__init__(self,
initial_population,
properties,
eval_kwargs={},
crossovers=None,
mutators=None,
fragment_libs='all',
script_evaluator=None,
generations=25,
population=10,
crossover_rate=90.0,
mutation_rate=2.0,
selection='roulette_wheel',
tournament_size=2,
terminators=['unproductive', 'all_properties'],
num_unproductive=10,
scaling='sigma_truncation',
elitism=1,
random_seed=None,
no_minimize=False,
num_ret_structures=1,
molecule_history=False,
molecule_history_file='history.mae',
tpp_ga=1,
logger=None)
(Constructor)
|
|
Create an instance.
- Parameters:
initial_population (list) - the initial population of schrodinger.structure.Structureproperties (list of Property) - the properties to be optimizedeval_kwargs (dict) - a dictionary of key value pairs that will be passed onto
scriptlauncher.addScriptArgs(), both keys and values must be
single strings, values can contain whitespacecrossovers (list) - contains two-element tuples each of which holds a crossover
operator to be used in the optimization, i.e. Crossover() method,
along with a weightmutators (list) - contains two-element tuples each of which holds a mutation
operator to be used in the optimization, i.e. Mutation() method,
along with a weightfragment_libs (list) - strings specifying fragment libraries to be used, can be either
module constants from FRAGMENT_LIBS.keys() (or ALL if all of
those are desired) or the names of Maestro files (including the
file extensions) containing fragments collected by the userscript_evaluator (method) - the Evaluator method to be called to score individuals during the
optimization, takes a StructureGenome and returns a
launcher.Launchergenerations (int) - the number of generations for which to run the optimizationpopulation (int) - the population size to use in the optimization, can be
less-than-or-equal-to the length of initial_populationcrossover_rate (float) - the rate of crossover as a percentagemutation_rate (float) - the rate of mutation as a percentageselection (str) - the selection protocol used to select individuals to the gene
pool for the upcoming generationtournament_size (int) - the size of tournament to use if using tournament based
selection, unused if a tournament based selection is not being
usedterminators (list) - list of strings that specify the termination protocols to be used
to terminate the optimization, typically more than one is
specified only if the unproductive protocol is being usednum_unproductive (int) - if the unproductive protocol is being used to terminate the
optimization then this integer specifies how many unproductive
cycles are allowed before terminating, unused if a different
termination protocol is usedscaling (str) - specifies the scaling protocol to use, scaling scales the raw
scores of the individuals to produce fitness scores to ease
selection in cases where raw scores are nearly equalelitism (int) - specify the number of elite individuals guaranteed to be added to
the gene pool for the upcoming generation, zero disables elitismrandom_seed (None or float) - the random seed, if None then system time will be usedno_minimize (bool) - specify that the offspring structures generated by the crossover
and mutation operators not be geometry optimized prior to
selectionnum_ret_structures (int) - typically and by default the single best structure will be
returned, increase this value from one to return this number of
best structures from a sorted listmolecule_history (bool) - this options specifies to create a Maestro file containing all
molecules from all generationsmolecule_history_file (str) - name to use for the molecule history filetpp_ga (int) - a threads-per-processor value, used to parallelize the scoring of
the individuals during the optimization, this option simply forks
out tpp_ga simultaneous processes that reside on the same node,
if your evaluation function supports parallelization and you wish
to utilize a second level of parallelization of each individual
evaluation then pass those arguments in eval_kwargslogger (logging.Logger) - output logger - Overrides:
object.__init__
|