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AguaClara/aguaclara | aguaclara/research/floc_model.py | num_coll_reqd | def num_coll_reqd(DIM_FRACTAL, material, DiamTarget):
"""Return the number of doubling collisions required.
Calculates the number of doubling collisions required to produce
a floc of diameter DiamTarget.
"""
return DIM_FRACTAL * np.log2(DiamTarget/material.Diameter) | python | def num_coll_reqd(DIM_FRACTAL, material, DiamTarget):
"""Return the number of doubling collisions required.
Calculates the number of doubling collisions required to produce
a floc of diameter DiamTarget.
"""
return DIM_FRACTAL * np.log2(DiamTarget/material.Diameter) | Return the number of doubling collisions required.
Calculates the number of doubling collisions required to produce
a floc of diameter DiamTarget. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L255-L261 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | sep_dist_floc | def sep_dist_floc(ConcAluminum, ConcClay, coag, material,
DIM_FRACTAL, DiamTarget):
"""Return separation distance as a function of floc size."""
return (material.Diameter
* (np.pi/(6
* frac_vol_floc_initial(ConcAluminum, ConcClay,
coag, material)
))**(1/3)
* (DiamTarget / material.Diameter)**(DIM_FRACTAL / 3)
) | python | def sep_dist_floc(ConcAluminum, ConcClay, coag, material,
DIM_FRACTAL, DiamTarget):
"""Return separation distance as a function of floc size."""
return (material.Diameter
* (np.pi/(6
* frac_vol_floc_initial(ConcAluminum, ConcClay,
coag, material)
))**(1/3)
* (DiamTarget / material.Diameter)**(DIM_FRACTAL / 3)
) | Return separation distance as a function of floc size. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L266-L275 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | frac_vol_floc | def frac_vol_floc(ConcAluminum, ConcClay, coag, DIM_FRACTAL,
material, DiamTarget):
"""Return the floc volume fraction."""
return (frac_vol_floc_initial(ConcAluminum, ConcClay, coag, material)
* (DiamTarget / material.Diameter)**(3-DIM_FRACTAL)
) | python | def frac_vol_floc(ConcAluminum, ConcClay, coag, DIM_FRACTAL,
material, DiamTarget):
"""Return the floc volume fraction."""
return (frac_vol_floc_initial(ConcAluminum, ConcClay, coag, material)
* (DiamTarget / material.Diameter)**(3-DIM_FRACTAL)
) | Return the floc volume fraction. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L280-L285 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | dens_floc_init | def dens_floc_init(ConcAluminum, ConcClay, coag, material):
"""Return the density of the initial floc.
Initial floc is made primarily of the primary colloid and nanoglobs.
"""
return (conc_floc(ConcAluminum, ConcClay, coag).magnitude
/ frac_vol_floc_initial(ConcAluminum, ConcClay, coag, material)
) | python | def dens_floc_init(ConcAluminum, ConcClay, coag, material):
"""Return the density of the initial floc.
Initial floc is made primarily of the primary colloid and nanoglobs.
"""
return (conc_floc(ConcAluminum, ConcClay, coag).magnitude
/ frac_vol_floc_initial(ConcAluminum, ConcClay, coag, material)
) | Return the density of the initial floc.
Initial floc is made primarily of the primary colloid and nanoglobs. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L289-L296 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | ratio_area_clay_total | def ratio_area_clay_total(ConcClay, material, DiamTube, RatioHeightDiameter):
"""Return the surface area of clay normalized by total surface area.
Total surface area is a combination of clay and reactor wall
surface areas. This function is used to estimate how much coagulant
actually goes to the clay.
:param ConcClay: Concentration of clay in suspension
:type ConcClay: float
:param material: Type of clay in suspension, e.g. floc_model.Clay
:type material: floc_model.Material
:param DiamTube: Diameter of flocculator tube (assumes tube flocculator for calculation of reactor surface area)
:type DiamTube: float
:param RatioHeightDiameter: Dimensionless ratio describing ratio of clay height to clay diameter
:type RatioHeightDiameter: float
:return: The ratio of clay surface area to total available surface area (accounting for reactor walls)
:rtype: float
"""
return (1
/ (1
+ (2 * material.Diameter
/ (3 * DiamTube * ratio_clay_sphere(RatioHeightDiameter)
* (ConcClay / material.Density)
)
)
)
) | python | def ratio_area_clay_total(ConcClay, material, DiamTube, RatioHeightDiameter):
"""Return the surface area of clay normalized by total surface area.
Total surface area is a combination of clay and reactor wall
surface areas. This function is used to estimate how much coagulant
actually goes to the clay.
:param ConcClay: Concentration of clay in suspension
:type ConcClay: float
:param material: Type of clay in suspension, e.g. floc_model.Clay
:type material: floc_model.Material
:param DiamTube: Diameter of flocculator tube (assumes tube flocculator for calculation of reactor surface area)
:type DiamTube: float
:param RatioHeightDiameter: Dimensionless ratio describing ratio of clay height to clay diameter
:type RatioHeightDiameter: float
:return: The ratio of clay surface area to total available surface area (accounting for reactor walls)
:rtype: float
"""
return (1
/ (1
+ (2 * material.Diameter
/ (3 * DiamTube * ratio_clay_sphere(RatioHeightDiameter)
* (ConcClay / material.Density)
)
)
)
) | Return the surface area of clay normalized by total surface area.
Total surface area is a combination of clay and reactor wall
surface areas. This function is used to estimate how much coagulant
actually goes to the clay.
:param ConcClay: Concentration of clay in suspension
:type ConcClay: float
:param material: Type of clay in suspension, e.g. floc_model.Clay
:type material: floc_model.Material
:param DiamTube: Diameter of flocculator tube (assumes tube flocculator for calculation of reactor surface area)
:type DiamTube: float
:param RatioHeightDiameter: Dimensionless ratio describing ratio of clay height to clay diameter
:type RatioHeightDiameter: float
:return: The ratio of clay surface area to total available surface area (accounting for reactor walls)
:rtype: float | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L309-L336 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | gamma_coag | def gamma_coag(ConcClay, ConcAluminum, coag, material,
DiamTube, RatioHeightDiameter):
"""Return the coverage of clay with nanoglobs.
This function accounts for loss to the tube flocculator walls
and a poisson distribution on the clay given random hits by the
nanoglobs. The poisson distribution results in the coverage only
gradually approaching full coverage as coagulant dose increases.
:param ConcClay: Concentration of clay in suspension
:type ConcClay: float
:param ConcAluminum: Concentration of aluminum in solution
:type ConcAluminum: float
:param coag: Type of coagulant in solution, e.g. floc_model.PACl
:type coag: floc_model.Material
:param material: Type of clay in suspension, e.g. floc_model.Clay
:type material: floc_model.Material
:param DiamTube: Diameter of flocculator tube (assumes tube flocculator for calculation of reactor surface area)
:type DiamTube: float
:param RatioHeightDiameter: Dimensionless ratio of clay height to clay diameter
:type RatioHeightDiameter: float
:return: Fraction of the clay surface area that is coated with coagulant precipitates
:rtype: float
"""
return (1 - np.exp((
(-frac_vol_floc_initial(ConcAluminum, 0*u.kg/u.m**3, coag, material)
* material.Diameter)
/ (frac_vol_floc_initial(0*u.kg/u.m**3, ConcClay, coag, material)
* coag.Diameter))
* (1 / np.pi)
* (ratio_area_clay_total(ConcClay, material,
DiamTube, RatioHeightDiameter)
/ ratio_clay_sphere(RatioHeightDiameter))
)) | python | def gamma_coag(ConcClay, ConcAluminum, coag, material,
DiamTube, RatioHeightDiameter):
"""Return the coverage of clay with nanoglobs.
This function accounts for loss to the tube flocculator walls
and a poisson distribution on the clay given random hits by the
nanoglobs. The poisson distribution results in the coverage only
gradually approaching full coverage as coagulant dose increases.
:param ConcClay: Concentration of clay in suspension
:type ConcClay: float
:param ConcAluminum: Concentration of aluminum in solution
:type ConcAluminum: float
:param coag: Type of coagulant in solution, e.g. floc_model.PACl
:type coag: floc_model.Material
:param material: Type of clay in suspension, e.g. floc_model.Clay
:type material: floc_model.Material
:param DiamTube: Diameter of flocculator tube (assumes tube flocculator for calculation of reactor surface area)
:type DiamTube: float
:param RatioHeightDiameter: Dimensionless ratio of clay height to clay diameter
:type RatioHeightDiameter: float
:return: Fraction of the clay surface area that is coated with coagulant precipitates
:rtype: float
"""
return (1 - np.exp((
(-frac_vol_floc_initial(ConcAluminum, 0*u.kg/u.m**3, coag, material)
* material.Diameter)
/ (frac_vol_floc_initial(0*u.kg/u.m**3, ConcClay, coag, material)
* coag.Diameter))
* (1 / np.pi)
* (ratio_area_clay_total(ConcClay, material,
DiamTube, RatioHeightDiameter)
/ ratio_clay_sphere(RatioHeightDiameter))
)) | Return the coverage of clay with nanoglobs.
This function accounts for loss to the tube flocculator walls
and a poisson distribution on the clay given random hits by the
nanoglobs. The poisson distribution results in the coverage only
gradually approaching full coverage as coagulant dose increases.
:param ConcClay: Concentration of clay in suspension
:type ConcClay: float
:param ConcAluminum: Concentration of aluminum in solution
:type ConcAluminum: float
:param coag: Type of coagulant in solution, e.g. floc_model.PACl
:type coag: floc_model.Material
:param material: Type of clay in suspension, e.g. floc_model.Clay
:type material: floc_model.Material
:param DiamTube: Diameter of flocculator tube (assumes tube flocculator for calculation of reactor surface area)
:type DiamTube: float
:param RatioHeightDiameter: Dimensionless ratio of clay height to clay diameter
:type RatioHeightDiameter: float
:return: Fraction of the clay surface area that is coated with coagulant precipitates
:rtype: float | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L339-L373 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | gamma_humic_acid_to_coag | def gamma_humic_acid_to_coag(ConcAl, ConcNatOrgMat, NatOrgMat, coag):
"""Return the fraction of the coagulant that is coated with humic acid.
:param ConcAl: Concentration of alumninum in solution
:type ConcAl: float
:param ConcNatOrgMat: Concentration of natural organic matter in solution
:type ConcNatOrgMat: float
:param NatOrgMat: type of natural organic matter, e.g. floc_model.HumicAcid
:type NatOrgMat: floc_model.Material
:param coag: Type of coagulant in solution, e.g. floc_model.PACl
:type coag: floc_model.Material
:return: fraction of the coagulant that is coated with humic acid
:rtype: float
"""
return min(((ConcNatOrgMat / conc_precipitate(ConcAl, coag).magnitude)
* (coag.Density / NatOrgMat.Density)
* (coag.Diameter / (4 * NatOrgMat.Diameter))
),
1) | python | def gamma_humic_acid_to_coag(ConcAl, ConcNatOrgMat, NatOrgMat, coag):
"""Return the fraction of the coagulant that is coated with humic acid.
:param ConcAl: Concentration of alumninum in solution
:type ConcAl: float
:param ConcNatOrgMat: Concentration of natural organic matter in solution
:type ConcNatOrgMat: float
:param NatOrgMat: type of natural organic matter, e.g. floc_model.HumicAcid
:type NatOrgMat: floc_model.Material
:param coag: Type of coagulant in solution, e.g. floc_model.PACl
:type coag: floc_model.Material
:return: fraction of the coagulant that is coated with humic acid
:rtype: float
"""
return min(((ConcNatOrgMat / conc_precipitate(ConcAl, coag).magnitude)
* (coag.Density / NatOrgMat.Density)
* (coag.Diameter / (4 * NatOrgMat.Diameter))
),
1) | Return the fraction of the coagulant that is coated with humic acid.
:param ConcAl: Concentration of alumninum in solution
:type ConcAl: float
:param ConcNatOrgMat: Concentration of natural organic matter in solution
:type ConcNatOrgMat: float
:param NatOrgMat: type of natural organic matter, e.g. floc_model.HumicAcid
:type NatOrgMat: floc_model.Material
:param coag: Type of coagulant in solution, e.g. floc_model.PACl
:type coag: floc_model.Material
:return: fraction of the coagulant that is coated with humic acid
:rtype: float | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L377-L396 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | pacl_term | def pacl_term(DiamTube, ConcClay, ConcAl, ConcNatOrgMat, NatOrgMat,
coag, material, RatioHeightDiameter):
"""Return the fraction of the surface area that is covered with coagulant
that is not covered with humic acid.
:param DiamTube: Diameter of the dosing tube
:type Diamtube: float
:param ConcClay: Concentration of clay in solution
:type ConcClay: float
:param ConcAl: Concentration of alumninum in solution
:type ConcAl: float
:param ConcNatOrgMat: Concentration of natural organic matter in solution
:type ConcNatOrgMat: float
:param NatOrgMat: type of natural organic matter, e.g. floc_model.HumicAcid
:type NatOrgMat: floc_model.Material
:param coag: Type of coagulant in solution, e.g. floc_model.PACl
:type coag: floc_model.Material
:param material: Type of clay in suspension, e.g. floc_model.Clay
:type material: floc_model.Material
:param RatioHeightDiameter: Dimensionless ratio of clay height to clay diameter
:type RatioHeightDiameter: float
:return: fraction of the surface area that is covered with coagulant that is not covered with humic acid
:rtype: float
"""
return (gamma_coag(ConcClay, ConcAl, coag, material, DiamTube,
RatioHeightDiameter)
* (1 - gamma_humic_acid_to_coag(ConcAl, ConcNatOrgMat,
NatOrgMat, coag))
) | python | def pacl_term(DiamTube, ConcClay, ConcAl, ConcNatOrgMat, NatOrgMat,
coag, material, RatioHeightDiameter):
"""Return the fraction of the surface area that is covered with coagulant
that is not covered with humic acid.
:param DiamTube: Diameter of the dosing tube
:type Diamtube: float
:param ConcClay: Concentration of clay in solution
:type ConcClay: float
:param ConcAl: Concentration of alumninum in solution
:type ConcAl: float
:param ConcNatOrgMat: Concentration of natural organic matter in solution
:type ConcNatOrgMat: float
:param NatOrgMat: type of natural organic matter, e.g. floc_model.HumicAcid
:type NatOrgMat: floc_model.Material
:param coag: Type of coagulant in solution, e.g. floc_model.PACl
:type coag: floc_model.Material
:param material: Type of clay in suspension, e.g. floc_model.Clay
:type material: floc_model.Material
:param RatioHeightDiameter: Dimensionless ratio of clay height to clay diameter
:type RatioHeightDiameter: float
:return: fraction of the surface area that is covered with coagulant that is not covered with humic acid
:rtype: float
"""
return (gamma_coag(ConcClay, ConcAl, coag, material, DiamTube,
RatioHeightDiameter)
* (1 - gamma_humic_acid_to_coag(ConcAl, ConcNatOrgMat,
NatOrgMat, coag))
) | Return the fraction of the surface area that is covered with coagulant
that is not covered with humic acid.
:param DiamTube: Diameter of the dosing tube
:type Diamtube: float
:param ConcClay: Concentration of clay in solution
:type ConcClay: float
:param ConcAl: Concentration of alumninum in solution
:type ConcAl: float
:param ConcNatOrgMat: Concentration of natural organic matter in solution
:type ConcNatOrgMat: float
:param NatOrgMat: type of natural organic matter, e.g. floc_model.HumicAcid
:type NatOrgMat: floc_model.Material
:param coag: Type of coagulant in solution, e.g. floc_model.PACl
:type coag: floc_model.Material
:param material: Type of clay in suspension, e.g. floc_model.Clay
:type material: floc_model.Material
:param RatioHeightDiameter: Dimensionless ratio of clay height to clay diameter
:type RatioHeightDiameter: float
:return: fraction of the surface area that is covered with coagulant that is not covered with humic acid
:rtype: float | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L401-L430 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | dens_floc | def dens_floc(ConcAl, ConcClay, DIM_FRACTAL, DiamTarget, coag, material, Temp):
"""Calculate floc density as a function of size."""
WaterDensity = pc.density_water(Temp).magnitude
return ((dens_floc_init(ConcAl, ConcClay, coag, material).magnitude
- WaterDensity
)
* (material.Diameter / DiamTarget)**(3 - DIM_FRACTAL)
+ WaterDensity
) | python | def dens_floc(ConcAl, ConcClay, DIM_FRACTAL, DiamTarget, coag, material, Temp):
"""Calculate floc density as a function of size."""
WaterDensity = pc.density_water(Temp).magnitude
return ((dens_floc_init(ConcAl, ConcClay, coag, material).magnitude
- WaterDensity
)
* (material.Diameter / DiamTarget)**(3 - DIM_FRACTAL)
+ WaterDensity
) | Calculate floc density as a function of size. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L505-L513 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | vel_term_floc | def vel_term_floc(ConcAl, ConcClay, coag, material, DIM_FRACTAL,
DiamTarget, Temp):
"""Calculate floc terminal velocity."""
WaterDensity = pc.density_water(Temp).magnitude
return (((pc.gravity.magnitude * material.Diameter**2)
/ (18 * PHI_FLOC * pc.viscosity_kinematic(Temp).magnitude)
)
* ((dens_floc_init(ConcAl, ConcClay, coag, material).magnitude
- WaterDensity
)
/ WaterDensity
)
* (DiamTarget / material.Diameter) ** (DIM_FRACTAL - 1)
) | python | def vel_term_floc(ConcAl, ConcClay, coag, material, DIM_FRACTAL,
DiamTarget, Temp):
"""Calculate floc terminal velocity."""
