From 29d4bf227d2036a023cb55e22b58a37311daf66c Mon Sep 17 00:00:00 2001 From: kstaats Date: Sun, 1 Apr 2018 23:58:49 -0700 Subject: [PATCH] organisation and naming updates --- RELEASE_NOTES.txt | 39 + karoo_gp_base_class.py | 4365 ++++++++++++++++++++-------------------- karoo_gp_main.py | 210 +- karoo_gp_server.py | 48 +- 4 files changed, 2354 insertions(+), 2308 deletions(-) diff --git a/RELEASE_NOTES.txt b/RELEASE_NOTES.txt index cd601ec..144f969 100644 --- a/RELEASE_NOTES.txt +++ b/RELEASE_NOTES.txt @@ -1,3 +1,42 @@ +2018 04/01 + +Two major updates, as follows: + +1) I have returned to the basic structure of Karoo GP and worked to make it properly Pythonic. For those of you who +recognised the error in my ways, I appreciate your patience. What I have updated with this v1.1 release does not +improve performance nor stability (already rock solid), rather, it keeps properly trained developers from cringing +when they read my code. + +In the prior versions, I used one of the loose functions of Python that allows the initiation of a variable outside of +the base_class. Meaning, I imported the base_class to karoo_gp_main.py and karoo_gp_server.py, called a dozen global +variables by "gp.___" and as such initiated them from the user scripts. Yes, it works. But it caused a certain +individual with whom I worked at the SKA to yell at me and shake her head (and I had just met her a few days prior). +I don't like being yelled at, so I fixed it. + +Now, all user configurations are set as local variables and their values are passed to the base_class. + +To make thing a bit easier to read and follow, I have renamed and reorganized several functions (methods) as follows: + +a) Created a new category for data and archiving, moving/renaming all related functions to fx_data_. + +b) Renamed all fx_gen_ to fx_init_ as this is the initial population. + +c) Moved the four top-level functions used to construct the next generation of trees into their own fx_nextgen_ +category: fx_karoo_reproduce, fx_nextgen_point_mutate, fx_nextgen_branch_mutate, fx_nextgen_crossover + +The final breakdown is as follows: + fx_karoo_ Methods to Run Karoo GP + fx_data_ Methods to Load and Archive Data + fx_init_ Methods to Construct the 1st Generation + fx_eval_ Methods to Evaluate a Tree + fx_fitness_ Methods to Train and Test a Tree for Fitness + fx_nextgen_ Methods to Construct the next Generation + fx_evolve_ Methods to Evolve a Population + fx_display_ Methods to Visualize a Tree + +That's it! --kai + + 2018 02/27 Updated the Python library versions and improved some explanation of Operators and Operands in the User Guide. diff --git a/karoo_gp_base_class.py b/karoo_gp_base_class.py index f5cc993..146d31f 100644 --- a/karoo_gp_base_class.py +++ b/karoo_gp_base_class.py @@ -2,7 +2,7 @@ # Define the methods and global variables used by Karoo GP # by Kai Staats, MSc; see LICENSE.md # Thanks to Emmanuel Dufourq and Arun Kumar for support during 2014-15 devel; TensorFlow support provided by Iurii Milovanov -# version 1.0.8 +# version 1.1 ''' A NOTE TO THE NEWBIE, EXPERT, AND BRAVE @@ -24,6 +24,8 @@ from sympy import sympify from datetime import datetime from collections import OrderedDict +# import karoo_gp_pause as fx + # np.random.seed(1000) # for reproducibility @@ -74,113 +76,112 @@ class Base_GP(object): (defined below) by the prefix 'fx_' followed by an object and action, as in 'fx_display_tree()', with a few expections, such as 'fx_fitness_gene_pool'. - The categories (denoted by +++ banners +++) are as follows: - 'karoo_gp' A single method which conducts an entire run. Employed only by karoo_gp_server.py - 'fx_karoo_' Methods to Run Karoo GP - 'fx_gen_' Methods to Generate a Tree - 'fx_eval_' Methods to Evaluate a Tree - 'fx_fitness_' Methods to Train and Test a Tree for Fitness - 'fx_evolve_' Methods to Evolve a Population - 'fx_display_' Methods to Display a Tree - 'fx_archive_' Methods to Archive + The method categories (denoted by +++ banners +++) are as follows: + fx_karoo_ Methods to Run Karoo GP + fx_data_ Methods to Load and Archive Data + fx_init_ Methods to Construct the 1st Generation + fx_eval_ Methods to Evaluate a Tree + fx_fitness_ Methods to Train and Test a Tree for Fitness + fx_nextgen_ Methods to Construct the next Generation + fx_evolve_ Methods to Evolve a Population + fx_display_ Methods to Visualize a Tree - ### Global variables used for data management ### - 'gp.data_train' store train data for processing in TF - 'gp.data_test' store test data for processing in TF - 'gp.tf_device' set TF computation backend device (CPU or GPU) - 'gp.tf_device_log' employed for TensorFlow debugging - - 'gp.data_train_cols' number of cols in the TRAINING data (see 'fx_karoo_data_load', below) - 'gp.data_train_rows' number of rows in the TRAINING data (see 'fx_karoo_data_load', below) - 'gp.data_test_cols' number of cols in the TEST data (see 'fx_karoo_data_load', below) - 'gp.data_test_rows' number of rows in the TEST data (see 'fx_karoo_data_load', below) - - 'gp.functions' user defined functions (operators) from the associated files/[functions].csv - 'gp.terminals' user defined variables (operands) from the top row of the associated [data].csv - 'gp.coeff' user defined coefficients (NOT YET IN USE) - 'gp.fitness_type' fitness type - 'gp.datetime' date-time stamp of when the unique directory is created - 'gp.path' full path to the unique directory created with each run - 'gp.dataset' local path and dataset filename - - ### Global variables initiated and/or used by Sympy ### - 'gp.algo_raw' a Sympy string which represents a flattened tree - 'gp.algo_sym' a Sympy executable version of algo_raw - 'gp.fittest_dict' a dictionary of the most fit trees, compiled during fitness function execution - - ### Global variables used for evolutionary management ### - 'gp.population_a' the root generation from which Trees are chosen for mutation and reproduction - 'gp.population_b' the generation constructed from gp.population_a (recyled) - 'gp.gene_pool' once-per-generation assessment of trees that meet min and max boundary conditions - 'gp.generation_id' simple n + 1 increment - 'gp.fitness_type' set in 'fx_karoo_data_load' as either a minimising or maximising function - 'gp.tree' axis-1, 13 element Numpy array that defines each Tree, stored in 'gp.population' - 'gp.pop_*' 13 variables that define each Tree (see 'fx_gen_tree_initialise') - - ### Error checks ### - You can quickly locate all error checks by searching for 'ERROR!' in this and all classes. - + Error checks are quickly located by searching for 'ERROR!' ''' def __init__(self): - - ### Global variables instantiated in karoo_gp_main.py and karoo_gp_server.py ### - self.kernel = '' # fitness function - self.tree_depth_max = 0 # maximum Tree depth for the entire run; limits bloat - self.tree_depth_min = 0 # minimum number of nodes - self.tree_pop_max = 0 # maximum number of Trees per generation - self.generation_max = 0 # maximum number of generations - self.tourn_size = 0 # number of Trees selected for each tournament + + ''' + ### Global variables used for data management ### + self.data_train store train data for processing in TF + self.data_test store test data for processing in TF + self.tf_device set TF computation backend device (CPU or GPU) + self.tf_device_log employed for TensorFlow debugging + + self.data_train_cols number of cols in the TRAINING data (see fx_data_load, below) + self.data_train_rows number of rows in the TRAINING data (see fx_data_load, below) + self.data_test_cols number of cols in the TEST data (see fx_data_load, below) + self.data_test_rows number of rows in the TEST data (see fx_data_load, below) + + self.functions user defined functions (operators) from the associated files/[functions].csv + self.terminals user defined variables (operands) from the top row of the associated [data].csv + self.coeff user defined coefficients (NOT YET IN USE) + self.fitness_type fitness type + self.datetime date-time stamp of when the unique directory is created + self.path full path to the unique directory created with each run + self.dataset local path and dataset filename - self.evolve_repro = 0 # quantity of a population generated through Reproduction - self.evolve_point = 0 # quantity of a population generated through Point Mutation - self.evolve_branch = 0 # quantity of a population generated through Branch Mutation - self.evolve_cross = 0 # quantity of a population generated through Crossover + ### Global variables used for evolutionary management ### + self.population_a the root generation from which Trees are chosen for mutation and reproduction + self.population_b the generation constructed from gp.population_a (recyled) + self.gene_pool once-per-generation assessment of trees that meet min and max boundary conditions + self.generation_id simple n + 1 increment + self.fitness_type set in 'fx_data_load' as either a minimising or maximising function + self.tree axis-1, 13 element Numpy array that defines each Tree, stored in 'gp.population' + self.pop_* 13 variables that define each Tree (see 'fx_init_tree_initialise') + ''' - self.display = '' # display mode is set to (s)ilent # level of on-screen feedback - self.precision = 0 # the number of floating points for the round function in 'fx_fitness_eval' - - # self.karoo_gp(tree_type, tree_depth_base, filename) # used by karoo_gp_server.py to launch an entire run - - - ### Global variables instantiated in the classes ### self.algo_raw = [] # the raw expression generated by Sympy per Tree -- CONSIDER MAKING THIS VARIABLE LOCAL self.algo_sym = [] # the expression generated by Sympy per Tree -- CONSIDER MAKING THIS VARIABLE LOCAL self.fittest_dict = {} # all Trees which share the best fitness score - self.gene_pool = [] # store all Tree IDs for use by Tournament self.class_labels = 0 # the number of true class labels (data_y) return - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Run Karoo GP | - #++++++++++++++++++++++++++++++++++++++++++ + #+++++++++++++++++++++++++++++++++++++++++++++ + # Methods to Run Karoo GP | + #+++++++++++++++++++++++++++++++++++++++++++++ - def karoo_gp(self, tree_type, tree_depth_base, filename): + def fx_karoo_gp(self, kernel, tree_type, tree_depth_base, tree_depth_max, tree_depth_min, tree_pop_max, generation_max, tourn_size, filename, evolve_repro, evolve_point, evolve_branch, evolve_cross, display, precision, app): ''' - This method enables the engagement of the entire Karoo GP application. It is used exclusively by the server script - karoo_gp_server.py (not by the desktop script karoo_gp_main.py). Instead of returning the user to the pause menu, - this script terminates at the command-line, providing support for bash and chron job execution. + This method enables the engagement of the entire Karoo GP application. It is used by both the desktop and server + scripts. Instead of returning the user to the pause menu, this script terminates at the command-line, providing + support for bash and chron job execution. - Arguments required: tree_type, tree_depth_base, filename + Arguments required: kernel, tree_type, tree_depth_base, tree_depth_max, tree_depth_min, tree_pop_max, + generation_max, tourn_size, filename, evolve_repro, evolve_point, evolve_branch, evolve_cross, display, + precision, and 'm' for the Desktop or 's' for the Server app): ''' - - self.karoo_banner() - start = time.time() # start the clock for the timer + + # set variables to those values passed from the user interface scripts + self.kernel = kernel # fitness function + # tree_type is passed between methods to construct specific trees + # tree_depth_base is passed between methods to construct specific trees + self.tree_depth_max = tree_depth_max # maximum Tree depth for the entire run; limits bloat + self.tree_depth_min = tree_depth_min # minimum number of nodes + self.tree_pop_max = tree_pop_max # maximum number of Trees per generation + self.generation_max = generation_max # maximum number of generations + self.tourn_size = tourn_size # number of Trees selected for each tournament + # filename is passed between methods to work with specific populations + self.evolve_repro = evolve_repro # quantity of a population generated through Reproduction + self.evolve_point = evolve_point # quantity of a population generated through Point Mutation + self.evolve_branch = evolve_branch # quantity of a population generated through Branch Mutation + self.evolve_cross = evolve_cross # quantity of a population generated through Crossover + self.display = display # display mode is set to (s)ilent # level of on-screen feedback + self.precision = precision # the number of floating points for the round function in 'fx_fitness_eval' + + start = time.time() # start the timer # construct first generation of Trees - self.fx_karoo_data_load(filename) + self.fx_data_load(filename) self.generation_id = 1 # set initial generation ID self.population_a = ['Karoo GP by Kai Staats, Generation ' + str(self.generation_id)] # list to store all Tree arrays, one generation at a time - self.fx_karoo_construct(tree_type, tree_depth_base) # construct the first population of Trees + self.fx_init_construct(tree_type, tree_depth_base) # construct the first population of Trees + + if self.kernel == 'p': # EOL for Play mode + self.fx_display_tree(self.tree) # print the current Tree + self.fx_data_tree_write(self.population_a, 'a') # save this one Tree to disk + sys.exit() + + else: print '\n We have constructed a population of', self.tree_pop_max,'Trees for Generation 1\n' # evaluate first generation of Trees print '\n Evaluate the first generation of Trees ...' self.fx_fitness_gym(self.population_a) # generate expression, evaluate fitness, compare fitness - self.fx_archive_tree_write(self.population_a, 'a') # save the first generation of Trees to disk + self.fx_data_tree_write(self.population_a, 'a') # save the first generation of Trees to disk # evolve subsequent generations of Trees for self.generation_id in range(2, self.generation_max + 1): # loop through 'generation_max' @@ -189,362 +190,73 @@ class Base_GP(object): self.population_b = ['Karoo GP by Kai Staats, Evolving Generation'] # initialise population_b to host the next generation self.fx_fitness_gene_pool() # generate the viable gene pool (compares against gp.tree_depth_min) - self.fx_karoo_reproduce() # method 1 - Reproduction - self.fx_karoo_point_mutate() # method 2 - Point Mutation - self.fx_karoo_branch_mutate() # method 3 - Branch Mutation - self.fx_karoo_crossover() # method 4 - Crossover + self.fx_nextgen_reproduce() # method 1 - Reproduction + self.fx_nextgen_point_mutate() # method 2 - Point Mutation + self.fx_nextgen_branch_mutate() # method 3 - Branch Mutation + self.fx_nextgen_crossover() # method 4 - Crossover self.fx_eval_generation() # evaluate all Trees in a single generation self.population_a = self.fx_evolve_pop_copy(self.population_b, ['Karoo GP by Kai Staats, Generation ' + str(self.generation_id)]) # "End of line, man!" --CLU - print '\n \033[36m Karoo GP has an ellapsed time of \033[0;0m\033[31m%f\033[0;0m' % (time.time() - start), '\033[0;0m' - self.fx_archive_tree_write(self.population_b, 'f') # save the final generation of Trees to disk - self.fx_archive_params_write('Server') # save run-time parameters to disk - - print '\n \033[3m Congrats!\033[0;0m Your multi-generational Karoo GP run is complete.\n' - sys.exit() # return Karoo GP to the command line to support bash and chron job execution - - # return - - - def karoo_banner(self): - - ''' - This method makes Karoo GP look old-school cool! - - Arguments required: none - ''' - - os.system('clear') - - print '\n\033[36m\033[1m' - print '\t ** ** ****** ***** ****** ****** ****** ******' - print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' - print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' - print '\t **** ******** ****** ** ** ** ** ** *** *******' - print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' - print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' - print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' - print '\t ** ** ** ** ** ** ****** ****** ****** **' - print '\033[0;0m' - print '\t\033[36m Genetic Programming in Python - by Kai Staats, version 1.0.7\033[0;0m' + print '\n \033[36m Karoo GP has an ellapsed time of \033[0;0m\033[31m%f\033[0;0m' % (time.time() - start), '\033[0;0m' # mark the timer + self.fx_data_tree_write(self.population_b, 'f') # save the final generation of Trees to disk + + print '\n\033[3m "It is not the strongest of the species that survive, nor the most intelligent,\033[0;0m' + print '\033[3m but the one most responsive to change."\033[0;0m --Charles Darwin' + print '' + print '\033[3m Congrats!\033[0;0m Your Karoo GP run is complete.\n' + if app == 's': self.fx_data_params_write('Server') + else: + self.fx_data_params_write('Desktop') + print '\n\t\033[36m Type \033[1m?\033[0;0m\033[36m to review your options or \033[1mq\033[0;0m\033[36m to quit.