Module qsarmodelingpy.ga

Variable selection with Genetic Algorithm.

Functions

def checkLen(min, max)

def initIndividual(icls, imin, imax, size)

def returnIndices(individual)

Classes

class Ga (X: pandas.core.frame.DataFrame, y: pandas.core.frame.DataFrame, nLV: int = None, scale: bool = True, min_size: int = 5, max_size: int = 25, size_population: int = 200, mig_rate: float = 0.2, cxpb: float = 0.5, mutpb: float = 0.2, ngen: int = 120)

Variable selection with Genetic Algorithm.

Args

X : DataFrame

The matrix with descriptors.

y : DataFrame, list, array

The dependent variable vector.

nLV : int, optional

Number of Latent Variables. Defaults to None.

scale : bool, optional

Defaults to True.

min_size : int, optional

Minimum number of variables in the model. Defaults to 5.

max_size : int, optional

Maximum number of variables in the model. Defaults to 25.

size_population : int, optional

Size of the population in each generation. Defaults to 200.

mig_rate : float, optional

Migration rate. Defaults to 0.2.

cxpb : float, optional

Crossover rate. Defaults to 0.5.

mutpb : float, optional

Mutation rate. Defaults to 0.2.

ngen : int, optional

Number of generations. Defaults to 120.

Methods

def evaluate(self, individual)

Do some hard computing on the individual

def run(self)

def savePop(self, file)

Saves population to file.

Args

file (str path, file-like, io): The filename to save population output.

def saveQ2(self, file)

Saves Q² output to file.

Args

file (str path, file-like, io): The filename to save Q² output.