WaterDensity = pc.density_water(Temp).magnitude
return (((pc.gravity.magnitude * material.Diameter**2)
/ (18 * PHI_FLOC * pc.viscosity_kinematic(Temp).magnitude)
)
* ((dens_floc_init(ConcAl, ConcClay, coag, material).magnitude
- WaterDensity
)
/ WaterDensity
)
* (DiamTarget / material.Diameter) ** (DIM_FRACTAL - 1)
) | Calculate floc terminal velocity. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L518-L531 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | diam_floc_vel_term | def diam_floc_vel_term(ConcAl, ConcClay, coag, material,
DIM_FRACTAL, VelTerm, Temp):
"""Calculate floc diamter as a function of terminal velocity."""
WaterDensity = pc.density_water(Temp).magnitude
return (material.Diameter * (((18 * VelTerm * PHI_FLOC
* pc.viscosity_kinematic(Temp).magnitude
)
/ (pc.gravity.magnitude * material.Diameter**2)
)
* (WaterDensity
/ (dens_floc_init(ConcAl, ConcClay, coag,
material).magnitude
- WaterDensity
)
)
) ** (1 / (DIM_FRACTAL - 1))
) | python | def diam_floc_vel_term(ConcAl, ConcClay, coag, material,
DIM_FRACTAL, VelTerm, Temp):
"""Calculate floc diamter as a function of terminal velocity."""
WaterDensity = pc.density_water(Temp).magnitude
return (material.Diameter * (((18 * VelTerm * PHI_FLOC
* pc.viscosity_kinematic(Temp).magnitude
)
/ (pc.gravity.magnitude * material.Diameter**2)
)
* (WaterDensity
/ (dens_floc_init(ConcAl, ConcClay, coag,
material).magnitude
- WaterDensity
)
)
) ** (1 / (DIM_FRACTAL - 1))
) | Calculate floc diamter as a function of terminal velocity. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L536-L552 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | time_col_laminar | def time_col_laminar(EnergyDis, Temp, ConcAl, ConcClay, coag, material,
DiamTarget, DiamTube, DIM_FRACTAL, RatioHeightDiameter):
"""Calculate single collision time for laminar flow mediated collisions.
Calculated as a function of floc size.
"""
return (((1/6) * ((6/np.pi)**(1/3))
* frac_vol_floc_initial(ConcAl, ConcClay, coag, material) ** (-2/3)
* (pc.viscosity_kinematic(Temp).magnitude / EnergyDis) ** (1 / 2)
* (DiamTarget / material.Diameter) ** (2*DIM_FRACTAL/3 - 2)
) # End of the numerator
/ (gamma_coag(ConcClay, ConcAl, coag, material, DiamTube,
RatioHeightDiameter)
) # End of the denominator
) | python | def time_col_laminar(EnergyDis, Temp, ConcAl, ConcClay, coag, material,
DiamTarget, DiamTube, DIM_FRACTAL, RatioHeightDiameter):
"""Calculate single collision time for laminar flow mediated collisions.
Calculated as a function of floc size.
"""
return (((1/6) * ((6/np.pi)**(1/3))
* frac_vol_floc_initial(ConcAl, ConcClay, coag, material) ** (-2/3)
* (pc.viscosity_kinematic(Temp).magnitude / EnergyDis) ** (1 / 2)
* (DiamTarget / material.Diameter) ** (2*DIM_FRACTAL/3 - 2)
) # End of the numerator
/ (gamma_coag(ConcClay, ConcAl, coag, material, DiamTube,
RatioHeightDiameter)
) # End of the denominator
) | Calculate single collision time for laminar flow mediated collisions.
Calculated as a function of floc size. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L558-L572 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | time_col_turbulent | def time_col_turbulent(EnergyDis, ConcAl, ConcClay, coag, material,
DiamTarget, DIM_FRACTAL):
"""Calculate single collision time for turbulent flow mediated collisions.
Calculated as a function of floc size.
"""
return((1/6) * (6/np.pi)**(1/9) * EnergyDis**(-1/3) * DiamTarget**(2/3)
* frac_vol_floc_initial(ConcAl, ConcClay, coag, material)**(-8/9)
* (DiamTarget / material.Diameter)**((8*(DIM_FRACTAL-3)) / 9)
) | python | def time_col_turbulent(EnergyDis, ConcAl, ConcClay, coag, material,
DiamTarget, DIM_FRACTAL):
"""Calculate single collision time for turbulent flow mediated collisions.
Calculated as a function of floc size.
"""
return((1/6) * (6/np.pi)**(1/9) * EnergyDis**(-1/3) * DiamTarget**(2/3)
* frac_vol_floc_initial(ConcAl, ConcClay, coag, material)**(-8/9)
* (DiamTarget / material.Diameter)**((8*(DIM_FRACTAL-3)) / 9)
) | Calculate single collision time for turbulent flow mediated collisions.
Calculated as a function of floc size. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L577-L586 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | diam_kolmogorov | def diam_kolmogorov(EnergyDis, Temp, ConcAl, ConcClay, coag, material,
DIM_FRACTAL):
"""Return the size of the floc with separation distances equal to
the Kolmogorov length and the inner viscous length scale.
"""
return (material.Diameter
* ((eta_kolmogorov(EnergyDis, Temp).magnitude / material.Diameter)
* ((6 * frac_vol_floc_initial(ConcAl, ConcClay, coag, material))
/ np.pi
)**(1/3)
)**(3 / DIM_FRACTAL)
) | python | def diam_kolmogorov(EnergyDis, Temp, ConcAl, ConcClay, coag, material,
DIM_FRACTAL):
"""Return the size of the floc with separation distances equal to
the Kolmogorov length and the inner viscous length scale.
"""
return (material.Diameter
* ((eta_kolmogorov(EnergyDis, Temp).magnitude / material.Diameter)
* ((6 * frac_vol_floc_initial(ConcAl, ConcClay, coag, material))
/ np.pi
)**(1/3)
)**(3 / DIM_FRACTAL)
) | Return the size of the floc with separation distances equal to
the Kolmogorov length and the inner viscous length scale. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L602-L613 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | dean_number | def dean_number(PlantFlow, IDTube, RadiusCoil, Temp):
"""Return the Dean Number.
The Dean Number is a dimensionless parameter that is the unfortunate
combination of Reynolds and tube curvature. It would have been better
to keep the Reynolds number and define a simple dimensionless geometric
parameter.
"""
return (reynolds_rapid_mix(PlantFlow, IDTube, Temp)
* (IDTube / (2 * RadiusCoil))**(1/2)
) | python | def dean_number(PlantFlow, IDTube, RadiusCoil, Temp):
"""Return the Dean Number.
The Dean Number is a dimensionless parameter that is the unfortunate
combination of Reynolds and tube curvature. It would have been better
to keep the Reynolds number and define a simple dimensionless geometric
parameter.
"""
return (reynolds_rapid_mix(PlantFlow, IDTube, Temp)
* (IDTube / (2 * RadiusCoil))**(1/2)
) | Return the Dean Number.
The Dean Number is a dimensionless parameter that is the unfortunate
combination of Reynolds and tube curvature. It would have been better
to keep the Reynolds number and define a simple dimensionless geometric
parameter. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L669-L679 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | g_coil | def g_coil(FlowPlant, IDTube, RadiusCoil, Temp):
"""We need a reference for this.
Karen's thesis likely has this equation and the reference.
"""
return (g_straight(FlowPlant, IDTube).magnitude
* (1 + 0.033 *
np.log10(dean_number(FlowPlant, IDTube, RadiusCoil, Temp)
) ** 4
) ** (1/2)
) | python | def g_coil(FlowPlant, IDTube, RadiusCoil, Temp):
"""We need a reference for this.
Karen's thesis likely has this equation and the reference.
"""
return (g_straight(FlowPlant, IDTube).magnitude
* (1 + 0.033 *
np.log10(dean_number(FlowPlant, IDTube, RadiusCoil, Temp)
) ** 4
) ** (1/2)
) | We need a reference for this.
Karen's thesis likely has this equation and the reference. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L683-L693 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | g_time_res | def g_time_res(FlowPlant, IDTube, RadiusCoil, LengthTube, Temp):
"""G Residence Time calculated for a coiled tube flocculator."""
return (g_coil(FlowPlant, IDTube, RadiusCoil, Temp).magnitude
* time_res_tube(IDTube, LengthTube, FlowPlant).magnitude
) | python | def g_time_res(FlowPlant, IDTube, RadiusCoil, LengthTube, Temp):
"""G Residence Time calculated for a coiled tube flocculator."""
return (g_coil(FlowPlant, IDTube, RadiusCoil, Temp).magnitude
* time_res_tube(IDTube, LengthTube, FlowPlant).magnitude
) | G Residence Time calculated for a coiled tube flocculator. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L703-L707 |
AguaClara/aguaclara | aguaclara/research/floc_model.py | Chemical.define_Precip | def define_Precip(self, diameter, density, molecweight, alumMPM):
"""Define a precipitate for the chemical.
:param diameter: Diameter of the precipitate in particulate form
:type diameter: float
:param density: Density of the material (mass/volume)
:type density: float
:param molecWeight: Molecular weight of the material (mass/mole)
:type molecWeight: float
:param alumMPM:
"""
self.PrecipDiameter = diameter
self.PrecipDensity = density
self.PrecipMolecWeight = molecweight
self.PrecipAluminumMPM = alumMPM | python | def define_Precip(self, diameter, density, molecweight, alumMPM):
"""Define a precipitate for the chemical.
:param diameter: Diameter of the precipitate in particulate form
:type diameter: float
:param density: Density of the material (mass/volume)
:type density: float
:param molecWeight: Molecular weight of the material (mass/mole)
:type molecWeight: float
:param alumMPM:
"""
self.PrecipDiameter = diameter
self.PrecipDensity = density
self.PrecipMolecWeight = molecweight
self.PrecipAluminumMPM = alumMPM | Define a precipitate for the chemical.
:param diameter: Diameter of the precipitate in particulate form
:type diameter: float
:param density: Density of the material (mass/volume)
:type density: float
:param molecWeight: Molecular weight of the material (mass/mole)
:type molecWeight: float
:param alumMPM: | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/research/floc_model.py#L59-L73 |
AguaClara/aguaclara | aguaclara/design/plant.py | Plant.ent_tank_a | def ent_tank_a(self):
"""Calculate the planview area of the entrance tank, given the volume of
the flocculator.
:returns: The planview area of the entrance tank.
:rtype: float * u.m ** 2
"""
# first guess planview area
a_new = 1 * u.m**2
a_ratio = 2 # set to >1+tolerance to start while loop
tolerance = 0.01
a_floc_pv = (
self.floc.vol /
(self.floc.downstream_H + (self.floc.HL / 2))
)
while a_ratio > (1 + tolerance):
a_et_pv = a_new
a_etf_pv = a_et_pv + a_floc_pv
w_tot = a_etf_pv / self.floc.max_L
w_chan = w_tot / self.floc.channel_n
a_new = self.floc.max_L * w_chan
a_ratio = a_new / a_et_pv
return a_new | python | def ent_tank_a(self):
"""Calculate the planview area of the entrance tank, given the volume of
the flocculator.
:returns: The planview area of the entrance tank.
:rtype: float * u.m ** 2
"""
# first guess planview area
a_new = 1 * u.m**2
a_ratio = 2 # set to >1+tolerance to start while loop
tolerance = 0.01
a_floc_pv = (
self.floc.vol /
(self.floc.downstream_H + (self.floc.HL / 2))
)
while a_ratio > (1 + tolerance):
a_et_pv = a_new
a_etf_pv = a_et_pv + a_floc_pv
w_tot = a_etf_pv / self.floc.max_L
w_chan = w_tot / self.floc.channel_n
a_new = self.floc.max_L * w_chan
a_ratio = a_new / a_et_pv
return a_new | Calculate the planview area of the entrance tank, given the volume of
the flocculator.
:returns: The planview area of the entrance tank.
:rtype: float * u.m ** 2 | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/design/plant.py#L24-L47 |
AguaClara/aguaclara | aguaclara/unit_process_design/lfom.py | n_lfom_rows | def n_lfom_rows(FLOW,HL_LFOM):
"""This equation states that the open area corresponding to one row can be
set equal to two orifices of diameter=row height. If there are more than
two orifices per row at the top of the LFOM then there are more orifices
than are convenient to drill and more than necessary for good accuracy.
Thus this relationship can be used to increase the spacing between the
rows and thus increase the diameter of the orifices. This spacing function
also sets the lower depth on the high flow rate LFOM with no accurate
flows below a depth equal to the first row height.
But it might be better to always set then number of rows to 10.
The challenge is to figure out a reasonable system of constraints that
reliably returns a valid solution.
"""
N_estimated = (HL_LFOM*np.pi/(2*width_stout(HL_LFOM,HL_LFOM)*FLOW))
variablerow = min(10,max(4,math.trunc(N_estimated.magnitude)))
# Forcing the LFOM to either have 4 or 8 rows, for design purposes
# If the hydraulic calculation finds that there should be 4 rows, then there
# will be 4 rows. If anything other besides 4 rows is found, then assign 8
# rows.
# This can be improved in the future.
if variablerow == 4:
variablerow = 4
else:
variablerow = 8
return variablerow | python | def n_lfom_rows(FLOW,HL_LFOM):
"""This equation states that the open area corresponding to one row can be
set equal to two orifices of diameter=row height. If there are more than
two orifices per row at the top of the LFOM then there are more orifices
than are convenient to drill and more than necessary for good accuracy.
Thus this relationship can be used to increase the spacing between the
rows and thus increase the diameter of the orifices. This spacing function
also sets the lower depth on the high flow rate LFOM with no accurate
flows below a depth equal to the first row height.
But it might be better to always set then number of rows to 10.
The challenge is to figure out a reasonable system of constraints that
reliably returns a valid solution.
"""
N_estimated = (HL_LFOM*np.pi/(2*width_stout(HL_LFOM,HL_LFOM)*FLOW))
variablerow = min(10,max(4,math.trunc(N_estimated.magnitude)))
# Forcing the LFOM to either have 4 or 8 rows, for design purposes
# If the hydraulic calculation finds that there should be 4 rows, then there
# will be 4 rows. If anything other besides 4 rows is found, then assign 8
# rows.
# This can be improved in the future.
if variablerow == 4:
variablerow = 4
else:
variablerow = 8
return variablerow | This equation states that the open area corresponding to one row can be
set equal to two orifices of diameter=row height. If there are more than
two orifices per row at the top of the LFOM then there are more orifices
than are convenient to drill and more than necessary for good accuracy.
Thus this relationship can be used to increase the spacing between the
rows and thus increase the diameter of the orifices. This spacing function
also sets the lower depth on the high flow rate LFOM with no accurate
flows below a depth equal to the first row height.
But it might be better to always set then number of rows to 10.
The challenge is to figure out a reasonable system of constraints that
reliably returns a valid solution. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/unit_process_design/lfom.py#L26-L51 |
AguaClara/aguaclara | aguaclara/unit_process_design/lfom.py | area_lfom_orifices_top | def area_lfom_orifices_top(FLOW,HL_LFOM):
"""Estimate the orifice area corresponding to the top row of orifices.
Another solution method is to use integration to solve this problem.
Here we use the width of the stout weir in the center of the top row
to estimate the area of the top orifice
"""
return ((FLOW*width_stout(HL_LFOM*u.m,HL_LFOM*u.m-0.5*dist_center_lfom_rows(FLOW,HL_LFOM)).magnitude *
dist_center_lfom_rows(FLOW,HL_LFOM).magnitude)) | python | def area_lfom_orifices_top(FLOW,HL_LFOM):
"""Estimate the orifice area corresponding to the top row of orifices.
Another solution method is to use integration to solve this problem.
Here we use the width of the stout weir in the center of the top row
to estimate the area of the top orifice
"""
return ((FLOW*width_stout(HL_LFOM*u.m,HL_LFOM*u.m-0.5*dist_center_lfom_rows(FLOW,HL_LFOM)).magnitude *
dist_center_lfom_rows(FLOW,HL_LFOM).magnitude)) | Estimate the orifice area corresponding to the top row of orifices.
Another solution method is to use integration to solve this problem.
Here we use the width of the stout weir in the center of the top row
to estimate the area of the top orifice | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/unit_process_design/lfom.py#L76-L83 |
AguaClara/aguaclara | aguaclara/unit_process_design/lfom.py | n_lfom_orifices_per_row_max | def n_lfom_orifices_per_row_max(FLOW,HL_LFOM,drill_bits):
"""A bound on the number of orifices allowed in each row.
The distance between consecutive orifices must be enough to retain
structural integrity of the pipe.
"""
return math.floor(math.pi * (pipe.ID_SDR(
nom_diam_lfom_pipe(FLOW, HL_LFOM), design.lfom.SDR_LFOM).magnitude)
/ (orifice_diameter(FLOW, HL_LFOM, drill_bits).magnitude +
aguaclara.design.lfom.ORIFICE_S.magnitude)) | python | def n_lfom_orifices_per_row_max(FLOW,HL_LFOM,drill_bits):
"""A bound on the number of orifices allowed in each row.
The distance between consecutive orifices must be enough to retain
structural integrity of the pipe.
"""
return math.floor(math.pi * (pipe.ID_SDR(
nom_diam_lfom_pipe(FLOW, HL_LFOM), design.lfom.SDR_LFOM).magnitude)
/ (orifice_diameter(FLOW, HL_LFOM, drill_bits).magnitude +
aguaclara.design.lfom.ORIFICE_S.magnitude)) | A bound on the number of orifices allowed in each row.
The distance between consecutive orifices must be enough to retain
structural integrity of the pipe. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/unit_process_design/lfom.py#L99-L107 |
AguaClara/aguaclara | aguaclara/unit_process_design/lfom.py | height_lfom_orifices | def height_lfom_orifices(FLOW,HL_LFOM,drill_bits):
"""Calculates the height of the center of each row of orifices.
The bottom of the bottom row orifices is at the zero elevation
point of the LFOM so that the flow goes to zero when the water height
is at zero.
"""
return (np.arange((orifice_diameter(FLOW,HL_LFOM,drill_bits)*0.5),
HL_LFOM,
(dist_center_lfom_rows(FLOW,HL_LFOM)))) | python | def height_lfom_orifices(FLOW,HL_LFOM,drill_bits):
"""Calculates the height of the center of each row of orifices.