\033[0;0m\n' + self.fx_karoo_pause(1) + return - def fx_karoo_data_load(self, filename): + def fx_karoo_continue(self, next_gen_start): ''' - The data and function .csv files are loaded according to the fitness function kernel selected by the user. An - alternative dataset may be loaded at launch, by appending a command line argument. The data is then split into - both TRAINING and TEST segments in order to validate the success of the GP training run. Datasets less than - 10 rows will not be split, rather copied in full to both TRAINING and TEST as it is assumed you are conducting - a system validation run, as with the built-in MATCH kernel and associated dataset. + This method enables the launch of another full run of Karoo GP, but starting with a seed generation + instead of with a randomly generated first population. This can be used at the end of a standard run to + continue the evoluationary process, or after having recovered a set of trees from a prior run. - Arguments required: filename (of the dataset) + Arguments required: next_gen_start ''' - ### 1) load the associated data set, operators, operands, fitness type, and coefficients ### + for self.generation_id in range(next_gen_start, self.generation_max + 1): # evolve additional generations of Trees - # full_path = os.path.realpath(__file__); cwd = os.path.dirname(full_path) # Good idea Marco :) - cwd = os.getcwd() - - data_dict = {'c':cwd + '/files/data_CLASSIFY.csv', 'r':cwd + '/files/data_REGRESS.csv', 'm':cwd + '/files/data_MATCH.csv', 'p':cwd + '/files/data_PLAY.csv'} - - if len(sys.argv) == 1: # load data from the default karoo_gp/files/ directory - data_x = np.loadtxt(data_dict[self.kernel], skiprows = 1, delimiter = ',', dtype = float); data_x = data_x[:,0:-1] # load all but the right-most column - data_y = np.loadtxt(data_dict[self.kernel], skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels) - header = open(data_dict[self.kernel],'r') # read only the top row of parameters - self.dataset = data_dict[self.kernel] # copy the name only + print '\n Evolve a population of Trees for Generation', self.generation_id, '...' + self.population_b = ['Karoo GP by Kai Staats, Evolving Generation'] # initialise population_b to host the next generation - elif len(sys.argv) == 2: # load an external data file - data_x = np.loadtxt(sys.argv[1], skiprows = 1, delimiter = ',', dtype = float); data_x = data_x[:,0:-1] # load all but the right-most column - data_y = np.loadtxt(sys.argv[1], skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels) - header = open(sys.argv[1],'r') # read only the top row of parameters - self.dataset = sys.argv[1] # copy the name only + self.fx_fitness_gene_pool() # generate the viable gene pool (compares against gp.tree_depth_min) + self.fx_nextgen_reproduce() # method 1 - Reproduction + self.fx_nextgen_point_mutate() # method 2 - Point Mutation + self.fx_nextgen_branch_mutate() # method 3 - Branch Mutation + self.fx_nextgen_crossover() # method 4 - Crossover + self.fx_eval_generation() # evaluate all Trees in a single generation - elif len(sys.argv) > 2: # receive filename and additional arguments from karoo_gp_server.py via argparse - data_x = np.loadtxt(filename, skiprows = 1, delimiter = ',', dtype = float); data_x = data_x[:,0:-1] # load all but the right-most column - data_y = np.loadtxt(filename, skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels) - header = open(filename,'r') # read only the top row of parameters - self.dataset = filename # copy the name only + self.population_a = self.fx_evolve_pop_copy(self.population_b, ['Karoo GP by Kai Staats, Generation ' + str(self.generation_id)]) - fitt_dict = {'c':'max', 'r':'min', 'm':'max', 'p':''} - self.fitness_type = fitt_dict[self.kernel] # load fitness type - - func_dict = {'c':cwd + '/files/operators_CLASSIFY.csv', 'r':cwd + '/files/operators_REGRESS.csv', 'm':cwd + '/files/operators_MATCH.csv', 'p':cwd + '/files/operators_PLAY.csv'} - self.functions = np.loadtxt(func_dict[self.kernel], delimiter=',', skiprows=1, dtype = str) # load the user defined functions (operators) - self.terminals = header.readline().split(','); self.terminals[-1] = self.terminals[-1].replace('\n','') # load the user defined terminals (operands) - self.class_labels = len(np.unique(data_y)) # load the user defined true labels for classification or solutions for regression - #self.coeff = np.loadtxt(cwd + '/files/coefficients.csv', delimiter=',', skiprows=1, dtype = str) # load the user defined coefficients - NOT USED YET - - - ### 2) from the dataset, extract TRAINING and TEST data ### - - if len(data_x) < 11: # for small datasets we will not split them into TRAINING and TEST components - data_train = np.c_[data_x, data_y] - data_test = np.c_[data_x, data_y] - - else: # if larger than 10, we run the data through the SciKit Learn's 'random split' function - x_train, x_test, y_train, y_test = skcv.train_test_split(data_x, data_y, test_size = 0.2) # 80/20 TRAIN/TEST split - data_x, data_y = [], [] # clear from memory - - data_train = np.c_[x_train, y_train] # recombine each row of data with its associated class label (right column) - x_train, y_train = [], [] # clear from memory - - data_test = np.c_[x_test, y_test] # recombine each row of data with its associated class label (right column) - x_test, y_test = [], [] # clear from memory - - self.data_train_cols = len(data_train[0,:]) # qty count - self.data_train_rows = len(data_train[:,0]) # qty count - self.data_test_cols = len(data_test[0,:]) # qty count - self.data_test_rows = len(data_test[:,0]) # qty count - - - ### 3) load TRAINING and TEST data for TensorFlow processing - tested 2017 02/02 - - self.data_train = data_train # Store train data for processing in TF - self.data_test = data_test # Store test data for processing in TF - self.tf_device = "/gpu:0" # Set TF computation backend device (CPU or GPU); gpu:n = 1st, 2nd, or ... GPU device - self.tf_device_log = False # TF device usage logging (for debugging) - - - ### 4) create a unique directory and initialise all .csv files ### - self.datetime = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') - self.path = os.path.join(cwd, 'runs/', filename.split('.')[0] + '_' + self.datetime + '/') # generate a unique directory name - if not os.path.isdir(self.path): os.makedirs(self.path) # make a unique directory - - self.filename = {} # a dictionary to hold .csv filenames - - self.filename.update( {'a':self.path + 'population_a.csv'} ) - target = open(self.filename['a'], 'w') # initialise the .csv file for population 'a' (foundation) + # "End of line, man!" --CLU + target = open(self.filename['f'], 'w') # reset the .csv file for the final population target.close() - self.filename.update( {'b':self.path + 'population_b.csv'} ) - target = open(self.filename['b'], 'w') # initialise the .csv file for population 'b' (evolving) - target.close() + self.fx_data_tree_write(self.population_b, 'f') # save the final generation of Trees to disk - self.filename.update( {'f':self.path + 'population_f.csv'} ) - target = open(self.filename['f'], 'w') # initialise the .csv file for the final population (test) - target.close() + print '\n\033[3m "It is not the strongest of the species that survive, nor the most intelligent,\033[0;0m' + print '\033[3m but the one most responsive to change."\033[0;0m --Charles Darwin' + print '' + print '\033[3m Congrats!\033[0;0m Your Karoo GP run is complete.\n' + print '\033[36m Type \033[1m?\033[0;0m\033[36m to review your options or \033[1mq\033[0;0m\033[36m to quit.\033[0;0m\n' - self.filename.update( {'s':self.path + 'population_s.csv'} ) - # do NOT initialise this .csv file, as it is retained for loading a previous run (recover) + self.fx_karoo_pause(1) return - def fx_karoo_data_recover(self, population): - - ''' - This method is used to load a saved population of Trees, as invoked through the (pause) menu where population_s - replaces population_a in the /[path]/karoo_gp/runs/ directory. - - Arguments required: population size - ''' - - with open(population, 'rb') as csv_file: - target = csv.reader(csv_file, delimiter=',') - n = 0 # track row count - - for row in target: - - n = n + 1 - if n == 1: pass # skip first empty row - - elif n == 2: - self.population_a = [row] # write header to population_a - - else: - if row == []: - self.tree = np.array([[]]) # initialise Tree array - - else: - if self.tree.shape[1] == 0: - self.tree = np.append(self.tree, [row], axis = 1) # append first row to Tree - - else: - self.tree = np.append(self.tree, [row], axis = 0) # append subsequent rows to Tree - - if self.tree.shape[0] == 13: - self.population_a.append(self.tree) # append complete Tree to population list - - print self.population_a - - return - - - def fx_karoo_construct(self, tree_type, tree_depth_base): - - ''' - As used by the method 'karoo_gp', this method constructs the initial population based upon the user-defined - Tree type and initial, maximum Tree depth ('tree_depth_base'). "Ramped half/half" was defined by John Koza as - a means of building maximum diversity in the initial population. There are equal numbers of Full and Grow - methods trees, and an equal spread of Trees across depths 1 to 'tree_depth_base'. - - Arguments required: tree_type, tree_depth_base - ''' - - if self.display == 'i' or self.display == 'g': - print '\n\t Type \033[1m?\033[0;0m at any (pause) to review your options, or \033[1mENTER\033[0;0m to continue.\033[0;0m' - self.fx_karoo_pause(0) - - if tree_type == 'r': # Ramped 50/50 - - TREE_ID = 1 - for n in range(1, int((self.tree_pop_max / 2) / tree_depth_base) + 1): # split the population into equal parts - for depth in range(1, tree_depth_base + 1): # build 2 Trees ats each depth - self.fx_gen_tree_build(TREE_ID, 'f', depth) # build a Full Tree - self.fx_archive_tree_append(self.tree) # append Tree to the list 'gp.population_a' - TREE_ID = TREE_ID + 1 - - self.fx_gen_tree_build(TREE_ID, 'g', depth) # build a Grow Tree - self.fx_archive_tree_append(self.tree) # append Tree to the list 'gp.population_a' - TREE_ID = TREE_ID + 1 - - if TREE_ID < self.tree_pop_max: # eg: split 100 by 2*3 and it will produce only 96 Trees ... - for n in range(self.tree_pop_max - TREE_ID + 1): # ... so we complete the run - self.fx_gen_tree_build(TREE_ID, 'g', tree_depth_base) - self.fx_archive_tree_append(self.tree) - TREE_ID = TREE_ID + 1 - - else: pass - - else: # Full or Grow - for TREE_ID in range(1, self.tree_pop_max + 1): - self.fx_gen_tree_build(TREE_ID, tree_type, tree_depth_base) # build the 1st generation of Trees - self.fx_archive_tree_append(self.tree) - - return - - - def fx_karoo_reproduce(self): - - ''' - Through tournament selection, a single Tree from the prior generation is copied without mutation to the next - generation. This is analogous to a member of the prior generation directly entering the gene pool of the - subsequent (younger) generation. - - Arguments required: none - ''' - - if self.display != 's': - if self.display == 'i': print '' - print ' Perform', self.evolve_repro, 'Reproductions ...' - if self.display == 'i': self.fx_karoo_pause(0) - - for n in range(self.evolve_repro): # quantity of Trees to be copied without mutation - tourn_winner = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for each reproduction - tourn_winner = self.fx_evolve_fitness_wipe(tourn_winner) # wipe fitness data - self.population_b.append(tourn_winner) # append array to next generation population of Trees - - return - - - def fx_karoo_point_mutate(self): - - ''' - Through tournament selection, a copy of a Tree from the prior generation mutates before being added to the - next generation. In this method, a single point is selected for mutation while maintaining function nodes as - functions (operators) and terminal nodes as terminals (variables). The size and shape of the Tree will remain - identical. - - Arguments required: none - ''' - - if self.display != 's': - if self.display == 'i': print '' - print ' Perform', self.evolve_point, 'Point Mutations ...' - if self.display == 'i': self.fx_karoo_pause(0) - - for n in range(self.evolve_point): # quantity of Trees to be generated through mutation - tourn_winner = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for each mutation - tourn_winner, node = self.fx_evolve_point_mutate(tourn_winner) # perform point mutation; return single point for record keeping - self.population_b.append(tourn_winner) # append array to next generation population of Trees - - return - - - def fx_karoo_branch_mutate(self): - - ''' - Through tournament selection, a copy of a Tree from the prior generation mutates before being added to the - next generation. Unlike Point Mutation, in this method an entire branch is selected. If the evolutionary run is - designated as Full, the size and shape of the Tree will remain identical, each node mutated sequentially, where - functions remain functions and terminals remain terminals. If the evolutionary run is designated as Grow or - Ramped Half/Half, the size and shape of the Tree may grow smaller or larger, but it may not exceed - tree_depth_max as defined by the user. - - Arguments required: none - ''' - - if self.display != 's': - if self.display == 'i': print '' - print ' Perform', self.evolve_branch, 'Full or Grow Mutations ...' - if self.display == 'i': self.fx_karoo_pause(0) - - for n in range(self.evolve_branch): # quantity of Trees to be generated through mutation - tourn_winner = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for each mutation - branch = self.fx_evolve_branch_select(tourn_winner) # select point of mutation and all nodes beneath - - # TEST & DEBUG: comment the top or bottom to force all Full or all Grow methods - - if tourn_winner[1][1] == 'f': # perform Full method mutation on 'tourn_winner' - tourn_winner = self.fx_evolve_full_mutate(tourn_winner, branch) - - elif tourn_winner[1][1] == 'g': # perform Grow method mutation on 'tourn_winner' - tourn_winner = self.fx_evolve_grow_mutate(tourn_winner, branch) - - self.population_b.append(tourn_winner) # append array to next generation population of Trees - - return - - - def fx_karoo_crossover(self): - - ''' - Through tournament selection, two trees are selected as parents to produce two offspring. Within each parent - Tree a branch is selected. Parent A is copied, with its selected branch deleted. Parent B's branch is then - copied to the former location of Parent A's branch and inserted (grafted). The size and shape of the child - Tree may be smaller or larger than either of the parents, but may not exceed 'tree_depth_max' as defined - by the user. - - This process combines genetic code from two parent Trees, both of which were chosen by the tournament process - as having a higher fitness than the average population. Therefore, there is a chance their offspring will - provide an improvement in total fitness. In most GP applications, Crossover is the most commonly applied - evolutionary operator (~70-80%). - - For those who like to watch, select 'db' (debug mode) at the launch of Karoo GP or at any (pause). - - Arguments required: none - ''' - - if self.display != 's': - if self.display == 'i': print '' - print ' Perform', self.evolve_cross, 'Crossovers ...' - if self.display == 'i': self.fx_karoo_pause(0) - - for n in range(self.evolve_cross / 2): # quantity of Trees to be generated through Crossover, accounting for 2 children each - - parent_a = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for 'parent_a' - branch_a = self.fx_evolve_branch_select(parent_a) # select branch within 'parent_a', to copy to 'parent_b' and receive a branch from 'parent_b' - - parent_b = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for 'parent_b' - branch_b = self.fx_evolve_branch_select(parent_b) # select branch within 'parent_b', to copy to 'parent_a' and receive a branch from 'parent_a' - - parent_c = np.copy(parent_a); branch_c = np.copy(branch_a) # else the Crossover mods affect the parent Trees, due to how Python manages '=' - parent_d = np.copy(parent_b); branch_d = np.copy(branch_b) # else the Crossover mods affect the parent Trees, due to how Python manages '=' - - offspring_1 = self.fx_evolve_crossover(parent_a, branch_a, parent_b, branch_b) # perform Crossover - self.population_b.append(offspring_1) # append the 1st child to next generation of Trees - - offspring_2 = self.fx_evolve_crossover(parent_d, branch_d, parent_c, branch_c) # perform Crossover - self.population_b.append(offspring_2) # append the 2nd child to next generation of Trees - - return - - def fx_karoo_pause(self, eol): ''' @@ -789,14 +501,14 @@ class Base_GP(object): try: query = raw_input('\n\t Overwrite the current population with population_s? ') if query not in ['y','n']: raise ValueError() - if query == 'y': self.fx_karoo_data_recover(self.filename['s']); break + if query == 'y': self.fx_data_recover(self.filename['s']); break elif query == 'n': break except ValueError: print '\n\t\033[32m Enter (y)es or (n)o. Try again ...\033[0;0m' elif pause == 'w': # write the evolving population_b to disk if self.generation_id > 1: - self.fx_archive_tree_write(self.population_b, 'b') + self.fx_data_tree_write(self.population_b, 'b') print '\t\033[36m All current members of the evolving population_b saved to .csv\033[0;0m' else: print '\n\t\033[36m The evolving population_b does not yet exist\033[0;0m' @@ -804,17 +516,17 @@ class Base_GP(object): elif pause == 'q': if eol == 0: # if the GP run is not at the final generation - query = raw_input('\n\t \033[32mThe current population_b will be lost!\033[0;0m\n\n\t Are you certain you want to quit? (y/n) ') + query = raw_input('\n\t\033[32m The current population_b will be lost!