find_best_food.py

#    This file is part of DEAP.
#
#    DEAP is free software: you can redistribute it and/or modify
#    it under the terms of the GNU Lesser General Public License as
#    published by the Free Software Foundation, either version 3 of
#    the License, or (at your option) any later version.
#
#    DEAP is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
#    GNU Lesser General Public License for more details.
#
#    You should have received a copy of the GNU Lesser General Public
#    License along with DEAP. If not, see <http://www.gnu.org/licenses/>.


#    example which maximizes the sum of a list of integers
#    each of which can be 0 or 1

path_to_food = "Food/"

ban_items = [
    "Ecoylent"
    , "Transglutaminase"
    , "Milk"
    # , "Bear S U P R E M E"
    # , "Elk Wellington"
    # , "Fried Hare Haunches"
    # , "Boiled Sausage"
    # , "Seared Meat"
    # , "Corn Fritters"
]
craftstations = [
    "CampfireObject"
    , "BakeryOvenObject"
    , "StoveObject"
    , "CastIronStoveObject"
    , "ButcheryTableObject"
    , "MillObject"
    , "KitchenObject"
    # , "LaboratoryObject"
]
global max_food_combinations, ban_items

generations = 250
max_food_combinations = 8

import random

from deap import base
from deap import creator
from deap import tools
import multiprocessing

from os import listdir
import re




REGEX_FOOD = r"FriendlyName\s*{\s*get\s*{\s*return\s*\"(?P<name>.*?)\";\s*}\s*}.*Carbs\s*=\s*(?P<carbs>\d*),\s*Fat\s*=\s*(?P<fat>\d*),\s*Protein\s*=\s*(?P<protein>\d*),\s*Vitamins\s*=\s*(?P<vitamins>\d*).*float\s*Calories\s*{\s*get\s*{\s*return\s*(?P<calories>\d*);\s*}\s*}.*CraftingComponent.AddRecipe\(typeof\((?P<craft>.*?)\),\s*this\);"



def load_data(path):
    global ban_items
    # get file-list
    data = []
    for _file in listdir(path):
        with open(path + _file) as f:
            cs_txt = f.read()
            m = re.finditer(REGEX_FOOD, cs_txt, re.DOTALL)
            for matchNum, match in enumerate(m):
                matchNum = matchNum + 1
            if(match.group("name") not in ban_items):
                if(len(only_craft) == 0 or match.group("craft") in craftstations):
                    if(not (int(match.group("carbs")) == 0 and \
                    int(match.group("fat"))      == 0 and \
                    int(match.group("protein"))  == 0 and \
                    int(match.group("vitamins")) == 0)): # dont add zero food
                        data.append({
                            "name": match.group("name"),
                            "craft": match.group("craft"),
                            "carbs": int(match.group("carbs")),
                            "fat": int(match.group("fat")),
                            "protein": int(match.group("protein")),
                            "vitamins": int(match.group("vitamins")),
                            "calories": int(match.group("calories")),
                            "nutrients": int(match.group("carbs")) + int(match.group("fat")) + int(match.group("protein")) + int(match.group("vitamins"))
                        })
    return data

global data
data = load_data(path_to_food)

creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

toolbox = base.Toolbox()
pool = multiprocessing.Pool()
toolbox.register("map", pool.map)
# Attribute generator
#                      define 'attr_bool' to be an attribute ('gene')
#                      which corresponds to integers sampled uniformly
#                      from the range [0,1] (i.e. 0 or 1 with equal
#                      probability)
toolbox.register("attr_bool", random.randint, 0, len(data)+1)

# Structure initializers
#                         define 'individual' to be an individual
#                         consisting of 100 'attr_bool' elements ('genes')
toolbox.register("individual", tools.initRepeat, creator.Individual,
    toolbox.attr_bool, max_food_combinations)

# define the population to be a list of individuals
toolbox.register("population", tools.initRepeat, list, toolbox.individual)

# the goal ('fitness') function to be maximized
def evalOneMax(gene):
    """
    Helper method used to return the fitness for the chromosome based
    on its gene.
    """
    all_nutrients_cal = 0
    all_cal = 0

    weight_fat = 0
    weight_carbs = 0
    weight_protein = 0
    weight_vitamins = 0

    modificator = 0

    for gen in gene:
        if(gen < len(data)):
            all_nutrients_cal += (data[gen]["nutrients"] * data[gen]["calories"])
            all_cal += data[gen]["calories"]

            weight_fat      += (data[gen]["calories"] * data[gen]["fat"])
            weight_carbs    += (data[gen]["calories"] * data[gen]["carbs"])
            weight_protein  += (data[gen]["calories"] * data[gen]["protein"])
            weight_vitamins += (data[gen]["calories"] * data[gen]["vitamins"])
            modificator -= 0.2 # less is more!