The bottom of the bottom row orifices is at the zero elevation
point of the LFOM so that the flow goes to zero when the water height
is at zero.
"""
return (np.arange((orifice_diameter(FLOW,HL_LFOM,drill_bits)*0.5),
HL_LFOM,
(dist_center_lfom_rows(FLOW,HL_LFOM)))) | Calculates the height of the center of each row of orifices.
The bottom of the bottom row orifices is at the zero elevation
point of the LFOM so that the flow goes to zero when the water height
is at zero. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/unit_process_design/lfom.py#L115-L123 |
AguaClara/aguaclara | aguaclara/unit_process_design/lfom.py | flow_lfom_actual | def flow_lfom_actual(FLOW,HL_LFOM,drill_bits,Row_Index_Submerged,N_LFOM_Orifices):
"""Calculates the flow for a given number of submerged rows of orifices
harray is the distance from the water level to the center of the orifices
when the water is at the max level
"""
D_LFOM_Orifices=orifice_diameter(FLOW, HL_LFOM, drill_bits).magnitude
row_height=dist_center_lfom_rows(FLOW, HL_LFOM).magnitude
harray = (np.linspace(row_height, HL_LFOM, n_lfom_rows(FLOW, HL_LFOM))) - 0.5 * D_LFOM_Orifices
FLOW_new = 0
for i in range(Row_Index_Submerged+1):
FLOW_new = FLOW_new + (N_LFOM_Orifices[i] * (
pc.flow_orifice_vert(D_LFOM_Orifices, harray[Row_Index_Submerged-i],
con.VC_ORIFICE_RATIO).magnitude))
return FLOW_new | python | def flow_lfom_actual(FLOW,HL_LFOM,drill_bits,Row_Index_Submerged,N_LFOM_Orifices):
"""Calculates the flow for a given number of submerged rows of orifices
harray is the distance from the water level to the center of the orifices
when the water is at the max level
"""
D_LFOM_Orifices=orifice_diameter(FLOW, HL_LFOM, drill_bits).magnitude
row_height=dist_center_lfom_rows(FLOW, HL_LFOM).magnitude
harray = (np.linspace(row_height, HL_LFOM, n_lfom_rows(FLOW, HL_LFOM))) - 0.5 * D_LFOM_Orifices
FLOW_new = 0
for i in range(Row_Index_Submerged+1):
FLOW_new = FLOW_new + (N_LFOM_Orifices[i] * (
pc.flow_orifice_vert(D_LFOM_Orifices, harray[Row_Index_Submerged-i],
con.VC_ORIFICE_RATIO).magnitude))
return FLOW_new | Calculates the flow for a given number of submerged rows of orifices
harray is the distance from the water level to the center of the orifices
when the water is at the max level | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/unit_process_design/lfom.py#L126-L139 |
AguaClara/aguaclara | aguaclara/core/utility.py | round_sf | def round_sf(number, digits):
"""Returns inputted value rounded to number of significant figures desired.
:param number: Value to be rounded
:type number: float
:param digits: number of significant digits to be rounded to.
:type digits: int
"""
units = None
try:
num = number.magnitude
units = number.units
except AttributeError:
num = number
try:
if (units != None):
rounded_num = round(num, digits - int(floor(log10(abs(num)))) - 1) * units
else:
rounded_num = round(num, digits - int(floor(log10(abs(num)))) - 1)
return rounded_num
except ValueError: # Prevents an error with log10(0)
if (units != None):
return 0 * units
else:
return 0 | python | def round_sf(number, digits):
"""Returns inputted value rounded to number of significant figures desired.
:param number: Value to be rounded
:type number: float
:param digits: number of significant digits to be rounded to.
:type digits: int
"""
units = None
try:
num = number.magnitude
units = number.units
except AttributeError:
num = number
try:
if (units != None):
rounded_num = round(num, digits - int(floor(log10(abs(num)))) - 1) * units
else:
rounded_num = round(num, digits - int(floor(log10(abs(num)))) - 1)
return rounded_num
except ValueError: # Prevents an error with log10(0)
if (units != None):
return 0 * units
else:
return 0 | Returns inputted value rounded to number of significant figures desired.
:param number: Value to be rounded
:type number: float
:param digits: number of significant digits to be rounded to.
:type digits: int | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/core/utility.py#L12-L37 |
AguaClara/aguaclara | aguaclara/core/utility.py | stepceil_with_units | def stepceil_with_units(param, step, unit):
"""This function returns the smallest multiple of 'step' greater than or
equal to 'param' and outputs the result in Pint units.
This function is unit-aware and functions without requiring translation
so long as 'param' and 'unit' are of the same dimensionality.
"""
counter = 0 * unit
while counter < param.to(unit):
counter += step * unit
return counter | python | def stepceil_with_units(param, step, unit):
"""This function returns the smallest multiple of 'step' greater than or
equal to 'param' and outputs the result in Pint units.
This function is unit-aware and functions without requiring translation
so long as 'param' and 'unit' are of the same dimensionality.
"""
counter = 0 * unit
while counter < param.to(unit):
counter += step * unit
return counter | This function returns the smallest multiple of 'step' greater than or
equal to 'param' and outputs the result in Pint units.
This function is unit-aware and functions without requiring translation
so long as 'param' and 'unit' are of the same dimensionality. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/core/utility.py#L40-L49 |
AguaClara/aguaclara | aguaclara/core/utility.py | list_handler | def list_handler(HandlerResult="nparray"):
"""Wraps a function to handle list inputs."""
def decorate(func):
def wrapper(*args, **kwargs):
"""Run through the wrapped function once for each array element.
:param HandlerResult: output type. Defaults to numpy arrays.
"""
sequences = []
enumsUnitCheck = enumerate(args)
argsList = list(args)
#This for loop identifies pint unit objects and strips them
#of their units.
for num, arg in enumsUnitCheck:
if type(arg) == type(1 * u.m):
argsList[num] = arg.to_base_units().magnitude
enumsUnitless = enumerate(argsList)
#This for loop identifies arguments that are sequences and
#adds their index location to the list 'sequences'.
for num, arg in enumsUnitless:
if isinstance(arg, (list, tuple, np.ndarray)):
sequences.append(num)
#If there are no sequences to iterate through, simply return
#the function.
if len(sequences) == 0:
result = func(*args, **kwargs)
else:
#iterant keeps track of how many times we've iterated and
#limiter stops the loop once we've iterated as many times
#as there are list elements. Without this check, a few
#erroneous runs will occur, appending the last couple values
#to the end of the list multiple times.
#
#We only care about the length of sequences[0] because this
#function is recursive, and sequences[0] is always the relevant
#sequences for any given run.
limiter = len(argsList[sequences[0]])
iterant = 0
result = []
for num in sequences:
for arg in argsList[num]:
if iterant >= limiter:
break
#We can safely replace the entire list argument
#with a single element from it because of the looping
#we're doing. We redefine the object, but that
#definition remains within this namespace and does
#not penetrate further up the function.
argsList[num] = arg
#Here we dive down the rabbit hole. This ends up
#creating a multi-dimensional array shaped by the
#sizes and shapes of the lists passed.
result.append(wrapper(*argsList,
HandlerResult=HandlerResult, **kwargs))
iterant += 1
#HandlerResult allows the user to specify what type to
#return the generated sequence as. It defaults to numpy
#arrays because functions tend to handle them better, but if
#the user does not wish to import numpy the base Python options
#are available to them.
if HandlerResult == "nparray":
result = np.array(result)
elif HandlerResult == "tuple":
result = tuple(result)
elif HandlerResult == "list":
result == list(result)
return result
return wrapper
return decorate | python | def list_handler(HandlerResult="nparray"):
"""Wraps a function to handle list inputs."""
def decorate(func):
def wrapper(*args, **kwargs):
"""Run through the wrapped function once for each array element.
:param HandlerResult: output type. Defaults to numpy arrays.
"""
sequences = []
enumsUnitCheck = enumerate(args)
argsList = list(args)
#This for loop identifies pint unit objects and strips them
#of their units.
for num, arg in enumsUnitCheck:
if type(arg) == type(1 * u.m):
argsList[num] = arg.to_base_units().magnitude
enumsUnitless = enumerate(argsList)
#This for loop identifies arguments that are sequences and
#adds their index location to the list 'sequences'.
for num, arg in enumsUnitless:
if isinstance(arg, (list, tuple, np.ndarray)):
sequences.append(num)
#If there are no sequences to iterate through, simply return
#the function.
if len(sequences) == 0:
result = func(*args, **kwargs)
else:
#iterant keeps track of how many times we've iterated and
#limiter stops the loop once we've iterated as many times
#as there are list elements. Without this check, a few
#erroneous runs will occur, appending the last couple values
#to the end of the list multiple times.
#
#We only care about the length of sequences[0] because this
#function is recursive, and sequences[0] is always the relevant
#sequences for any given run.
limiter = len(argsList[sequences[0]])
iterant = 0
result = []
for num in sequences:
for arg in argsList[num]:
if iterant >= limiter:
break
#We can safely replace the entire list argument
#with a single element from it because of the looping
#we're doing. We redefine the object, but that
#definition remains within this namespace and does
#not penetrate further up the function.
argsList[num] = arg
#Here we dive down the rabbit hole. This ends up
#creating a multi-dimensional array shaped by the
#sizes and shapes of the lists passed.
result.append(wrapper(*argsList,
HandlerResult=HandlerResult, **kwargs))
iterant += 1
#HandlerResult allows the user to specify what type to
#return the generated sequence as. It defaults to numpy
#arrays because functions tend to handle them better, but if
#the user does not wish to import numpy the base Python options
#are available to them.
if HandlerResult == "nparray":
result = np.array(result)
elif HandlerResult == "tuple":
result = tuple(result)
elif HandlerResult == "list":
result == list(result)
return result
return wrapper
return decorate | Wraps a function to handle list inputs. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/core/utility.py#L64-L132 |
AguaClara/aguaclara | aguaclara/core/utility.py | check_range | def check_range(*args):
"""
Check whether passed paramters fall within approved ranges.
Does not return anything, but will raise an error if a parameter falls
outside of its defined range.
Input should be passed as an array of sequences, with each sequence
having three elements:
[0] is the value being checked,
[1] is the range parameter(s) within which the value should fall, and
[2] is the name of the parameter, for better error messages.
If [2] is not supplied, "Input" will be appended as a generic name.
Range requests that this function understands are listed in the
knownChecks sequence.
"""
knownChecks = ('>0', '>=0', '0-1', '<0', '<=0', 'int', 'boolean')
for arg in args:
#Converts arg to a mutable list
arg = [*arg]
if len(arg) == 1:
#arg[1] details what range the parameter should fall within; if
#len(arg) is 1 that means a validity was not specified and the
#parameter should not have been passed in its current form
raise TypeError("No range-validity parameter provided.")
elif len(arg) == 2:
#Appending 'Input" to the end allows us to give more descriptive
#error messages that do not fail if no description was supplied.
arg.append("Input")
#This ensures that all whitespace is removed before checking if the
#request is understood
arg[1] = "".join(arg[1].lower().split())
#This block checks that each range request is understood.
#If the request is a compound one, it must be separated into individual
#requests for validity comprehension
for i in arg[1].split(","):
if i not in knownChecks:
raise RuntimeError("Unknown parameter validation "
"request: {0}.".format(i))
if not isinstance(arg[0], (list, tuple, np.ndarray)):
arg[0] = [arg[0]]
for i in arg[0]:
if '>0' in arg[1] and i <= 0:
raise ValueError("{1} is {0} but must be greater than "
"0.".format(i, arg[2]))
if '>=0' in arg[1] and i <0:
raise ValueError("{1} is {0} but must be 0 or "
"greater.".format(i, arg[2]))
if '0-1' in arg[1] and not 0 <= i <= 1:
raise ValueError("{1} is {0} but must be between 0 and "
"1.".format(i, arg[2]))
if '<0' in arg[1] and i >= 0:
raise ValueError("{1} is {0} but must be less than "
"0.".format(i, arg[2]))
if '<=0' in arg[1] and i >0:
raise ValueError("{1} is {0} but must be 0 or "
"less.".format(i, arg[2]))
if 'int' in arg[1] and int(i) != i:
raise TypeError("{1} is {0} but must be a numeric "
"integer.".format(i, arg[2]))
if 'boolean' in arg[1] and type(i) != bool:
raise TypeError("{1} is {0} but must be a "
"boolean.".format(i, arg[2])) | python | def check_range(*args):
"""
Check whether passed paramters fall within approved ranges.
Does not return anything, but will raise an error if a parameter falls
outside of its defined range.
Input should be passed as an array of sequences, with each sequence
having three elements:
[0] is the value being checked,
[1] is the range parameter(s) within which the value should fall, and
[2] is the name of the parameter, for better error messages.
If [2] is not supplied, "Input" will be appended as a generic name.
Range requests that this function understands are listed in the
knownChecks sequence.
"""
knownChecks = ('>0', '>=0', '0-1', '<0', '<=0', 'int', 'boolean')
for arg in args:
#Converts arg to a mutable list
arg = [*arg]
if len(arg) == 1:
#arg[1] details what range the parameter should fall within; if
#len(arg) is 1 that means a validity was not specified and the
#parameter should not have been passed in its current form
raise TypeError("No range-validity parameter provided.")
elif len(arg) == 2:
#Appending 'Input" to the end allows us to give more descriptive
#error messages that do not fail if no description was supplied.
arg.append("Input")
#This ensures that all whitespace is removed before checking if the
#request is understood
arg[1] = "".join(arg[1].lower().split())
#This block checks that each range request is understood.
#If the request is a compound one, it must be separated into individual
#requests for validity comprehension
for i in arg[1].split(","):
if i not in knownChecks:
raise RuntimeError("Unknown parameter validation "
"request: {0}.".format(i))
if not isinstance(arg[0], (list, tuple, np.ndarray)):
arg[0] = [arg[0]]
for i in arg[0]:
if '>0' in arg[1] and i <= 0:
raise ValueError("{1} is {0} but must be greater than "
"0.".format(i, arg[2]))
if '>=0' in arg[1] and i <0:
raise ValueError("{1} is {0} but must be 0 or "
"greater.".format(i, arg[2]))
if '0-1' in arg[1] and not 0 <= i <= 1:
raise ValueError("{1} is {0} but must be between 0 and "
"1.".format(i, arg[2]))
if '<0' in arg[1] and i >= 0:
raise ValueError("{1} is {0} but must be less than "
"0.".format(i, arg[2]))
if '<=0' in arg[1] and i >0:
raise ValueError("{1} is {0} but must be 0 or "
"less.".format(i, arg[2]))
if 'int' in arg[1] and int(i) != i:
raise TypeError("{1} is {0} but must be a numeric "
"integer.".format(i, arg[2]))
if 'boolean' in arg[1] and type(i) != bool:
raise TypeError("{1} is {0} but must be a "
"boolean.".format(i, arg[2])) | Check whether passed paramters fall within approved ranges.
Does not return anything, but will raise an error if a parameter falls
outside of its defined range.
Input should be passed as an array of sequences, with each sequence
having three elements:
[0] is the value being checked,
[1] is the range parameter(s) within which the value should fall, and
[2] is the name of the parameter, for better error messages.
If [2] is not supplied, "Input" will be appended as a generic name.
Range requests that this function understands are listed in the
knownChecks sequence. | https://github.com/AguaClara/aguaclara/blob/8dd4e734768b166a7fc2b60388a24df2f93783fc/aguaclara/core/utility.py#L135-L198 |
fracpete/python-weka-wrapper3 | python/weka/clusterers.py | main | def main():
"""
Runs a clusterer from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Performs clustering from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-X", metavar="heap", dest="heap", help="max heap size for jvm, e.g., 512m")
parser.add_argument("-t", metavar="train", dest="train", required=True, help="training set file")
parser.add_argument("-T", metavar="test", dest="test", help="test set file")
parser.add_argument("-d", metavar="outmodel", dest="outmodel", help="model output file name")
parser.add_argument("-l", metavar="inmodel", dest="inmodel", help="model input file name")
parser.add_argument("-p", metavar="attributes", dest="attributes", help="attribute range")
parser.add_argument("-x", metavar="num folds", dest="numfolds", help="number of folds")
parser.add_argument("-s", metavar="seed", dest="seed", help="seed value for randomization")
parser.add_argument("-c", metavar="class index", dest="classindex", help="1-based class attribute index")
parser.add_argument("-g", metavar="graph", dest="graph", help="graph output file (if supported)")
parser.add_argument("clusterer", help="clusterer classname, e.g., weka.clusterers.SimpleKMeans")
parser.add_argument("option", nargs=argparse.REMAINDER, help="additional clusterer options")
parsed = parser.parse_args()
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
params = []
if parsed.train is not None:
params.extend(["-t", parsed.train])
if parsed.test is not None:
params.extend(["-T", parsed.test])
if parsed.outmodel is not None:
params.extend(["-d", parsed.outmodel])
if parsed.inmodel is not None:
params.extend(["-l", parsed.inmodel])
if parsed.attributes is not None:
params.extend(["-p", parsed.attributes])
if parsed.numfolds is not None:
params.extend(["-x", parsed.numfolds])
if parsed.seed is not None:
params.extend(["-s", parsed.seed])
if parsed.classindex is not None:
params.extend(["-c", parsed.classindex])
if parsed.graph is not None:
params.extend(["-g", parsed.graph])
jvm.start(jars, max_heap_size=parsed.heap, packages=True)
logger.debug("Commandline: " + join_options(sys.argv[1:]))
try:
clusterer = Clusterer(classname=parsed.clusterer)
if len(parsed.option) > 0:
clusterer.options = parsed.option
print(ClusterEvaluation.evaluate_clusterer(clusterer, params))
except Exception as e:
print(e)
finally:
jvm.stop() | python | def main():
"""
Runs a clusterer from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Performs clustering from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-X", metavar="heap", dest="heap", help="max heap size for jvm, e.g., 512m")
parser.add_argument("-t", metavar="train", dest="train", required=True, help="training set file")
parser.add_argument("-T", metavar="test", dest="test", help="test set file")
parser.add_argument("-d", metavar="outmodel", dest="outmodel", help="model output file name")
parser.add_argument("-l", metavar="inmodel", dest="inmodel", help="model input file name")
parser.add_argument("-p", metavar="attributes", dest="attributes", help="attribute range")
parser.add_argument("-x", metavar="num folds", dest="numfolds", help="number of folds")
parser.add_argument("-s", metavar="seed", dest="seed", help="seed value for randomization")
parser.add_argument("-c", metavar="class index", dest="classindex", help="1-based class attribute index")
parser.add_argument("-g", metavar="graph", dest="graph", help="graph output file (if supported)")
parser.add_argument("clusterer", help="clusterer classname, e.g., weka.clusterers.SimpleKMeans")
parser.add_argument("option", nargs=argparse.REMAINDER, help="additional clusterer options")
parsed = parser.parse_args()
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
params = []
if parsed.train is not None:
params.extend(["-t", parsed.train])
if parsed.test is not None:
params.extend(["-T", parsed.test])
if parsed.outmodel is not None:
params.extend(["-d", parsed.outmodel])
if parsed.inmodel is not None:
params.extend(["-l", parsed.inmodel])
if parsed.attributes is not None:
params.extend(["-p", parsed.attributes])
if parsed.numfolds is not None:
params.extend(["-x", parsed.numfolds])
if parsed.seed is not None:
params.extend(["-s", parsed.seed])
if parsed.classindex is not None:
params.extend(["-c", parsed.classindex])
if parsed.graph is not None:
params.extend(["-g", parsed.graph])
jvm.start(jars, max_heap_size=parsed.heap, packages=True)
logger.debug("Commandline: " + join_options(sys.argv[1:]))
try:
clusterer = Clusterer(classname=parsed.clusterer)
if len(parsed.option) > 0:
clusterer.options = parsed.option
print(ClusterEvaluation.evaluate_clusterer(clusterer, params))
except Exception as e:
print(e)
finally:
jvm.stop() | Runs a clusterer from the command-line. Calls JVM start/stop automatically.