\033[0;0m\n\n\t Are you certain you want to quit? (y/n) ') if query == 'y': - self.fx_archive_params_write('Desktop') # save run-time parameters to disk + self.fx_data_params_write('Desktop') # save run-time parameters to disk sys.exit() # quit the script without saving population_b else: break else: # if the GP run is complete query = raw_input('\n\t Are you certain you want to quit? (y/n) ') if query == 'y': - print '\n\t \033[32mYour Trees and runtime parameters are archived in karoo_gp/runs/\033[0;0m' - self.fx_archive_params_write('Desktop') # save run-time parameters to disk + print '\n\t\033[32m Your Trees and runtime parameters are archived in karoo_gp/runs/\033[0;0m' + self.fx_data_params_write('Desktop') # save run-time parameters to disk sys.exit() else: self.fx_karoo_pause(1) @@ -824,1764 +536,153 @@ class Base_GP(object): return - def fx_karoo_continue(self, next_gen_start): + #+++++++++++++++++++++++++++++++++++++++++++++ + # Methods to Load and Archive Data | + #+++++++++++++++++++++++++++++++++++++++++++++ + + def fx_data_load(self, filename): ''' - This method enables the launch of another full run of Karoo GP, but starting with a seed generation - instead of with a randomly generated first population. This can be used at the end of a standard run to - continue the evoluationary process, or after having recovered a set of trees from a prior run. + The data and function .csv files are loaded according to the fitness function kernel selected by the user. An + alternative dataset may be loaded at launch, by appending a command line argument. The data is then split into + both TRAINING and TEST segments in order to validate the success of the GP training run. Datasets less than + 10 rows will not be split, rather copied in full to both TRAINING and TEST as it is assumed you are conducting + a system validation run, as with the built-in MATCH kernel and associated dataset. - Arguments required: next_gen_start + Arguments required: filename (of the dataset) ''' - for self.generation_id in range(next_gen_start, self.generation_max + 1): # evolve additional generations of Trees + ### 1) load the associated data set, operators, operands, fitness type, and coefficients ### - print '\n Evolve a population of Trees for Generation', self.generation_id, '...' - self.population_b = ['Karoo GP by Kai Staats, Evolving Generation'] # initialise population_b to host the next generation + # full_path = os.path.realpath(__file__); cwd = os.path.dirname(full_path) # Good idea Marco :) + cwd = os.getcwd() + + data_dict = {'c':cwd + '/files/data_CLASSIFY.csv', 'r':cwd + '/files/data_REGRESS.csv', 'm':cwd + '/files/data_MATCH.csv', 'p':cwd + '/files/data_PLAY.csv'} + + if len(sys.argv) == 1: # load data from the default karoo_gp/files/ directory + data_x = np.loadtxt(data_dict[self.kernel], skiprows = 1, delimiter = ',', dtype = float); data_x = data_x[:,0:-1] # load all but the right-most column + data_y = np.loadtxt(data_dict[self.kernel], skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels) + header = open(data_dict[self.kernel],'r') # open file to be read (below) + self.dataset = data_dict[self.kernel] # copy the name only - self.fx_fitness_gene_pool() # generate the viable gene pool (compares against gp.tree_depth_min) - self.fx_karoo_reproduce() # method 1 - Reproduction - self.fx_karoo_point_mutate() # method 2 - Point Mutation - self.fx_karoo_branch_mutate() # method 3 - Branch Mutation - self.fx_karoo_crossover() # method 4 - Crossover - self.fx_eval_generation() # evaluate all Trees in a single generation + elif len(sys.argv) == 2: # load an external data file + data_x = np.loadtxt(sys.argv[1], skiprows = 1, delimiter = ',', dtype = float); data_x = data_x[:,0:-1] # load all but the right-most column + data_y = np.loadtxt(sys.argv[1], skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels) + header = open(sys.argv[1],'r') # open file to be read (below) + self.dataset = sys.argv[1] # copy the name only - self.population_a = self.fx_evolve_pop_copy(self.population_b, ['Karoo GP by Kai Staats, Generation ' + str(self.generation_id)]) + elif len(sys.argv) > 2: # receive filename and additional arguments from karoo_gp_server.py via argparse + data_x = np.loadtxt(filename, skiprows = 1, delimiter = ',', dtype = float); data_x = data_x[:,0:-1] # load all but the right-most column + data_y = np.loadtxt(filename, skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels) + header = open(filename,'r') # open file to be read (below) + self.dataset = filename # copy the name only - # "End of line, man!" --CLU - target = open(self.filename['f'], 'w') # reset the .csv file for the final population + fitt_dict = {'c':'max', 'r':'min', 'm':'max', 'p':''} + self.fitness_type = fitt_dict[self.kernel] # load fitness type + + func_dict = {'c':cwd + '/files/operators_CLASSIFY.csv', 'r':cwd + '/files/operators_REGRESS.csv', 'm':cwd + '/files/operators_MATCH.csv', 'p':cwd + '/files/operators_PLAY.csv'} + self.functions = np.loadtxt(func_dict[self.kernel], delimiter=',', skiprows=1, dtype = str) # load the user defined functions (operators) + self.terminals = header.readline().split(','); self.terminals[-1] = self.terminals[-1].replace('\n','') # load the user defined terminals (operands) + self.class_labels = len(np.unique(data_y)) # load the user defined true labels for classification or solutions for regression + #self.coeff = np.loadtxt(cwd + '/files/coefficients.csv', delimiter=',', skiprows=1, dtype = str) # load the user defined coefficients - NOT USED YET + + + ### 2) from the dataset, extract TRAINING and TEST data ### + + if len(data_x) < 11: # for small datasets we will not split them into TRAINING and TEST components + data_train = np.c_[data_x, data_y] + data_test = np.c_[data_x, data_y] + + else: # if larger than 10, we run the data through the SciKit Learn's 'random split' function + x_train, x_test, y_train, y_test = skcv.train_test_split(data_x, data_y, test_size = 0.2) # 80/20 TRAIN/TEST split + data_x, data_y = [], [] # clear from memory + + data_train = np.c_[x_train, y_train] # recombine each row of data with its associated class label (right column) + x_train, y_train = [], [] # clear from memory + + data_test = np.c_[x_test, y_test] # recombine each row of data with its associated class label (right column) + x_test, y_test = [], [] # clear from memory + + self.data_train_cols = len(data_train[0,:]) # qty count + self.data_train_rows = len(data_train[:,0]) # qty count + self.data_test_cols = len(data_test[0,:]) # qty count + self.data_test_rows = len(data_test[:,0]) # qty count + + + ### 3) load TRAINING and TEST data for TensorFlow processing - tested 2017 02/02 + + self.data_train = data_train # Store train data for processing in TF + self.data_test = data_test # Store test data for processing in TF + self.tf_device = "/gpu:0" # Set TF computation backend device (CPU or GPU); gpu:n = 1st, 2nd, or ... GPU device + self.tf_device_log = False # TF device usage logging (for debugging) + + + ### 4) create a unique directory and initialise all .csv files ### + self.datetime = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + self.path = os.path.join(cwd, 'runs/', filename.split('.')[0] + '_' + self.datetime + '/') # generate a unique directory name + if not os.path.isdir(self.path): os.makedirs(self.path) # make a unique directory + + self.filename = {} # a dictionary to hold .csv filenames + + self.filename.update( {'a':self.path + 'population_a.csv'} ) + target = open(self.filename['a'], 'w') # initialise the .csv file for population 'a' (foundation) target.close() - self.fx_archive_tree_write(self.population_b, 'f') # save the final generation of Trees to disk - self.fx_karoo_eol() + self.filename.update( {'b':self.path + 'population_b.csv'} ) + target = open(self.filename['b'], 'w') # initialise the .csv file for population 'b' (evolving) + target.close() + + self.filename.update( {'f':self.path + 'population_f.csv'} ) + target = open(self.filename['f'], 'w') # initialise the .csv file for the final population (test) + target.close() + + self.filename.update( {'s':self.path + 'population_s.csv'} ) + # do NOT initialise this .csv file, as it is retained for loading a previous run (recover) return - def fx_karoo_eol(self): - - ''' - The very last method to run in Karoo GP. - - Arguments required: none - ''' - - print '\n\033[3m "It is not the strongest of the species that survive, nor the most intelligent,\033[0;0m' - print '\033[3m but the one most responsive to change."\033[0;0m --Charles Darwin' - print '' - print '\033[3m Congrats!\033[0;0m Your multi-generational Karoo GP run is complete.\n' - print '\033[36m Type \033[1m?\033[0;0m\033[36m to review your options or \033[1mq\033[0;0m\033[36m to quit.\033[0;0m\n' - self.fx_karoo_pause(1) - - return - + def fx_data_recover(self, population): - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Generate a new Tree | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_gen_tree_initialise(self, TREE_ID, tree_type, tree_depth_base): - ''' - Assign 13 global variables to the array 'tree'. + This method is used to load a saved population of Trees, as invoked through the (pause) menu where population_s + replaces population_a in the /[path]/karoo_gp/runs/ directory. - Build the array 'tree' with 13 rows and initally, just 1 column of labels. This array will grow as each new - node is appended. The values of this array are stored as string characters. Numbers will be forced to integers - at the point of execution. - - This method is called by 'fx_gen_tree_build'. - - Arguments required: TREE_ID, tree_type, tree_depth_base + Arguments required: population size ''' - self.pop_TREE_ID = TREE_ID # pos 0: a unique identifier for each tree - self.pop_tree_type = tree_type # pos 1: a global constant based upon the initial user setting - self.pop_tree_depth_base = tree_depth_base # pos 2: a global variable which conveys 'tree_depth_base' as unique to each new Tree - self.pop_NODE_ID = 1 # pos 3: unique identifier for each node; this is the INDEX KEY to this array - self.pop_node_depth = 0 # pos 4: depth of each node when committed to the array - self.pop_node_type = '' # pos 5: root, function, or terminal - self.pop_node_label = '' # pos 6: operator [+, -, *, ...] or terminal [a, b, c, ...] - self.pop_node_parent = '' # pos 7: parent node - self.pop_node_arity = '' # pos 8: number of nodes attached to each non-terminal node - self.pop_node_c1 = '' # pos 9: child node 1 - self.pop_node_c2 = '' # pos 10: child node 2 - self.pop_node_c3 = '' # pos 11: child node 3 (assumed max of 3 with boolean operator 'if') - self.pop_fitness = '' # pos 12: fitness score following Tree evaluation - - self.tree = np.array([ ['TREE_ID'],['tree_type'],['tree_depth_base'],['NODE_ID'],['node_depth'],['node_type'],['node_label'],['node_parent'],['node_arity'],['node_c1'],['node_c2'],['node_c3'],['fitness'] ]) - - return + with open(population, 'rb') as csv_file: + target = csv.reader(csv_file, delimiter=',') + n = 0 # track row count + + for row in target: + + n = n + 1 + if n == 1: pass # skip first empty row - - ### Root Node ### - - def fx_gen_root_node_build(self): - - ''' - Build the Root node for the initial population. - - This method is called by 'fx_gen_tree_build'. - - Arguments required: none - ''' - - self.fx_gen_function_select() # select the operator for root - - if self.pop_node_arity == 1: # 1 child - self.pop_node_c1 = 2 - self.pop_node_c2 = '' - self.pop_node_c3 = '' - - elif self.pop_node_arity == 2: # 2 children - self.pop_node_c1 = 2 - self.pop_node_c2 = 3 - self.pop_node_c3 = '' - - elif self.pop_node_arity == 3: # 3 children - self.pop_node_c1 = 2 - self.pop_node_c2 = 3 - self.pop_node_c3 = 4 - - else: print '\n\t\033[31m ERROR! In fx_gen_root_node_build: pop_node_arity =', self.pop_node_arity, '\033[0;0m'; self.fx_karoo_pause(0) - - self.pop_node_type = 'root' - - self.fx_gen_node_commit() - - return - - - ### Function Nodes ### - - def fx_gen_function_node_build(self): - - ''' - Build the Function nodes for the intial population. - - This method is called by 'fx_gen_tree_build'. - - Arguments required: none - ''' - - for i in range(1, self.pop_tree_depth_base): # increment depth, from 1 through 'tree_depth_base' - 1 - - self.pop_node_depth = i # increment 'node_depth' - - parent_arity_sum = 0 - prior_sibling_arity = 0 # reset for 'c_buffer' in 'children_link' - prior_siblings = 0 # reset for 'c_buffer' in 'children_link' - - for j in range(1, len(self.tree[3])): # increment through all nodes (exclude 0) in array 'tree' - - if int(self.tree[4][j]) == self.pop_node_depth - 1: # find parent nodes which reside at the prior depth - parent_arity_sum = parent_arity_sum + int(self.tree[8][j]) # sum arities of all parent nodes at the prior depth + elif n == 2: + self.population_a = [row] # write header to population_a - # (do *not* merge these 2 "j" loops or it gets all kinds of messed up) - - for j in range(1, len(self.tree[3])): # increment through all nodes (exclude 0) in array 'tree' - - if int(self.tree[4][j]) == self.pop_node_depth - 1: # find parent nodes which reside at the prior depth - - for k in range(1, int(self.tree[8][j]) + 1): # increment through each degree of arity for each parent node - self.pop_node_parent = int(self.tree[3][j]) # set the parent 'NODE_ID' ... - prior_sibling_arity = self.fx_gen_function_gen(parent_arity_sum, prior_sibling_arity, prior_siblings) # ... generate a Function ndoe - prior_siblings = prior_siblings + 1 # sum sibling nodes (current depth) who will spawn their own children (cousins? :) - - return - - - def fx_gen_function_gen(self, parent_arity_sum, prior_sibling_arity, prior_siblings): - - ''' - Generate a single Function node for the initial population. - - This method is called by 'fx_gen_function_node_build'. - - Arguments required: parent_arity_sum, prior_sibling_arity, prior_siblings - ''' - - if self.pop_tree_type == 'f': # user defined as (f)ull - self.fx_gen_function_select() # retrieve a function - self.fx_gen_child_link(parent_arity_sum, prior_sibling_arity, prior_siblings) # establish links to children - - elif self.pop_tree_type == 'g': # user defined as (g)row - rnd = np.random.randint(2) - - if rnd == 0: # randomly selected as Function - self.fx_gen_function_select() # retrieve a function - self.fx_gen_child_link(parent_arity_sum, prior_sibling_arity, prior_siblings) # establish links to children - - elif rnd == 1: # randomly selected as Terminal - self.fx_gen_terminal_select() # retrieve a terminal - self.pop_node_c1 = '' - self.pop_node_c2 = '' - self.pop_node_c3 = '' - - self.fx_gen_node_commit() # commit new node to array - prior_sibling_arity = prior_sibling_arity + self.pop_node_arity # sum the arity of prior siblings - - return prior_sibling_arity - - - def fx_gen_function_select(self): - - ''' - Define a single Function (operator extracted from the associated functions.csv) for the initial population. - - This method is called by 'fx_gen_function_gen' and 'fx_gen_root_node_build'. - - Arguments required: none - ''' - - self.pop_node_type = 'func' - rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators - self.pop_node_label = self.functions[rnd][0] - self.pop_node_arity = int(self.functions[rnd][1]) - - return - - - ### Terminal Nodes ### - - def fx_gen_terminal_node_build(self): - - ''' - Build the Terminal nodes for the intial population. - - This method is called by 'fx_gen_tree_build'. - - Arguments required: none - ''' - - self.pop_node_depth = self.pop_tree_depth_base # set the final node_depth (same as 'gp.pop_node_depth' + 1) - - for j in range(1, len(self.tree[3]) ): # increment through all nodes (exclude 0) in array 'tree' - - if int(self.tree[4][j]) == self.pop_node_depth - 1: # find parent nodes which reside at the prior depth - - for k in range(1,(int(self.tree[8][j]) + 1)): # increment through each degree of arity for each parent node - self.pop_node_parent = int(self.tree[3][j]) # set the parent 'NODE_ID' ... - self.fx_gen_terminal_gen() # ... generate a Terminal node - - return - - - def fx_gen_terminal_gen(self): - - ''' - Generate a single Terminal node for the initial population. - - This method is called by 'fx_gen_terminal_node_build'. - - Arguments required: none - ''' - - self.fx_gen_terminal_select() # retrieve a terminal - self.pop_node_c1 = '' - self.pop_node_c2 = '' - self.pop_node_c3 = '' - - self.fx_gen_node_commit() # commit new node to array - - return - - - def fx_gen_terminal_select(self): - - ''' - Define a single Terminal (variable extracted from the top row of the associated TRAINING data) - - This method is called by 'fx_gen_terminal_gen' and 'fx_gen_function_gen'. - - Arguments required: none - ''' - - self.pop_node_type = 'term' - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - self.pop_node_label = self.terminals[rnd] - self.pop_node_arity = 0 - - return - - - ### The Lovely Children ### - - def fx_gen_child_link(self, parent_arity_sum, prior_sibling_arity, prior_siblings): - - ''' - Link each parent node to its children in the intial population. - - This method is called by 'fx_gen_function_gen'. - - Arguments required: parent_arity_sum, prior_sibling_arity, prior_siblings - ''' - - c_buffer = 0 - - for n in range(1, len(self.tree[3]) ): # increment through all nodes (exclude 0) in array 'tree' - - if int(self.tree[4][n]) == self.pop_node_depth - 1: # find all nodes that reside at the prior (parent) 'node_depth' - - c_buffer = self.pop_NODE_ID + (parent_arity_sum + prior_sibling_arity - prior_siblings) # One algo to rule the world! - - if self.pop_node_arity == 0: # terminal in a Grow Tree - self.pop_node_c1 = '' - self.pop_node_c2 = '' - self.pop_node_c3 = '' - - elif self.pop_node_arity == 1: # 1 child - self.pop_node_c1 = c_buffer - self.pop_node_c2 = '' - self.pop_node_c3 = '' - - elif self.pop_node_arity == 2: # 2 children - self.pop_node_c1 = c_buffer - self.pop_node_c2 = c_buffer + 1 - self.pop_node_c3 = '' - - elif self.pop_node_arity == 3: # 3 children - self.pop_node_c1 = c_buffer - self.pop_node_c2 = c_buffer + 1 - self.pop_node_c3 = c_buffer + 2 - - else: print '\n\t\033[31m ERROR! In fx_gen_child_link: pop_node_arity =', self.pop_node_arity, '\033[0;0m'; self.fx_karoo_pause(0) - - return - - - def fx_gen_node_commit(self): - - ''' - Commit the values of a new node (root, function, or terminal) to the array 'tree'. - - This method is called by 'fx_gen_root_node_build' and 'fx_gen_function_gen' and 'fx_gen_terminal_gen'. - - Arguments required: none - ''' - - self.tree = np.append(self.tree, [ [self.pop_TREE_ID],[self.pop_tree_type],[self.pop_tree_depth_base],[self.pop_NODE_ID],[self.pop_node_depth],[self.pop_node_type],[self.pop_node_label],[self.pop_node_parent],[self.pop_node_arity],[self.pop_node_c1],[self.pop_node_c2],[self.pop_node_c3],[self.pop_fitness] ], 1) - - self.pop_NODE_ID = self.pop_NODE_ID + 1 - - return - - - def fx_gen_tree_build(self, TREE_ID, tree_type, tree_depth_base): - - ''' - This method combines 4 sub-methods into a single method for ease of deployment. It is designed to executed - within a loop such that an entire population is built. However, it may also be run from the command line, - passing a single TREE_ID to the method. - - 'tree_type' is either (f)ull or (g)row. Note, however, that when the user selects 'ramped 50/50' at launch, - it is still (f) or (g) which are passed to this method. - - This method is called by a 'fx_evolve_crossover' and 'fx_evolve_grow_mutate' and 'fx_karoo_construct'. - - Arguments required: TREE_ID, tree_type, tree_depth_base - ''' - - self.fx_gen_tree_initialise(TREE_ID, tree_type, tree_depth_base) # initialise a new Tree - self.fx_gen_root_node_build() # build the Root node - self.fx_gen_function_node_build() # build the Function nodes - self.fx_gen_terminal_node_build() # build the Terminal nodes - - return # each Tree is written to 'gp.tree' - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Evaluate a Tree | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_eval_poly(self, tree): - - ''' - Evaluate a Tree and generate its multivariate expression (both raw and Sympified). - - We need to extract the variables from the expression. However, these variables are no longer correlated - to the original variables listed across the top of each column of data.csv. Therefore, we must re-assign - the respective values for each subsequent row in the data .csv, for each Tree's unique expression. - - Arguments required: tree - ''' - - self.algo_raw = self.fx_eval_label(tree, 1) # pass the root 'node_id', then flatten the Tree to a string - self.algo_sym = sympify(self.algo_raw) # convert string to a functional expression (the coolest line in Karoo! :) - - return - - - def fx_eval_label(self, tree, node_id): - - ''' - Evaluate all or part of a Tree (starting at node_id) and return a raw mutivariate expression ('algo_raw'). - - In the main code, this method is called once per Tree, but may be called at any time to prepare an expression - for any full or partial (branch) Tree contained in 'population'. - - Pass the starting node for recursion via the local variable 'node_id' where the local variable 'tree' is a - copy of the Tree you desire to evaluate. - - Arguments required: tree, node_id - ''' - - # if tree[6, node_id] == 'not': tree[6, node_id] = ', not' # temp until this can be fixed at data_load - - node_id = int(node_id) - - if tree[8, node_id] == '0': # arity of 0 for the pattern '[term]' - return '(' + tree[6, node_id] + ')' # 'node_label' (function or terminal) - - else: - if tree[8, node_id] == '1': # arity of 1 for the explicit pattern 'not [term]' - return self.fx_eval_label(tree, tree[9, node_id]) + tree[6, node_id] - - elif tree[8, node_id] == '2': # arity of 2 for the pattern '[func] [term] [func]' - return self.fx_eval_label(tree, tree[9, node_id]) + tree[6, node_id] + self.fx_eval_label(tree, tree[10, node_id]) - - elif tree[8, node_id] == '3': # arity of 3 for the explicit pattern 'if [term] then [term] else [term]' - return tree[6, node_id] + self.fx_eval_label(tree, tree[9, node_id]) + ' then ' + self.fx_eval_label(tree, tree[10, node_id]) + ' else ' + self.fx_eval_label(tree, tree[11, node_id]) + else: + if row == []: + self.tree = np.array([[]]) # initialise Tree array - - def fx_eval_id(self, tree, node_id): - - ''' - Evaluate all or part of a Tree and return a list of all 'NODE_ID's. - - This method generates a list of all 'NODE_ID's from the given Node and below. It is used primarily to generate - 'branch' for the multi-generational mutation of Trees. - - Pass the starting node for recursion via the local variable 'node_id' where the local variable 'tree' is a copy - of the Tree you desire to evaluate. - - Arguments required: tree, node_id - ''' - - node_id = int(node_id) - - if tree[8, node_id] == '0': # arity of 0 for the pattern '[NODE_ID]' - return tree[3, node_id] # 'NODE_ID' - - else: - if tree[8, node_id] == '1': # arity of 1 for the pattern '[NODE_ID], [NODE_ID]' - return tree[3, node_id] + ', ' + self.fx_eval_id(tree, tree[9, node_id]) - - elif tree[8, node_id] == '2': # arity of 2 for the pattern '[NODE_ID], [NODE_ID], [NODE_ID]' - return tree[3, node_id] + ', ' + self.fx_eval_id(tree, tree[9, node_id]) + ', ' + self.fx_eval_id(tree, tree[10, node_id]) - - elif tree[8, node_id] == '3': # arity of 3 for the pattern '[NODE_ID], [NODE_ID], [NODE_ID], [NODE_ID]' - return tree[3, node_id] + ', ' + self.fx_eval_id(tree, tree[9, node_id]) + ', ' + self.fx_eval_id(tree, tree[10, node_id]) + ', ' + self.fx_eval_id(tree, tree[11, node_id]) - - - def fx_eval_generation(self): - - ''' - This method invokes the evaluation of an entire generation of Trees, as engaged by karoo_gp_server.py and the - 'cont' function of karoo_go_main.py. It automatically evaluates population_b before invoking the copy of _b to _a. - - Arguments required: none - ''' - - if self.display != 's': - if self.display == 'i': print '' - print '\n Evaluate all Trees in Generation', self.generation_id - if self.display == 'i': self.fx_karoo_pause(0) - - self.fx_evolve_tree_renum(self.population_b) # population renumber - self.fx_fitness_gym(self.population_b) # run 'fx_eval', 'fx_fitness', 'fx_fitness_store', and fitness record - self.fx_archive_tree_write(self.population_b, 'a') # archive current population as foundation for next generation - - if self.display != 's': - print '\n Copy gp.population_b to gp.population_a\n' - - return - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Train and Test a Tree | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_fitness_gym(self, population): - - ''' - Part 1 evaluates each expression against the data, line for line. This is the most time consuming and - computationally expensive part of genetic programming. When GPUs are available, the performance can increase - by many orders of magnitude for datasets measured in millions of data. - - Part 2 evaluates every Tree in each generation to determine which have the best, overall fitness score. This - could be the highest or lowest depending upon if the fitness function is maximising (higher is better) or - minimising (lower is better). The total fitness score is then saved with each Tree in the external .csv file. - - Part 3 compares the fitness of each Tree to the prior best fit in order to track those that improve with each - comparison. For matching functions, all the Trees will have the same fitness score, but they may present more - than one solution. For minimisation and maximisation functions, the final Tree should present the best overall - fitness for that generation. It is important to note that Part 3 does *not* in any way influence the Tournament - Selection which is a stand-alone process. - - Arguments required: population - ''' - - fitness_best = 0 - self.fittest_dict = {} - time_sum = 0 - - for tree_id in range(1, len(population)): - - ### PART 1 - GENERATE MULTIVARIATE EXPRESSION FOR EACH TREE ### - self.fx_eval_poly(population[tree_id]) # extract the expression - if self.display not in ('s'): print '\t\033[36mTree', population[tree_id][0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' - - - ### PART 2 - EVALUATE FITNESS FOR EACH TREE AGAINST TRAINING DATA ### - fitness = 0 - - expr = str(self.algo_sym) # get sympified expression and process it with TF - tested 2017 02/02 - result = self.fx_fitness_eval(expr, self.data_train) - fitness = result['fitness'] # extract fitness score - - if self.display == 'i': - print '\t \033[36m with fitness sum:\033[1m', fitness, '\033[0;0m\n' - - self.fx_fitness_store(population[tree_id], fitness) # store Fitness with each Tree - - - ### PART 3 - COMPARE FITNESS OF ALL TREES IN CURRENT GENERATION ### - if self.kernel == 'c': # display best fit Trees for the CLASSIFY kernel - if fitness >= fitness_best: # find the Tree with Maximum fitness score - fitness_best = fitness # set best fitness score - self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary if fitness >= prior - - elif self.kernel == 'r': # display best fit Trees for the REGRESSION kernel - if fitness_best == 0: fitness_best = fitness # set the baseline first time through - if fitness <= fitness_best: # find the Tree with Minimum fitness score - fitness_best = fitness # set best fitness score - self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary if fitness <= prior - - elif self.kernel == 'm': # display best fit Trees for the MATCH kernel - if fitness == self.data_train_rows: # find the Tree with a perfect match for all data rows - fitness_best = fitness # set best fitness score - self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary if all rows match - - # elif self.kernel == '[other]': # display best fit Trees for the [other] kernel - # if fitness [>=, <=] fitness_best: # find the Tree with [Maximum or Minimum] fitness score - # fitness_best = fitness # set best fitness score - # self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary - - print '\n\033[36m ', len(self.fittest_dict.keys()), 'trees\033[1m', np.sort(self.fittest_dict.keys()), '\033[0;0m\033[36moffer the highest fitness scores.\033[0;0m' - if self.display == 'g': self.fx_karoo_pause(0) - - return - - - def fx_fitness_eval(self, expr, data, get_pred_labels = False): - - ''' - Computes tree expression using TensorFlow (TF) returning results and fitness scores. - - This method orchestrates most of the TF routines by parsing input string 'expression' and converting it into a TF - operation graph which is then processed in an isolated TF session to compute the results and corresponding fitness - values. - - 'self.tf_device' - controls which device will be used for computations (CPU or GPU). - 'self.tf_device_log' - controls device placement logging (debug only). - - Args: - 'expr' - a string containing math expression to be computed on the data. Variable names should match corresponding - terminal names in 'self.terminals'. - - 'data' - an 'n by m' matrix of the data points containing n observations and m features per observation. - Variable order should match corresponding order of terminals in 'self.terminals'. - - 'get_pred_labels' - a boolean flag which controls whether the predicted labels should be extracted from the - evolved results. This applies only to the CLASSIFY kernel and defaults to 'False'. - - Returns: - A dict mapping keys to the following outputs: - 'result' - an array of the results of applying given expression to the data - 'pred_labels' - an array of the predicted labels extracted from the results; defined only for CLASSIFY kernel, else None - 'solution' - an array of the solution values extracted from the data (variable 's' in the dataset) - 'pairwise_fitness' - an array of the element-wise results of applying corresponding fitness kernel function - 'fitness' - aggregated scalar fitness score - - Arguments required: expr, data - ''' - - # Initialize TensorFlow session - tf.reset_default_graph() # Reset TF internal state and cache (after previous processing) - config = tf.ConfigProto(log_device_placement=self.tf_device_log, allow_soft_placement=True) - config.gpu_options.allow_growth = True - - with tf.Session(config=config) as sess: - with sess.graph.device(self.tf_device): - - # 1 - Load data into TF vectors - tensors = {} - for i in range(len(self.terminals)): - var = self.terminals[i] - tensors[var] = tf.constant(data[:, i], dtype=tf.float32) # converts data into vectors - - # 2- Transform string expression into TF operation graph - result = self.fx_fitness_expr_parse(expr, tensors) - pred_labels = tf.no_op() # a placeholder, applies only to CLASSIFY kernel - solution = tensors['s'] # solution value is assumed to be stored in 's' terminal - - # 3- Add fitness computation into TF graph - if self.kernel == 'c': # CLASSIFY kernel - - ''' - Creates element-wise fitness computation TensorFlow (TF) sub-graph for CLASSIFY kernel. - - This method uses the 'sympified' (SymPy) expression ('algo_sym') created in 'fx_eval_poly' and the data set - loaded at run-time to evaluate the fitness of the selected kernel. - - This multiclass classifer compares each row of a given Tree to the known solution, comparing predicted labels - generated by Karoo GP against the true classs labels. This method is able to work with any number of class - labels, from 2 to n. The left-most bin includes -inf. The right-most bin includes +inf. Those inbetween are - by default confined to the spacing of 1.0 each, as defined by: - - (solution - 1) < result <= solution - - The skew adjusts the boundaries of the bins such that they fall on both the negative and positive sides of the - origin. At the time of this writing, an odd number of class labels will generate an extra bin on the positive - side of origin as it has not yet been determined the effect of enabling the middle bin to include both a - negative and positive result. - - Arguments required: result, solution - ''' - - # was breaking with upgrade from Tensorflow 1.1 to 1.3; fixed by Iurii by replacing [] with () as of 20171026 - # if get_pred_labels: pred_labels = tf.map_fn(self.fx_fitness_labels_map, result, dtype = [tf.int32, tf.string], swap_memory = True) - if get_pred_labels: pred_labels = tf.map_fn(self.fx_fitness_labels_map, result, dtype = (tf.int32, tf.string), swap_memory = True) - - skew = (self.class_labels / 2) - 1 - - rule11 = tf.equal(solution, 0) - rule12 = tf.less_equal(result, 0 - skew) - rule13 = tf.logical_and(rule11, rule12) - - rule21 = tf.equal(solution, self.class_labels - 1) - rule22 = tf.greater(result, solution - 1 - skew) - rule23 = tf.logical_and(rule21, rule22) - - rule31 = tf.less(solution - 1 - skew, result) - rule32 = tf.less_equal(result, solution - skew) - rule33 = tf.logical_and(rule31, rule32) - - pairwise_fitness = tf.cast(tf.logical_or(tf.logical_or(rule13, rule23), rule33), tf.int32) - - - elif self.kernel == 'r': # REGRESSION kernel - - ''' - A very, very basic REGRESSION kernel which is not designed to perform well in the real world. It requires - that you raise the minimum node count to keep it from converging on the value of '1'. Consider writing or - integrating a more sophisticated kernel. - ''' - - pairwise_fitness = tf.abs(solution - result) - - - elif self.kernel == 'm': # MATCH kernel - - ''' - This is used for demonstration purposes only. - ''' - - # pairwise_fitness = tf.cast(tf.equal(solution, result), tf.int32) # breaks due to floating points - RTOL, ATOL = 1e-05, 1e-08 # fixes above issue by checking if a float value lies within a range of values - pairwise_fitness = tf.cast(tf.less_equal(tf.abs(solution - result), ATOL + RTOL * tf.abs(result)), tf.int32) - - # elif self.kernel == '[other]': # [OTHER] kernel - # pairwise_fitness = tf.cast(tf.___(solution, result) - - else: raise Exception('Kernel type is wrong or missing. You entered {}'.format(self.kernel)) - - fitness = tf.reduce_sum(pairwise_fitness) - - # Process TF graph and collect the results - result, pred_labels, solution, fitness, pairwise_fitness = sess.run([result, pred_labels, solution, fitness, pairwise_fitness]) - - return {'result': result, 'pred_labels': pred_labels, 'solution': solution, 'fitness': float(fitness), 'pairwise_fitness': pairwise_fitness} - - - def fx_fitness_expr_parse(self, expr, tensors): - - ''' - Extract expression tree from the string algo_sym and transform into TensorFlow (TF) graph. - - Arguments required: expr, tensors - ''' - - tree = ast.parse(expr, mode='eval').body - - return self.fx_fitness_node_parse(tree, tensors) - - - def fx_fitness_chain_bool(self, values, operation, tensors): - - ''' - Chains a sequence of boolean operations (e.g. 'a and b and c') into a single TensorFlow (TF) sub graph. - - Arguments required: values, operation, tensors - ''' - - x = tf.cast(self.fx_fitness_node_parse(values[0], tensors), tf.bool) - if len(values) > 1: - return operation(x, self.fx_fitness_chain_bool(values[1:], operation, tensors)) - else: - return x - - - def fx_fitness_chain_compare(self, comparators, ops, tensors): - - ''' - Chains a sequence of comparison operations (e.g. 'a > b < c') into a single TensorFlow (TF) sub graph. - - Arguments required: comparators, ops, tensors - ''' - - x = self.fx_fitness_node_parse(comparators[0], tensors) - y = self.fx_fitness_node_parse(comparators[1], tensors) - if len(comparators) > 2: - return tf.logical_and(operators[type(ops[0])](x, y), self.fx_fitness_chain_compare(comparators[1:], ops[1:], tensors)) - else: - return operators[type(ops[0])](x, y) - - - def fx_fitness_node_parse(self, node, tensors): - - ''' - Recursively transforms parsed expression tree into TensorFlow (TF) graph. - - Arguments required: node, tensors - ''' - - if isinstance(node, ast.Name): # - return tensors[node.id] - - elif isinstance(node, ast.Num): # - shape = tensors[tensors.keys()[0]].get_shape() - return tf.constant(node.n, shape=shape, dtype=tf.float32) - - elif isinstance(node, ast.BinOp): # , e.g., x + y - return operators[type(node.op)](self.fx_fitness_node_parse(node.left, tensors), self.fx_fitness_node_parse(node.right, tensors)) - - elif isinstance(node, ast.UnaryOp): # e.g., -1 - return operators[type(node.op)](self.fx_fitness_node_parse(node.operand, tensors)) - - elif isinstance(node, ast.Call): # () e.g., sin(x) - return operators[node.func.id](*[self.fx_fitness_node_parse(arg, tensors) for arg in node.args]) - - elif isinstance(node, ast.BoolOp): # e.g. x or y - return self.fx_fitness_chain_bool(node.values, operators[type(node.op)], tensors) - - elif isinstance(node, ast.Compare): # e.g., a > z - return self.fx_fitness_chain_compare([node.left] + node.comparators, node.ops, tensors) - - else: raise TypeError(node) - - - def fx_fitness_labels_map(self, result): - - ''' - For the CLASSIFY kernel, creates a TensorFlow (TF) sub-graph defined as a sequence of boolean conditions based upon - the quantity of true class labels provided in the data .csv. Outputs an array of tuples containing the predicted - labels based upon the result and corresponding boolean condition triggered. - - For comparison, the original (pre-TensorFlow) cod follows: - - skew = (self.class_labels / 2) - 1 # '-1' keeps a binary classification splitting over the origin - if solution == 0 and result <= 0 - skew; fitness = 1: # check for first class (the left-most bin) - elif solution == self.class_labels - 1 and result > solution - 1 - skew; fitness = 1: # check for last class (the right-most bin) - elif solution - 1 - skew < result <= solution - skew; fitness = 1: # check for class bins between first and last - else: fitness = 0 # no class match - - Arguments required: result - ''' - - skew = (self.class_labels / 2) - 1 - label_rules = {self.class_labels - 1: (tf.constant(self.class_labels - 1), tf.constant(' > {}'.format(self.class_labels - 2 - skew)))} - - for class_label in range(self.class_labels - 2, 0, -1): - cond = (class_label - 1 - skew < result) & (result <= class_label - skew) - label_rules[class_label] = tf.cond(cond, lambda: (tf.constant(class_label), tf.constant(' <= {}'.format(class_label - skew))), lambda: label_rules[class_label + 1]) - - pred_label = tf.cond(result <= 0 - skew, lambda: (tf.constant(0), tf.constant(' <= {}'.format(0 - skew))), lambda: label_rules[1]) - - return pred_label - - - def fx_fitness_store(self, tree, fitness): - - ''' - Records the fitness and length of the raw algorithm (multivariate expression) to the Numpy array. Parsimony can - be used to apply pressure to the evolutionary process to select from a set of trees with the same fitness function - the one(s) with the simplest (shortest) multivariate expression. - - Arguments required: tree, fitness - ''' - - fitness = float(fitness) - fitness = round(fitness, self.precision) - - tree[12][1] = fitness # store the fitness with each tree - tree[12][2] = len(str(self.algo_raw)) # store the length of the raw algo for parsimony - # if len(tree[3]) > 4: # if the Tree array is wide enough -- SEE SCRATCHPAD - - return - - - def fx_fitness_tournament(self, tourn_size): - - ''' - Multiple contenders ('tourn_size') are randomly selected and then compared for their respective fitness, as - determined in 'fx_fitness_gym'. The tournament is engaged to select a single Tree for each invocation of the - genetic operators: reproduction, mutation (point, branch), and crossover (sexual reproduction). - - The original Tournament Selection drew directly from the foundation generation (gp.generation_a). However, - with the introduction of a minimum number of nodes as defined by the user ('gp.tree_depth_min'), - 'gp.gene_pool' limits the Trees to those which meet all criteria. - - Stronger boundary parameters (a reduced gap between the min and max number of nodes) may invoke more compact - solutions, but also runs the risk of elitism, even total population die-off where a healthy population once existed. - - Arguments required: tourn_size - ''' - - tourn_test = 0 - # short_test = 0 # an incomplete parsimony test (seeking shortest solution) - - if self.display == 'i': print '\n\tEnter the tournament ...' - - for n in range(tourn_size): - # tree_id = np.random.randint(1, self.tree_pop_max + 1) # former method of selection from the unfiltered population - rnd = np.random.randint(len(self.gene_pool)) # select one Tree at random from the gene pool - tree_id = int(self.gene_pool[rnd]) - - fitness = float(self.population_a[tree_id][12][1]) # extract the fitness from the array - fitness = round(fitness, self.precision) # force 'result' and 'solution' to the same number of floating points - - if self.fitness_type == 'max': # if the fitness function is Maximising - - # first time through, 'tourn_test' will be initialised below - - if fitness > tourn_test: # if the current Tree's 'fitness' is greater than the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '>', tourn_test, 'and leads\033[0;0m' - tourn_lead = tree_id # set 'TREE_ID' for the new leader - tourn_test = fitness # set 'fitness' of the new leader - # short_test = int(self.population_a[tree_id][12][2]) # set len(algo_raw) of new leader - - elif fitness == tourn_test: # if the current Tree's 'fitness' is equal to the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '=', tourn_test, 'and leads\033[0;0m' - tourn_lead = tree_id # in case there is no variance in this tournament - # tourn_test remains unchanged - - # NEED TO ADD: option for parsimony - # if int(self.population_a[tree_id][12][2]) < short_test: - # short_test = int(self.population_a[tree_id][12][2]) # set len(algo_raw) of new leader - # print '\t\033[36m with improved parsimony score of:\033[1m', short_test, '\033[0;0m' - - elif fitness < tourn_test: # if the current Tree's 'fitness' is less than the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '<', tourn_test, 'and is ignored\033[0;0m' - # tourn_lead remains unchanged - # tourn_test remains unchanged - - else: print '\n\t\033[31m ERROR! In fx_fitness_tournament: fitness =', fitness, 'and tourn_test =', tourn_test, '\033[0;0m'; self.fx_karoo_pause(0) - - - elif self.fitness_type == 'min': # if the fitness function is Minimising - - if tourn_test == 0: # first time through, 'tourn_test' is given a baseline value - tourn_test = fitness - - if fitness < tourn_test: # if the current Tree's 'fitness' is less than the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '<', tourn_test, 'and leads\033[0;0m' - tourn_lead = tree_id # set 'TREE_ID' for the new leader - tourn_test = fitness # set 'fitness' of the new leader - - elif fitness == tourn_test: # if the current Tree's 'fitness' is equal to the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '=', tourn_test, 'and leads\033[0;0m' - tourn_lead = tree_id # in case there is no variance in this tournament - # tourn_test remains unchanged - - elif fitness > tourn_test: # if the current Tree's 'fitness' is greater than the priors' - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '>', tourn_test, 'and is ignored\033[0;0m' - # tourn_lead remains unchanged - # tourn_test remains unchanged - - else: print '\n\t\033[31m ERROR! In fx_fitness_tournament: fitness =', fitness, 'and tourn_test =', tourn_test, '\033[0;0m'; self.fx_karoo_pause(0) - - - tourn_winner = np.copy(self.population_a[tourn_lead]) # copy full Tree so as to not inadvertantly modify the original tree - - if self.display == 'i': print '\n\t\033[36mThe winner of the tournament is Tree:\033[1m', tourn_winner[0][1], '\033[0;0m' - - return tourn_winner - - - def fx_fitness_gene_pool(self): - - ''' - The gene pool was introduced as means by which advanced users could define additional constraints on the evolved - functions, in an effort to guide the evolutionary process. The first constraint introduced is the 'mininum number - of nodes' parameter (gp.tree_depth_min). This defines the minimum number of nodes (in the context of Karoo, this - refers to both functions (operators) and terminals (operands)). - - When the minimum node count is human guided, it can keep the solution from defaulting to a local minimum, as with - 't/t' in the Kepler problem, by forcing a more complex solution. If you find that when engaging the Regression - kernel you are met with a solution which is too simple (eg: linear instead of non-linear), try increasing the - minimum number of nodes (with the launch of Karoo, or mid-stream by way of the pause menu). - - What's more, you can add additional constraints to the Gene Pool, thereby customizing how the next generation is - selected. - - At this time, the gene pool does *not* limit the number of times any given Tree may be selected for mutation or - reproduction nor does it take into account parsimony (seeking the simplest multivariate expression). - - This method is automatically invoked with every Tournament Selection ('fx_fitness_tournament'). - - Arguments required: none - ''' - - self.gene_pool = [] - if self.display == 'i': print '\n Prepare a viable gene pool ...'; self.fx_karoo_pause(0) - - for tree_id in range(1, len(self.population_a)): - - self.fx_eval_poly(self.population_a[tree_id]) # extract the expression - - if len(self.population_a[tree_id][3])-1 >= self.tree_depth_min and self.algo_sym != 1: # check if Tree meets the requirements - if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has >=', self.tree_depth_min, 'nodes and is added to the gene pool\033[0;0m' - self.gene_pool.append(self.population_a[tree_id][0][1]) - - if len(self.gene_pool) > 0 and self.display == 'i': print '\n\t The total population of the gene pool is', len(self.gene_pool); self.fx_karoo_pause(0) - - elif len(self.gene_pool) <= 0: # the evolutionary constraints were too tight, killing off the entire population - # self.generation_id = self.generation_id - 1 # revert the increment of the 'generation_id' - # self.generation_max = self.generation_id # catch the unused "cont" values in the 'fx_karoo_pause' method - print "\n\t\033[31m\033[3m 'They're dead Jim. They're all dead!'\033[0;0m There are no Trees in the gene pool. You should archive your populations and (q)uit."; self.fx_karoo_pause(0) - - return - - - def fx_fitness_test_classify(self, result): - - ''' - Print the Precision-Recall and Confusion Matrix for a CLASSIFICATION run against the test data. - - From scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html - Precision (P) = true_pos / true_pos + false_pos - Recall (R) = true_pos / true_pos + false_neg - harmonic mean of Precision and Recall (F1) = 2(P x R) / (P + R) - - From scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html - y_pred = result, the predicted labels generated by Karoo GP - y_true = solution, the true labels associated with the data - - Arguments required: result - ''' - - for i in range(len(result['result'])): - print '\t\033[36m Data row {} predicts class:\033[1m {} ({} True)\033[0;0m\033[36m as {:.2f}{}\033[0;0m'.format(i, int(result['pred_labels'][0][i]), int(result['solution'][i]), result['result'][i], result['pred_labels'][1][i]) - - print '\n Fitness score: {}'.format(result['fitness']) - print '\n Precision-Recall report:\n', skm.classification_report(result['solution'], result['pred_labels'][0]) - print ' Confusion matrix:\n', skm.confusion_matrix(result['solution'], result['pred_labels'][0]) - - return - - - def fx_fitness_test_regress(self, result): - - ''' - Print the Fitness score and Mean Squared Error for a REGRESSION run against the test data. - ''' - - for i in range(len(result['result'])): - print '\t\033[36m Data row {} predicts value:\033[1m {:.2f} ({:.2f} True)\033[0;0m'.format(i, result['result'][i], result['solution'][i]) - - MSE, fitness = skm.mean_squared_error(result['result'], result['solution']), result['fitness'] - print '\n\t Regression fitness score: {}'.format(fitness) - print '\t Mean Squared Error: {}'.format(MSE) - - return - - - def fx_fitness_test_match(self, result): - - ''' - Print the accuracy for a MATCH kernel run against the test data. - ''' - - for i in range(len(result['result'])): - print '\t\033[36m Data row {} predicts match:\033[1m {:.2f} ({:.2f} True)\033[0;0m'.format(i, result['result'][i], result['solution'][i]) - - print '\n\tMatching fitness score: {}'.format(result['fitness']) - - return - - - # def fx_fitness_test_[other](self, result): - - # ''' - # Print the [statistical measure] for a [OTHER] kernel run against the test data. - # ''' - - # for i in range(len(result['result'])): - # print '\t\033[36m Data row {} predicts value:\033[1m {} ({} label)\033[0;0m'.format(i, int(result['result'][i]), int(result['solution'][i])) - - # print '\n\tFitness score: {}'.format(result['fitness']) - - # return - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Evolve a Population | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_evolve_point_mutate(self, tree): - - ''' - Mutate a single point in any Tree (Grow or Full). - - Arguments required: tree - ''' - - node = np.random.randint(1, len(tree[3])) # randomly select a point in the Tree (including root) - if self.display == 'i': print '\t\033[36m with', tree[5][node], 'node\033[1m', tree[3][node], '\033[0;0m\033[36mchosen for mutation\n\033[0;0m' - elif self.display == 'db': print '\n\n\033[33m *** Point Mutation *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree - - if tree[5][node] == 'root': - rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators - tree[6][node] = self.functions[rnd][0] # replace function (operator) - - elif tree[5][node] == 'func': - rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators - tree[6][node] = self.functions[rnd][0] # replace function (operator) - - elif tree[5][node] == 'term': - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - tree[6][node] = self.terminals[rnd] # replace terminal (variable) - - else: print '\n\t\033[31m ERROR! In fx_evolve_point_mutate, node_type =', tree[5][node], '\033[0;0m'; self.fx_karoo_pause(0) - - tree = self.fx_evolve_fitness_wipe(tree) # wipe fitness data - - if self.display == 'db': print '\n\033[36m This is tourn_winner after node\033[1m', node, '\033[0;0m\033[36mmutation and updates:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - return tree, node # 'node' is returned only to be assigned to the 'tourn_trees' record keeping - - - def fx_evolve_full_mutate(self, tree, branch): - - ''' - Mutate a branch of a Full method Tree. - - The full mutate method is straight-forward. A branch was generated and passed to this method. As the size and - shape of the Tree must remain identical, each node is mutated sequentially (copied from the new Tree to replace - the old, node for node), where functions remain functions and terminals remain terminals. - - Arguments required: tree, branch - ''' - - if self.display == 'db': print '\n\n\033[33m *** Full Mutation: function to function *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree - - for n in range(len(branch)): - - # 'root' is not made available for Full mutation as this would build an entirely new Tree - - if tree[5][branch[n]] == 'func': - if self.display == 'i': print '\t\033[36m from\033[1m', tree[5][branch[n]], '\033[0;0m\033[36mto\033[1m func \033[0;0m' + else: + if self.tree.shape[1] == 0: + self.tree = np.append(self.tree, [row], axis = 1) # append first row to Tree - rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators - tree[6][branch[n]] = self.functions[rnd][0] # replace function (operator) - - elif tree[5][branch[n]] == 'term': - if self.display == 'i': print '\t\033[36m from\033[1m', tree[5][branch[n]], '\033[0;0m\033[36mto\033[1m term \033[0;0m' + else: + self.tree = np.append(self.tree, [row], axis = 0) # append subsequent rows to Tree - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - tree[6][branch[n]] = self.terminals[rnd] # replace terminal (variable) - - tree = self.fx_evolve_fitness_wipe(tree) # wipe fitness data - - if self.display == 'db': print '\n\033[36m This is tourn_winner after nodes\033[1m', branch, '\033[0;0m\033[36mwere mutated and updated:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - return tree - - - def fx_evolve_grow_mutate(self, tree, branch): - - ''' - Mutate a branch of a Grow method Tree. - - A branch is selected within a given tree. - - If the point of mutation ('branch_top') resides at 'tree_depth_max', we do not need to grow a new tree. As the - methods for building trees always assume root (node 0) to be a function, we need only mutate this terminal node - to another terminal node, and this branch mutate method is complete. - - If the top of that branch is a terminal which does not reside at 'tree_depth_max', then it may either remain a - terminal (in which case a new value is randomly assigned) or it may mutate into a function. If it becomes a - function, a new branch (mini-tree) is generated to be appended to that nodes current location. The same is true - for function-to-function mutation. Either way, the new branch will be only as deep as allowed by the distance - from it's branch_top to the bottom of the tree. - - If however a function mutates into a