Source code for stk.ea.fitness_calculators.property_vector

"""
Property Vector
===============

"""

from .fitness_calculator import FitnessCalculator


[docs]class PropertyVector(FitnessCalculator): """ Uses multiple molecular properties as a fitness value. Examples -------- *Calculating Fitness Values* .. testcode:: calculating-fitness-values import stk # First, create the functions which calculate the properties # of molecules. def get_num_atoms(molecule): return molecule.get_num_atoms() def get_num_bonds(molecule): return molecule.get_num_bonds() def get_diameter(molecule): return molecule.get_maximum_diameter() # Next, create the fitness calculator. fitness_calculator = stk.PropertyVector( property_functions=( get_num_atoms, get_num_bonds, get_diameter, ), ) # Calculate the fitness value of a molecule. # "value" is a tuple, holding the number of atoms, number of # bonds and the diameter of the molecule. value = fitness_calculator.get_fitness_value( molecule=stk.BuildingBlock('BrCCBr'), ) .. testcode:: calculating-fitness-values :hide: _bb = stk.BuildingBlock('BrCCBr') assert value == ( _bb.get_num_atoms(), _bb.get_num_bonds(), _bb.get_maximum_diameter(), ) *Storing Fitness Values in a Database* Sometimes you want to store fitness values in a database, you can do this by providing the `output_database` parameter. .. testsetup:: storing-fitness-values-in-a-database import stk # Change the database used, so that when a developer # runs the doctests locally, their "stk" database is not # contaminated. _test_database = '_stk_doctest_database' _old_init = stk.ValueMongoDb stk.ValueMongoDb = lambda mongo_client, collection: ( _old_init( mongo_client=mongo_client, database=_test_database, collection=collection, ) ) # Change the database MongoClient will connect to. import os import pymongo _mongo_client = pymongo.MongoClient _mongodb_uri = os.environ.get( 'MONGODB_URI', 'mongodb://localhost:27017/' ) pymongo.MongoClient = lambda: _mongo_client(_mongodb_uri) .. testcode:: storing-fitness-values-in-a-database import stk import pymongo # Create a database which stores the fitness value of each # molecule. fitness_db = stk.ValueMongoDb( # This connects to a local database - so make sure you have # local MongoDB server running. You can also connect to # a remote MongoDB with MongoClient(), read to pymongo # docs to see how to do that. mongo_client=pymongo.MongoClient(), collection='fitness_values', ) # Define the functions which calculate molecular properties. def get_num_atoms(molecule): return molecule.get_num_atoms() def get_num_bonds(molecule): return molecule.get_num_bonds() def get_diameter(molecule): return molecule.get_maximum_diameter() # Create the fitness calculator. fitness_calculator = stk.PropertyVector( property_functions=( get_num_atoms, get_num_bonds, get_diameter, ), output_database=fitness_db, ) # Calculate fitness values. value1 = fitness_calculator.get_fitness_value( molecule=stk.BuildingBlock('BrCCBr'), ) # You can retrieve the fitness values from the database. value2 = fitness_db.get(stk.BuildingBlock('BrCCBr')) .. testcode:: storing-fitness-values-in-a-database :hide: assert value1 == tuple(value2) .. testcleanup:: storing-fitness-values-in-a-database stk.ValueMongoDb = _old_init pymongo.MongoClient().drop_database(_test_database) pymongo.MongoClient = _mongo_client *Caching Fitness Values* Usually, if you calculate the fitness value of a molecule, you do not want to re-calculate it, because this may be expensive, and the fitness value is going to be the same anyway. By using the `input_database` parameter, together with the `output_database` parameter, you can make sure you store and retrieve calculated fitness values instead of repeating the same calculation multiple times. The `input_database` is checked before a calculation happens, to see if the value already exists, while the `output_database` has the calculated fitness value deposited into it. .. testsetup:: caching-fitness-values import stk # Change the database used, so that when a developer # runs the doctests locally, their "stk" database is not # contaminated. _test_database = '_stk_doctest_database' _old_init = stk.ValueMongoDb stk.ValueMongoDb = lambda mongo_client, collection: ( _old_init( mongo_client=mongo_client, database=_test_database, collection=collection, ) ) # Change the database MongoClient will connect to. import os import pymongo _mongo_client = pymongo.MongoClient _mongodb_uri = os.environ.get( 'MONGODB_URI', 'mongodb://localhost:27017/' ) pymongo.MongoClient = lambda: _mongo_client(_mongodb_uri) .. testcode:: caching-fitness-values import stk import pymongo # You can use the same database for both the input_database # and output_database parameters. fitness_db = stk.ValueMongoDb( # This connects to a local database - so make sure you have # local MongoDB server running. You can also connect to # a remote MongoDB with MongoClient(), read to pymongo # docs to see how to do that. mongo_client=pymongo.MongoClient(), collection='fitness_values', ) # Define the functions which calculate molecular properties. def get_num_atoms(molecule): return molecule.get_num_atoms() def get_num_bonds(molecule): return molecule.get_num_bonds() def get_diameter(molecule): return molecule.get_maximum_diameter() # Create the fitness calculator. fitness_calculator = stk.PropertyVector( property_functions=( get_num_atoms, get_num_bonds, get_diameter, ), input_database=fitness_db, output_database=fitness_db, ) # Assuming that a fitness value for this molecule was not # deposited into the database in a previous session, this # will calculate the fitness value. value1 = fitness_calculator.get_fitness_value( molecule=stk.BuildingBlock('BrCCBr'), ) # This will not re-calculate the fitness value, instead, # value1 will be retrieved from the database. value2 = fitness_calculator.get_fitness_value( molecule=stk.BuildingBlock('BrCCBr'), ) .. testcode:: caching-fitness-values :hide: value3 = fitness_calculator.get_fitness_value( molecule=stk.BuildingBlock('BrCCBr'), ) assert value2 is value3 .. testcleanup:: caching-fitness-values stk.ValueMongoDb = _old_init pymongo.MongoClient().drop_database(_test_database) pymongo.MongoClient = _mongo_client """
[docs] def __init__( self, property_functions, input_database=None, output_database=None, ): """ Initialize a :class:`.PropertyVector` instance. Parameters ---------- property_functions: :class:`tuple` of :class:`callable` A group of :class:`function`, each of which is used to calculate a single property of the molecule. Each function must take one parameter, `mol`, which accepts a :class:`.Molecule` object. This is the molecule used to calculate the property. input_database : :class:`.ValueDatabase`, optional A database to check before calling `fitness_function`. If a fitness value exists for a molecule in the database, the stored value is returned, instead of calling `fitness_function`. output_database : :class:`.ValueDatabase`, optional A database into which the calculate fitness value is placed. """ self._property_functions = property_functions self._input_database = input_database self._output_database = output_database
[docs] def get_fitness_value(self, molecule): if self._input_database is not None: try: fitness_value = self._input_database.get(molecule) except KeyError: fitness_value = tuple( property_function(molecule) for property_function in self._property_functions ) else: fitness_value = tuple( property_function(molecule) for property_function in self._property_functions ) if self._output_database is not None: self._output_database.put(molecule, fitness_value) return fitness_value