skcriteria.preprocessing.filters module

Normalization through the distance to distance function.

class skcriteria.preprocessing.filters.SKCByCriteriaFilterABC(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCTransformerABC

Abstract class capable of filtering alternatives based on criteria values.

This abstract class require to redefine _coerce_filters and _make_mask, instead of _transform_data.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

property criteria_filters

Conditions on which the alternatives will be evaluated.

It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

property ignore_missing_criteria

If the value is True the filter ignores the lack of a required criterion.

If the value is False, the lack of a criterion causes the filter to fail.

class skcriteria.preprocessing.filters.Filter(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCByCriteriaFilterABC

Function based filter.

This class accepts as a filter any arbitrary function that receives as a parameter a as a parameter a criterion and returns a mask of the same size as the number of the number of alternatives in the decision matrix.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

Examples

>>> from skcriteria.preprocess import filters

 >>> dm = skc.mkdm(
...     matrix=[
...         [7, 5, 35],
...         [5, 4, 26],
...         [5, 6, 28],
...         [1, 7, 30],
...         [5, 8, 30]
...     ],
...     objectives=[max, max, min],
...     alternatives=["PE", "JN", "AA", "MM", "FN"],
...     criteria=["ROE", "CAP", "RI"],
... )

>>> tfm = filters.Filter({
...     "ROE": lambda e: e > 1,
...     "RI": lambda e: e >= 28,
... })
>>> tfm.transform(dm)
   ROE[▲ 2.0] CAP[▲ 4.0] RI[▼ 1.0]
PE          7          5        35
AA          5          6        28
FN          5          8        30
[3 Alternatives x 3 Criteria]

class skcriteria.preprocessing.filters.SKCArithmeticFilterABC(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCByCriteriaFilterABC

Provide a common behavior to make filters based on the same comparator.

This abstract class require to redefine _filter method, and this will apply to each criteria separately.

This class is designed to implement in general arithmetic comparisons of “==”, “!=”, “>”, “>=”, “<”, “<=” taking advantage of the functions provided by numpy (e.g. np.greater_equal()).

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

Notes

The filter implemented with this class are slightly faster than function-based filters.

class skcriteria.preprocessing.filters.FilterGT(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCArithmeticFilterABC

Keeps the alternatives for which the criteria value are greater than a value.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

Notes

The filter implemented with this class are slightly faster than function-based filters.

Examples

>>> from skcriteria.preprocess import filters

>>> dm = skc.mkdm(
...     matrix=[
...         [7, 5, 35],
...         [5, 4, 26],
...         [5, 6, 28],
...         [1, 7, 30],
...         [5, 8, 30]
...     ],
...     objectives=[max, max, min],
...     alternatives=["PE", "JN", "AA", "MM", "FN"],
...     criteria=["ROE", "CAP", "RI"],
... )

>>> tfm = filters.FilterGT({"ROE": 1, "RI": 27})
>>> tfm.transform(dm)
   ROE[▲ 2.0] CAP[▲ 4.0] RI[▼ 1.0]
PE          7          5        35
AA          5          6        28
FN          5          8        30
[3 Alternatives x 3 Criteria]

class skcriteria.preprocessing.filters.FilterGE(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCArithmeticFilterABC

Keeps the alternatives for which the criteria value are greater or equal than a value.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

Notes

The filter implemented with this class are slightly faster than function-based filters.

Examples

>>> from skcriteria.preprocess import filters

>>> dm = skc.mkdm(
...     matrix=[
...         [7, 5, 35],
...         [5, 4, 26],
...         [5, 6, 28],
...         [1, 7, 30],
...         [5, 8, 30]
...     ],
...     objectives=[max, max, min],
...     alternatives=["PE", "JN", "AA", "MM", "FN"],
...     criteria=["ROE", "CAP", "RI"],
... )

>>> tfm = filters.FilterGE({"ROE": 1, "RI": 27})
>>> tfm.transform(dm)
   ROE[▲ 2.0] CAP[▲ 4.0] RI[▼ 1.0]
PE          7          5        35
AA          5          6        28
MM          1          7        30
FN          5          8        30
[4 Alternatives x 3 Criteria]

class skcriteria.preprocessing.filters.FilterLT(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCArithmeticFilterABC

Keeps the alternatives for which the criteria value are less than a value.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

Notes

The filter implemented with this class are slightly faster than function-based filters.

Examples

>>> from skcriteria.preprocess import filters

>>> dm = skc.mkdm(
...     matrix=[
...         [7, 5, 35],
...         [5, 4, 26],
...         [5, 6, 28],
...         [1, 7, 30],
...         [5, 8, 30]
...     ],
...     objectives=[max, max, min],
...     alternatives=["PE", "JN", "AA", "MM", "FN"],
...     criteria=["ROE", "CAP", "RI"],
... )

>>> tfm = filters.FilterLT({"RI": 28})
>>> tfm.transform(dm)
   ROE[▲ 2.0] CAP[▲ 4.0] RI[▼ 1.0]
JN          5          4        26
[1 Alternatives x 3 Criteria]

class skcriteria.preprocessing.filters.FilterLE(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCArithmeticFilterABC

Keeps the alternatives for which the criteria value are less or equal than a value.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

Notes

The filter implemented with this class are slightly faster than function-based filters.

Examples

>>> from skcriteria.preprocess import filters

>>> dm = skc.mkdm(
...     matrix=[
...         [7, 5, 35],
...         [5, 4, 26],
...         [5, 6, 28],
...         [1, 7, 30],
...         [5, 8, 30]
...     ],
...     objectives=[max, max, min],
...     alternatives=["PE", "JN", "AA", "MM", "FN"],
...     criteria=["ROE", "CAP", "RI"],
... )

>>> tfm = filters.FilterLE({"RI": 28})
>>> tfm.transform(dm)
   ROE[▲ 2.0] CAP[▲ 4.0] RI[▼ 1.0]
JN          5          4        26
AA          5          6        28
[2 Alternatives x 3 Criteria]

class skcriteria.preprocessing.filters.FilterEQ(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCArithmeticFilterABC

Keeps the alternatives for which the criteria value are equal than a value.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

Notes

The filter implemented with this class are slightly faster than function-based filters.

Examples

>>> from skcriteria.preprocess import filters

>>> dm = skc.mkdm(
...     matrix=[
...         [7, 5, 35],
...         [5, 4, 26],
...         [5, 6, 28],
...         [1, 7, 30],
...         [5, 8, 30]
...     ],
...     objectives=[max, max, min],
...     alternatives=["PE", "JN", "AA", "MM", "FN"],
...     criteria=["ROE", "CAP", "RI"],
... )

>>> tfm = filters.FilterEQ({"CAP": 7, "RI": 30})
>>> tfm.transform(dm)
   ROE[▲ 2.0] CAP[▲ 4.0] RI[▼ 1.0]
MM          1          7        30
[1 Alternatives x 3 Criteria]

class skcriteria.preprocessing.filters.FilterNE(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCArithmeticFilterABC

Keeps the alternatives for which the criteria value are not equal than a value.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

Notes

The filter implemented with this class are slightly faster than function-based filters.

Examples

>>> from skcriteria.preprocess import filters

>>> dm = skc.mkdm(
...     matrix=[
...         [7, 5, 35],
...         [5, 4, 26],
...         [5, 6, 28],
...         [1, 7, 30],
...         [5, 8, 30]
...     ],
...     objectives=[max, max, min],
...     alternatives=["PE", "JN", "AA", "MM", "FN"],
...     criteria=["ROE", "CAP", "RI"],
... )

>>> tfm = filters.FilterNE({"CAP": 7, "RI": 30})
>>> tfm.transform(dm)
   ROE[▲ 2.0] CAP[▲ 4.0] RI[▼ 1.0]
PE          7          5        35
JN          5          4        26
AA          5          6        28
[3 Alternatives x 3 Criteria]

class skcriteria.preprocessing.filters.SKCSetFilterABC(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCByCriteriaFilterABC

Provide a common behavior to make filters based on set operations.

