+----------------+-----------------------------------+-----------+----------------------+
          | alternative   |   index |   value | criteria       | result                            | type_dm   | hurwicz_coeficient   |
          |---------------+---------+---------+----------------+-----------------------------------+-----------+----------------------|
          | ALT_A         |       0 |    5000 | maximax        | {'ALT_A': 5000, 'ALT_B': 500}     | DMUU      |                      |
          | ALT_A         |       0 |     100 | maximin        | {'ALT_A': 100, 'ALT_B': 50}       | DMUU      |                      |
          | ALT_A         |       0 |    4020 | hurwicz        | {'ALT_A': 4020.0, 'ALT_B': 410.0} | DMUU      | 0.8                  |
          | ALT_A         |       0 |     400 | minimax-regret | {'ALT_A': 400, 'ALT_B': 4950}     | DMUU      |                      |
          +---------------+---------+---------+----------------+-----------------------------------+-----------+----------------------+
        
          dmuu.calc_clean()
          print(dmuu.pretty_calc())
          +----+----------------+-----------+-----------+-----------+
          |    | alternatives   |   STATE A |   STATE B |   STATE C |
          |----+----------------+-----------+-----------+-----------|
          |  0 | ALT_A          |      5000 |      2000 |       100 |
          |  1 | ALT_B          |        50 |        50 |       500 |
          +----+----------------+-----------+-----------+-----------+
        
        
        
        Module for Multi-Criteria Decision Aid (MCDA)
        ---------------------------------------------
        
        **MCDA**: Class Module for Multi-Criteria Decision-Aid
        
        Attributes:
          - df_original 
          - weights
          - signals
          - df_normalized
          - df_weighted
          - df_pis
          - df_nis
          - df_distances
          - df_decision
        
        **MCDA basis methods**:
        
        - dataframe(alt_data, alt_labels=[], state_labels=[])
        - set_signals([MIN, MIN, MAX])
        - set_normalization(default=RootSumSquared\_)
        
        Normalization constants: 
          ZScore\_, MinMax\_, Logistic\_, Max\_, Sum\_, RootSumSquared\_
        
        **MCDA weights determination methods**:
        
        - set_weights_manually([])
        - set_weights_by_entropy(normalization_method_for_entropy=Default)
        - set_weights_by_ranking_A() 
        - set_weights_by_ranking_B() 
        - set_weights_by_ranking_B_POW(default=0)
        - set_weights_by_ranking_C()
        
        *Ranking methods A, B, B_POW and C need criteria ordered by importance C1> c2> C3 ...*
        
        **Decision-Making methods**:
        
        - topsis()
        - wsm()
        - wpm()
        - waspas(lambda=0.5)
        
        **Properties**
        
        - pretty_original(tablefmt='psql')
        - pretty_normalized(tablefmt='psql')
        - pretty_weighted(tablefmt='psql')
        - pretty_Xis(tablefmt='psql')
        - pretty_decision(tablefmt='psql')
        
        tablefmt: "psql" or "latex" or "html" 
        
        Quick Start for MCDA
        --------------------
        
        .. code-block:: python
          
          from scikitmcda.mcda import MCDA
          from scikitmcda.constants import MAX, MIN, ZScore_, MinMax_, Logistic_, Max_, Sum_, RootSumSquared_ 
        
        
          mcda = MCDA()
        
          mcda.dataframe([[90, 20, 86],
                          [120, 8, 120],
                          [70, 12, 90]],
                          ["ALTERNATIVE A", "ALTERNATIVE B", "ALTERNATIVE C"],
                          ["COST", "TIME", "SPEED"]
                          )
        
          print(mcda.pretty_original())
          +----+----------------+--------+--------+---------+
          |    | alternatives   |   COST |   TIME |   SPEED |
          |----+----------------+--------+--------+---------|
          |  0 | ALTERNATIVE A  |     90 |     20 |      86 |
          |  1 | ALTERNATIVE B  |    120 |      8 |     120 |
          |  2 | ALTERNATIVE C  |     70 |     12 |      90 |
          +----+----------------+--------+--------+---------+
        
          # defining weights and signals for decision by TOPSIS 
          mcda.set_weights_manually([0.5, 0.3, 0.2])
          # or mcda.set_weights_by_entropy()
          
          mcda.set_signals([MIN, MIN, MAX])
          mcda.set_normalization_method(RootSumSquared_)
          mcda.topsis()
        
          print(mcda.pretty_normalized())
          +----+----------------+----------+----------+----------+
          |    | alternatives   |     COST |     TIME |    SPEED |
          |----+----------------+----------+----------+----------|
          |  0 | ALTERNATIVE A  | 0.54371  | 0.811107 | 0.497384 |
          |  1 | ALTERNATIVE B  | 0.724947 | 0.324443 | 0.694024 |
          |  2 | ALTERNATIVE C  | 0.422885 | 0.486664 | 0.520518 |
          +----+----------------+----------+----------+----------+
        
          print(mcda.pretty_weighted())
          +----+----------------+----------+-----------+-----------+
          |    | alternatives   |     COST |      TIME |     SPEED |
          |----+----------------+----------+-----------+-----------|
          |  0 | ALTERNATIVE A  | 0.271855 | 0.243332  | 0.0994768 |
          |  1 | ALTERNATIVE B  | 0.362473 | 0.0973329 | 0.138805  |
          |  2 | ALTERNATIVE C  | 0.211443 | 0.145999  | 0.104104  |
          +----+----------------+----------+-----------+-----------+
        
          print(mcda.pretty_Xis())
          +-----+----------+-----------+-----------+
          |     |     COST |      TIME |     SPEED |
          |-----+----------+-----------+-----------|
          | PIS | 0.211443 | 0.0973329 | 0.138805  |
          | NIS | 0.362473 | 0.243332  | 0.0994768 |
          +-----+----------+-----------+-----------+
        
          print(mcda.pretty_decision())
          +----+----------------+-------------+--------+
          |    | alternatives   |   euclidian |   rank |
          |----+----------------+-------------+--------|
          |  0 | ALTERNATIVE C  |    0.945809 |      1 |
          |  1 | ALTERNATIVE B  |    0.413933 |      2 |
          |  2 | ALTERNATIVE A  |    0.35164  |      3 |
          +----+----------------+-------------+--------+
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