Use -h to see all options. | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/clusterers.py#L383-L439 |
fracpete/python-weka-wrapper3 | python/weka/clusterers.py | Clusterer.update_clusterer | def update_clusterer(self, inst):
"""
Updates the clusterer with the instance.
:param inst: the Instance to update the clusterer with
:type inst: Instance
"""
if self.is_updateable:
javabridge.call(self.jobject, "updateClusterer", "(Lweka/core/Instance;)V", inst.jobject)
else:
logger.critical(classes.get_classname(self.jobject) + " is not updateable!") | python | def update_clusterer(self, inst):
"""
Updates the clusterer with the instance.
:param inst: the Instance to update the clusterer with
:type inst: Instance
"""
if self.is_updateable:
javabridge.call(self.jobject, "updateClusterer", "(Lweka/core/Instance;)V", inst.jobject)
else:
logger.critical(classes.get_classname(self.jobject) + " is not updateable!") | Updates the clusterer with the instance.
:param inst: the Instance to update the clusterer with
:type inst: Instance | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/clusterers.py#L78-L88 |
fracpete/python-weka-wrapper3 | python/weka/clusterers.py | Clusterer.update_finished | def update_finished(self):
"""
Signals the clusterer that updating with new data has finished.
"""
if self.is_updateable:
javabridge.call(self.jobject, "updateFinished", "()V")
else:
logger.critical(classes.get_classname(self.jobject) + " is not updateable!") | python | def update_finished(self):
"""
Signals the clusterer that updating with new data has finished.
"""
if self.is_updateable:
javabridge.call(self.jobject, "updateFinished", "()V")
else:
logger.critical(classes.get_classname(self.jobject) + " is not updateable!") | Signals the clusterer that updating with new data has finished. | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/clusterers.py#L90-L97 |
fracpete/python-weka-wrapper3 | python/weka/clusterers.py | Clusterer.distribution_for_instance | def distribution_for_instance(self, inst):
"""
Peforms a prediction, returning the cluster distribution.
:param inst: the Instance to get the cluster distribution for
:type inst: Instance
:return: the cluster distribution
:rtype: float[]
"""
pred = self.__distribution(inst.jobject)
return javabridge.get_env().get_double_array_elements(pred) | python | def distribution_for_instance(self, inst):
"""
Peforms a prediction, returning the cluster distribution.
:param inst: the Instance to get the cluster distribution for
:type inst: Instance
:return: the cluster distribution
:rtype: float[]
"""
pred = self.__distribution(inst.jobject)
return javabridge.get_env().get_double_array_elements(pred) | Peforms a prediction, returning the cluster distribution.
:param inst: the Instance to get the cluster distribution for
:type inst: Instance
:return: the cluster distribution
:rtype: float[] | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/clusterers.py#L110-L120 |
fracpete/python-weka-wrapper3 | python/weka/clusterers.py | ClusterEvaluation.cluster_assignments | def cluster_assignments(self):
"""
Return an array of cluster assignments corresponding to the most recent set of instances clustered.
:return: the cluster assignments
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "getClusterAssignments", "()[D")
if array is None:
return None
else:
return javabridge.get_env().get_double_array_elements(array) | python | def cluster_assignments(self):
"""
Return an array of cluster assignments corresponding to the most recent set of instances clustered.
:return: the cluster assignments
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "getClusterAssignments", "()[D")
if array is None:
return None
else:
return javabridge.get_env().get_double_array_elements(array) | Return an array of cluster assignments corresponding to the most recent set of instances clustered.
:return: the cluster assignments
:rtype: ndarray | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/clusterers.py#L297-L308 |
fracpete/python-weka-wrapper3 | python/weka/clusterers.py | ClusterEvaluation.classes_to_clusters | def classes_to_clusters(self):
"""
Return the array (ordered by cluster number) of minimum error class to cluster mappings.
:return: the mappings
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "getClassesToClusters", "()[I")
if array is None:
return None
else:
return javabridge.get_env().get_int_array_elements(array) | python | def classes_to_clusters(self):
"""
Return the array (ordered by cluster number) of minimum error class to cluster mappings.
:return: the mappings
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "getClassesToClusters", "()[I")
if array is None:
return None
else:
return javabridge.get_env().get_int_array_elements(array) | Return the array (ordered by cluster number) of minimum error class to cluster mappings.
:return: the mappings
:rtype: ndarray | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/clusterers.py#L331-L342 |
fracpete/python-weka-wrapper3 | python/weka/clusterers.py | ClusterEvaluation.crossvalidate_model | def crossvalidate_model(cls, clusterer, data, num_folds, rnd):
"""
Cross-validates the clusterer and returns the loglikelihood.
:param clusterer: the clusterer instance to evaluate
:type clusterer: Clusterer
:param data: the data to evaluate on
:type data: Instances
:param num_folds: the number of folds
:type num_folds: int
:param rnd: the random number generator to use
:type rnd: Random
:return: the cross-validated loglikelihood
:rtype: float
"""
return javabridge.static_call(
"Lweka/clusterers/ClusterEvaluation;", "crossValidateModel",
"(Lweka/clusterers/DensityBasedClusterer;Lweka/core/Instances;ILjava/util/Random;)D",
clusterer.jobject, data.jobject, num_folds, rnd.jobject) | python | def crossvalidate_model(cls, clusterer, data, num_folds, rnd):
"""
Cross-validates the clusterer and returns the loglikelihood.
:param clusterer: the clusterer instance to evaluate
:type clusterer: Clusterer
:param data: the data to evaluate on
:type data: Instances
:param num_folds: the number of folds
:type num_folds: int
:param rnd: the random number generator to use
:type rnd: Random
:return: the cross-validated loglikelihood
:rtype: float
"""
return javabridge.static_call(
"Lweka/clusterers/ClusterEvaluation;", "crossValidateModel",
"(Lweka/clusterers/DensityBasedClusterer;Lweka/core/Instances;ILjava/util/Random;)D",
clusterer.jobject, data.jobject, num_folds, rnd.jobject) | Cross-validates the clusterer and returns the loglikelihood.
:param clusterer: the clusterer instance to evaluate
:type clusterer: Clusterer
:param data: the data to evaluate on
:type data: Instances
:param num_folds: the number of folds
:type num_folds: int
:param rnd: the random number generator to use
:type rnd: Random
:return: the cross-validated loglikelihood
:rtype: float | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/clusterers.py#L362-L380 |
fracpete/python-weka-wrapper3 | python/weka/core/serialization.py | deepcopy | def deepcopy(obj):
"""
Creates a deep copy of the JavaObject (or derived class) or JB_Object.
:param obj: the object to create a copy of
:type obj: object
:return: the copy, None if failed to copy
:rtype: object
"""
if isinstance(obj, JavaObject):
wrapped = True
jobject = obj.jobject
else:
wrapped = False
jobject = obj
try:
serialized = javabridge.make_instance("weka/core/SerializedObject", "(Ljava/lang/Object;)V", jobject)
jcopy = javabridge.call(serialized, "getObject", "()Ljava/lang/Object;")
if wrapped:
jcopy = obj.__class__(jobject=jcopy)
return jcopy
except JavaException as e:
print("Failed to create copy of " + classes.get_classname(obj) + ": " + str(e))
return None | python | def deepcopy(obj):
"""
Creates a deep copy of the JavaObject (or derived class) or JB_Object.
:param obj: the object to create a copy of
:type obj: object
:return: the copy, None if failed to copy
:rtype: object
"""
if isinstance(obj, JavaObject):
wrapped = True
jobject = obj.jobject
else:
wrapped = False
jobject = obj
try:
serialized = javabridge.make_instance("weka/core/SerializedObject", "(Ljava/lang/Object;)V", jobject)
jcopy = javabridge.call(serialized, "getObject", "()Ljava/lang/Object;")
if wrapped:
jcopy = obj.__class__(jobject=jcopy)
return jcopy
except JavaException as e:
print("Failed to create copy of " + classes.get_classname(obj) + ": " + str(e))
return None | Creates a deep copy of the JavaObject (or derived class) or JB_Object.
:param obj: the object to create a copy of
:type obj: object
:return: the copy, None if failed to copy
:rtype: object | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/serialization.py#L27-L50 |
fracpete/python-weka-wrapper3 | python/weka/core/serialization.py | read_all | def read_all(filename):
"""
Reads the serialized objects from disk. Caller must wrap objects in appropriate Python wrapper classes.
:param filename: the file with the serialized objects
:type filename: str
:return: the list of JB_OBjects
:rtype: list
"""
array = javabridge.static_call(
"Lweka/core/SerializationHelper;", "readAll",
"(Ljava/lang/String;)[Ljava/lang/Object;",
filename)
if array is None:
return None
else:
return javabridge.get_env().get_object_array_elements(array) | python | def read_all(filename):
"""
Reads the serialized objects from disk. Caller must wrap objects in appropriate Python wrapper classes.
:param filename: the file with the serialized objects
:type filename: str
:return: the list of JB_OBjects
:rtype: list
"""
array = javabridge.static_call(
"Lweka/core/SerializationHelper;", "readAll",
"(Ljava/lang/String;)[Ljava/lang/Object;",
filename)
if array is None:
return None
else:
return javabridge.get_env().get_object_array_elements(array) | Reads the serialized objects from disk. Caller must wrap objects in appropriate Python wrapper classes.
:param filename: the file with the serialized objects
:type filename: str
:return: the list of JB_OBjects
:rtype: list | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/serialization.py#L68-L84 |
fracpete/python-weka-wrapper3 | python/weka/core/serialization.py | write | def write(filename, jobject):
"""
Serializes the object to disk. JavaObject instances get automatically unwrapped.
:param filename: the file to serialize the object to
:type filename: str
:param jobject: the object to serialize
:type jobject: JB_Object or JavaObject
"""
if isinstance(jobject, JavaObject):
jobject = jobject.jobject
javabridge.static_call(
"Lweka/core/SerializationHelper;", "write",
"(Ljava/lang/String;Ljava/lang/Object;)V",
filename, jobject) | python | def write(filename, jobject):
"""
Serializes the object to disk. JavaObject instances get automatically unwrapped.
:param filename: the file to serialize the object to
:type filename: str
:param jobject: the object to serialize
:type jobject: JB_Object or JavaObject
"""
if isinstance(jobject, JavaObject):
jobject = jobject.jobject
javabridge.static_call(
"Lweka/core/SerializationHelper;", "write",
"(Ljava/lang/String;Ljava/lang/Object;)V",
filename, jobject) | Serializes the object to disk. JavaObject instances get automatically unwrapped.
:param filename: the file to serialize the object to
:type filename: str
:param jobject: the object to serialize
:type jobject: JB_Object or JavaObject | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/serialization.py#L87-L101 |
fracpete/python-weka-wrapper3 | python/weka/associations.py | Item.decrease_frequency | def decrease_frequency(self, frequency=None):
"""
Decreases the frequency.
:param frequency: the frequency to decrease by, 1 if None
:type frequency: int
"""
if frequency is None:
javabridge.call(self.jobject, "decreaseFrequency", "()V")
else:
javabridge.call(self.jobject, "decreaseFrequency", "(I)V", frequency) | python | def decrease_frequency(self, frequency=None):
"""
Decreases the frequency.
:param frequency: the frequency to decrease by, 1 if None
:type frequency: int
"""
if frequency is None:
javabridge.call(self.jobject, "decreaseFrequency", "()V")
else:
javabridge.call(self.jobject, "decreaseFrequency", "(I)V", frequency) | Decreases the frequency.
:param frequency: the frequency to decrease by, 1 if None
:type frequency: int | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/associations.py#L84-L94 |
fracpete/python-weka-wrapper3 | python/weka/associations.py | Item.increase_frequency | def increase_frequency(self, frequency=None):
"""
Increases the frequency.
:param frequency: the frequency to increase by, 1 if None
:type frequency: int
"""
if frequency is None:
javabridge.call(self.jobject, "increaseFrequency", "()V")
else:
javabridge.call(self.jobject, "increaseFrequency", "(I)V", frequency) | python | def increase_frequency(self, frequency=None):
"""
Increases the frequency.
:param frequency: the frequency to increase by, 1 if None
:type frequency: int
"""
if frequency is None:
javabridge.call(self.jobject, "increaseFrequency", "()V")
else:
javabridge.call(self.jobject, "increaseFrequency", "(I)V", frequency) | Increases the frequency.
:param frequency: the frequency to increase by, 1 if None
:type frequency: int | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/associations.py#L96-L106 |
fracpete/python-weka-wrapper3 | python/weka/associations.py | AssociationRule.consequence | def consequence(self):
"""
Get the the consequence.
:return: the consequence, list of Item objects
:rtype: list
"""
items = javabridge.get_collection_wrapper(
javabridge.call(self.jobject, "getConsequence", "()Ljava/util/Collection;"))
result = []
for item in items:
result.append(Item(item))
return result | python | def consequence(self):
"""
Get the the consequence.
:return: the consequence, list of Item objects
:rtype: list
"""
items = javabridge.get_collection_wrapper(
javabridge.call(self.jobject, "getConsequence", "()Ljava/util/Collection;"))
result = []
for item in items:
result.append(Item(item))
return result | Get the the consequence.
:return: the consequence, list of Item objects
:rtype: list | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/associations.py#L275-L287 |
fracpete/python-weka-wrapper3 | python/weka/associations.py | Associator.can_produce_rules | def can_produce_rules(self):
"""
Checks whether association rules can be generated.
:return: whether scheme implements AssociationRulesProducer interface and
association rules can be generated
:rtype: bool
"""
if not self.check_type(self.jobject, "weka.associations.AssociationRulesProducer"):
return False
return javabridge.call(self.jobject, "canProduceRules", "()Z") | python | def can_produce_rules(self):
"""
Checks whether association rules can be generated.
:return: whether scheme implements AssociationRulesProducer interface and
association rules can be generated
:rtype: bool
"""
if not self.check_type(self.jobject, "weka.associations.AssociationRulesProducer"):
return False
return javabridge.call(self.jobject, "canProduceRules", "()Z") | Checks whether association rules can be generated.
:return: whether scheme implements AssociationRulesProducer interface and
association rules can be generated
:rtype: bool | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/associations.py#L552-L562 |
fracpete/python-weka-wrapper3 | python/weka/associations.py | Associator.association_rules | def association_rules(self):
"""
Returns association rules that were generated. Only if implements AssociationRulesProducer.
:return: the association rules that were generated
:rtype: AssociationRules
"""
if not self.check_type(self.jobject, "weka.associations.AssociationRulesProducer"):
return None
return AssociationRules(
javabridge.call(self.jobject, "getAssociationRules", "()Lweka/associations/AssociationRules;")) | python | def association_rules(self):
"""
Returns association rules that were generated. Only if implements AssociationRulesProducer.
:return: the association rules that were generated
:rtype: AssociationRules
"""
if not self.check_type(self.jobject, "weka.associations.AssociationRulesProducer"):
return None
return AssociationRules(
javabridge.call(self.jobject, "getAssociationRules", "()Lweka/associations/AssociationRules;")) | Returns association rules that were generated. Only if implements AssociationRulesProducer.
:return: the association rules that were generated
:rtype: AssociationRules | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/associations.py#L564-L574 |
fracpete/python-weka-wrapper3 | python/weka/associations.py | Associator.rule_metric_names | def rule_metric_names(self):
"""
Returns the rule metric names of the association rules. Only if implements AssociationRulesProducer.
:return: the metric names
:rtype: list
"""
if not self.check_type(self.jobject, "weka.associations.AssociationRulesProducer"):
return None
return string_array_to_list(
javabridge.call(self.jobject, "getRuleMetricNames", "()[Ljava/lang/String;")) | python | def rule_metric_names(self):
"""
Returns the rule metric names of the association rules. Only if implements AssociationRulesProducer.
