skcriteria.core.data
module¶
Data abstraction layer.
This module defines the DecisionMatrix object, which internally encompasses the alternative matrix, weights and objectives (MIN, MAX) of the criteria.
- class skcriteria.core.data.DecisionMatrix(data_df, objectives, weights)[source]¶
Bases:
object
Representation of all data needed in the MCDA analysis.
This object gathers everything necessary to represent a data set used in MCDA:
An alternative matrix where each row is an alternative and each column is of a different criteria.
An optimization objective (Minimize, Maximize) for each criterion.
A weight for each criterion.
An independent type of data for each criterion
DecisionMatrix has two main forms of construction:
Use the default constructor of the DecisionMatrix class
pandas.DataFrame
where the index is the alternatives and the columns are the criteria; an iterable with the objectives with the same amount of elements that columns/criteria has the dataframe; and an iterable with the weights also with the same amount of elements as criteria.>>> import pandas as pd >>> from skcriteria import DecisionMatrix, mkdm
>>> data_df = pd.DataFrame( ... [[1, 2, 3], [4, 5, 6]], ... index=["A0", "A1"], ... columns=["C0", "C1", "C2"] ... ) >>> objectives = [min, max, min] >>> weights = [1, 1, 1]
>>> dm = DecisionMatrix(data_df, objectives, weights) >>> dm C0[▼ 1.0] C1[▲ 1.0] C2[▲ 1.0] A0 1 2 3 A1 4 5 6 [2 Alternatives x 3 Criteria]
Use the classmethod DecisionMatrix.from_mcda_data which requests the data in a more natural way for this type of analysis (the weights, the criteria / alternative names, and the data types are optional)
>>> DecisionMatrix.from_mcda_data( ... [[1, 2, 3], [4, 5, 6]], ... [min, max, min], ... [1, 1, 1]) C0[▼ 1.0] C1[▲ 1.0] C2[▲ 1.0] A0 1 2 3 A1 4 5 6 [2 Alternatives x 3 Criteria]
For simplicity a function is offered at the module level analogous to
from_mcda_data
calledmkdm
(make decision matrix).
- Parameters
data_df (
pandas.DatFrame
) – Dataframe where the index is the alternatives and the columns are the criteria.objectives (
numpy.ndarray
) – Aan iterable with the targets with sense of optimality of every criteria (You can use any alias defined in Objective) the same length as columns/criteria has the data_df.weights (
numpy.ndarray
) – An iterable with the weights also with the same amount of elements as criteria.
- aequals(other, rtol=1e-05, atol=1e-08, equal_nan=False)[source]¶
Return True if the decision matrix 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 returnTrue
.If
other
is not instance of ‘DecisionMatrix’, has different shape ‘criteria’, ‘alternatives’ or ‘objectives’ returnsFalse
.Next check the ‘weights’ and the matrix itself using the provided tolerance.
- Parameters
other (
skcriteria.DecisionMatrix
) – Other instance 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 dm 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()
- property alternatives¶
Names of the alternatives.
- copy(**kwargs)[source]¶
Return a deep copy of the current DecisionMatrix.
This method is also useful for manually modifying the values of the DecisionMatrix object.
- Parameters
kwargs – The same parameters supported by
from_mcda_data()
. The values provided replace the existing ones in the obSject to be copied.- Returns
A new decision matrix.
- Return type
- property criteria¶
Names of the criteria.
- describe(**kwargs)[source]¶
Generate descriptive statistics.
Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding
NaN
values.- Parameters
pandas.DataFrame.describe() (Same parameters as) –
- Returns
Summary statistics of DecisionMatrix provided.
- Return type
pandas.DataFrame
- property dtypes¶
Dtypes of the criteria.
- equals(other)[source]¶
Return True if the decision matrix are equal.
This method calls DecisionMatrix.aquals whitout tolerance.
- Parameters
other (
skcriteria.DecisionMatrix
) – Other instance to compare.- Returns
equals – Returns True if the two dm are equals.
- Return type
bool:py:class:
See also
aequals
,numpy.isclose()
,numpy.all()
,numpy.any()
,numpy.equal()
,numpy.allclose()
- classmethod from_mcda_data(matrix, objectives, weights=None, alternatives=None, criteria=None, dtypes=None)[source]¶
Create a new DecisionMatrix object.
This method receives the parts of the matrix, in what conceptually the matrix of alternatives is usually divided
- Parameters
matrix (Iterable) – The matrix of alternatives. Where every row is an alternative and every column is a criteria.
objectives (Iterable) – The array with the sense of optimality of every criteria. You can use any alias provided by the objective class.
weights (Iterable o None (default
None
)) – Optional weights of the criteria. If isNone
all the criteria are weighted with 1.alternatives (Iterable o None (default
None
)) – Optional names of the alternatives. If isNone
, al the alternatives are names “A[n]” where n is the number of the row of matrix statring at 0.criteria (Iterable o None (default
None
)) – Optional names of the criteria. If isNone
, al the alternatives are names “C[m]” where m is the number of the columns of matrix statring at 0.dtypes (Iterable o None (default
None
)) – Optional types of the criteria. If is None, the type is inferred automatically by pandas.
