Changelog¶
Version 0.8¶
New The
skcriteria.cmp
package utilities to compare rankings.New The new package
skcriteria.datasets
include two datasets (one a toy and one real) to quickly start your experiments.New DecisionMatrix now can be sliced with a syntax similar of the pandas.DataFrame.
dm["c0"]
cut the $c0$ criteria.dm[["c0", "c2"]
cut the criteria $c0$ and $c2$.dm.loc["a0"]
cut the alternative $a0$.dm.loc[["a0", "a1"]]
cut the alternatives $a0$ and $a1$.dm.iloc[0:3]
cuts from the first to the third alternative.
New imputation methods for replacing missing data with substituted values. These methods are in the module
skcriteria.preprocessing.impute
.New results object now has a
to_series
method.Changed Behaviour: The ranks and kernels
equals
are now calledvalues_equals
. The newaequals
support tolerances to compare numpy arrays internally stored inextra_
, and theequals
method is equivalent toaequals(rtol=0, atol=0)
.We detected a bad behavior in ELECTRE2, so we decided to launch a
FutureWarning
when the class is instantiated. In the version after 0.8, a new implementation of ELECTRE2 will be provided.Multiple
__repr__
was improved to folow the Python recomendationCritic
weighter was renamed toCRITIC
(all capitals) to be consistent with the literature. The old class is still there but is deprecated.All the functions and classes of
skcriteria.preprocessing.distance
was moved toskcriteria.preprocessing.scalers
.The
StdWeighter
now uses the sample standar-deviation. From the numerical point of view, this does not generate any change, since the deviations are scaled by the sum. Computationally speaking there may be some difference from the ~5th decimal digit onwards.Two method of the
Objective
enum was deprecated and replaced:Objective.construct_from_alias()
->
Objective.from_alias()
(classmethod)Objective.to_string()
->
Objective.to_symbol()'
The deprecated methods will be removed in version 1.0.
Add a dominance plot
DecisionMatrix.plot.dominance()
.WeightedSumModel
raises aValueError
when some value $< 0$.Moved internal modules
skcriteria.core.methods.SKCTransformerABC
->
skcriteria.preprocessing.SKCTransformerABC
skcriteria.core.methods.SKCMatrixAndWeightTransformerABC
->
skcriteria.preprocessing.SKCMatrixAndWeightTransformerABC
Version 0.7¶
New method:
ELECTRE2
.New preprocessing strategy: A new way to transform from minimization to maximization criteria:
NegateMinimize()
which reverses the sign of the values of the criteria to be minimized (useful for not breaking distance relations in methods like TOPSIS). Additionally the previous we rename theMinimizeToMaximize()
transformer toInvertMinimize()
.Now the
RankingResult
, support repeated/tied rankings and some methods were implemented to deal with these cases.RankingResult.has_ties_
to see if there are tied values.RankingResult.ties_
to see how often values are repeated.RankingResult.untided_rank_
to get a ranking with no repeated values.repeated values.
KernelResult
now implements several new properties:kernel_alternatives_
to know which alternatives are in the kernel.kernel_size_
to know the number of alternatives in the kernel.kernel_where_
was replaced bykernelwhere_
to standardize the api.
Version 0.6¶
Support for Python 3.10.
All the objects of the project are now immutable by design, and can only be mutated troughs the
object.copy()
method.Dominance analysis tools (
DecisionMatrix.dominance
).The method
DecisionMatrix.describe()
was deprecated and will be removed in version 1.0.New statistics functionalities
DecisionMatrix.stats
accessor.The accessors are now cached in the
DecisionMatrix
.Tutorial for dominance and satisfaction analysis.
TOPSIS now support hyper-parameters to select different metrics.
Generalize the idea of accessors in scikit-criteria througth a common framework (
skcriteria.utils.accabc
module).New deprecation mechanism through the
skcriteria.utils.decorators.deprecated
decorator.
Version 0.5¶
In this version scikit-criteria was rewritten from scratch. Among other things:
The model implementation API was simplified.
The
Data
object was removed in favor ofDecisionMatrix
which implements many more useful features for MCDA.Plots were completely re-implemented using Seaborn.
Coverage was increased to 100%.
Pipelines concept was added (Thanks to Scikit-learn).
New documentation. The quick start is totally rewritten!
Full Changelog: https://github.com/quatrope/scikit-criteria/commits/0.5
Version 0.2¶
First OO stable version.
Version 0.1¶
Only functions.