:return: the metric names
:rtype: list
"""
if not self.check_type(self.jobject, "weka.associations.AssociationRulesProducer"):
return None
return string_array_to_list(
javabridge.call(self.jobject, "getRuleMetricNames", "()[Ljava/lang/String;")) | Returns the rule metric names of the association rules. Only if implements AssociationRulesProducer.
:return: the metric names
:rtype: list | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/associations.py#L577-L587 |
fracpete/python-weka-wrapper3 | python/weka/core/converters.py | loader_for_file | def loader_for_file(filename):
"""
Returns a Loader that can load the specified file, based on the file extension. None if failed to determine.
:param filename: the filename to get the loader for
:type filename: str
:return: the assoicated loader instance or None if none found
:rtype: Loader
"""
loader = javabridge.static_call(
"weka/core/converters/ConverterUtils", "getLoaderForFile",
"(Ljava/lang/String;)Lweka/core/converters/AbstractFileLoader;", filename)
if loader is None:
return None
else:
return Loader(jobject=loader) | python | def loader_for_file(filename):
"""
Returns a Loader that can load the specified file, based on the file extension. None if failed to determine.
:param filename: the filename to get the loader for
:type filename: str
:return: the assoicated loader instance or None if none found
:rtype: Loader
"""
loader = javabridge.static_call(
"weka/core/converters/ConverterUtils", "getLoaderForFile",
"(Ljava/lang/String;)Lweka/core/converters/AbstractFileLoader;", filename)
if loader is None:
return None
else:
return Loader(jobject=loader) | Returns a Loader that can load the specified file, based on the file extension. None if failed to determine.
:param filename: the filename to get the loader for
:type filename: str
:return: the assoicated loader instance or None if none found
:rtype: Loader | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/converters.py#L228-L243 |
fracpete/python-weka-wrapper3 | python/weka/core/converters.py | saver_for_file | def saver_for_file(filename):
"""
Returns a Saver that can load the specified file, based on the file extension. None if failed to determine.
:param filename: the filename to get the saver for
:type filename: str
:return: the associated saver instance or None if none found
:rtype: Saver
"""
saver = javabridge.static_call(
"weka/core/converters/ConverterUtils", "getSaverForFile",
"(Ljava/lang/String;)Lweka/core/converters/AbstractFileSaver;", filename)
if saver is None:
return None
else:
return Saver(jobject=saver) | python | def saver_for_file(filename):
"""
Returns a Saver that can load the specified file, based on the file extension. None if failed to determine.
:param filename: the filename to get the saver for
:type filename: str
:return: the associated saver instance or None if none found
:rtype: Saver
"""
saver = javabridge.static_call(
"weka/core/converters/ConverterUtils", "getSaverForFile",
"(Ljava/lang/String;)Lweka/core/converters/AbstractFileSaver;", filename)
if saver is None:
return None
else:
return Saver(jobject=saver) | Returns a Saver that can load the specified file, based on the file extension. None if failed to determine.
:param filename: the filename to get the saver for
:type filename: str
:return: the associated saver instance or None if none found
:rtype: Saver | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/converters.py#L262-L277 |
fracpete/python-weka-wrapper3 | python/weka/core/converters.py | save_any_file | def save_any_file(data, filename):
"""
Determines a Saver based on the the file extension. Returns whether successfully saved.
:param filename: the name of the file to save
:type filename: str
:param data: the data to save
:type data: Instances
:return: whether successfully saved
:rtype: bool
"""
saver = saver_for_file(filename)
if saver is None:
return False
else:
saver.save_file(data, filename)
return True | python | def save_any_file(data, filename):
"""
Determines a Saver based on the the file extension. Returns whether successfully saved.
:param filename: the name of the file to save
:type filename: str
:param data: the data to save
:type data: Instances
:return: whether successfully saved
:rtype: bool
"""
saver = saver_for_file(filename)
if saver is None:
return False
else:
saver.save_file(data, filename)
return True | Determines a Saver based on the the file extension. Returns whether successfully saved.
:param filename: the name of the file to save
:type filename: str
:param data: the data to save
:type data: Instances
:return: whether successfully saved
:rtype: bool | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/converters.py#L280-L296 |
fracpete/python-weka-wrapper3 | python/weka/core/converters.py | ndarray_to_instances | def ndarray_to_instances(array, relation, att_template="Att-#", att_list=None):
"""
Converts the numpy matrix into an Instances object and returns it.
:param array: the numpy ndarray to convert
:type array: numpy.darray
:param relation: the name of the dataset
:type relation: str
:param att_template: the prefix to use for the attribute names, "#" is the 1-based index,
"!" is the 0-based index, "@" the relation name
:type att_template: str
:param att_list: the list of attribute names to use
:type att_list: list
:return: the generated instances object
:rtype: Instances
"""
if len(numpy.shape(array)) != 2:
raise Exception("Number of array dimensions must be 2!")
rows, cols = numpy.shape(array)
# header
atts = []
if att_list is not None:
if len(att_list) != cols:
raise Exception(
"Number columns and provided attribute names differ: " + str(cols) + " != " + len(att_list))
for name in att_list:
att = Attribute.create_numeric(name)
atts.append(att)
else:
for i in range(cols):
name = att_template.replace("#", str(i+1)).replace("!", str(i)).replace("@", relation)
att = Attribute.create_numeric(name)
atts.append(att)
result = Instances.create_instances(relation, atts, rows)
# data
for i in range(rows):
inst = Instance.create_instance(array[i])
result.add_instance(inst)
return result | python | def ndarray_to_instances(array, relation, att_template="Att-#", att_list=None):
"""
Converts the numpy matrix into an Instances object and returns it.
:param array: the numpy ndarray to convert
:type array: numpy.darray
:param relation: the name of the dataset
:type relation: str
:param att_template: the prefix to use for the attribute names, "#" is the 1-based index,
"!" is the 0-based index, "@" the relation name
:type att_template: str
:param att_list: the list of attribute names to use
:type att_list: list
:return: the generated instances object
:rtype: Instances
"""
if len(numpy.shape(array)) != 2:
raise Exception("Number of array dimensions must be 2!")
rows, cols = numpy.shape(array)
# header
atts = []
if att_list is not None:
if len(att_list) != cols:
raise Exception(
"Number columns and provided attribute names differ: " + str(cols) + " != " + len(att_list))
for name in att_list:
att = Attribute.create_numeric(name)
atts.append(att)
else:
for i in range(cols):
name = att_template.replace("#", str(i+1)).replace("!", str(i)).replace("@", relation)
att = Attribute.create_numeric(name)
atts.append(att)
result = Instances.create_instances(relation, atts, rows)
# data
for i in range(rows):
inst = Instance.create_instance(array[i])
result.add_instance(inst)
return result | Converts the numpy matrix into an Instances object and returns it.
:param array: the numpy ndarray to convert
:type array: numpy.darray
:param relation: the name of the dataset
:type relation: str
:param att_template: the prefix to use for the attribute names, "#" is the 1-based index,
"!" is the 0-based index, "@" the relation name
:type att_template: str
:param att_list: the list of attribute names to use
:type att_list: list
:return: the generated instances object
:rtype: Instances | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/converters.py#L299-L340 |
fracpete/python-weka-wrapper3 | python/weka/core/converters.py | Loader.load_file | def load_file(self, dfile, incremental=False):
"""
Loads the specified file and returns the Instances object.
In case of incremental loading, only the structure.
:param dfile: the file to load
:type dfile: str
:param incremental: whether to load the dataset incrementally
:type incremental: bool
:return: the full dataset or the header (if incremental)
:rtype: Instances
:raises Exception: if the file does not exist
"""
self.enforce_type(self.jobject, "weka.core.converters.FileSourcedConverter")
self.incremental = incremental
if not javabridge.is_instance_of(dfile, "Ljava/io/File;"):
dfile = javabridge.make_instance(
"Ljava/io/File;", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(str(dfile)))
javabridge.call(self.jobject, "reset", "()V")
# check whether file exists, otherwise previously set file gets loaded again
sfile = javabridge.to_string(dfile)
if not os.path.exists(sfile):
raise Exception("Dataset file does not exist: " + str(sfile))
javabridge.call(self.jobject, "setFile", "(Ljava/io/File;)V", dfile)
if incremental:
self.structure = Instances(javabridge.call(self.jobject, "getStructure", "()Lweka/core/Instances;"))
return self.structure
else:
return Instances(javabridge.call(self.jobject, "getDataSet", "()Lweka/core/Instances;")) | python | def load_file(self, dfile, incremental=False):
"""
Loads the specified file and returns the Instances object.
In case of incremental loading, only the structure.
:param dfile: the file to load
:type dfile: str
:param incremental: whether to load the dataset incrementally
:type incremental: bool
:return: the full dataset or the header (if incremental)
:rtype: Instances
:raises Exception: if the file does not exist
"""
self.enforce_type(self.jobject, "weka.core.converters.FileSourcedConverter")
self.incremental = incremental
if not javabridge.is_instance_of(dfile, "Ljava/io/File;"):
dfile = javabridge.make_instance(
"Ljava/io/File;", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(str(dfile)))
javabridge.call(self.jobject, "reset", "()V")
# check whether file exists, otherwise previously set file gets loaded again
sfile = javabridge.to_string(dfile)
if not os.path.exists(sfile):
raise Exception("Dataset file does not exist: " + str(sfile))
javabridge.call(self.jobject, "setFile", "(Ljava/io/File;)V", dfile)
if incremental:
self.structure = Instances(javabridge.call(self.jobject, "getStructure", "()Lweka/core/Instances;"))
return self.structure
else:
return Instances(javabridge.call(self.jobject, "getDataSet", "()Lweka/core/Instances;")) | Loads the specified file and returns the Instances object.
In case of incremental loading, only the structure.
:param dfile: the file to load
:type dfile: str
:param incremental: whether to load the dataset incrementally
:type incremental: bool
:return: the full dataset or the header (if incremental)
:rtype: Instances
:raises Exception: if the file does not exist | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/converters.py#L60-L88 |
fracpete/python-weka-wrapper3 | python/weka/core/converters.py | Loader.load_url | def load_url(self, url, incremental=False):
"""
Loads the specified URL and returns the Instances object.
In case of incremental loading, only the structure.
:param url: the URL to load the data from
:type url: str
:param incremental: whether to load the dataset incrementally
:type incremental: bool
:return: the full dataset or the header (if incremental)
:rtype: Instances
"""
self.enforce_type(self.jobject, "weka.core.converters.URLSourcedLoader")
self.incremental = incremental
javabridge.call(self.jobject, "reset", "()V")
javabridge.call(self.jobject, "setURL", "(Ljava/lang/String;)V", str(url))
if incremental:
self.structure = Instances(javabridge.call(self.jobject, "getStructure", "()Lweka/core/Instances;"))
return self.structure
else:
return Instances(javabridge.call(self.jobject, "getDataSet", "()Lweka/core/Instances;")) | python | def load_url(self, url, incremental=False):
"""
Loads the specified URL and returns the Instances object.
In case of incremental loading, only the structure.
:param url: the URL to load the data from
:type url: str
:param incremental: whether to load the dataset incrementally
:type incremental: bool
:return: the full dataset or the header (if incremental)
:rtype: Instances
"""
self.enforce_type(self.jobject, "weka.core.converters.URLSourcedLoader")
self.incremental = incremental
javabridge.call(self.jobject, "reset", "()V")
javabridge.call(self.jobject, "setURL", "(Ljava/lang/String;)V", str(url))
if incremental:
self.structure = Instances(javabridge.call(self.jobject, "getStructure", "()Lweka/core/Instances;"))
return self.structure
else:
return Instances(javabridge.call(self.jobject, "getDataSet", "()Lweka/core/Instances;")) | Loads the specified URL and returns the Instances object.
In case of incremental loading, only the structure.
:param url: the URL to load the data from
:type url: str
:param incremental: whether to load the dataset incrementally
:type incremental: bool
:return: the full dataset or the header (if incremental)
:rtype: Instances | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/converters.py#L90-L110 |
fracpete/python-weka-wrapper3 | python/weka/core/converters.py | TextDirectoryLoader.load | def load(self):
"""
Loads the text files from the specified directory and returns the Instances object.
In case of incremental loading, only the structure.
:return: the full dataset or the header (if incremental)
:rtype: Instances
"""
javabridge.call(self.jobject, "reset", "()V")
return Instances(javabridge.call(self.jobject, "getDataSet", "()Lweka/core/Instances;")) | python | def load(self):
"""
Loads the text files from the specified directory and returns the Instances object.
In case of incremental loading, only the structure.
:return: the full dataset or the header (if incremental)
:rtype: Instances
"""
javabridge.call(self.jobject, "reset", "()V")
return Instances(javabridge.call(self.jobject, "getDataSet", "()Lweka/core/Instances;")) | Loads the text files from the specified directory and returns the Instances object.
In case of incremental loading, only the structure.
:return: the full dataset or the header (if incremental)
:rtype: Instances | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/converters.py#L168-L177 |
fracpete/python-weka-wrapper3 | python/weka/core/converters.py | Saver.save_file | def save_file(self, data, dfile):
"""
Saves the Instances object in the specified file.
:param data: the data to save
:type data: Instances
:param dfile: the file to save the data to
:type dfile: str
"""
self.enforce_type(self.jobject, "weka.core.converters.FileSourcedConverter")
if not javabridge.is_instance_of(dfile, "Ljava/io/File;"):
dfile = javabridge.make_instance(
"Ljava/io/File;", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(str(dfile)))
javabridge.call(self.jobject, "setFile", "(Ljava/io/File;)V", dfile)
javabridge.call(self.jobject, "setInstances", "(Lweka/core/Instances;)V", data.jobject)
javabridge.call(self.jobject, "writeBatch", "()V") | python | def save_file(self, data, dfile):
"""
Saves the Instances object in the specified file.
:param data: the data to save
:type data: Instances
:param dfile: the file to save the data to
:type dfile: str
"""
self.enforce_type(self.jobject, "weka.core.converters.FileSourcedConverter")
if not javabridge.is_instance_of(dfile, "Ljava/io/File;"):
dfile = javabridge.make_instance(
"Ljava/io/File;", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(str(dfile)))
javabridge.call(self.jobject, "setFile", "(Ljava/io/File;)V", dfile)
javabridge.call(self.jobject, "setInstances", "(Lweka/core/Instances;)V", data.jobject)
javabridge.call(self.jobject, "writeBatch", "()V") | Saves the Instances object in the specified file.
:param data: the data to save
:type data: Instances
:param dfile: the file to save the data to
:type dfile: str | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/converters.py#L210-L225 |
fracpete/python-weka-wrapper3 | python/weka/core/typeconv.py | string_array_to_list | def string_array_to_list(a):
"""
Turns the Java string array into Python unicode string list.
:param a: the string array to convert
:type a: JB_Object
:return: the string list
:rtype: list
"""
result = []
length = javabridge.get_env().get_array_length(a)
wrapped = javabridge.get_env().get_object_array_elements(a)
for i in range(length):
result.append(javabridge.get_env().get_string(wrapped[i]))
return result | python | def string_array_to_list(a):
"""
Turns the Java string array into Python unicode string list.
:param a: the string array to convert
:type a: JB_Object
:return: the string list
:rtype: list
"""
result = []
length = javabridge.get_env().get_array_length(a)
wrapped = javabridge.get_env().get_object_array_elements(a)
for i in range(length):
result.append(javabridge.get_env().get_string(wrapped[i]))
return result | Turns the Java string array into Python unicode string list.
:param a: the string array to convert
:type a: JB_Object
:return: the string list
:rtype: list | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/typeconv.py#L25-L39 |
fracpete/python-weka-wrapper3 | python/weka/core/typeconv.py | string_list_to_array | def string_list_to_array(l):
"""
Turns a Python unicode string list into a Java String array.
:param l: the string list
:type: list
:rtype: java string array
:return: JB_Object
"""
result = javabridge.get_env().make_object_array(len(l), javabridge.get_env().find_class("java/lang/String"))
for i in range(len(l)):
javabridge.get_env().set_object_array_element(result, i, javabridge.get_env().new_string_utf(l[i]))
return result | python | def string_list_to_array(l):
"""
Turns a Python unicode string list into a Java String array.
:param l: the string list
:type: list
:rtype: java string array
:return: JB_Object
"""
result = javabridge.get_env().make_object_array(len(l), javabridge.get_env().find_class("java/lang/String"))
for i in range(len(l)):
javabridge.get_env().set_object_array_element(result, i, javabridge.get_env().new_string_utf(l[i]))
return result | Turns a Python unicode string list into a Java String array.
:param l: the string list
:type: list
:rtype: java string array
:return: JB_Object | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/typeconv.py#L42-L54 |
fracpete/python-weka-wrapper3 | python/weka/core/typeconv.py | double_matrix_to_ndarray | def double_matrix_to_ndarray(m):
"""
Turns the Java matrix (2-dim array) of doubles into a numpy 2-dim array.
:param m: the double matrix
:type: JB_Object
:return: Numpy array
:rtype: numpy.darray
"""
rows = javabridge.get_env().get_object_array_elements(m)
num_rows = len(rows)
num_cols = javabridge.get_env().get_array_length(rows[0])
result = numpy.zeros(num_rows * num_cols).reshape((num_rows, num_cols))
i = 0
for row in rows:
elements = javabridge.get_env().get_double_array_elements(row)
n = 0
for element in elements:
result[i][n] = element
n += 1
i += 1
return result | python | def double_matrix_to_ndarray(m):
"""
Turns the Java matrix (2-dim array) of doubles into a numpy 2-dim array.
:param m: the double matrix
:type: JB_Object
:return: Numpy array
:rtype: numpy.darray
"""
rows = javabridge.get_env().get_object_array_elements(m)
num_rows = len(rows)
num_cols = javabridge.get_env().get_array_length(rows[0])
result = numpy.zeros(num_rows * num_cols).reshape((num_rows, num_cols))
i = 0
for row in rows:
elements = javabridge.get_env().get_double_array_elements(row)
n = 0
for element in elements:
result[i][n] = element
n += 1
i += 1
return result | Turns the Java matrix (2-dim array) of doubles into a numpy 2-dim array.
