skcriteria.cmp.ranks_rev.ranks_inv_check
module¶
Test Criterion #1 for evaluating the effectiveness MCDA method.
According to this criterion, the best alternative identified by the method should remain unchanged when a non-optimal alternative is replaced by a worse alternative, provided that the relative importance of each decision criterion remains the same.
- class skcriteria.cmp.ranks_rev.rank_inv_check.RankInvariantChecker(dmaker, *, repeat=1, allow_missing_alternatives=False, last_diff_strategy='median', random_state=None)[source]¶
Bases:
SKCMethodABC
Test Criterion #1 for evaluating the effectiveness MCDA method.
According to this criterion, the best alternative identified by the method should remain unchanged when a non-optimal alternative is replaced by a worse alternative, provided that the relative importance of each decision criterion remains the same.
To illustrate, suppose that the MCDA method has ranked a set of alternatives, and one of the alternatives, \(A_j\), is replaced by another alternative, \(A_j'\), which is less desirable than Ak. The MCDA method should still identify the same best alternative when the alternatives are re-ranked using the same method. Furthermore, the relative rankings of the remaining alternatives that were not changed should also remain the same.
The current implementation worsens each non-optimal alternative
repeat
times, and stores each resulting output in a collection for comparison with the original ranking. In essence, the test is run once for each suboptimal alternative.This class assumes that there is another suboptimal alternative \(A_j\) that is just the next worst alternative to \(A_k\), so that \(A_k \succ A_j\). Then it generates a mutation \(A_k'\) such that \(A_k'\) is worse than \(A_k\) but still better than \(A_j\) (\(A_k \succ A_k' \succ A_j\)). In the case that the worst alternative is reached, its degradation is limited by default with respect to the median of all limits of the previous alternatives mutations, in order not to break he distribution of each criterion.
- Parameters
dmaker (Decision maker - must implement the
evaluate()
method) – The MCDA method, or pipeline to evaluate.repeat (int, default = 1) –
How many times to mutate each suboptimal alternative.
The total number of rankings returned by this method is given by the number of alternatives in the decision matrix minus one multiplied by
repeat
.allow_missing_alternatives (bool, default = False) –
dmaker
can somehow return rankings with fewer alternatives than the original ones (using a pipeline that implements a filter, for example). By setting this parameter toTrue
, the invariance test allows for missing alternatives in a ranking to be added with a value of the maximum value of the ranking obtained + 1.On the other hand, if the value is
False
, when a ranking is missing an alternative, the test will fail with aValueError
.If more than one alternative is removed, all of them are added with the same value
last_diff_strategy (str or callable (default: "median").) – True if any mutation is allowed that does not possess all the alternatives of the original decision matrix.
random_state (int, numpy.random.default_rng or None (default: None)) – Controls the random state to generate variations in the sub-optimal alternatives.
- property dmaker¶
The MCDA method, or pipeline to evaluate.
- property repeat¶
How many times to mutate each suboptimal alternative.
- property allow_missing_alternatives¶
True if any mutation is allowed that does not possess all the alternatives of the original decision matrix.
- property last_diff_strategy¶
Since the least preferred alternative has no lower bound (since there is nothing immediately below it), this function calculates a limit ceiling based on the bounds of all the other suboptimal alternatives.
- property random_state¶
Controls the random state to generate variations in the sub-optimal alternatives.
- evaluate(dm)[source]¶
Executes a the invariance test.
- Parameters
dm (DecisionMatrix) – The decision matrix to be evaluated.
- Returns
An object containing multiple rankings of the alternatives, with information on any changes made to the original decision matrix in the extra_ attribute. Specifically, the extra_ attribute contains a an object in the key rrt1 that provides information on any changes made to the original decision matrix, including the the noise applied to worsen any sub-optimal alternative.
- Return type