This abstract class require to redefine _set_filter method, and this will apply to each criteria separately.

This class is designed to implement in general set comparison like “inclusion” and “exclusion”.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

class skcriteria.preprocessing.filters.FilterIn(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCSetFilterABC

Keeps the alternatives for which the criteria value are included in a set of values.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

Examples

>>> from skcriteria.preprocess import filters

>>> dm = skc.mkdm(
...     matrix=[
...         [7, 5, 35],
...         [5, 4, 26],
...         [5, 6, 28],
...         [1, 7, 30],
...         [5, 8, 30]
...     ],
...     objectives=[max, max, min],
...     alternatives=["PE", "JN", "AA", "MM", "FN"],
...     criteria=["ROE", "CAP", "RI"],
... )

>>> tfm = filters.FilterIn({"ROE": [7, 1], "RI": [30, 35]})
>>> tfm.transform(dm)
   ROE[▲ 2.0] CAP[▲ 4.0] RI[▼ 1.0]
PE          7          5        35
MM          1          7        30
[2 Alternatives x 3 Criteria]

class skcriteria.preprocessing.filters.FilterNotIn(criteria_filters, *, ignore_missing_criteria=False)

Bases: SKCSetFilterABC

Keeps the alternatives for which the criteria value are not included in a set of values.

Parameters:

• criteria_filters (dict) – It is a dictionary in which the key is the name of a criterion, and the value is the filter condition.

• ignore_missing_criteria (bool, default: False) – If True, it is ignored if a decision matrix does not have any particular criteria that should be filtered.

Examples

>>> from skcriteria.preprocess import filters

>>> dm = skc.mkdm(
...     matrix=[
...         [7, 5, 35],
...         [5, 4, 26],
...         [5, 6, 28],
...         [1, 7, 30],
...         [5, 8, 30]
...     ],
...     objectives=[max, max, min],
...     alternatives=["PE", "JN", "AA", "MM", "FN"],
...     criteria=["ROE", "CAP", "RI"],
... )

>>> tfm = filters.FilterNotIn({"ROE": [7, 1], "RI": [30, 35]})
>>> tfm.transform(dm)
   ROE[▲ 2.0] CAP[▲ 4.0] RI[▼ 1.0]
JN          5          4        26
AA          5          6        28
[2 Alternatives x 3 Criteria]

class skcriteria.preprocessing.filters.FilterNonDominated(*, strict=False)

Bases: SKCTransformerABC

Keeps the non dominated or non strictly-dominated alternatives.

In order to evaluate the dominance of an alternative a0 over an alternative a1, the algorithm evaluates that a0 is better in at least one criterion and that a1 is not better in any criterion than a0. In the case that strict = True it also evaluates that there are no equal criteria.

Parameters:

strict (bool, default False) – If True, strictly dominated alternatives are removed, otherwise all dominated alternatives are removed.

Examples

>>> from skcriteria.preprocess import filters

>>> dm = skc.mkdm(
...     matrix=[
...         [7, 5, 35],
...         [5, 4, 26],
...         [5, 6, 28],
...         [1, 7, 30],
...         [5, 8, 30]
...     ],
...     objectives=[max, max, min],
...     alternatives=["PE", "JN", "AA", "MM", "FN"],
...     criteria=["ROE", "CAP", "RI"],
... )

>>> tfm = filters.FilterNonDominated(strict=False)
>>> tfm.transform(dm)
   ROE[▲ 1.0] CAP[▲ 1.0] RI[▼ 1.0]
PE          7          5        35
JN          5          4        26
AA          5          6        28
FN          5          8        30
[4 Alternatives x 3 Criteria]

property strict

If the filter must remove the dominated or strictly-dominated alternatives.

transform(dm)

Perform transformation on dm.

Parameters:

• dm (skcriteria.data.DecisionMatrix)

• transform. (The decision matrix to)

Returns:

Transformed decision matrix.

Return type:

skcriteria.data.DecisionMatrix