:param m: the double matrix
:type: JB_Object
:return: Numpy array
:rtype: numpy.darray | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/typeconv.py#L57-L78 |
fracpete/python-weka-wrapper3 | python/weka/core/typeconv.py | enumeration_to_list | def enumeration_to_list(enm):
"""
Turns the java.util.Enumeration into a list.
:param enm: the enumeration to convert
:type enm: JB_Object
:return: the list
:rtype: list
"""
result = []
while javabridge.call(enm, "hasMoreElements", "()Z"):
result.append(javabridge.call(enm, "nextElement", "()Ljava/lang/Object;"))
return result | python | def enumeration_to_list(enm):
"""
Turns the java.util.Enumeration into a list.
:param enm: the enumeration to convert
:type enm: JB_Object
:return: the list
:rtype: list
"""
result = []
while javabridge.call(enm, "hasMoreElements", "()Z"):
result.append(javabridge.call(enm, "nextElement", "()Ljava/lang/Object;"))
return result | Turns the java.util.Enumeration into a list.
:param enm: the enumeration to convert
:type enm: JB_Object
:return: the list
:rtype: list | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/typeconv.py#L81-L93 |
fracpete/python-weka-wrapper3 | python/weka/attribute_selection.py | main | def main():
"""
Runs attribute selection from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Performs attribute selection from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-X", metavar="heap", dest="heap", help="max heap size for jvm, e.g., 512m")
parser.add_argument("-i", metavar="input", dest="input", required=True, help="input file")
parser.add_argument("-c", metavar="class index", dest="classindex", help="1-based class attribute index")
parser.add_argument("-s", metavar="search", dest="search", help="search method, classname and options")
parser.add_argument("-x", metavar="num folds", dest="numfolds", help="number of folds")
parser.add_argument("-n", metavar="seed", dest="seed", help="the seed value for randomization")
parser.add_argument("evaluator", help="evaluator classname, e.g., weka.attributeSelection.CfsSubsetEval")
parser.add_argument("option", nargs=argparse.REMAINDER, help="additional evaluator options")
parsed = parser.parse_args()
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
params = []
if parsed.input is not None:
params.extend(["-i", parsed.input])
if parsed.classindex is not None:
params.extend(["-c", parsed.classindex])
if parsed.search is not None:
params.extend(["-s", parsed.search])
if parsed.numfolds is not None:
params.extend(["-x", parsed.numfolds])
if parsed.seed is not None:
params.extend(["-n", parsed.seed])
jvm.start(jars, max_heap_size=parsed.heap, packages=True)
logger.debug("Commandline: " + join_options(sys.argv[1:]))
try:
evaluation = ASEvaluation(classname=parsed.evaluator)
if len(parsed.option) > 0:
evaluation.options = parsed.option
print(AttributeSelection.attribute_selection(evaluation, params))
except Exception as e:
print(e)
finally:
jvm.stop() | python | def main():
"""
Runs attribute selection from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Performs attribute selection from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-X", metavar="heap", dest="heap", help="max heap size for jvm, e.g., 512m")
parser.add_argument("-i", metavar="input", dest="input", required=True, help="input file")
parser.add_argument("-c", metavar="class index", dest="classindex", help="1-based class attribute index")
parser.add_argument("-s", metavar="search", dest="search", help="search method, classname and options")
parser.add_argument("-x", metavar="num folds", dest="numfolds", help="number of folds")
parser.add_argument("-n", metavar="seed", dest="seed", help="the seed value for randomization")
parser.add_argument("evaluator", help="evaluator classname, e.g., weka.attributeSelection.CfsSubsetEval")
parser.add_argument("option", nargs=argparse.REMAINDER, help="additional evaluator options")
parsed = parser.parse_args()
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
params = []
if parsed.input is not None:
params.extend(["-i", parsed.input])
if parsed.classindex is not None:
params.extend(["-c", parsed.classindex])
if parsed.search is not None:
params.extend(["-s", parsed.search])
if parsed.numfolds is not None:
params.extend(["-x", parsed.numfolds])
if parsed.seed is not None:
params.extend(["-n", parsed.seed])
jvm.start(jars, max_heap_size=parsed.heap, packages=True)
logger.debug("Commandline: " + join_options(sys.argv[1:]))
try:
evaluation = ASEvaluation(classname=parsed.evaluator)
if len(parsed.option) > 0:
evaluation.options = parsed.option
print(AttributeSelection.attribute_selection(evaluation, params))
except Exception as e:
print(e)
finally:
jvm.stop() | Runs attribute selection from the command-line. Calls JVM start/stop automatically.
Use -h to see all options. | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/attribute_selection.py#L310-L354 |
fracpete/python-weka-wrapper3 | python/weka/attribute_selection.py | ASSearch.search | def search(self, evaluation, data):
"""
Performs the search and returns the indices of the selected attributes.
:param evaluation: the evaluation algorithm to use
:type evaluation: ASEvaluation
:param data: the data to use
:type data: Instances
:return: the selected attributes (0-based indices)
:rtype: ndarray
"""
array = javabridge.call(
self.jobject, "search", "(Lweka/attributeSelection/ASEvaluation;Lweka/core/Instances;)[I",
evaluation.jobject, data.jobject)
if array is None:
return None
else:
javabridge.get_env().get_int_array_elements(array) | python | def search(self, evaluation, data):
"""
Performs the search and returns the indices of the selected attributes.
:param evaluation: the evaluation algorithm to use
:type evaluation: ASEvaluation
:param data: the data to use
:type data: Instances
:return: the selected attributes (0-based indices)
:rtype: ndarray
"""
array = javabridge.call(
self.jobject, "search", "(Lweka/attributeSelection/ASEvaluation;Lweka/core/Instances;)[I",
evaluation.jobject, data.jobject)
if array is None:
return None
else:
javabridge.get_env().get_int_array_elements(array) | Performs the search and returns the indices of the selected attributes.
:param evaluation: the evaluation algorithm to use
:type evaluation: ASEvaluation
:param data: the data to use
:type data: Instances
:return: the selected attributes (0-based indices)
:rtype: ndarray | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/attribute_selection.py#L54-L71 |
fracpete/python-weka-wrapper3 | python/weka/attribute_selection.py | ASEvaluation.post_process | def post_process(self, indices):
"""
Post-processes the evaluator with the selected attribute indices.
:param indices: the attribute indices list to use
:type indices: ndarray
:return: the processed indices
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "postProcess", "([I)[I", indices)
if array is None:
return None
else:
return javabridge.get_env().get_int_array_elements(array) | python | def post_process(self, indices):
"""
Post-processes the evaluator with the selected attribute indices.
:param indices: the attribute indices list to use
:type indices: ndarray
:return: the processed indices
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "postProcess", "([I)[I", indices)
if array is None:
return None
else:
return javabridge.get_env().get_int_array_elements(array) | Post-processes the evaluator with the selected attribute indices.
:param indices: the attribute indices list to use
:type indices: ndarray
:return: the processed indices
:rtype: ndarray | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/attribute_selection.py#L114-L127 |
fracpete/python-weka-wrapper3 | python/weka/attribute_selection.py | AttributeSelection.selected_attributes | def selected_attributes(self):
"""
Returns the selected attributes from the last run.
:return: the Numpy array of 0-based indices
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "selectedAttributes", "()[I")
if array is None:
return None
else:
return javabridge.get_env().get_int_array_elements(array) | python | def selected_attributes(self):
"""
Returns the selected attributes from the last run.
:return: the Numpy array of 0-based indices
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "selectedAttributes", "()[I")
if array is None:
return None
else:
return javabridge.get_env().get_int_array_elements(array) | Returns the selected attributes from the last run.
:return: the Numpy array of 0-based indices
:rtype: ndarray | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/attribute_selection.py#L215-L226 |
fracpete/python-weka-wrapper3 | python/weka/attribute_selection.py | AttributeSelection.ranked_attributes | def ranked_attributes(self):
"""
Returns the matrix of ranked attributes from the last run.
:return: the Numpy matrix
:rtype: ndarray
"""
matrix = javabridge.call(self.jobject, "rankedAttributes", "()[[D")
if matrix is None:
return None
else:
return typeconv.double_matrix_to_ndarray(matrix) | python | def ranked_attributes(self):
"""
Returns the matrix of ranked attributes from the last run.
:return: the Numpy matrix
:rtype: ndarray
"""
matrix = javabridge.call(self.jobject, "rankedAttributes", "()[[D")
if matrix is None:
return None
else:
return typeconv.double_matrix_to_ndarray(matrix) | Returns the matrix of ranked attributes from the last run.
:return: the Numpy matrix
:rtype: ndarray | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/attribute_selection.py#L259-L270 |
fracpete/python-weka-wrapper3 | python/weka/attribute_selection.py | AttributeSelection.reduce_dimensionality | def reduce_dimensionality(self, data):
"""
Reduces the dimensionality of the provided Instance or Instances object.
:param data: the data to process
:type data: Instances
:return: the reduced dataset
:rtype: Instances
"""
if type(data) is Instance:
return Instance(
javabridge.call(
self.jobject, "reduceDimensionality",
"(Lweka/core/Instance;)Lweka/core/Instance;", data.jobject))
else:
return Instances(
javabridge.call(
self.jobject, "reduceDimensionality",
"(Lweka/core/Instances;)Lweka/core/Instances;", data.jobject)) | python | def reduce_dimensionality(self, data):
"""
Reduces the dimensionality of the provided Instance or Instances object.
:param data: the data to process
:type data: Instances
:return: the reduced dataset
:rtype: Instances
"""
if type(data) is Instance:
return Instance(
javabridge.call(
self.jobject, "reduceDimensionality",
"(Lweka/core/Instance;)Lweka/core/Instance;", data.jobject))
else:
return Instances(
javabridge.call(
self.jobject, "reduceDimensionality",
"(Lweka/core/Instances;)Lweka/core/Instances;", data.jobject)) | Reduces the dimensionality of the provided Instance or Instances object.
:param data: the data to process
:type data: Instances
:return: the reduced dataset
:rtype: Instances | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/attribute_selection.py#L272-L290 |
fracpete/python-weka-wrapper3 | python/weka/plot/classifiers.py | generate_thresholdcurve_data | def generate_thresholdcurve_data(evaluation, class_index):
"""
Generates the threshold curve data from the evaluation object's predictions.
:param evaluation: the evaluation to obtain the predictions from
:type evaluation: Evaluation
:param class_index: the 0-based index of the class-label to create the plot for
:type class_index: int
:return: the generated threshold curve data
:rtype: Instances
"""
jtc = JavaObject.new_instance("weka.classifiers.evaluation.ThresholdCurve")
pred = javabridge.call(evaluation.jobject, "predictions", "()Ljava/util/ArrayList;")
result = Instances(
javabridge.call(jtc, "getCurve", "(Ljava/util/ArrayList;I)Lweka/core/Instances;", pred, class_index))
return result | python | def generate_thresholdcurve_data(evaluation, class_index):
"""
Generates the threshold curve data from the evaluation object's predictions.
:param evaluation: the evaluation to obtain the predictions from
:type evaluation: Evaluation
:param class_index: the 0-based index of the class-label to create the plot for
:type class_index: int
:return: the generated threshold curve data
:rtype: Instances
"""
jtc = JavaObject.new_instance("weka.classifiers.evaluation.ThresholdCurve")
pred = javabridge.call(evaluation.jobject, "predictions", "()Ljava/util/ArrayList;")
result = Instances(
javabridge.call(jtc, "getCurve", "(Ljava/util/ArrayList;I)Lweka/core/Instances;", pred, class_index))
return result | Generates the threshold curve data from the evaluation object's predictions.
:param evaluation: the evaluation to obtain the predictions from
:type evaluation: Evaluation
:param class_index: the 0-based index of the class-label to create the plot for
:type class_index: int
:return: the generated threshold curve data
:rtype: Instances | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/plot/classifiers.py#L101-L116 |
fracpete/python-weka-wrapper3 | python/weka/plot/classifiers.py | get_thresholdcurve_data | def get_thresholdcurve_data(data, xname, yname):
"""
Retrieves x and y columns from of the data generated by the weka.classifiers.evaluation.ThresholdCurve
class.
:param data: the threshold curve data
:type data: Instances
:param xname: the name of the X column
:type xname: str
:param yname: the name of the Y column
:type yname: str
:return: tuple of x and y arrays
:rtype: tuple
"""
xi = data.attribute_by_name(xname).index
yi = data.attribute_by_name(yname).index
x = []
y = []
for i in range(data.num_instances):
inst = data.get_instance(i)
x.append(inst.get_value(xi))
y.append(inst.get_value(yi))
return x, y | python | def get_thresholdcurve_data(data, xname, yname):
"""
Retrieves x and y columns from of the data generated by the weka.classifiers.evaluation.ThresholdCurve
class.
:param data: the threshold curve data
:type data: Instances
:param xname: the name of the X column
:type xname: str
:param yname: the name of the Y column
:type yname: str
:return: tuple of x and y arrays
:rtype: tuple
"""
xi = data.attribute_by_name(xname).index
yi = data.attribute_by_name(yname).index
x = []
y = []
for i in range(data.num_instances):
inst = data.get_instance(i)
x.append(inst.get_value(xi))
y.append(inst.get_value(yi))
return x, y | Retrieves x and y columns from of the data generated by the weka.classifiers.evaluation.ThresholdCurve
class.
:param data: the threshold curve data
:type data: Instances
:param xname: the name of the X column
:type xname: str
:param yname: the name of the Y column
:type yname: str
:return: tuple of x and y arrays
:rtype: tuple | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/plot/classifiers.py#L119-L141 |
fracpete/python-weka-wrapper3 | python/weka/plot/classifiers.py | plot_roc | def plot_roc(evaluation, class_index=None, title=None, key_loc="lower right", outfile=None, wait=True):
"""
Plots the ROC (receiver operator characteristics) curve for the given predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param evaluation: the evaluation to obtain the predictions from
:type evaluation: Evaluation
:param class_index: the list of 0-based indices of the class-labels to create the plot for
:type class_index: list
:param title: an optional title
:type title: str
:param key_loc: the position string for the key
:type key_loc: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
if class_index is None:
class_index = [0]
ax = None
for cindex in class_index:
data = generate_thresholdcurve_data(evaluation, cindex)
head = evaluation.header
area = get_auc(data)
x, y = get_thresholdcurve_data(data, "False Positive Rate", "True Positive Rate")
if ax is None:
fig, ax = plt.subplots()
ax.set_xlabel("False Positive Rate")
ax.set_ylabel("True Positive Rate")
if title is None:
title = "ROC"
ax.set_title(title)
ax.grid(True)
fig.canvas.set_window_title(title)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plot_label = head.class_attribute.value(cindex) + " (AUC: %0.4f)" % area
ax.plot(x, y, label=plot_label)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
plt.draw()
plt.legend(loc=key_loc, shadow=True)
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show() | python | def plot_roc(evaluation, class_index=None, title=None, key_loc="lower right", outfile=None, wait=True):
"""
Plots the ROC (receiver operator characteristics) curve for the given predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param evaluation: the evaluation to obtain the predictions from
:type evaluation: Evaluation
:param class_index: the list of 0-based indices of the class-labels to create the plot for
:type class_index: list
:param title: an optional title
:type title: str
:param key_loc: the position string for the key
:type key_loc: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
if class_index is None:
class_index = [0]
ax = None
for cindex in class_index:
data = generate_thresholdcurve_data(evaluation, cindex)
head = evaluation.header
area = get_auc(data)
x, y = get_thresholdcurve_data(data, "False Positive Rate", "True Positive Rate")
if ax is None:
fig, ax = plt.subplots()
ax.set_xlabel("False Positive Rate")
ax.set_ylabel("True Positive Rate")
if title is None:
title = "ROC"
ax.set_title(title)
ax.grid(True)
fig.canvas.set_window_title(title)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plot_label = head.class_attribute.value(cindex) + " (AUC: %0.4f)" % area
ax.plot(x, y, label=plot_label)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
plt.draw()
plt.legend(loc=key_loc, shadow=True)
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show() | Plots the ROC (receiver operator characteristics) curve for the given predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param evaluation: the evaluation to obtain the predictions from
:type evaluation: Evaluation
:param class_index: the list of 0-based indices of the class-labels to create the plot for
:type class_index: list
:param title: an optional title
:type title: str
:param key_loc: the position string for the key
:type key_loc: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/plot/classifiers.py#L170-L219 |
fracpete/python-weka-wrapper3 | python/weka/plot/classifiers.py | plot_learning_curve | def plot_learning_curve(classifiers, train, test=None, increments=100, metric="percent_correct",
title="Learning curve", label_template="[#] @ $", key_loc="lower right",
outfile=None, wait=True):
"""
Plots a learning curve.
