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, orientation)[source]

Bases: BaseResult

score: efficiency
id:
dmu:
weight:
x_slack:
y_slack:
orientation: “in” for input-oriented, “out” for output-oriented

dmu: DMU

property has_slack: bool

id: int

property is_efficient: bool

orientation: Literal['in', 'out']

score: float

property virtual_dmu: DMU

Returns the virtual DMU (projection onto the efficient frontier).

The virtual DMU represents the target values that an inefficient DMU should achieve to become efficient, based on DEA theory.

For input-oriented models:

projected_input = score * original_input - input_slack
projected_output = original_output + output_slack

For output-oriented models:

projected_input = original_input - input_slack
projected_output = score * original_output + output_slack

Returns:: A new DMU object with projected input and output values
Return type:: DMU

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

Pyfrontier.domain.type module

class Pyfrontier.domain.type.Frontier(_frontier)[source]

Bases: object

Frontier type with validation for DEA models.

property value: Literal['CRS', 'VRS', 'NDRS', 'NIRS']: Returns the validated frontier value.

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, orientation)[source]

Bases: BaseResult

score: efficiency
id:
dmu:
weight:
x_slack:
y_slack:
orientation: “in” for input-oriented, “out” for output-oriented

dmu: DMU

property has_slack: bool

id: int

property is_efficient: bool

orientation: Literal['in', 'out']

score: float

property virtual_dmu: DMU

Returns the virtual DMU (projection onto the efficient frontier).

The virtual DMU represents the target values that an inefficient DMU should achieve to become efficient, based on DEA theory.

For input-oriented models:

projected_input = score * original_input - input_slack
projected_output = original_output + output_slack

For output-oriented models:

projected_input = original_input - input_slack
projected_output = score * original_output + output_slack

Returns:: A new DMU object with projected input and output values
Return type:: DMU

weights: List[float]

x_slack: List[float]

y_slack: List[float]

class Pyfrontier.domain.Frontier(_frontier)[source]

Bases: object

Frontier type with validation for DEA models.

property value: Literal['CRS', 'VRS', 'NDRS', 'NIRS']: Returns the validated frontier value.

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