skcriteria.madm
deprecated package¶
Warning
This package is deprecated, and is simply an alias for the skcriteria.agg
package.
Therefore
from skcriteria.madm.similarity import TOPSIS
from skcriteria.madm import electre
Is equivalent to
from skcriteria.agg.similarity import TOPSIS
from skcriteria.agg import electre
MCDA aggregation methods and internal machinery.
This Deprecated backward compatibility layer around skcriteria.agg.
Deprecated since version 0.8.5: ‘skcriteria.madm’ module is deprecated, use ‘skcriteria.agg’ instead
- class skcriteria.madm.KernelResult(method, alternatives, values, extra)[source]¶
Bases:
ResultABC
Separates the alternatives between good (kernel) and bad.
This type of results is used by methods that select which alternatives are good and bad. The good alternatives are called “kernel”
- Parameters:
method (str) – Name of the method that generated the result.
alternatives (array-like) – Names of the alternatives evaluated.
values (array-like) – Values assigned to each alternative by the method, where the i-th value refers to the valuation of the i-th. alternative.
extra (dict-like) – Extra information provided by the method regarding the evaluation of the alternatives.
- property kernel_¶
Alias for
values
.
- property kernel_size_¶
How many alternatives has the kernel.
- property kernel_where_¶
Indexes of the alternatives that are part of the kernel.
- property kernelwhere_¶
Indexes of the alternatives that are part of the kernel.
Deprecated since version 0.7: Use
kernel_where_
instead
- property kernel_alternatives_¶
Return the names of alternatives in the kernel.
- class skcriteria.madm.RankResult(method, alternatives, values, extra)[source]¶
Bases:
ResultABC
Ranking of alternatives.
This type of results is used by methods that generate a ranking of alternatives.
- Parameters:
method (str) – Name of the method that generated the result.
alternatives (array-like) – Names of the alternatives evaluated.
values (array-like) – Values assigned to each alternative by the method, where the i-th value refers to the valuation of the i-th. alternative.
extra (dict-like) – Extra information provided by the method regarding the evaluation of the alternatives.
- property has_ties_¶
Return True if two alternatives shares the same ranking.
- property ties_¶
Counter object that counts how many times each value appears.
- property rank_¶
Alias for
values
.
- property untied_rank_¶
Ranking whitout ties.
if the ranking has ties this property assigns unique and consecutive values in the ranking. This method only assigns the values using the command
numpy.argsort(rank_) + 1
.
- class skcriteria.madm.ResultABC(method, alternatives, values, extra)[source]¶
Bases:
DiffEqualityMixin
Base class to implement different types of results.
Any evaluation of the DecisionMatrix is expected to result in an object that extends the functionalities of this class.
- Parameters:
method (str) – Name of the method that generated the result.
alternatives (array-like) – Names of the alternatives evaluated.
values (array-like) – Values assigned to each alternative by the method, where the i-th value refers to the valuation of the i-th. alternative.
extra (dict-like) – Extra information provided by the method regarding the evaluation of the alternatives.
- property values¶
Values assigned to each alternative by the method.
The i-th value refers to the valuation of the i-th. alternative.
- property method¶
Name of the method that generated the result.
- property alternatives¶
Names of the alternatives evaluated.
- property extra_¶
Additional information about the result.
Note
e_
is an alias for this property
- property e_¶
Additional information about the result.
Note
e_
is an alias for this property
- property shape¶
Tuple with (number_of_alternatives, ).
rank.shape <==> np.shape(rank)
- diff(other, rtol=1e-05, atol=1e-08, equal_nan=False, check_dtypes=False)[source]¶
Return the difference between two objects within a tolerance.
This method should be implemented by subclasses to define how differences between objects are calculated.
The tolerance parameters rtol and atol, equal_nan, and check_dtypes are provided to be used by the numpy and pandas equality functions. These parameters allow you to customize the behavior of the equality comparison, such as setting the relative and absolute tolerance for numeric comparisons, considering NaN values as equal, and checking for the data type of the objects being compared.
- Parameters:
other (object) – The object to compare to.
rtol (float, optional) – The relative tolerance parameter. Default is 1e-05.
atol (float, optional) – The absolute tolerance parameter. Default is 1e-08.
equal_nan (bool, optional) – Whether to consider NaN values as equal. Default is True.
check_dtypes (bool, optional) – Whether to check the data type of the objects. Default is False.
- Returns:
The difference between the current and the other object.
- Return type:
the_diff
See also
equals
,aequals
,numpy.isclose()
,numpy.all()
,numpy.any()
,numpy.equal()
,numpy.allclose()
Notes
The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added together to compare against the absolute difference between a and b.
NaNs are treated as equal if they are in the same place and if
equal_nan=True
. Infs are treated as equal if they are in the same place and of the same sign in both arrays.
- class skcriteria.madm.SKCDecisionMakerABC[source]¶
Bases:
SKCMethodABC
Abstract class for all decisor based methods in scikit-criteria.