- Returns
A new decision matrix.
- Return type
Example
>>> DecisionMatrix.from_mcda_data( ... [[1, 2, 3], [4, 5, 6]], ... [min, max, min], ... [1, 1, 1]) C0[▼ 1.0] C1[▲ 1.0] C2[▲ 1.0] A0 1 2 3 A1 4 5 6 [2 Alternatives x 3 Criteria]
For simplicity a function is offered at the module level analogous to
from_mcda_data
calledmkdm
(make decision matrix).Notes
This functionality generates more sensitive defaults than using the constructor of the DecisionMatrix class but is slower.
- property iobjectives¶
Objectives of the criteria as
int
.Minimize = Objective.MIN.value
Maximize = Objective.MAX.value
- property matrix¶
Alternatives matrix as pandas DataFrame.
The matrix excludes weights and objectives.
If you want to create a DataFrame with objetvies and weights, use
DecisionMatrix.to_dataframe()
- property objectives¶
Objectives of the criteria as
Objective
instances.
- property plot¶
Plot accessor.
- property shape¶
Return a tuple with (number_of_alternatives, number_of_criteria).
dm.shape <==> np.shape(dm)
- to_dataframe()[source]¶
Convert the entire DecisionMatrix into a dataframe.
The objectives and weights ara added as rows before the alternatives.
- Returns
A Decision matrix as pandas DataFrame.
- Return type
pd.DataFrame
Example
>>> dm = DecisionMatrix.from_mcda_data( >>> dm ... [[1, 2, 3], [4, 5, 6]], ... [min, max, min], ... [1, 1, 1]) C0[▼ 1.0] C1[▲ 1.0] C2[▲ 1.0] A0 1 2 3 A1 4 5 6 >>> dm.to_dataframe() C0 C1 C2 objectives MIN MAX MIN weights 1.0 1.0 1.0 A0 1 2 3 A1 4 5 6
- to_dict()[source]¶
Return a dict representation of the data.
All the values are represented as numpy array.
- property weights¶
Weights of the criteria.
- class skcriteria.core.data.KernelResult(method, alternatives, values, extra)[source]¶
Bases:
skcriteria.core.data.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 kernelwhere_¶
Indexes of the alternatives that are part of the kernel.
- class skcriteria.core.data.Objective(value)[source]¶
Bases:
enum.Enum
Representation of criteria objectives (Minimize, Maximize).
- MAX = 1¶
Internal representation of maximize criteria
- MIN = -1¶
Internal representation of minimize criteria
- class skcriteria.core.data.RankResult(method, alternatives, values, extra)[source]¶
Bases:
skcriteria.core.data.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 rank_¶
Alias for
values
.
- class skcriteria.core.data.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 alternatives¶
Names of the alternatives evaluated.
- property e_¶
Additional information about the result.
Note
e_
is an alias for this property
- equals(other)[source]¶
Check if the alternatives and ranking are the same.
The method doesn’t check the method or the extra parameters.
- property extra_¶
Additional information about the result.
Note
e_
is an alias for this property
- property method¶
Name of the method that generated the result.
- property shape¶
Tuple with (number_of_alternatives, number_of_alternatives).
rank.shape <==> np.shape(rank)
- property values¶
Values assigned to each alternative by the method.
The i-th value refers to the valuation of the i-th. alternative.
- skcriteria.core.data.mkdm(matrix, objectives, weights=None, alternatives=None, criteria=None, dtypes=None)[source]¶
Create a new DecisionMatrix object.
This method receives the parts of the matrix, in what conceptually the matrix of alternatives is usually divided
- Parameters
matrix (Iterable) – The matrix of alternatives. Where every row is an alternative and every column is a criteria.
objectives (Iterable) – The array with the sense of optimality of every criteria. You can use any alias provided by the objective class.
weights (Iterable o None (default
None
)) – Optional weights of the criteria. If isNone
all the criteria are weighted with 1.alternatives (Iterable o None (default
None
)) – Optional names of the alternatives. If isNone
, al the alternatives are names “A[n]” where n is the number of the row of matrix statring at 0.criteria (Iterable o None (default
None
)) – Optional names of the criteria. If isNone
, al the alternatives are names “C[m]” where m is the number of the columns of matrix statring at 0.dtypes (Iterable o None (default
None
)) – Optional types of the criteria. If is None, the type is inferred automatically by pandas.
- Returns
A new decision matrix.
- Return type
Example
>>> DecisionMatrix.from_mcda_data( ... [[1, 2, 3], [4, 5, 6]], ... [min, max, min], ... [1, 1, 1]) C0[▼ 1.0] C1[▲ 1.0] C2[▲ 1.0] A0 1 2 3 A1 4 5 6 [2 Alternatives x 3 Criteria]
For simplicity a function is offered at the module level analogous to
from_mcda_data
calledmkdm
(make decision matrix).Notes
This functionality generates more sensitive defaults than using the constructor of the DecisionMatrix class but is slower.