dealib.dea.utils package

Submodules

dealib.dea.utils.options module

class dealib.dea.utils.options.Orientation(value)

Class to configure efficiency orientation.

input - input efficiency

output - output efficiency

input = 0
output = 1
class dealib.dea.utils.options.RTS(value)

Class to configure returns to scale assumption.

vrs - Variable returns to scale

crs - Constant returns to scale

drs - Decreasing returns to scale

irs - Increasing returns to scale

crs = 1
drs = 2
irs = 3
vrs = 0

dealib.dea.utils.wrappers module

class dealib.dea.utils.wrappers.Efficiency(rts: Optional[dealib.dea.utils.options.RTS] = None, orientation: Optional[dealib.dea.utils.options.Orientation] = None, transpose: Optional[bool] = None, direct: Optional[numpy.ndarray[Any, numpy.dtype[float]]] = None, eff: Optional[numpy.ndarray[Any, numpy.dtype[float]]] = None, objval: Optional[numpy.ndarray[Any, numpy.dtype[float]]] = None, lambdas: Optional[numpy.ndarray[Any, numpy.dtype[float]]] = None, sx: Optional[numpy.ndarray[Any, numpy.dtype[float]]] = None, sy: Optional[numpy.ndarray[Any, numpy.dtype[float]]] = None, slack: Optional[numpy.ndarray[Any, numpy.dtype[float]]] = None, ux: Optional[numpy.ndarray[Any, numpy.dtype[float]]] = None, vy: Optional[numpy.ndarray[Any, numpy.dtype[float]]] = None)

Object that is returned by dea, add, direct, mea, mea, mea.

Parameters

  • rts (RTS) – The return to scale assumption as in the option RTS in the call.

  • orientation (Orientation) – The efficiency orientation as in the call.

  • transpose (bool) – As in the call.

  • direct (1-d array, 2-d array) – Direction used for an estimating of efficiencies.

  • eff (1-d array, 2-d array) – The efficiencies. Note when DIRECT is used then the efficiencies are not Farrell efficiencies but rather excess values in DIRECT units of measurement.

  • objval (1-d array) – The objective value as returned from the LP program; normally the same as eff, but for slack it is the sum of the slacks.

  • lambdas (2-d array) – The lambdas, i.e. the weight of the peers, for each firm.

  • sx (2-d array) – A matrix for input slacks for each firm.

  • sy (2-d array) – A matrix for output slacks for each firm.

  • slack (2-d array) – A matrix of slacks for each firm.

  • ux (2-d array) – Dual variable for input.

  • vy (2-d array) – Dual variable for output.

direct: numpy.ndarray[Any, numpy.dtype[float]] = None
eff: numpy.ndarray[Any, numpy.dtype[float]] = None
lambdas: numpy.ndarray[Any, numpy.dtype[float]] = None
objval: numpy.ndarray[Any, numpy.dtype[float]] = None
orientation: dealib.dea.utils.options.Orientation = None
rts: dealib.dea.utils.options.RTS = None
slack: numpy.ndarray[Any, numpy.dtype[float]] = None
sx: numpy.ndarray[Any, numpy.dtype[float]] = None
sy: numpy.ndarray[Any, numpy.dtype[float]] = None
transpose: bool = None
ux: numpy.ndarray[Any, numpy.dtype[float]] = None
vy: numpy.ndarray[Any, numpy.dtype[float]] = None
class dealib.dea.utils.wrappers.Malmquist(m: numpy.ndarray[Any, numpy.dtype[float]], tc: numpy.ndarray[Any, numpy.dtype[float]], ec: numpy.ndarray[Any, numpy.dtype[float]], mq: numpy.ndarray[Any, numpy.dtype[float]], e00: numpy.ndarray[Any, numpy.dtype[float]], e10: numpy.ndarray[Any, numpy.dtype[float]], e11: numpy.ndarray[Any, numpy.dtype[float]], e01: numpy.ndarray[Any, numpy.dtype[float]])

Object that is returned by malmq.

Parameters

  • m (1-d array) – Malmquist index for productivity.

  • tc (1-d array) – Index for technology change.

  • ec (1-d array) – Index for efficiency change.

  • mq (1-d array) – Malmquist index for productivity; same as m.

  • e00 (1-d array) – The efficiencies for period 0 with reference technology from period 0.

  • e10 (1-d array) – The efficiencies for period 1 with reference technology from period 0.

  • e11 (1-d array) – The efficiencies for period 1 with reference technology from period 1.

  • e01 (1-d array) – The efficiencies for period 0 with reference technology from period 1.

e00: numpy.ndarray[Any, numpy.dtype[float]]
e01: numpy.ndarray[Any, numpy.dtype[float]]
e10: numpy.ndarray[Any, numpy.dtype[float]]
e11: numpy.ndarray[Any, numpy.dtype[float]]
ec: numpy.ndarray[Any, numpy.dtype[float]]
m: numpy.ndarray[Any, numpy.dtype[float]]
mq: numpy.ndarray[Any, numpy.dtype[float]]
tc: numpy.ndarray[Any, numpy.dtype[float]]