    if(all_cal == 0):
        return 0,

    fat = weight_fat / all_cal
    carbs = weight_carbs / all_cal
    protein = weight_protein / all_cal
    vitamins = weight_vitamins / all_cal

    balance = (sum([fat, carbs, protein, vitamins]) / (max([fat, carbs, protein, vitamins])*4))*2.0

    return (((all_nutrients_cal / all_cal) * balance)+modificator),

#----------
# Operator registration
#----------
# register the goal / fitness function
toolbox.register("evaluate", evalOneMax)

# register the crossover operator
toolbox.register("mate", tools.cxTwoPoint)

# register a mutation operator with a probability to
# flip each attribute/gene of 0.05
toolbox.register("mutate", tools.mutFlipBit, indpb=0.1)

# operator for selecting individuals for breeding the next
# generation: each individual of the current generation
# is replaced by the 'fittest' (best) of three individuals
# drawn randomly from the current generation.
toolbox.register("select", tools.selTournament, tournsize=3)

#----------

def main():
    # random.seed(64)

    # create an initial population of 300 individuals (where
    # each individual is a list of integers)
    pop = toolbox.population(n=2500)

    # CXPB  is the probability with which two individuals
    #       are crossed
    #
    # MUTPB is the probability for mutating an individual
    CXPB, MUTPB = 0.5, 0.5

    print("Start of evolution")

    # Evaluate the entire population
    fitnesses = list(map(toolbox.evaluate, pop))
    for ind, fit in zip(pop, fitnesses):
        ind.fitness.values = fit

    print("  Evaluated %i individuals" % len(pop))

    # Extracting all the fitnesses of
    fits = [ind.fitness.values[0] for ind in pop]

    # Variable keeping track of the number of generations
    g = 0

    best_ind = None
    best_fit = 0

    # Begin the evolution
    while g < generations:
        # A new generation
        g = g + 1
        print("-- Generation %i --" % g)

        # Select the next generation individuals
        offspring = toolbox.select(pop, len(pop))
        # Clone the selected individuals
        offspring = list(map(toolbox.clone, offspring))

        # Apply crossover and mutation on the offspring
        for child1, child2 in zip(offspring[::2], offspring[1::2]):

            # cross two individuals with probability CXPB
            if random.random() < CXPB:
                toolbox.mate(child1, child2)

                # fitness values of the children
                # must be recalculated later
                del child1.fitness.values
                del child2.fitness.values

        for mutant in offspring:

            # mutate an individual with probability MUTPB
            if random.random() < MUTPB:
                toolbox.mutate(mutant)
                del mutant.fitness.values

        # Evaluate the individuals with an invalid fitness
        invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
        fitnesses = map(toolbox.evaluate, invalid_ind)
        for ind, fit in zip(invalid_ind, fitnesses):
            ind.fitness.values = fit

        print("  Evaluated %i individuals" % len(invalid_ind))

        # The population is entirely replaced by the offspring
        pop[:] = offspring

        # Gather all the fitnesses in one list and print the stats
        fits = [ind.fitness.values[0] for ind in pop]

        length = len(pop)
        mean = sum(fits) / length
        sum2 = sum(x*x for x in fits)
        std = abs(sum2 / length - mean**2)**0.5

        print("  Min %s" % min(fits))
        print("  Max %s" % max(fits))
        print("  Avg %s" % mean)
        print("  Std %s" % std)

        if(max(fits) > best_fit):
            best_ind = tools.selBest(pop, 1)[0]
            best_fit = best_ind.fitness.values[0]

    print("-- End of (successful) evolution --")

    # best_ind = tools.selBest(pop, 1)[0]
    print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))
    for val in best_ind:
        if(val < len(data)):
            print(data[val]["name"])

if __name__ == "__main__":
    main()

trying to run this on windows machine... getting errors:

S:\eco>python find_best_food.py
find_best_food.py:43: SyntaxWarning: name 'ban_items' is assigned to before global declaration
  global max_food_combinations, ban_items
C:\Python27\lib\site-packages\deap\tools\_hypervolume\pyhv.py:33: ImportWarning: Falling back to the python version of hypervolume module. Expect this to be very slow.
  "module. Expect this to be very slow.", ImportWarning)
Traceback (most recent call last):
  File "find_best_food.py", line 94, in <module>
    data = load_data(path_to_food)
  File "find_best_food.py", line 76, in load_data
    if(len(only_craft) == 0 or match.group("craft") in craftstations):
NameError: global name 'only_craft' is not defined

doesn't look like only_craft is defined anywhere, if I remove the only_craft part in the if statement i think it loops indefinitely.

is their any requirements that I may be missing ?
Python 2.7.14
installed deap via pip:

Name: deap
Version: 1.2.2
Summary: Distributed Evolutionary Algorithms in Python
Home-page: https://www.github.com/deap
Author: deap Development Team
Author-email: [email protected]
License: LGPL
Location: c:\python27\lib\site-packages
Requires: