Pyfrontier.domain package

Submodules

Pyfrontier.domain.assurance_region module

class Pyfrontier.domain.assurance_region.AssuranceRegion(type, index_a, index_b, coefficient, operator)[source]

Bases: object

  • x_a/x_b =< coefficient

  • coefficient <= x_a/x_b

coefficient: float
index_a: int
index_b: int
operator: Literal['=<', '>=']
type: Literal['input', 'output']

Pyfrontier.domain.dmu module

class Pyfrontier.domain.dmu.BooleanInput(_value)[source]

Bases: object

property value
class Pyfrontier.domain.dmu.DMU(input, output, id)[source]

Bases: object

id: int
input: ndarray
output: ndarray
class Pyfrontier.domain.dmu.DMUSet(inputs, outputs, index=nan)[source]

Bases: object

Dataset for DEA

property N
get_id(o)[source]
index: ndarray = nan
inputs: ndarray
property m
outputs: ndarray
property s

Pyfrontier.domain.metrics module

Pyfrontier.domain.parallel module

class Pyfrontier.domain.parallel.MultiProcessor(_solver_function, _n_dmus)[source]

Bases: object

solve(n_jobs)[source]
Return type:

List[BaseResult]

class Pyfrontier.domain.parallel.NumberOfJobs(_n_jobs=1)[source]

Bases: object

property value: int

Pyfrontier.domain.result module

class Pyfrontier.domain.result.AdditiveResult(score, id, dmu, x_slack, y_slack, weights)[source]

Bases: BaseResult

  • score: efficiency

  • id:

  • dmu:

  • x_slack:

  • y_slack:

  • weights: lambda

dmu: DMU
id: int
property is_efficient: bool
score: float
weights: List[float]
x_slack: List[float]
y_slack: List[float]
class Pyfrontier.domain.result.BaseResult(score, id, dmu)[source]

Bases: ABC

dmu: DMU
id: int
score: float
class Pyfrontier.domain.result.EnvelopResult(score, id, dmu, weights, x_slack, y_slack)[source]

Bases: BaseResult

  • score: efficiency

  • id:

  • dmu:

  • weight:

  • x_slack:

  • y_slack:

dmu: DMU
property has_slack: bool
id: int
property is_efficient: bool
score: float
weights: List[float]
x_slack: List[float]
y_slack: List[float]
class Pyfrontier.domain.result.MultipleResult(score, id, dmu, x_weight, y_weight, bias)[source]

Bases: BaseResult

  • score: efficiency

  • id:

  • dmu:

  • x_weight:

  • y_weight:

  • bias:

bias: float
dmu: DMU
id: int
property is_efficient: bool
score: float
x_weight: List[float]
y_weight: List[float]

Pyfrontier.domain.slack_weight module

class Pyfrontier.domain.slack_weight.SlackWeight(_weights=<factory>, _n_dim=0)[source]

Bases: object

property value: ndarray

Module contents

class Pyfrontier.domain.AdditiveResult(score, id, dmu, x_slack, y_slack, weights)[source]

Bases: BaseResult

  • score: efficiency

  • id:

  • dmu:

  • x_slack:

  • y_slack:

  • weights: lambda

dmu: DMU
id: int
property is_efficient: bool
score: float
weights: List[float]
x_slack: List[float]
y_slack: List[float]
class Pyfrontier.domain.AssuranceRegion(type, index_a, index_b, coefficient, operator)[source]

Bases: object

  • x_a/x_b =< coefficient

  • coefficient <= x_a/x_b

coefficient: float
index_a: int
index_b: int
operator: Literal['=<', '>=']
type: Literal['input', 'output']
class Pyfrontier.domain.BooleanInput(_value)[source]

Bases: object

property value
class Pyfrontier.domain.DMU(input, output, id)[source]

Bases: object

id: int
input: ndarray
output: ndarray
class Pyfrontier.domain.DMUSet(inputs, outputs, index=nan)[source]

Bases: object

Dataset for DEA

property N
get_id(o)[source]
index: ndarray = nan
inputs: ndarray
property m
outputs: ndarray
property s
class Pyfrontier.domain.EnvelopResult(score, id, dmu, weights, x_slack, y_slack)[source]

Bases: BaseResult

  • score: efficiency

  • id:

  • dmu:

  • weight:

  • x_slack:

  • y_slack:

dmu: DMU
property has_slack: bool
id: int
property is_efficient: bool
score: float
weights: List[float]
x_slack: List[float]
y_slack: List[float]
class Pyfrontier.domain.MultiProcessor(_solver_function, _n_dmus)[source]

Bases: object

solve(n_jobs)[source]
Return type:

List[BaseResult]

class Pyfrontier.domain.MultipleResult(score, id, dmu, x_weight, y_weight, bias)[source]

Bases: BaseResult

  • score: efficiency

  • id:

  • dmu:

  • x_weight:

  • y_weight:

  • bias:

bias: float
dmu: DMU
id: int
property is_efficient: bool
score: float
x_weight: List[float]
y_weight: List[float]
class Pyfrontier.domain.NumberOfJobs(_n_jobs=1)[source]

Bases: object

property value: int
class Pyfrontier.domain.SlackWeight(_weights=<factory>, _n_dim=0)[source]

Bases: object

property value: ndarray