:param classifiers: list of Classifier template objects
:type classifiers: list of Classifier
:param train: dataset to use for the building the classifier, used for evaluating it test set None
:type train: Instances
:param test: optional dataset (or list of datasets) to use for the testing the built classifiers
:type test: list or Instances
:param increments: the increments (>= 1: # of instances, <1: percentage of dataset)
:type increments: float
:param metric: the name of the numeric metric to plot (Evaluation.<metric>)
:type metric: str
:param title: the title for the plot
:type title: str
:param label_template: the template for the label in the plot
(#: 1-based index of classifier, @: full classname, !: simple classname,
$: options, *: 1-based index of test set)
:type label_template: str
:param key_loc: the location string for the key
:type key_loc: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
if not train.has_class():
logger.error("Training set has no class attribute set!")
return
if increments >= 1:
inc = increments
else:
inc = round(train.num_instances * increments)
if test is None:
tst = [train]
elif isinstance(test, list):
tst = test
elif isinstance(test, Instances):
tst = [test]
else:
logger.error("Expected list or Instances object, instead: " + type(test))
return
for t in tst:
if train.equal_headers(t) is not None:
logger.error("Training and test set are not compatible: " + train.equal_headers(t))
return
steps = []
cls = []
evls = {}
for classifier in classifiers:
cl = Classifier.make_copy(classifier)
cls.append(cl)
evls[cl] = {}
for t in tst:
evls[cl][t] = []
for i in range(train.num_instances):
if (i > 0) and (i % inc == 0):
steps.append(i+1)
for cl in cls:
# train
if cl.is_updateable:
if i == 0:
tr = Instances.copy_instances(train, 0, 1)
cl.build_classifier(tr)
else:
cl.update_classifier(train.get_instance(i))
else:
if (i > 0) and (i % inc == 0):
tr = Instances.copy_instances(train, 0, i + 1)
cl.build_classifier(tr)
# evaluate
if (i > 0) and (i % inc == 0):
for t in tst:
evl = Evaluation(t)
evl.test_model(cl, t)
evls[cl][t].append(getattr(evl, metric))
fig, ax = plt.subplots()
ax.set_xlabel("# of instances")
ax.set_ylabel(metric)
ax.set_title(title)
fig.canvas.set_window_title(title)
ax.grid(True)
i = 0
for cl in cls:
evlpertest = evls[cl]
i += 1
n = 0
for t in tst:
evl = evlpertest[t]
n += 1
plot_label = label_template.\
replace("#", str(i)).\
replace("*", str(n)).\
replace("@", cl.classname).\
replace("!", cl.classname[cl.classname.rfind(".") + 1:]).\
replace("$", join_options(cl.config))
ax.plot(steps, evl, label=plot_label)
plt.draw()
plt.legend(loc=key_loc, shadow=True)
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show() | python | def plot_learning_curve(classifiers, train, test=None, increments=100, metric="percent_correct",
title="Learning curve", label_template="[#] @ $", key_loc="lower right",
outfile=None, wait=True):
"""
Plots a learning curve.
:param classifiers: list of Classifier template objects
:type classifiers: list of Classifier
:param train: dataset to use for the building the classifier, used for evaluating it test set None
:type train: Instances
:param test: optional dataset (or list of datasets) to use for the testing the built classifiers
:type test: list or Instances
:param increments: the increments (>= 1: # of instances, <1: percentage of dataset)
:type increments: float
:param metric: the name of the numeric metric to plot (Evaluation.<metric>)
:type metric: str
:param title: the title for the plot
:type title: str
:param label_template: the template for the label in the plot
(#: 1-based index of classifier, @: full classname, !: simple classname,
$: options, *: 1-based index of test set)
:type label_template: str
:param key_loc: the location string for the key
:type key_loc: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
if not train.has_class():
logger.error("Training set has no class attribute set!")
return
if increments >= 1:
inc = increments
else:
inc = round(train.num_instances * increments)
if test is None:
tst = [train]
elif isinstance(test, list):
tst = test
elif isinstance(test, Instances):
tst = [test]
else:
logger.error("Expected list or Instances object, instead: " + type(test))
return
for t in tst:
if train.equal_headers(t) is not None:
logger.error("Training and test set are not compatible: " + train.equal_headers(t))
return
steps = []
cls = []
evls = {}
for classifier in classifiers:
cl = Classifier.make_copy(classifier)
cls.append(cl)
evls[cl] = {}
for t in tst:
evls[cl][t] = []
for i in range(train.num_instances):
if (i > 0) and (i % inc == 0):
steps.append(i+1)
for cl in cls:
# train
if cl.is_updateable:
if i == 0:
tr = Instances.copy_instances(train, 0, 1)
cl.build_classifier(tr)
else:
cl.update_classifier(train.get_instance(i))
else:
if (i > 0) and (i % inc == 0):
tr = Instances.copy_instances(train, 0, i + 1)
cl.build_classifier(tr)
# evaluate
if (i > 0) and (i % inc == 0):
for t in tst:
evl = Evaluation(t)
evl.test_model(cl, t)
evls[cl][t].append(getattr(evl, metric))
fig, ax = plt.subplots()
ax.set_xlabel("# of instances")
ax.set_ylabel(metric)
ax.set_title(title)
fig.canvas.set_window_title(title)
ax.grid(True)
i = 0
for cl in cls:
evlpertest = evls[cl]
i += 1
n = 0
for t in tst:
evl = evlpertest[t]
n += 1
plot_label = label_template.\
replace("#", str(i)).\
replace("*", str(n)).\
replace("@", cl.classname).\
replace("!", cl.classname[cl.classname.rfind(".") + 1:]).\
replace("$", join_options(cl.config))
ax.plot(steps, evl, label=plot_label)
plt.draw()
plt.legend(loc=key_loc, shadow=True)
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show() | Plots a learning curve.
:param classifiers: list of Classifier template objects
:type classifiers: list of Classifier
:param train: dataset to use for the building the classifier, used for evaluating it test set None
:type train: Instances
:param test: optional dataset (or list of datasets) to use for the testing the built classifiers
:type test: list or Instances
:param increments: the increments (>= 1: # of instances, <1: percentage of dataset)
:type increments: float
:param metric: the name of the numeric metric to plot (Evaluation.<metric>)
:type metric: str
:param title: the title for the plot
:type title: str
:param label_template: the template for the label in the plot
(#: 1-based index of classifier, @: full classname, !: simple classname,
$: options, *: 1-based index of test set)
:type label_template: str
:param key_loc: the location string for the key
:type key_loc: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/plot/classifiers.py#L274-L388 |
fracpete/python-weka-wrapper3 | python/weka/core/packages.py | all_packages | def all_packages():
"""
Returns a list of all packages.
:return: the list of packages
:rtype: list
"""
establish_cache()
result = []
pkgs = javabridge.get_collection_wrapper(
javabridge.static_call(
"weka/core/WekaPackageManager", "getAllPackages", "()Ljava/util/List;"))
for pkge in pkgs:
result.append(Package(pkge))
return result | python | def all_packages():
"""
Returns a list of all packages.
:return: the list of packages
:rtype: list
"""
establish_cache()
result = []
pkgs = javabridge.get_collection_wrapper(
javabridge.static_call(
"weka/core/WekaPackageManager", "getAllPackages", "()Ljava/util/List;"))
for pkge in pkgs:
result.append(Package(pkge))
return result | Returns a list of all packages.
:return: the list of packages
:rtype: list | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/packages.py#L228-L242 |
fracpete/python-weka-wrapper3 | python/weka/core/packages.py | install_package | def install_package(pkge, version="Latest"):
"""
The list of packages to install.
:param pkge: the name of the repository package, a URL (http/https) or a zip file
:type pkge: str
:param version: in case of the repository packages, the version
:type version: str
:return: whether successfully installed
:rtype: bool
"""
establish_cache()
if pkge.startswith("http://") or pkge.startswith("https://"):
url = javabridge.make_instance(
"java/net/URL", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(pkge))
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromURL",
"(Ljava/net/URL;[Ljava/io/PrintStream;)Ljava/lang/String;", url, []) is None
elif pkge.lower().endswith(".zip"):
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromArchive",
"(Ljava/lang/String;[Ljava/io/PrintStream;)Ljava/lang/String;", pkge, []) is None
else:
return javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromRepository",
"(Ljava/lang/String;Ljava/lang/String;[Ljava/io/PrintStream;)Z", pkge, version, []) | python | def install_package(pkge, version="Latest"):
"""
The list of packages to install.
:param pkge: the name of the repository package, a URL (http/https) or a zip file
:type pkge: str
:param version: in case of the repository packages, the version
:type version: str
:return: whether successfully installed
:rtype: bool
"""
establish_cache()
if pkge.startswith("http://") or pkge.startswith("https://"):
url = javabridge.make_instance(
"java/net/URL", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(pkge))
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromURL",
"(Ljava/net/URL;[Ljava/io/PrintStream;)Ljava/lang/String;", url, []) is None
elif pkge.lower().endswith(".zip"):
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromArchive",
"(Ljava/lang/String;[Ljava/io/PrintStream;)Ljava/lang/String;", pkge, []) is None
else:
return javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromRepository",
"(Ljava/lang/String;Ljava/lang/String;[Ljava/io/PrintStream;)Z", pkge, version, []) | The list of packages to install.
:param pkge: the name of the repository package, a URL (http/https) or a zip file
:type pkge: str
:param version: in case of the repository packages, the version
:type version: str
:return: whether successfully installed
:rtype: bool | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/packages.py#L279-L304 |
fracpete/python-weka-wrapper3 | python/weka/core/packages.py | is_installed | def is_installed(name):
"""
Checks whether a package with the name is already installed.
:param name: the name of the package
:type name: str
:return: whether the package is installed
:rtype: bool
"""
pkgs = installed_packages()
for pkge in pkgs:
if pkge.name == name:
return True
return False | python | def is_installed(name):
"""
Checks whether a package with the name is already installed.
:param name: the name of the package
:type name: str
:return: whether the package is installed
:rtype: bool
"""
pkgs = installed_packages()
for pkge in pkgs:
if pkge.name == name:
return True
return False | Checks whether a package with the name is already installed.
:param name: the name of the package
:type name: str
:return: whether the package is installed
:rtype: bool | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/packages.py#L322-L335 |
fracpete/python-weka-wrapper3 | python/weka/core/packages.py | Package.dependencies | def dependencies(self):
"""
Returns the dependencies of the package.
:return: the list of Dependency objects
:rtype: list of Dependency
"""
result = []
dependencies = javabridge.get_collection_wrapper(
javabridge.call(self.jobject, "getDependencies", "()Ljava/util/List;"))
for dependency in dependencies:
result.append(Dependency(dependency))
return result | python | def dependencies(self):
"""
Returns the dependencies of the package.
:return: the list of Dependency objects
:rtype: list of Dependency
"""
result = []
dependencies = javabridge.get_collection_wrapper(
javabridge.call(self.jobject, "getDependencies", "()Ljava/util/List;"))
for dependency in dependencies:
result.append(Dependency(dependency))
return result | Returns the dependencies of the package.
:return: the list of Dependency objects
:rtype: list of Dependency | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/packages.py#L58-L70 |
fracpete/python-weka-wrapper3 | python/weka/core/packages.py | PackageConstraint.check_constraint | def check_constraint(self, pkge=None, constr=None):
"""
Checks the constraints.
:param pkge: the package to check
:type pkge: Package
:param constr: the package constraint to check
:type constr: PackageConstraint
"""
if not pkge is None:
return javabridge.call(
self.jobject, "checkConstraint", "(Lweka/core/packageManagement/Package;)Z", pkge.jobject)
if not constr is None:
return javabridge.call(
self.jobject, "checkConstraint", "(Lweka/core/packageManagement/PackageConstraint;)Z", pkge.jobject)
raise Exception("Either package or package constraing must be provided!") | python | def check_constraint(self, pkge=None, constr=None):
"""
Checks the constraints.
:param pkge: the package to check
:type pkge: Package
:param constr: the package constraint to check
:type constr: PackageConstraint
"""
if not pkge is None:
return javabridge.call(
self.jobject, "checkConstraint", "(Lweka/core/packageManagement/Package;)Z", pkge.jobject)
if not constr is None:
return javabridge.call(
self.jobject, "checkConstraint", "(Lweka/core/packageManagement/PackageConstraint;)Z", pkge.jobject)
raise Exception("Either package or package constraing must be provided!") | Checks the constraints.
:param pkge: the package to check
:type pkge: Package
:param constr: the package constraint to check
:type constr: PackageConstraint | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/packages.py#L133-L148 |
fracpete/python-weka-wrapper3 | python/weka/core/database.py | InstanceQuery.custom_properties | def custom_properties(self, props):
"""
Sets the custom properties file to use.
:param props: the props file
:type props: str
"""
fprops = javabridge.make_instance("java/io/File", "(Ljava/lang/String;)V", props)
javabridge.call(self.jobject, "setCustomPropsFile", "(Ljava/io/File;)V", fprops) | python | def custom_properties(self, props):
"""
Sets the custom properties file to use.
:param props: the props file
:type props: str
"""
fprops = javabridge.make_instance("java/io/File", "(Ljava/lang/String;)V", props)
javabridge.call(self.jobject, "setCustomPropsFile", "(Ljava/io/File;)V", fprops) | Sets the custom properties file to use.
:param props: the props file
:type props: str | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/database.py#L132-L140 |
fracpete/python-weka-wrapper3 | python/weka/core/database.py | InstanceQuery.retrieve_instances | def retrieve_instances(self, query=None):
"""
Executes either the supplied query or the one set via options (or the 'query' property).
:param query: query to execute if not the currently set one
:type query: str
:return: the generated dataq
:rtype: Instances
"""
if query is None:
data = javabridge.call(self.jobject, "retrieveInstances", "()Lweka/core/Instances;")
else:
data = javabridge.call(self.jobject, "retrieveInstances", "(Ljava/lang/String;)Lweka/core/Instances;")
return Instances(data) | python | def retrieve_instances(self, query=None):
"""
Executes either the supplied query or the one set via options (or the 'query' property).
:param query: query to execute if not the currently set one
:type query: str
:return: the generated dataq
:rtype: Instances
"""
if query is None:
data = javabridge.call(self.jobject, "retrieveInstances", "()Lweka/core/Instances;")
else:
data = javabridge.call(self.jobject, "retrieveInstances", "(Ljava/lang/String;)Lweka/core/Instances;")
return Instances(data) | Executes either the supplied query or the one set via options (or the 'query' property).
:param query: query to execute if not the currently set one
:type query: str
:return: the generated dataq
:rtype: Instances | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/database.py#L182-L195 |
fracpete/python-weka-wrapper3 | python/weka/core/capabilities.py | Capabilities.owner | def owner(self):
"""
Returns the owner of these capabilities, if any.
:return: the owner, can be None
:rtype: JavaObject
"""
obj = javabridge.call(self.jobject, "getOwner", "()Lweka/core/CapabilitiesHandler;")
if obj is None:
return None
else:
return JavaObject(jobject=obj) | python | def owner(self):
"""
Returns the owner of these capabilities, if any.
:return: the owner, can be None
:rtype: JavaObject
"""
obj = javabridge.call(self.jobject, "getOwner", "()Lweka/core/CapabilitiesHandler;")
if obj is None:
return None
else:
return JavaObject(jobject=obj) | Returns the owner of these capabilities, if any.
:return: the owner, can be None
:rtype: JavaObject | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/capabilities.py#L113-L124 |
fracpete/python-weka-wrapper3 | python/weka/core/capabilities.py | Capabilities.dependencies | def dependencies(self):
"""
Returns all the dependencies.
:return: the dependency list
:rtype: list
"""
result = []
iterator = javabridge.iterate_java(javabridge.call(self.jobject, "dependencies", "()Ljava/util/Iterator;"))
for c in iterator:
result.append(Capability(c))
return result | python | def dependencies(self):
"""
Returns all the dependencies.
:return: the dependency list
:rtype: list
"""
result = []
iterator = javabridge.iterate_java(javabridge.call(self.jobject, "dependencies", "()Ljava/util/Iterator;"))
for c in iterator:
result.append(Capability(c))
return result | Returns all the dependencies.
:return: the dependency list
:rtype: list | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/capabilities.py#L181-L192 |
fracpete/python-weka-wrapper3 | python/weka/core/capabilities.py | Capabilities.for_instances | def for_instances(cls, data, multi=None):
"""
returns a Capabilities object specific for this data. The minimum number of instances is not set, the check
for multi-instance data is optional.
:param data: the data to generate the capabilities for
:type data: Instances
:param multi: whether to check the structure, too
:type multi: bool
:return: the generated capabilities
:rtype: Capabilities
"""
if multi is None:
return Capabilities(javabridge.static_call(
"weka/core/Capabilities", "forInstances",
"(Lweka/core/Instances;)Lweka/core/Capabilities;", data.jobject))
else:
return Capabilities(javabridge.static_call(
"weka/core/Capabilities", "forInstances",
"(Lweka/core/Instances;Z)Lweka/core/Capabilities;", data.jobject, multi)) | python | def for_instances(cls, data, multi=None):
"""
returns a Capabilities object specific for this data. The minimum number of instances is not set, the check
for multi-instance data is optional.
:param data: the data to generate the capabilities for
:type data: Instances
:param multi: whether to check the structure, too
:type multi: bool
:return: the generated capabilities
:rtype: Capabilities
"""
if multi is None:
return Capabilities(javabridge.static_call(
"weka/core/Capabilities", "forInstances",
"(Lweka/core/Instances;)Lweka/core/Capabilities;", data.jobject))
else:
return Capabilities(javabridge.static_call(
"weka/core/Capabilities", "forInstances",
"(Lweka/core/Instances;Z)Lweka/core/Capabilities;", data.jobject, multi)) | returns a Capabilities object specific for this data. The minimum number of instances is not set, the check
for multi-instance data is optional.
