edo.operators package

Submodules

edo.operators.crossover module

Functions for the crossover process.

edo.operators.crossover.crossover(parent1, parent2, col_limits, families, random_state, prob=0.5)

Blend the information from two parents to create a new Individual. Dimensions are inherited first, forming a “skeleton” that is filled with column-metadata pairs. These pairs are selected from either parent uniformly. Missing values are filled in as necessary.

Parameters

parent1Individual

The first individual to be blended.

parent2Individual

The second individual to be blended.

col_limitslist

Lower and upper bounds on the number of columns offspring can have. Used in case of tuple limits.

familieslist

Families of distributions with which to create new columns. Used in case of tuple column limits.

random_statenumpy.random.RandomState

The PRNG associated with the offspring.

probfloat, optional

The cut-off probability with which to inherit dimensions from parent1 over parent2.

Returns

——-

offspringIndividual

A new individual formed from the dimensions and columns of its parents.

edo.operators.mutation module

Functions related to the mutation operator.

edo.operators.mutation.mutate_ncols(dataframe, metadata, col_limits, families, weights, random_state, prob)

Mutate the number of columns an individual has by adding a new column and/or dropping a column at random. In either case, the bounds defined in col_limits cannot be exceeded.

edo.operators.mutation.mutate_nrows(dataframe, metadata, row_limits, random_state, prob)

Mutate the number of rows an individual has by adding a new row and/or dropping a row at random so as not to exceed the bounds of row_limits.

edo.operators.mutation.mutate_values(dataframe, metadata, random_state, prob)

Iterate over the values of dataframe and mutate them each with probability prob. Mutating a value is done by resampling from the associated column distribution in metadata.

edo.operators.mutation.mutation(individual, prob, row_limits, col_limits, families, weights=None)

Mutate an individual. Here, the characteristics of an individual can be split into two parts: their dimensions, and their values. Each of these parts is mutated in a different way using the same probability, prob.

Parameters

individualIndividual

The individual to be mutated.

probfloat

The probability with which any characteristic of individual should be mutated.

row_limitslist

Lower and upper limits on the number of rows an individual can have.

col_limitslist

Lower and upper limits on the number of columns an individual can have.

families: list

Families of distributions with which to create new columns.

weightslist, optional

Probabilities with which to sample a distribution families. If None, sample uniformly.

Returns

mutantIndividual

A (potentially) mutated individual.

edo.operators.selection module

The selection operator.

edo.operators.selection.selection(population, pop_fitness, best_prop, lucky_prop, random_state, maximise=False)

Given a population, select a proportion of the “best” individuals and another of the “lucky” individuals (if they are available) to form a set of potential parents.

Parameters

populationlist

All current individuals.

pop_fitnesslist

The fitness of each individual in population.

best_propfloat

The proportion of the fittest individuals in population to be selected.

lucky_propfloat

The proportion of lucky individuals left in population to be selected after the “best” have been selected.

maximisebool, optional

Determines whether an individual’s fitness should be maximal or not. Defaults to False.

Returns

parentsdict

The individuals chosen to potentially become parents and their index in the current population.

edo.operators.shrink module

Functions for shrinking the search space.

edo.operators.shrink.shrink(parents, families, itr, shrinkage)

Given the current progress of the evolutionary algorithm, shrink its search space, i.e. the parameter spaces for each of the distribution classes in families.

Parameters

parentslist of Individual instances

The parent individuals for this iteration.

familieslist of Distribution instances

The families of distributions to be shrunk.

itrint

The current iteration.

shrinkagefloat

The shrinkage factor between 0 and 1.

Returns

familieslist of Distribution instances

The altered families.

edo.operators.util module

A collection of functions for use across several operators.

edo.operators.util.get_family_counts(metadata, families)

Get the number of instances in metadata that belong to each family in families.

Module contents

edo.operators.crossover(parent1, parent2, col_limits, families, random_state, prob=0.5)

Blend the information from two parents to create a new Individual. Dimensions are inherited first, forming a “skeleton” that is filled with column-metadata pairs. These pairs are selected from either parent uniformly. Missing values are filled in as necessary.

Parameters

parent1Individual

The first individual to be blended.

parent2Individual

The second individual to be blended.

col_limitslist

Lower and upper bounds on the number of columns offspring can have. Used in case of tuple limits.

familieslist

Families of distributions with which to create new columns. Used in case of tuple column limits.

random_statenumpy.random.RandomState

The PRNG associated with the offspring.

probfloat, optional

The cut-off probability with which to inherit dimensions from parent1 over parent2.

Returns

——-

offspringIndividual

A new individual formed from the dimensions and columns of its parents.

edo.operators.mutation(individual, prob, row_limits, col_limits, families, weights=None)

Mutate an individual. Here, the characteristics of an individual can be split into two parts: their dimensions, and their values. Each of these parts is mutated in a different way using the same probability, prob.

Parameters

individualIndividual

The individual to be mutated.

probfloat

The probability with which any characteristic of individual should be mutated.

row_limitslist

Lower and upper limits on the number of rows an individual can have.

col_limitslist

Lower and upper limits on the number of columns an individual can have.

families: list

Families of distributions with which to create new columns.

weightslist, optional

Probabilities with which to sample a distribution families. If None, sample uniformly.

Returns

mutantIndividual

A (potentially) mutated individual.

edo.operators.selection(population, pop_fitness, best_prop, lucky_prop, random_state, maximise=False)

Given a population, select a proportion of the “best” individuals and another of the “lucky” individuals (if they are available) to form a set of potential parents.

Parameters

populationlist

All current individuals.

pop_fitnesslist

The fitness of each individual in population.

best_propfloat

The proportion of the fittest individuals in population to be selected.

lucky_propfloat

The proportion of lucky individuals left in population to be selected after the “best” have been selected.

maximisebool, optional

Determines whether an individual’s fitness should be maximal or not. Defaults to False.

Returns

parentsdict

The individuals chosen to potentially become parents and their index in the current population.

edo.operators.shrink(parents, families, itr, shrinkage)

Given the current progress of the evolutionary algorithm, shrink its search space, i.e. the parameter spaces for each of the distribution classes in families.

Parameters

parentslist of Individual instances

The parent individuals for this iteration.

familieslist of Distribution instances

The families of distributions to be shrunk.

itrint

The current iteration.

shrinkagefloat

The shrinkage factor between 0 and 1.

Returns

familieslist of Distribution instances

The altered families.