skcriteria.madm._madm_base module

Core functionalities to create madm decision-maker classes.

class skcriteria.madm._madm_base.SKCDecisionMakerABC[source]

Bases: SKCMethodABC

Abstract class for all decisor based methods in scikit-criteria.

evaluate(dm)[source]

Validate the dm and calculate and evaluate the alternatives.

Parameters

dm (skcriteria.data.DecisionMatrix) – Decision matrix on which the ranking will be calculated.

Returns

Ranking.

Return type

skcriteria.data.RankResult

class skcriteria.madm._madm_base.ResultABC(method, alternatives, values, extra)[source]

Bases: object

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

to_series()[source]

The result as pandas.Series.

property shape

Tuple with (number_of_alternatives, ).

rank.shape <==> np.shape(rank)

values_equals(other)[source]

Check if the alternatives and ranking are the same.

The method doesn’t check the method or the extra parameters.

aequals(other, rtol=1e-05, atol=1e-08, equal_nan=False)[source]

Return True if the result are equal within a tolerance.

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.

The proceeds as follows:

  • If other is the same object return True.

  • If other is not instance of ‘DecisionMatrix’, has different shape ‘criteria’, ‘alternatives’ or ‘objectives’ returns False.

  • Next check the ‘weights’ and the matrix itself using the provided tolerance.

Parameters

  • other (Result) – Other result to compare.

  • rtol (float) – The relative tolerance parameter (see Notes in numpy.allclose()).

  • atol (float) – The absolute tolerance parameter (see Notes in numpy.allclose()).

  • equal_nan (bool) – Whether to compare NaN’s as equal. If True, NaN’s in dm will be considered equal to NaN’s in other in the output array.

Returns

aequals – Returns True if the two result are equal within the given tolerance; False otherwise.

Return type

bool:py:class:

See also

equals, numpy.isclose(), numpy.all(), numpy.any(), numpy.equal(), numpy.allclose()

equals(other)[source]

Return True if the results are equal.

This method calls aquals without tolerance.

Parameters

other (skcriteria.DecisionMatrix) – Other instance to compare.

Returns

equals – Returns True if the two results are equals.

Return type

bool:py:class:

See also

aequals, numpy.isclose(), numpy.all(), numpy.any(), numpy.equal(), numpy.allclose()

class skcriteria.madm._madm_base.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.

to_series(*, untied=False)[source]

The result as pandas.Series.

class skcriteria.madm._madm_base.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.