:param data: the data to generate the capabilities for
:type data: Instances
:param multi: whether to check the structure, too
:type multi: bool
:return: the generated capabilities
:rtype: Capabilities | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/capabilities.py#L428-L447 |
fracpete/python-weka-wrapper3 | python/weka/plot/dataset.py | scatter_plot | def scatter_plot(data, index_x, index_y, percent=100.0, seed=1, size=50, title=None, outfile=None, wait=True):
"""
Plots two attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param data: the dataset
:type data: Instances
:param index_x: the 0-based index of the attribute on the x axis
:type index_x: int
:param index_y: the 0-based index of the attribute on the y axis
:type index_y: int
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param size: the size of the circles in point
:type size: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
# collect data
x = []
y = []
if data.class_index == -1:
c = None
else:
c = []
for i in range(data.num_instances):
inst = data.get_instance(i)
x.append(inst.get_value(index_x))
y.append(inst.get_value(index_y))
if c is not None:
c.append(inst.get_value(inst.class_index))
# plot data
fig, ax = plt.subplots()
if c is None:
ax.scatter(x, y, s=size, alpha=0.5)
else:
ax.scatter(x, y, c=c, s=size, alpha=0.5)
ax.set_xlabel(data.attribute(index_x).name)
ax.set_ylabel(data.attribute(index_y).name)
if title is None:
title = "Attribute scatter plot"
if percent != 100:
title += " (%0.1f%%)" % percent
ax.set_title(title)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
ax.grid(True)
fig.canvas.set_window_title(data.relationname)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show() | python | def scatter_plot(data, index_x, index_y, percent=100.0, seed=1, size=50, title=None, outfile=None, wait=True):
"""
Plots two attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param data: the dataset
:type data: Instances
:param index_x: the 0-based index of the attribute on the x axis
:type index_x: int
:param index_y: the 0-based index of the attribute on the y axis
:type index_y: int
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param size: the size of the circles in point
:type size: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
# collect data
x = []
y = []
if data.class_index == -1:
c = None
else:
c = []
for i in range(data.num_instances):
inst = data.get_instance(i)
x.append(inst.get_value(index_x))
y.append(inst.get_value(index_y))
if c is not None:
c.append(inst.get_value(inst.class_index))
# plot data
fig, ax = plt.subplots()
if c is None:
ax.scatter(x, y, s=size, alpha=0.5)
else:
ax.scatter(x, y, c=c, s=size, alpha=0.5)
ax.set_xlabel(data.attribute(index_x).name)
ax.set_ylabel(data.attribute(index_y).name)
if title is None:
title = "Attribute scatter plot"
if percent != 100:
title += " (%0.1f%%)" % percent
ax.set_title(title)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
ax.grid(True)
fig.canvas.set_window_title(data.relationname)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show() | Plots two attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param data: the dataset
:type data: Instances
:param index_x: the 0-based index of the attribute on the x axis
:type index_x: int
:param index_y: the 0-based index of the attribute on the y axis
:type index_y: int
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param size: the size of the circles in point
:type size: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/plot/dataset.py#L27-L93 |
fracpete/python-weka-wrapper3 | python/weka/plot/dataset.py | line_plot | def line_plot(data, atts=None, percent=100.0, seed=1, title=None, outfile=None, wait=True):
"""
Uses the internal format to plot the dataset, one line per instance.
:param data: the dataset
:type data: Instances
:param atts: the list of 0-based attribute indices of attributes to plot
:type atts: list
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
fig = plt.figure()
if atts is None:
x = []
for i in range(data.num_attributes):
x.append(i)
else:
x = atts
ax = fig.add_subplot(111)
ax.set_xlabel("attributes")
ax.set_ylabel("value")
ax.grid(True)
for index_y in range(data.num_instances):
y = []
for index_x in x:
y.append(data.get_instance(index_y).get_value(index_x))
ax.plot(x, y, "o-", alpha=0.5)
if title is None:
title = data.relationname
if percent != 100:
title += " (%0.1f%%)" % percent
fig.canvas.set_window_title(title)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show() | python | def line_plot(data, atts=None, percent=100.0, seed=1, title=None, outfile=None, wait=True):
"""
Uses the internal format to plot the dataset, one line per instance.
:param data: the dataset
:type data: Instances
:param atts: the list of 0-based attribute indices of attributes to plot
:type atts: list
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
fig = plt.figure()
if atts is None:
x = []
for i in range(data.num_attributes):
x.append(i)
else:
x = atts
ax = fig.add_subplot(111)
ax.set_xlabel("attributes")
ax.set_ylabel("value")
ax.grid(True)
for index_y in range(data.num_instances):
y = []
for index_x in x:
y.append(data.get_instance(index_y).get_value(index_x))
ax.plot(x, y, "o-", alpha=0.5)
if title is None:
title = data.relationname
if percent != 100:
title += " (%0.1f%%)" % percent
fig.canvas.set_window_title(title)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show() | Uses the internal format to plot the dataset, one line per instance.
:param data: the dataset
:type data: Instances
:param atts: the list of 0-based attribute indices of attributes to plot
:type atts: list
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/plot/dataset.py#L168-L221 |
fracpete/python-weka-wrapper3 | python/weka/filters.py | Filter.filter | def filter(self, data):
"""
Filters the dataset(s). When providing a list, this can be used to create compatible train/test sets,
since the filter only gets initialized with the first dataset and all subsequent datasets get transformed
using the same setup.
NB: inputformat(Instances) must have been called beforehand.
:param data: the Instances to filter
:type data: Instances or list of Instances
:return: the filtered Instances object(s)
:rtype: Instances or list of Instances
"""
if isinstance(data, list):
result = []
for d in data:
result.append(Instances(javabridge.static_call(
"Lweka/filters/Filter;", "useFilter",
"(Lweka/core/Instances;Lweka/filters/Filter;)Lweka/core/Instances;",
d.jobject, self.jobject)))
return result
else:
return Instances(javabridge.static_call(
"Lweka/filters/Filter;", "useFilter",
"(Lweka/core/Instances;Lweka/filters/Filter;)Lweka/core/Instances;",
data.jobject, self.jobject)) | python | def filter(self, data):
"""
Filters the dataset(s). When providing a list, this can be used to create compatible train/test sets,
since the filter only gets initialized with the first dataset and all subsequent datasets get transformed
using the same setup.
NB: inputformat(Instances) must have been called beforehand.
:param data: the Instances to filter
:type data: Instances or list of Instances
:return: the filtered Instances object(s)
:rtype: Instances or list of Instances
"""
if isinstance(data, list):
result = []
for d in data:
result.append(Instances(javabridge.static_call(
"Lweka/filters/Filter;", "useFilter",
"(Lweka/core/Instances;Lweka/filters/Filter;)Lweka/core/Instances;",
d.jobject, self.jobject)))
return result
else:
return Instances(javabridge.static_call(
"Lweka/filters/Filter;", "useFilter",
"(Lweka/core/Instances;Lweka/filters/Filter;)Lweka/core/Instances;",
data.jobject, self.jobject)) | Filters the dataset(s). When providing a list, this can be used to create compatible train/test sets,
since the filter only gets initialized with the first dataset and all subsequent datasets get transformed
using the same setup.
NB: inputformat(Instances) must have been called beforehand.
:param data: the Instances to filter
:type data: Instances or list of Instances
:return: the filtered Instances object(s)
:rtype: Instances or list of Instances | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/filters.py#L122-L147 |
fracpete/python-weka-wrapper3 | python/weka/filters.py | MultiFilter.filters | def filters(self):
"""
Returns the list of base filters.
:return: the filter list
:rtype: list
"""
objects = javabridge.get_env().get_object_array_elements(
javabridge.call(self.jobject, "getFilters", "()[Lweka/filters/Filter;"))
result = []
for obj in objects:
result.append(Filter(jobject=obj))
return result | python | def filters(self):
"""
Returns the list of base filters.
:return: the filter list
:rtype: list
"""
objects = javabridge.get_env().get_object_array_elements(
javabridge.call(self.jobject, "getFilters", "()[Lweka/filters/Filter;"))
result = []
for obj in objects:
result.append(Filter(jobject=obj))
return result | Returns the list of base filters.
:return: the filter list
:rtype: list | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/filters.py#L201-L213 |
fracpete/python-weka-wrapper3 | python/weka/filters.py | MultiFilter.filters | def filters(self, filters):
"""
Sets the base filters.
:param filters: the list of base filters to use
:type filters: list
"""
obj = []
for fltr in filters:
obj.append(fltr.jobject)
javabridge.call(self.jobject, "setFilters", "([Lweka/filters/Filter;)V", obj) | python | def filters(self, filters):
"""
Sets the base filters.
:param filters: the list of base filters to use
:type filters: list
"""
obj = []
for fltr in filters:
obj.append(fltr.jobject)
javabridge.call(self.jobject, "setFilters", "([Lweka/filters/Filter;)V", obj) | Sets the base filters.
:param filters: the list of base filters to use
:type filters: list | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/filters.py#L216-L226 |
fracpete/python-weka-wrapper3 | python/weka/flow/container.py | Container.generate_help | def generate_help(self):
"""
Generates a help string for this container.
:return: the help string
:rtype: str
"""
result = []
result.append(self.__class__.__name__)
result.append(re.sub(r'.', '=', self.__class__.__name__))
result.append("")
result.append("Supported value names:")
for a in self.allowed:
result.append(a)
return '\n'.join(result) | python | def generate_help(self):
"""
Generates a help string for this container.
:return: the help string
:rtype: str
"""
result = []
result.append(self.__class__.__name__)
result.append(re.sub(r'.', '=', self.__class__.__name__))
result.append("")
result.append("Supported value names:")
for a in self.allowed:
result.append(a)
return '\n'.join(result) | Generates a help string for this container.
:return: the help string
:rtype: str | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/container.py#L84-L98 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | Transformer.post_execute | def post_execute(self):
"""
Gets executed after the actual execution.
:return: None if successful, otherwise error message
:rtype: str
"""
result = super(Transformer, self).post_execute()
if result is None:
self._input = None
return result | python | def post_execute(self):
"""
Gets executed after the actual execution.
:return: None if successful, otherwise error message
:rtype: str
"""
result = super(Transformer, self).post_execute()
if result is None:
self._input = None
return result | Gets executed after the actual execution.
:return: None if successful, otherwise error message
:rtype: str | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L55-L65 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | LoadDataset.quickinfo | def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "incremental: " + str(self.config["incremental"]) \
+ ", custom: " + str(self.config["use_custom_loader"]) \
+ ", loader: " + base.to_commandline(self.config["custom_loader"]) | python | def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "incremental: " + str(self.config["incremental"]) \
+ ", custom: " + str(self.config["use_custom_loader"]) \
+ ", loader: " + base.to_commandline(self.config["custom_loader"]) | Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L131-L140 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | LoadDataset.fix_config | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
opt = "incremental"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to load the dataset incrementally (bool)."
opt = "use_custom_loader"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to use a custom loader."
opt = "custom_loader"
if opt not in options:
options[opt] = converters.Loader(classname="weka.core.converters.ArffLoader")
if opt not in self.help:
self.help[opt] = "The custom loader to use (Loader)."
return super(LoadDataset, self).fix_config(options) | python | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
opt = "incremental"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to load the dataset incrementally (bool)."
opt = "use_custom_loader"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to use a custom loader."
opt = "custom_loader"
if opt not in options:
options[opt] = converters.Loader(classname="weka.core.converters.ArffLoader")
if opt not in self.help:
self.help[opt] = "The custom loader to use (Loader)."
return super(LoadDataset, self).fix_config(options) | Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L142-L169 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | LoadDataset.check_input | def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if token is None:
raise Exception(self.full_name + ": No token provided!")
if isinstance(token.payload, str):
return
raise Exception(self.full_name + ": Unhandled class: " + classes.get_classname(token.payload)) | python | def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if token is None:
raise Exception(self.full_name + ": No token provided!")
if isinstance(token.payload, str):
return
raise Exception(self.full_name + ": Unhandled class: " + classes.get_classname(token.payload)) | Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L171-L182 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | LoadDataset.do_execute | def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
fname = str(self.input.payload)
if not os.path.exists(fname):
return "File '" + fname + "' does not exist!"
if not os.path.isfile(fname):
return "Location '" + fname + "' is not a file!"
if self.resolve_option("use_custom_loader"):
self._loader = self.resolve_option("custom_loader")
else:
self._loader = converters.loader_for_file(fname)
dataset = self._loader.load_file(fname, incremental=bool(self.resolve_option("incremental")))
if not self.resolve_option("incremental"):
self._output.append(Token(dataset))
else:
self._iterator = self._loader.__iter__()
return None | python | def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
fname = str(self.input.payload)
if not os.path.exists(fname):
return "File '" + fname + "' does not exist!"
if not os.path.isfile(fname):
return "Location '" + fname + "' is not a file!"
if self.resolve_option("use_custom_loader"):
self._loader = self.resolve_option("custom_loader")
else:
self._loader = converters.loader_for_file(fname)
dataset = self._loader.load_file(fname, incremental=bool(self.resolve_option("incremental")))
if not self.resolve_option("incremental"):
self._output.append(Token(dataset))
else:
self._iterator = self._loader.__iter__()
return None | The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L184-L205 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | LoadDataset.output | def output(self):
"""
Returns the next available output token.
:return: the next token, None if none available
:rtype: Token
"""
if self._iterator is not None:
try:
inst = self._iterator.next()
result = Token(inst)
except Exception as e:
self._iterator = None
result = None
else:
result = super(LoadDataset, self).output()
return result | python | def output(self):
"""
Returns the next available output token.
:return: the next token, None if none available
:rtype: Token
"""
if self._iterator is not None:
try:
inst = self._iterator.next()
result = Token(inst)
except Exception as e:
self._iterator = None
result = None
else:
result = super(LoadDataset, self).output()
return result | Returns the next available output token.
:return: the next token, None if none available
:rtype: Token | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L216-L232 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | LoadDataset.stop_execution | def stop_execution(self):
"""
Triggers the stopping of the object.
"""
super(LoadDataset, self).stop_execution()
self._loader = None
self._iterator = None | python | def stop_execution(self):
"""
Triggers the stopping of the object.
"""
super(LoadDataset, self).stop_execution()
self._loader = None
self._iterator = None | Triggers the stopping of the object. | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L234-L240 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | LoadDataset.wrapup | def wrapup(self):
"""
Finishes up after execution finishes, does not remove any graphical output.
"""
self._loader = None
self._iterator = None
super(LoadDataset, self).wrapup() | python | def wrapup(self):
"""
Finishes up after execution finishes, does not remove any graphical output.
"""
self._loader = None
self._iterator = None
super(LoadDataset, self).wrapup() | Finishes up after execution finishes, does not remove any graphical output. | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L242-L248 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | SetStorageValue.fix_config | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(SetStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The storage value name for storing the payload under (string)."
return options | python | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(SetStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The storage value name for storing the payload under (string)."
return options | Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L286-L303 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | SetStorageValue.do_execute | def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
self.storagehandler.storage[self.resolve_option("storage_name")] = self.input.payload
self._output.append(self.input)
return None | python | def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
self.storagehandler.storage[self.resolve_option("storage_name")] = self.input.payload
self._output.append(self.input)
return None | The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L305-L316 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | DeleteStorageValue.fix_config | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(DeleteStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the storage value to delete (string)."
return options | python | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(DeleteStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the storage value to delete (string)."
return options | Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L354-L371 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | InitStorageValue.fix_config | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(InitStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the storage value to delete (string)."
opt = "value"
if opt not in options:
options[opt] = "1"
if opt not in self.help:
self.help[opt] = "The initial value (string)."
return options | python | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(InitStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the storage value to delete (string)."
opt = "value"
if opt not in options:
options[opt] = "1"
if opt not in self.help:
self.help[opt] = "The initial value (string)."
return options | Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L422-L445 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | InitStorageValue.do_execute | def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
self.storagehandler.storage[self.resolve_option("storage_name")] = eval(str(self.resolve_option("value")))
self._output.append(self.input)
return None | python | def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
self.storagehandler.storage[self.resolve_option("storage_name")] = eval(str(self.resolve_option("value")))
self._output.append(self.input)
return None | The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L447-L458 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | UpdateStorageValue.fix_config | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(UpdateStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the storage value to update (string)."
opt = "expression"
if opt not in options:
options[opt] = "int({X} + 1)"
if opt not in self.help:
self.help[opt] = "The expression for updating the storage value; use {X} for current value (string)."
return options | python | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(UpdateStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the storage value to update (string)."
opt = "expression"
if opt not in options:
options[opt] = "int({X} + 1)"
if opt not in self.help:
self.help[opt] = "The expression for updating the storage value; use {X} for current value (string)."
return options | Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L499-L522 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | UpdateStorageValue.do_execute | def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
expr = str(self.resolve_option("expression")).replace(
"{X}", str(self.storagehandler.storage[str(self.resolve_option("storage_name"))]))
expr = self.storagehandler.expand(expr)
self.storagehandler.storage[self.resolve_option("storage_name")] = eval(expr)
self._output.append(self.input)
return None | python | def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
expr = str(self.resolve_option("expression")).replace(
"{X}", str(self.storagehandler.storage[str(self.resolve_option("storage_name"))]))
expr = self.storagehandler.expand(expr)
self.storagehandler.storage[self.resolve_option("storage_name")] = eval(expr)
self._output.append(self.input)
return None | The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L524-L538 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | MathExpression.fix_config | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(MathExpression, self).fix_config(options)
opt = "expression"
if opt not in options:
options[opt] = "{X}"
if opt not in self.help:
self.help[opt] = "The mathematical expression to evaluate (string)."
return options | python | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(MathExpression, self).fix_config(options)
opt = "expression"
if opt not in options:
options[opt] = "{X}"
if opt not in self.help:
self.help[opt] = "The mathematical expression to evaluate (string)."
return options | Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L581-L598 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | MathExpression.do_execute | def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
expr = str(self.resolve_option("expression"))
expr = expr.replace("{X}", str(self.input.payload))
self._output.append(Token(eval(expr)))
return None | python | def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
expr = str(self.resolve_option("expression"))
expr = expr.replace("{X}", str(self.input.payload))
self._output.append(Token(eval(expr)))
return None | The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L600-L610 |
fracpete/python-weka-wrapper3 | python/weka/flow/transformer.py | ClassSelector.fix_config | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(ClassSelector, self).fix_config(options)
opt = "index"
if opt not in options:
options[opt] = "last"
if opt not in self.help:
self.help[opt] = "The class index (1-based number); 'first' and 'last' are accepted as well (string)."
opt = "unset"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to unset the class index (bool)."
return options | python | def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(ClassSelector, self).fix_config(options)
opt = "index"
if opt not in options:
options[opt] = "last"
if opt not in self.help:
self.help[opt] = "The class index (1-based number); 'first' and 'last' are accepted as well (string)."
opt = "unset"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to unset the class index (bool)."
return options | Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/flow/transformer.py#L649-L672 |
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