terminal, the entire branch beneath the function is deleted from the array - and the entire array is updated, to fix parent/child links, associated arities, and node IDs. - - Arguments required: tree, branch - ''' - - branch_top = int(branch[0]) # replaces 2 instances, below; tested 2016 07/09 - branch_depth = self.tree_depth_max - int(tree[4][branch_top]) # 'tree_depth_max' - depth at 'branch_top' to set max potential size of new branch - 2016 07/10 - - if branch_depth < 0: # this has never occured ... yet - print '\n\t\033[31m ERROR! In fx_evolve_grow_mutate: branch_depth < 0\033[0;0m' - print '\t branch_depth =', branch_depth; self.fx_karoo_pause(0) - - elif branch_depth == 0: # the point of mutation ('branch_top') chosen resides at the maximum allowable depth, so mutate term to term - - if self.display == 'i': print '\t\033[36m max depth branch node\033[1m', tree[3][branch_top], '\033[0;0m\033[36mmutates from \033[1mterm\033[0;0m \033[36mto \033[1mterm\033[0;0m\n' - if self.display == 'db': print '\n\n\033[33m *** Grow Mutation: terminal to terminal *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree - - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - tree[6][branch_top] = self.terminals[rnd] # replace terminal (variable) - - if self.display == 'db': print '\n\033[36m This is tourn_winner after terminal\033[1m', branch_top, '\033[0;0m\033[36mmutation, branch deletion, and updates:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - else: # the point of mutation ('branch_top') chosen is at least one degree of depth from the maximum allowed - - # type_mod = '[func or term]' # TEST & DEBUG: force to 'func' or 'term' and comment the next 3 lines - rnd = np.random.randint(2) - if rnd == 0: type_mod = 'func' # randomly selected as Function - elif rnd == 1: type_mod = 'term' # randomly selected as Terminal - - if type_mod == 'term': # mutate 'branch_top' to a terminal and delete all nodes beneath (no subsequent nodes are added to this branch) - - if self.display == 'i': print '\t\033[36m branch node\033[1m', tree[3][branch_top], '\033[0;0m\033[36mmutates from\033[1m', tree[5][branch_top], '\033[0;0m\033[36mto\033[1m term \n\033[0;0m' - if self.display == 'db': print '\n\n\033[33m *** Grow Mutation: branch_top to terminal *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree - - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - tree[5][branch_top] = 'term' # replace type ('func' to 'term' or 'term' to 'term') - tree[6][branch_top] = self.terminals[rnd] # replace label - - tree = np.delete(tree, branch[1:], axis = 1) # delete all nodes beneath point of mutation ('branch_top') - tree = self.fx_evolve_node_arity_fix(tree) # fix all node arities - tree = self.fx_evolve_child_link_fix(tree) # fix all child links - tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's - - if self.display == 'db': print '\n\033[36m This is tourn_winner after terminal\033[1m', branch_top, '\033[0;0m\033[36mmutation, branch deletion, and updates:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - - if type_mod == 'func': # mutate 'branch_top' to a function (a new 'gp.tree' will be copied, node by node, into 'tourn_winner') - - if self.display == 'i': print '\t\033[36m branch node\033[1m', tree[3][branch_top], '\033[0;0m\033[36mmutates from\033[1m', tree[5][branch_top], '\033[0;0m\033[36mto\033[1m func \n\033[0;0m' - if self.display == 'db': print '\n\n\033[33m *** Grow Mutation: branch_top to function *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree - - self.fx_gen_tree_build('mutant', self.pop_tree_type, branch_depth) # build new Tree ('gp.tree') with a maximum depth which matches 'branch' - - if self.display == 'db': print '\n\033[36m This is the new Tree to be inserted at node\033[1m', branch_top, '\033[0;0m\033[36min tourn_winner:\033[0;0m\n', self.tree; self.fx_karoo_pause(0) - - # because we already know the maximum depth to which this branch can grow, there is no need to prune after insertion - tree = self.fx_evolve_branch_top_copy(tree, branch) # copy root of new 'gp.tree' to point of mutation ('branch_top') in 'tree' ('tourn_winner') - tree = self.fx_evolve_branch_body_copy(tree) # copy remaining nodes in new 'gp.tree' to 'tree' ('tourn_winner') - - tree = self.fx_evolve_fitness_wipe(tree) # wipe fitness data - - return tree - - - def fx_evolve_crossover(self, parent, branch_x, offspring, branch_y): - - ''' - Refer to the method 'fx_karoo_crossover' for a full description of the genetic operator Crossover. - - This method is called twice to produce 2 offspring per pair of parent Trees. Note that in the method - 'karoo_fx_crossover' the parent/branch relationships are swapped from the first run to the second, such that - this method receives swapped components to produce the alternative offspring. Therefore 'parent_b' is first - passed to 'offspring' which will receive 'branch_a'. With the second run, 'parent_a' is passed to 'offspring' which - will receive 'branch_b'. - - Arguments required: parent, branch_x, offspring, branch_y (parents_a / _b, branch_a / _b from 'fx_karoo_crossover') - ''' - - crossover = int(branch_x[0]) # pointer to the top of the 1st parent branch passed from 'fx_karoo_crossover' - branch_top = int(branch_y[0]) # pointer to the top of the 2nd parent branch passed from 'fx_karoo_crossover' - - if self.display == 'db': print '\n\n\033[33m *** Crossover *** \033[0;0m' - - if len(branch_x) == 1: # if the branch from the parent contains only one node (terminal) - - if self.display == 'i': print '\t\033[36m terminal crossover from \033[1mparent', parent[0][1], '\033[0;0m\033[36mto \033[1moffspring', offspring[0][1], '\033[0;0m\033[36mat node\033[1m', branch_top, '\033[0;0m' - - if self.display == 'db': - print '\n\033[36m In a copy of one parent:\033[0;0m\n', offspring - print '\n\033[36m ... we remove nodes\033[1m', branch_y, '\033[0;0m\033[36mand replace node\033[1m', branch_top, '\033[0;0m\033[36mwith a terminal from branch_x\033[0;0m'; self.fx_karoo_pause(0) - - offspring[5][branch_top] = 'term' # replace type - offspring[6][branch_top] = parent[6][crossover] # replace label with that of a particular node in 'branch_x' - offspring[8][branch_top] = 0 # set terminal arity - - offspring = np.delete(offspring, branch_y[1:], axis = 1) # delete all nodes beneath point of mutation ('branch_top') - offspring = self.fx_evolve_child_link_fix(offspring) # fix all child links - offspring = self.fx_evolve_node_renum(offspring) # renumber all 'NODE_ID's - - if self.display == 'db': print '\n\033[36m This is the resulting offspring:\033[0;0m\n', offspring; self.fx_karoo_pause(0) - - - else: # we are working with a branch from 'parent' >= depth 1 (min 3 nodes) - - if self.display == 'i': print '\t\033[36m branch crossover from \033[1mparent', parent[0][1], '\033[0;0m\033[36mto \033[1moffspring', offspring[0][1], '\033[0;0m\033[36mat node\033[1m', branch_top, '\033[0;0m' - - # self.fx_gen_tree_build('test', 'f', 2) # TEST & DEBUG: disable the next 'self.tree ...' line - self.tree = self.fx_evolve_branch_copy(parent, branch_x) # generate stand-alone 'gp.tree' with properties of 'branch_x' - - if self.display == 'db': - print '\n\033[36m From one parent:\033[0;0m\n', parent - print '\n\033[36m ... we copy branch_x\033[1m', branch_x, '\033[0;0m\033[36mas a new, sub-tree:\033[0;0m\n', self.tree; self.fx_karoo_pause(0) - - if self.display == 'db': - print '\n\033[36m ... and insert it into a copy of the second parent in place of the selected branch\033[1m', branch_y,':\033[0;0m\n', offspring; self.fx_karoo_pause(0) - - offspring = self.fx_evolve_branch_top_copy(offspring, branch_y) # copy root of 'branch_y' ('gp.tree') to 'offspring' - offspring = self.fx_evolve_branch_body_copy(offspring) # copy remaining nodes in 'branch_y' ('gp.tree') to 'offspring' - offspring = self.fx_evolve_tree_prune(offspring, self.tree_depth_max) # prune to the max Tree depth + adjustment - tested 2016 07/10 - - offspring = self.fx_evolve_fitness_wipe(offspring) # wipe fitness data - - return offspring - - - def fx_evolve_branch_select(self, tree): - - ''' - Select all nodes in the 'tourn_winner' Tree at and below the randomly selected starting point. - - While Grow mutation uses this method to select a region of the 'tourn_winner' to delete, Crossover uses this - method to select a region of the 'tourn_winner' which is then converted to a stand-alone tree. As such, it is - imperative that the nodes be in the correct order, else all kinds of bad things happen. - - Arguments required: tree - ''' - - branch = np.array([]) # the array is necessary in order to len(branch) when 'branch' has only one element - branch_top = np.random.randint(2, len(tree[3])) # randomly select a non-root node - branch_eval = self.fx_eval_id(tree, branch_top) # generate tuple of 'branch_top' and subseqent nodes - branch_symp = sympify(branch_eval) # convert string into something useful - branch = np.append(branch, branch_symp) # append list to array - - branch = np.sort(branch) # sort nodes in branch for Crossover. - - if self.display == 'i': print '\t \033[36mwith nodes\033[1m', branch, '\033[0;0m\033[36mchosen for mutation\033[0;0m' - - return branch - - - def fx_evolve_branch_top_copy(self, tree, branch): - - ''' - Copy the point of mutation ('branch_top') from 'gp.tree' to 'tree'. - - This method works with 3 inputs: local 'tree' is being modified; local 'branch' is a section of 'tree' which - will be removed; and global 'gp.tree' (recycling from initial population generation) is the new Tree to be - copied into 'tree', replacing 'branch'. - - This method is used in both Grow Mutation and Crossover. - - Arguments required: tree, branch - ''' - - branch_top = int(branch[0]) - - tree[5][branch_top] = 'func' # update type ('func' to 'term' or 'term' to 'term'); this modifies gp.tree[5[1] from 'root' to 'func' - tree[6][branch_top] = self.tree[6][1] # copy node_label from new tree - tree[8][branch_top] = self.tree[8][1] # copy node_arity from new tree - - tree = np.delete(tree, branch[1:], axis = 1) # delete all nodes beneath point of mutation ('branch_top') - - c_buffer = self.fx_evolve_c_buffer(tree, branch_top) # generate c_buffer for point of mutation ('branch_top') - tree = self.fx_evolve_child_insert(tree, branch_top, c_buffer) # insert new nodes - tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's - - if self.display == 'db': - print '\n\t ... inserted node 1 of', len(self.tree[3])-1 - print '\n\033[36m This is the Tree after a new node is inserted:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - return tree - - - def fx_evolve_branch_body_copy(self, tree): - - ''' - Copy the body of 'gp.tree' to 'tree', one node at a time. - - This method works with 3 inputs: local 'tree' is being modified; local 'branch' is a section of 'tree' which - will be removed; and global 'gp.tree' (recycling from initial population generation) is the new Tree to be - copied into 'tree', replacing 'branch'. - - This method is used in both Grow Mutation and Crossover. - - Arguments required: tree - ''' - - node_count = 2 # set node count for 'gp.tree' to 2 as the new root has already replaced 'branch_top' in 'fx_evolve_branch_top_copy' - - while node_count < len(self.tree[3]): # increment through all nodes in the new Tree ('gp.tree'), starting with node 2 - - for j in range(1, len(tree[3])): # increment through all nodes in tourn_winner ('tree') - - if self.display == 'db': print '\tScanning tourn_winner node_id:', j - - if tree[5][j] == '': - tree[5][j] = self.tree[5][node_count] # copy 'node_type' from branch to tree - tree[6][j] = self.tree[6][node_count] # copy 'node_label' from branch to tree - tree[8][j] = self.tree[8][node_count] # copy 'node_arity' from branch to tree - - if tree[5][j] == 'term': - tree = self.fx_evolve_child_link_fix(tree) # fix all child links - tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's + if self.tree.shape[0] == 13: + self.population_a.append(self.tree) # append complete Tree to population list - if tree[5][j] == 'func': - c_buffer = self.fx_evolve_c_buffer(tree, j) # generate 'c_buffer' for point of mutation ('branch_top') - tree = self.fx_evolve_child_insert(tree, j, c_buffer) # insert new nodes - tree = self.fx_evolve_child_link_fix(tree) # fix all child links - tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's - - if self.display == 'db': - print '\n\t ... inserted node', node_count, 'of', len(self.tree[3])-1 - print '\n\033[36m This is the Tree after a new node is inserted:\033[0;0m\n', tree; self.fx_karoo_pause(0) - - node_count = node_count + 1 # exit loop when 'node_count' reaches the number of columns in the array 'gp.tree' - - return tree - - - def fx_evolve_branch_copy(self, tree, branch): - - ''' - This method prepares a stand-alone Tree as a copy of the given branch. - - This method is used with Crossover. - - Arguments required: tree, branch - ''' - - new_tree = np.array([ ['TREE_ID'],['tree_type'],['tree_depth_base'],['NODE_ID'],['node_depth'],['node_type'],['node_label'],['node_parent'],['node_arity'],['node_c1'],['node_c2'],['node_c3'],['fitness'] ]) - - # tested 2015 06/08 - for n in range(len(branch)): - - node = branch[n] - branch_top = int(branch[0]) - - TREE_ID = 'copy' - tree_type = tree[1][1] - tree_depth_base = int(tree[4][branch[-1]]) - int(tree[4][branch_top]) # subtract depth of 'branch_top' from the last in 'branch' - NODE_ID = tree[3][node] - node_depth = int(tree[4][node]) - int(tree[4][branch_top]) # subtract the depth of 'branch_top' from the current node depth - node_type = tree[5][node] - node_label = tree[6][node] - node_parent = '' # updated by 'fx_evolve_parent_link_fix', below - node_arity = tree[8][node] - node_c1 = '' # updated by 'fx_evolve_child_link_fix', below - node_c2 = '' - node_c3 = '' - fitness = '' - - new_tree = np.append(new_tree, [ [TREE_ID],[tree_type],[tree_depth_base],[NODE_ID],[node_depth],[node_type],[node_label],[node_parent],[node_arity],[node_c1],[node_c2],[node_c3],[fitness] ], 1) - - new_tree = self.fx_evolve_node_renum(new_tree) - new_tree = self.fx_evolve_child_link_fix(new_tree) - new_tree = self.fx_evolve_parent_link_fix(new_tree) - new_tree = self.fx_archive_tree_clean(new_tree) - - return new_tree - - - def fx_evolve_c_buffer(self, tree, node): - - ''' - This method serves the very important function of determining the links from parent to child for any given - node. The single, simple formula [parent_arity_sum + prior_sibling_arity - prior_siblings] perfectly determines - the correct position of the child node, already in place or to be inserted, no matter the depth nor complexity - of the tree. - - This method is currently called from the evolution methods, but will soon (I hope) be called from the first - generation Tree generation methods (above) such that the same method may be used repeatedly. - - Arguments required: tree, node - ''' - - parent_arity_sum = 0 - prior_sibling_arity = 0 - prior_siblings = 0 - - for n in range(1, len(tree[3])): # increment through all nodes (exclude 0) in array 'tree' - - if int(tree[4][n]) == int(tree[4][node])-1: # find parent nodes at the prior depth - if tree[8][n] != '': parent_arity_sum = parent_arity_sum + int(tree[8][n]) # sum arities of all parent nodes at the prior depth - - if int(tree[4][n]) == int(tree[4][node]) and int(tree[3][n]) < int(tree[3][node]): # find prior siblings at the current depth - if tree[8][n] != '': prior_sibling_arity = prior_sibling_arity + int(tree[8][n]) # sum prior sibling arity - prior_siblings = prior_siblings + 1 # sum quantity of prior siblings - - c_buffer = node + (parent_arity_sum + prior_sibling_arity - prior_siblings) # One algo to rule the world! - - return c_buffer - - - def fx_evolve_child_link(self, tree, node, c_buffer): - - ''' - Link each parent node to its children. - - Arguments required: tree, node, c_buffer - ''' - - if int(tree[3][node]) == 1: c_buffer = c_buffer + 1 # if root (node 1) is passed through this method - - if tree[8][node] != '': - - if int(tree[8][node]) == 0: # if arity = 0 - tree[9][node] = '' - tree[10][node] = '' - tree[11][node] = '' - - elif int(tree[8][node]) == 1: # if arity = 1 - tree[9][node] = c_buffer - tree[10][node] = '' - tree[11][node] = '' - - elif int(tree[8][node]) == 2: # if arity = 2 - tree[9][node] = c_buffer - tree[10][node] = c_buffer + 1 - tree[11][node] = '' - - elif int(tree[8][node]) == 3: # if arity = 3 - tree[9][node] = c_buffer - tree[10][node] = c_buffer + 1 - tree[11][node] = c_buffer + 2 - - else: print '\n\t\033[31m ERROR! In fx_evolve_child_link: node', node, 'has arity', tree[8][node]; self.fx_karoo_pause(0) - - return tree - - - def fx_evolve_child_link_fix(self, tree): - - ''' - In a given Tree, fix 'node_c1', 'node_c2', 'node_c3' for all nodes. - - This is required anytime the size of the array 'gp.tree' has been modified, as with both Grow and Full mutation. - - Arguments required: tree - ''' - - # tested 2015 06/04 - for node in range(1, len(tree[3])): - - c_buffer = self.fx_evolve_c_buffer(tree, node) # generate c_buffer for each node - tree = self.fx_evolve_child_link(tree, node, c_buffer) # update child links for each node - - return tree - - - def fx_evolve_child_insert(self, tree, node, c_buffer): - - ''' - Insert child nodes. - - Arguments required: tree, node, c_buffer - ''' - - if int(tree[8][node]) == 0: # if arity = 0 - print '\n\t\033[31m ERROR! In fx_evolve_child_insert: node', node, 'has arity 0\033[0;0m'; self.fx_karoo_pause(0) - - elif int(tree[8][node]) == 1: # if arity = 1 - tree = np.insert(tree, c_buffer, '', axis=1) # insert node for 'node_c1' - tree[3][c_buffer] = c_buffer # node ID - tree[4][c_buffer] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer] = int(tree[3][node]) # parent ID - - elif int(tree[8][node]) == 2: # if arity = 2 - tree = np.insert(tree, c_buffer, '', axis=1) # insert node for 'node_c1' - tree[3][c_buffer] = c_buffer # node ID - tree[4][c_buffer] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer] = int(tree[3][node]) # parent ID - - tree = np.insert(tree, c_buffer + 1, '', axis=1) # insert node for 'node_c2' - tree[3][c_buffer + 1] = c_buffer + 1 # node ID - tree[4][c_buffer + 1] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer + 1] = int(tree[3][node]) # parent ID - - elif int(tree[8][node]) == 3: # if arity = 3 - tree = np.insert(tree, c_buffer, '', axis=1) # insert node for 'node_c1' - tree[3][c_buffer] = c_buffer # node ID - tree[4][c_buffer] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer] = int(tree[3][node]) # parent ID - - tree = np.insert(tree, c_buffer + 1, '', axis=1) # insert node for 'node_c2' - tree[3][c_buffer + 1] = c_buffer + 1 # node ID - tree[4][c_buffer + 1] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer + 1] = int(tree[3][node]) # parent ID - - tree = np.insert(tree, c_buffer + 2, '', axis=1) # insert node for 'node_c3' - tree[3][c_buffer + 2] = c_buffer + 2 # node ID - tree[4][c_buffer + 2] = int(tree[4][node]) + 1 # node_depth - tree[7][c_buffer + 2] = int(tree[3][node]) # parent ID - - else: print '\n\t\033[31m ERROR! In fx_evolve_child_insert: node', node, 'arity > 3\033[0;0m'; self.fx_karoo_pause(0) - - return tree - - - def fx_evolve_parent_link_fix(self, tree): - - ''' - In a given Tree, fix 'parent_id' for all nodes. - - This is automatically handled in all mutations except with Crossover due to the need to copy branches 'a' and - 'b' to their own trees before inserting them into copies of the parents. - - Technically speaking, the 'node_parent' value is not used by any methods. The parent ID can be completely out - of whack and the expression will work perfectly. This is maintained for the sole purpose of granting the user - a friendly, makes-sense interface which can be read in both directions. - - Arguments required: tree - ''' - - ### THIS METHOD MAY NOT BE REQUIRED AS SORTING 'branch' SEEMS TO HAVE FIXED 'parent_id' ### - - # tested 2015 06/05 - for node in range(1, len(tree[3])): - - if tree[9][node] != '': - child = int(tree[9][node]) - tree[7][child] = node - - if tree[10][node] != '': - child = int(tree[10][node]) - tree[7][child] = node - - if tree[11][node] != '': - child = int(tree[11][node]) - tree[7][child] = node - - return tree - - - def fx_evolve_node_arity_fix(self, tree): - - ''' - In a given Tree, fix 'node_arity' for all nodes labeled 'term' but with arity 2. - - This is required after a function has been replaced by a terminal, as may occur with both Grow mutation and - Crossover. - - Arguments required: tree - ''' - - # tested 2015 05/31 - for n in range(1, len(tree[3])): # increment through all nodes (exclude 0) in array 'tree' - - if tree[5][n] == 'term': # check for discrepency - tree[8][n] = '0' # set arity to 0 - tree[9][n] = '' # wipe 'node_c1' - tree[10][n] = '' # wipe 'node_c2' - tree[11][n] = '' # wipe 'node_c3' - - return tree - - - def fx_evolve_node_renum(self, tree): - - ''' - Renumber all 'NODE_ID' in a given tree. - - This is required after a new generation is evolved as the NODE_ID numbers are carried forward from the previous - generation but are no longer in order. - - Arguments required: tree - ''' - - for n in range(1, len(tree[3])): - - tree[3][n] = n # renumber all Trees in given population - - return tree - - - def fx_evolve_fitness_wipe(self, tree): - - ''' - Remove all fitness data from a given tree. - - This is required after a new generation is evolved as the fitness of the same Tree prior to its mutation will - no longer apply. - - Arguments required: tree - ''' - - tree[12][1:] = '' # wipe fitness data - - return tree - - - def fx_evolve_tree_prune(self, tree, depth): - - ''' - This method reduces the depth of a Tree. Used with Crossover, the input value 'branch' can be a partial Tree - (branch) or a full tree, and it will operate correctly. The input value 'depth' becomes the new maximum depth, - where depth is defined as the local maximum + the user defined adjustment. - - Arguments required: tree, depth - ''' - - nodes = [] - - # tested 2015 06/08 - for n in range(1, len(tree[3])): - - if int(tree[4][n]) == depth and tree[5][n] == 'func': - rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals - tree[5][n] = 'term' # mutate type 'func' to 'term' - tree[6][n] = self.terminals[rnd] # replace label - - elif int(tree[4][n]) > depth: # record nodes deeper than the maximum allowed Tree depth - nodes.append(n) - - else: pass # as int(tree[4][n]) < depth and will remain untouched - - tree = np.delete(tree, nodes, axis = 1) # delete nodes deeper than the maximum allowed Tree depth - tree = self.fx_evolve_node_arity_fix(tree) # fix all node arities - - return tree - - - def fx_evolve_tree_renum(self, population): - - ''' - Renumber all 'TREE_ID' in a given population. - - This is required after a new generation is evolved as the TREE_ID numbers are carried forward from the previous - generation but are no longer in order. - - Arguments required: population - ''' - - for tree_id in range(1, len(population)): - - population[tree_id][0][1] = tree_id # renumber all Trees in given population - - return population - - - def fx_evolve_pop_copy(self, pop_a, title): - - ''' - Copy one population to another. - - Simply copying a list of arrays generates a pointer to the original list. Therefore we must append each array - to a new, empty array and then build a list of those new arrays. - - Arguments required: pop_a, title - ''' - - pop_b = [title] # an empty list stores a copy of the prior generation - - for tree in range(1, len(pop_a)): # increment through each Tree in the current population - - tree_copy = np.copy(pop_a[tree]) # copy each array in the current population - pop_b.append(tree_copy) # add each copied Tree to the new population list - - return pop_b - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Display a Tree | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_display_tree(self, tree): - - ''' - Display all or part of a Tree on-screen. - - This method displays all sequential node_ids from 'start' node through bottom, within the given tree. - - Arguments required: tree - ''' - - ind = '' - print '\n\033[1m\033[36m Tree ID', int(tree[0][1]), '\033[0;0m' - - for depth in range(0, self.tree_depth_max + 1): # increment through all possible Tree depths - tested 2016 07/09 - print '\n', ind,'\033[36m Tree Depth:', depth, 'of', tree[2][1], '\033[0;0m' - - for node in range(1, len(tree[3])): # increment through all nodes (redundant, I know) - if int(tree[4][node]) == depth: - print '' - print ind,'\033[1m\033[36m NODE:', tree[3][node], '\033[0;0m' - print ind,' type:', tree[5][node] - print ind,' label:', tree[6][node], '\tparent node:', tree[7][node] - print ind,' arity:', tree[8][node], '\tchild node(s):', tree[9][node], tree[10][node], tree[11][node] - - ind = ind + '\t' - - print '' - self.fx_eval_poly(tree) # generate the raw and sympified equation for the entire Tree - print '\t\033[36mTree', tree[0][1], 'yields (raw):', self.algo_raw, '\033[0;0m' - print '\t\033[36mTree', tree[0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' + print self.population_a return - - def fx_display_branch(self, tree, start): - - ''' - Display a Tree branch on-screen. - This method displays all sequential node_ids from 'start' node through bottom, within the given branch. - - This method is not used by Karoo GP at this time. - - Arguments required: tree, start - ''' - - branch = np.array([]) # the array is necessary in order to len(branch) when 'branch' has only one element - branch_eval = self.fx_eval_id(tree, start) # generate tuple of given 'branch' - branch_symp = sympify(branch_eval) # convert string from tuple to list - branch = np.append(branch, branch_symp) # append list to array - ind = '' - - # for depth in range(int(tree[4][start]), int(tree[2][1]) + self.tree_depth_max + 1): # increment through all Tree depths - tested 2016 07/09 - for depth in range(int(tree[4][start]), self.tree_depth_max + 1): # increment through all Tree depths - tested 2016 07/09 - print '\n', ind,'\033[36m Tree Depth:', depth, 'of', tree[2][1], '\033[0;0m' - - for n in range(0, len(branch)): # increment through all nodes listed in the branch - node = branch[n] - - if int(tree[4][node]) == depth: - print '' - print ind,'\033[1m\033[36m NODE:', node, '\033[0;0m' - print ind,' type:', tree[5][node] - print ind,' label:', tree[6][node], '\tparent node:', tree[7][node] - print ind,' arity:', tree[8][node], '\tchild node(s):', tree[9][node], tree[10][node], tree[11][node] - - ind = ind + '\t' - - print '' - self.fx_eval_poly(tree) # generate the raw and sympified equation for the entire Tree - print '\t\033[36mTree', tree[0][1], 'yields (raw):', self.algo_raw, '\033[0;0m' - print '\t\033[36mTree', tree[0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' - - return - - - #++++++++++++++++++++++++++++++++++++++++++ - # Methods to Archive | - #++++++++++++++++++++++++++++++++++++++++++ - - def fx_archive_tree_clean(self, tree): + def fx_data_tree_clean(self, tree): ''' This method aesthetically cleans the Tree array, removing redundant data. @@ -2596,7 +697,7 @@ class Base_GP(object): return tree - def fx_archive_tree_append(self, tree): + def fx_data_tree_append(self, tree): ''' Append Tree array to the foundation Population. @@ -2604,13 +705,13 @@ class Base_GP(object): Arguments required: tree ''' - self.fx_archive_tree_clean(tree) # clean 'tree' prior to storing + self.fx_data_tree_clean(tree) # clean 'tree' prior to storing self.population_a.append(tree) # append 'tree' to population list return - def fx_archive_tree_write(self, population, key): + def fx_data_tree_write(self, population, key): ''' Save population_* to disk. @@ -2631,7 +732,7 @@ class Base_GP(object): return - def fx_archive_params_write(self, app): # tested 2017 02/13 + def fx_data_params_write(self, app): # tested 2017 02/13 ''' Save run-time configuration parameters to disk. @@ -2730,5 +831,1973 @@ class Base_GP(object): file.close() return + + + #+++++++++++++++++++++++++++++++++++++++++++++ + # Methods to Construct the 1st Generation | + #+++++++++++++++++++++++++++++++++++++++++++++ + + def fx_init_construct(self, tree_type, tree_depth_base): + + ''' + This method constructs the initial population of Tree type 'tree_type' and of the size tree_depth_base. The Tree + can be Full, Grow, or "Ramped Half/Half" as defined by John Koza. + + Called by: fx_karoo_gp + + Arguments required: tree_type, tree_depth_base + ''' + + if self.display == 'i' or self.display == 'g': + print '\n\t Type \033[1m?\033[0;0m at any (pause) to review your options, or \033[1mENTER\033[0;0m to continue.\033[0;0m' + self.fx_karoo_pause(0) + + if tree_type == 'r': # Ramped 50/50 + + TREE_ID = 1 + for n in range(1, int((self.tree_pop_max / 2) / tree_depth_base) + 1): # split the population into equal parts + for depth in range(1, tree_depth_base + 1): # build 2 Trees at each depth + self.fx_init_tree_build(TREE_ID, 'f', depth) # build a Full Tree + self.fx_data_tree_append(self.tree) # append Tree to the list 'gp.population_a' + TREE_ID = TREE_ID + 1 + + self.fx_init_tree_build(TREE_ID, 'g', depth) # build a Grow Tree + self.fx_data_tree_append(self.tree) # append Tree to the list 'gp.population_a' + TREE_ID = TREE_ID + 1 + + if TREE_ID < self.tree_pop_max: # eg: split 100 by 2*3 and it will produce only 96 Trees ... + for n in range(self.tree_pop_max - TREE_ID + 1): # ... so we complete the run + self.fx_init_tree_build(TREE_ID, 'g', tree_depth_base) + self.fx_data_tree_append(self.tree) + TREE_ID = TREE_ID + 1 + + else: pass + + else: # Full or Grow + for TREE_ID in range(1, self.tree_pop_max + 1): + self.fx_init_tree_build(TREE_ID, tree_type, tree_depth_base) # build the 1st generation of Trees + self.fx_data_tree_append(self.tree) + + return + + + def fx_init_tree_build(self, TREE_ID, tree_type, tree_depth_base): + + ''' + This method combines 4 sub-methods into a single method for ease of deployment. It is designed to executed + within a loop such that an entire population is built. However, it may also be run from the command line, + passing a single TREE_ID to the method. + + 'tree_type' is either (f)ull or (g)row. Note, however, that when the user selects 'ramped 50/50' at launch, + it is still (f) or (g) which are passed to this method. + + This method is called by: fx_init_construct, fx_evolve_crossover, fx_evolve_grow_mutate + + Arguments required: TREE_ID, tree_type, tree_depth_base + ''' + + self.fx_init_tree_initialise(TREE_ID, tree_type, tree_depth_base) # initialise a new Tree + self.fx_init_root_build() # build the Root node + self.fx_init_function_build() # build the Function nodes + self.fx_init_terminal_build() # build the Terminal nodes + + return # each Tree is written to 'gp.tree' + + + def fx_init_tree_initialise(self, TREE_ID, tree_type, tree_depth_base): + + ''' + Assign 13 global variables to the array 'tree'. + + Build the array 'tree' with 13 rows and initally, just 1 column of labels. This array will grow as each new + node is appended. The values of this array are stored as string characters. Numbers will be forced to integers + at the point of execution. + + Called by: fx_init_tree_build + + Arguments required: TREE_ID, tree_type, tree_depth_base + ''' + + self.pop_TREE_ID = TREE_ID # pos 0: a unique identifier for each tree + self.pop_tree_type = tree_type # pos 1: a global constant based upon the initial user setting + self.pop_tree_depth_base = tree_depth_base # pos 2: a global variable which conveys 'tree_depth_base' as unique to each new Tree + self.pop_NODE_ID = 1 # pos 3: unique identifier for each node; this is the INDEX KEY to this array + self.pop_node_depth = 0 # pos 4: depth of each node when committed to the array + self.pop_node_type = '' # pos 5: root, function, or terminal + self.pop_node_label = '' # pos 6: operator [+, -, *, ...] or terminal [a, b, c, ...] + self.pop_node_parent = '' # pos 7: parent node + self.pop_node_arity = '' # pos 8: number of nodes attached to each non-terminal node + self.pop_node_c1 = '' # pos 9: child node 1 + self.pop_node_c2 = '' # pos 10: child node 2 + self.pop_node_c3 = '' # pos 11: child node 3 (assumed max of 3 with boolean operator 'if') + self.pop_fitness = '' # pos 12: fitness score following Tree evaluation + + self.tree = np.array([ ['TREE_ID'],['tree_type'],['tree_depth_base'],['NODE_ID'],['node_depth'],['node_type'],['node_label'],['node_parent'],['node_arity'],['node_c1'],['node_c2'],['node_c3'],['fitness'] ]) + + return + + + ### Root Node ### + + def fx_init_root_build(self): + + ''' + Build the Root node for the initial population. + + Called by: fx_init_tree_build + + Arguments required: none + ''' + + self.fx_init_function_select() # select the operator for root + + if self.pop_node_arity == 1: # 1 child + self.pop_node_c1 = 2 + self.pop_node_c2 = '' + self.pop_node_c3 = '' + + elif self.pop_node_arity == 2: # 2 children + self.pop_node_c1 = 2 + self.pop_node_c2 = 3 + self.pop_node_c3 = '' + + elif self.pop_node_arity == 3: # 3 children + self.pop_node_c1 = 2 + self.pop_node_c2 = 3 + self.pop_node_c3 = 4 + + else: print '\n\t\033[31m ERROR! In fx_init_root_build: pop_node_arity =', self.pop_node_arity, '\033[0;0m'; self.fx_karoo_pause(0) + + self.pop_node_type = 'root' + + self.fx_init_node_commit() + + return + + + ### Function Nodes ### + + def fx_init_function_build(self): + + ''' + Build the Function nodes for the intial population. + + Called by: fx_init_tree_build + + Arguments required: none + ''' + + for i in range(1, self.pop_tree_depth_base): # increment depth, from 1 through 'tree_depth_base' - 1 + + self.pop_node_depth = i # increment 'node_depth' + + parent_arity_sum = 0 + prior_sibling_arity = 0 # reset for 'c_buffer' in 'children_link' + prior_siblings = 0 # reset for 'c_buffer' in 'children_link' + + for j in range(1, len(self.tree[3])): # increment through all nodes (exclude 0) in array 'tree' + + if int(self.tree[4][j]) == self.pop_node_depth - 1: # find parent nodes which reside at the prior depth + parent_arity_sum = parent_arity_sum + int(self.tree[8][j]) # sum arities of all parent nodes at the prior depth + + # (do *not* merge these 2 "j" loops or it gets all kinds of messed up) + + for j in range(1, len(self.tree[3])): # increment through all nodes (exclude 0) in array 'tree' + + if int(self.tree[4][j]) == self.pop_node_depth - 1: # find parent nodes which reside at the prior depth + + for k in range(1, int(self.tree[8][j]) + 1): # increment through each degree of arity for each parent node + self.pop_node_parent = int(self.tree[3][j]) # set the parent 'NODE_ID' ... + prior_sibling_arity = self.fx_init_function_gen(parent_arity_sum, prior_sibling_arity, prior_siblings) # ... generate a Function ndoe + prior_siblings = prior_siblings + 1 # sum sibling nodes (current depth) who will spawn their own children (cousins? :) + + return + + + def fx_init_function_gen(self, parent_arity_sum, prior_sibling_arity, prior_siblings): + + ''' + Generate a single Function node for the initial population. + + Called by fx_init_function_build + + Arguments required: parent_arity_sum, prior_sibling_arity, prior_siblings + ''' + + if self.pop_tree_type == 'f': # user defined as (f)ull + self.fx_init_function_select() # retrieve a function + self.fx_init_child_link(parent_arity_sum, prior_sibling_arity, prior_siblings) # establish links to children + + elif self.pop_tree_type == 'g': # user defined as (g)row + rnd = np.random.randint(2) + + if rnd == 0: # randomly selected as Function + self.fx_init_function_select() # retrieve a function + self.fx_init_child_link(parent_arity_sum, prior_sibling_arity, prior_siblings) # establish links to children + + elif rnd == 1: # randomly selected as Terminal + self.fx_init_terminal_select() # retrieve a terminal + self.pop_node_c1 = '' + self.pop_node_c2 = '' + self.pop_node_c3 = '' + + self.fx_init_node_commit() # commit new node to array + prior_sibling_arity = prior_sibling_arity + self.pop_node_arity # sum the arity of prior siblings + + return prior_sibling_arity + + + def fx_init_function_select(self): + + ''' + Define a single Function (operator extracted from the associated functions.csv) for the initial population. + + Called by: fx_init_function_gen, fx_init_root_build + + Arguments required: none + ''' + + self.pop_node_type = 'func' + rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators + self.pop_node_label = self.functions[rnd][0] + self.pop_node_arity = int(self.functions[rnd][1]) + + return + + + ### Terminal Nodes ### + + def fx_init_terminal_build(self): + + ''' + Build the Terminal nodes for the intial population. + + Called by: fx_init_tree_build + + Arguments required: none + ''' + + self.pop_node_depth = self.pop_tree_depth_base # set the final node_depth (same as 'gp.pop_node_depth' + 1) + + for j in range(1, len(self.tree[3]) ): # increment through all nodes (exclude 0) in array 'tree' + + if int(self.tree[4][j]) == self.pop_node_depth - 1: # find parent nodes which reside at the prior depth + + for k in range(1,(int(self.tree[8][j]) + 1)): # increment through each degree of arity for each parent node + self.pop_node_parent = int(self.tree[3][j]) # set the parent 'NODE_ID' ... + self.fx_init_terminal_gen() # ... generate a Terminal node + + return + + + def fx_init_terminal_gen(self): + + ''' + Generate a single Terminal node for the initial population. + + Called by: fx_init_terminal_build + + Arguments required: none + ''' + + self.fx_init_terminal_select() # retrieve a terminal + self.pop_node_c1 = '' + self.pop_node_c2 = '' + self.pop_node_c3 = '' + + self.fx_init_node_commit() # commit new node to array + + return + + + def fx_init_terminal_select(self): + + ''' + Define a single Terminal (variable extracted from the top row of the associated TRAINING data) + + Called by: fx_init_terminal_gen, fx_init_function_gen + + Arguments required: none + ''' + + self.pop_node_type = 'term' + rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals + self.pop_node_label = self.terminals[rnd] + self.pop_node_arity = 0 + + return + + + ### The Lovely Children ### + + def fx_init_child_link(self, parent_arity_sum, prior_sibling_arity, prior_siblings): + + ''' + Link each parent node to its children in the intial population. + + Called by: fx_init_function_gen + + Arguments required: parent_arity_sum, prior_sibling_arity, prior_siblings + ''' + + c_buffer = 0 + + for n in range(1, len(self.tree[3]) ): # increment through all nodes (exclude 0) in array 'tree' + + if int(self.tree[4][n]) == self.pop_node_depth - 1: # find all nodes that reside at the prior (parent) 'node_depth' + + c_buffer = self.pop_NODE_ID + (parent_arity_sum + prior_sibling_arity - prior_siblings) # One algo to rule the world! + + if self.pop_node_arity == 0: # terminal in a Grow Tree + self.pop_node_c1 = '' + self.pop_node_c2 = '' + self.pop_node_c3 = '' + + elif self.pop_node_arity == 1: # 1 child + self.pop_node_c1 = c_buffer + self.pop_node_c2 = '' + self.pop_node_c3 = '' + + elif self.pop_node_arity == 2: # 2 children + self.pop_node_c1 = c_buffer + self.pop_node_c2 = c_buffer + 1 + self.pop_node_c3 = '' + + elif self.pop_node_arity == 3: # 3 children + self.pop_node_c1 = c_buffer + self.pop_node_c2 = c_buffer + 1 + self.pop_node_c3 = c_buffer + 2 + + else: print '\n\t\033[31m ERROR! In fx_init_child_link: pop_node_arity =', self.pop_node_arity, '\033[0;0m'; self.fx_karoo_pause(0) + + return + + + def fx_init_node_commit(self): + + ''' + Commit the values of a new node (root, function, or terminal) to the array 'tree'. + + Called by: fx_init_root_build, fx_init_function_gen, fx_init_terminal_gen + + Arguments required: none + ''' + + self.tree = np.append(self.tree, [ [self.pop_TREE_ID],[self.pop_tree_type],[self.pop_tree_depth_base],[self.pop_NODE_ID],[self.pop_node_depth],[self.pop_node_type],[self.pop_node_label],[self.pop_node_parent],[self.pop_node_arity],[self.pop_node_c1],[self.pop_node_c2],[self.pop_node_c3],[self.pop_fitness] ], 1) + + self.pop_NODE_ID = self.pop_NODE_ID + 1 + + return + + + #+++++++++++++++++++++++++++++++++++++++++++++ + # Methods to Evaluate a Tree | + #+++++++++++++++++++++++++++++++++++++++++++++ + + def fx_eval_poly(self, tree): + + ''' + Evaluate a Tree and generate its multivariate expression (both raw and Sympified). + + We need to extract the variables from the expression. However, these variables are no longer correlated + to the original variables listed across the top of each column of data.csv. Therefore, we must re-assign + the respective values for each subsequent row in the data .csv, for each Tree's unique expression. + + Called by: fx_karoo_pause, fx_fitness_gym, fx_fitness_eval, fx_fitness_gene_pool, fx_display_tree + + Arguments required: tree + ''' + + self.algo_raw = self.fx_eval_label(tree, 1) # pass the root 'node_id', then flatten the Tree to a string + self.algo_sym = sympify(self.algo_raw) # convert string to a functional expression (the coolest line in Karoo! :) + + return + + + def fx_eval_label(self, tree, node_id): + + ''' + Evaluate all or part of a Tree (starting at node_id) and return a raw mutivariate expression ('algo_raw'). + + This method is called once per Tree, but may be called at any time to prepare an expression for any full or + partial (branch) Tree contained in 'population'. Pass the starting node for recursion via the local variable + 'node_id' where the local variable 'tree' is a copy of the Tree you desire to evaluate. + + Called by: fx_eval_poly, fx_eval_label (recursively) + + Arguments required: tree, node_id + ''' + + # if tree[6, node_id] == 'not': tree[6, node_id] = ', not' # temp until this can be fixed at data_load + + node_id = int(node_id) + + if tree[8, node_id] == '0': # arity of 0 for the pattern '[term]' + return '(' + tree[6, node_id] + ')' # 'node_label' (function or terminal) + + else: + if tree[8, node_id] == '1': # arity of 1 for the explicit pattern 'not [term]' + return self.fx_eval_label(tree, tree[9, node_id]) + tree[6, node_id] + + elif tree[8, node_id] == '2': # arity of 2 for the pattern '[func] [term] [func]' + return self.fx_eval_label(tree, tree[9, node_id]) + tree[6, node_id] + self.fx_eval_label(tree, tree[10, node_id]) + + elif tree[8, node_id] == '3': # arity of 3 for the explicit pattern 'if [term] then [term] else [term]' + return tree[6, node_id] + self.fx_eval_label(tree, tree[9, node_id]) + ' then ' + self.fx_eval_label(tree, tree[10, node_id]) + ' else ' + self.fx_eval_label(tree, tree[11, node_id]) + + + def fx_eval_id(self, tree, node_id): + + ''' + Evaluate all or part of a Tree and return a list of all 'NODE_ID's. + + This method generates a list of all 'NODE_ID's from the given Node and below. It is used primarily to generate + 'branch' for the multi-generational mutation of Trees. + + Pass the starting node for recursion via the local variable 'node_id' where the local variable 'tree' is a copy + of the Tree you desire to evaluate. + + Called by: fx_eval_id (recursively), fx_evolve_branch_select + + Arguments required: tree, node_id + ''' + + node_id = int(node_id) + + if tree[8, node_id] == '0': # arity of 0 for the pattern '[NODE_ID]' + return tree[3, node_id] # 'NODE_ID' + + else: + if tree[8, node_id] == '1': # arity of 1 for the pattern '[NODE_ID], [NODE_ID]' + return tree[3, node_id] + ', ' + self.fx_eval_id(tree, tree[9, node_id]) + + elif tree[8, node_id] == '2': # arity of 2 for the pattern '[NODE_ID], [NODE_ID], [NODE_ID]' + return tree[3, node_id] + ', ' + self.fx_eval_id(tree, tree[9, node_id]) + ', ' + self.fx_eval_id(tree, tree[10, node_id]) + + elif tree[8, node_id] == '3': # arity of 3 for the pattern '[NODE_ID], [NODE_ID], [NODE_ID], [NODE_ID]' + return tree[3, node_id] + ', ' + self.fx_eval_id(tree, tree[9, node_id]) + ', ' + self.fx_eval_id(tree, tree[10, node_id]) + ', ' + self.fx_eval_id(tree, tree[11, node_id]) + + + def fx_eval_generation(self): + + ''' + This method invokes the evaluation of an entire generation of Trees, as engaged by karoo_gp_server.py and the + 'cont' function of karoo_go_main.py. It automatically evaluates population_b before invoking the copy of _b to _a. + + Called by: fx_karoo_gp, fx_karoo_continue + + Arguments required: none + ''' + + if self.display != 's': + if self.display == 'i': print '' + print '\n Evaluate all Trees in Generation', self.generation_id + if self.display == 'i': self.fx_karoo_pause(0) + + self.fx_evolve_tree_renum(self.population_b) # population renumber + self.fx_fitness_gym(self.population_b) # run 'fx_eval', 'fx_fitness', 'fx_fitness_store', and fitness record + self.fx_data_tree_write(self.population_b, 'a') # archive current population as foundation for next generation + + if self.display != 's': + print '\n Copy gp.population_b to gp.population_a\n' + + return + + + #+++++++++++++++++++++++++++++++++++++++++++++ + # Methods to Train and Test a Tree | + #+++++++++++++++++++++++++++++++++++++++++++++ + + def fx_fitness_gym(self, population): + + ''' + Part 1 evaluates each expression against the data, line for line. This is the most time consuming and + computationally expensive part of genetic programming. When GPUs are available, the performance can increase + by many orders of magnitude for datasets measured in millions of data. + + Part 2 evaluates every Tree in each generation to determine which have the best, overall fitness score. This + could be the highest or lowest depending upon if the fitness function is maximising (higher is better) or + minimising (lower is better). The total fitness score is then saved with each Tree in the external .csv file. + + Part 3 compares the fitness of each Tree to the prior best fit in order to track those that improve with each + comparison. For matching functions, all the Trees will have the same fitness score, but they may present more + than one solution. For minimisation and maximisation functions, the final Tree should present the best overall + fitness for that generation. It is important to note that Part 3 does *not* in any way influence the Tournament + Selection which is a stand-alone process. + + Called by: + + Arguments required: population + ''' + + fitness_best = 0 + self.fittest_dict = {} + time_sum = 0 + + for tree_id in range(1, len(population)): + + ### PART 1 - GENERATE MULTIVARIATE EXPRESSION FOR EACH TREE ### + self.fx_eval_poly(population[tree_id]) # extract the expression + if self.display not in ('s'): print '\t\033[36mTree', population[tree_id][0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' + + + ### PART 2 - EVALUATE FITNESS FOR EACH TREE AGAINST TRAINING DATA ### + fitness = 0 + + expr = str(self.algo_sym) # get sympified expression and process it with TF - tested 2017 02/02 + result = self.fx_fitness_eval(expr, self.data_train) + fitness = result['fitness'] # extract fitness score + + if self.display == 'i': + print '\t \033[36m with fitness sum:\033[1m', fitness, '\033[0;0m\n' + + self.fx_fitness_store(population[tree_id], fitness) # store Fitness with each Tree + + + ### PART 3 - COMPARE FITNESS OF ALL TREES IN CURRENT GENERATION ### + if self.kernel == 'c': # display best fit Trees for the CLASSIFY kernel + if fitness >= fitness_best: # find the Tree with Maximum fitness score + fitness_best = fitness # set best fitness score + self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary if fitness >= prior + + elif self.kernel == 'r': # display best fit Trees for the REGRESSION kernel + if fitness_best == 0: fitness_best = fitness # set the baseline first time through + if fitness <= fitness_best: # find the Tree with Minimum fitness score + fitness_best = fitness # set best fitness score + self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary if fitness <= prior + + elif self.kernel == 'm': # display best fit Trees for the MATCH kernel + if fitness == self.data_train_rows: # find the Tree with a perfect match for all data rows + fitness_best = fitness # set best fitness score + self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary if all rows match + + # elif self.kernel == '[other]': # display best fit Trees for the [other] kernel + # if fitness [>=, <=] fitness_best: # find the Tree with [Maximum or Minimum] fitness score + # fitness_best = fitness # set best fitness score + # self.fittest_dict.update({tree_id:self.algo_sym}) # add to dictionary + + print '\n\033[36m ', len(self.fittest_dict.keys()), 'trees\033[1m', np.sort(self.fittest_dict.keys()), '\033[0;0m\033[36moffer the highest fitness scores.\033[0;0m' + if self.display == 'g': self.fx_karoo_pause(0) + + return + + + def fx_fitness_eval(self, expr, data, get_pred_labels = False): + + ''' + Computes tree expression using TensorFlow (TF) returning results and fitness scores. + + This method orchestrates most of the TF routines by parsing input string 'expression' and converting it into a TF + operation graph which is then processed in an isolated TF session to compute the results and corresponding fitness + values. + + 'self.tf_device' - controls which device will be used for computations (CPU or GPU). + 'self.tf_device_log' - controls device placement logging (debug only). + + Args: + 'expr' - a string containing math expression to be computed on the data. Variable names should match corresponding + terminal names in 'self.terminals'. + + 'data' - an 'n by m' matrix of the data points containing n observations and m features per observation. + Variable order should match corresponding order of terminals in 'self.terminals'. + + 'get_pred_labels' - a boolean flag which controls whether the predicted labels should be extracted from the + evolved results. This applies only to the CLASSIFY kernel and defaults to 'False'. + + Returns: + A dict mapping keys to the following outputs: + 'result' - an array of the results of applying given expression to the data + 'pred_labels' - an array of the predicted labels extracted from the results; defined only for CLASSIFY kernel, else None + 'solution' - an array of the solution values extracted from the data (variable 's' in the dataset) + 'pairwise_fitness' - an array of the element-wise results of applying corresponding fitness kernel function + 'fitness' - aggregated scalar fitness score + + Called by: + + Arguments required: expr, data + ''' + + # Initialize TensorFlow session + tf.reset_default_graph() # Reset TF internal state and cache (after previous processing) + config = tf.ConfigProto(log_device_placement=self.tf_device_log, allow_soft_placement=True) + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + with sess.graph.device(self.tf_device): + + # 1 - Load data into TF vectors + tensors = {} + for i in range(len(self.terminals)): + var = self.terminals[i] + tensors[var] = tf.constant(data[:, i], dtype=tf.float32) # converts data into vectors + + # 2- Transform string expression into TF operation graph + result = self.fx_fitness_expr_parse(expr, tensors) + pred_labels = tf.no_op() # a placeholder, applies only to CLASSIFY kernel + solution = tensors['s'] # solution value is assumed to be stored in 's' terminal + + # 3- Add fitness computation into TF graph + if self.kernel == 'c': # CLASSIFY kernel + + ''' + Creates element-wise fitness computation TensorFlow (TF) sub-graph for CLASSIFY kernel. + + This method uses the 'sympified' (SymPy) expression ('algo_sym') created in 'fx_eval_poly' and the data set + loaded at run-time to evaluate the fitness of the selected kernel. + + This multiclass classifer compares each row of a given Tree to the known solution, comparing predicted labels + generated by Karoo GP against the true classs labels. This method is able to work with any number of class + labels, from 2 to n. The left-most bin includes -inf. The right-most bin includes +inf. Those inbetween are + by default confined to the spacing of 1.0 each, as defined by: + + (solution - 1) < result <= solution + + The skew adjusts the boundaries of the bins such that they fall on both the negative and positive sides of the + origin. At the time of this writing, an odd number of class labels will generate an extra bin on the positive + side of origin as it has not yet been determined the effect of enabling the middle bin to include both a + negative and positive result. + + Called by: + + Arguments required: result, solution + ''' + + # was breaking with upgrade from Tensorflow 1.1 to 1.3; fixed by Iurii by replacing [] with () as of 20171026 + # if get_pred_labels: pred_labels = tf.map_fn(self.fx_fitness_labels_map, result, dtype = [tf.int32, tf.string], swap_memory = True) + if get_pred_labels: pred_labels = tf.map_fn(self.fx_fitness_labels_map, result, dtype = (tf.int32, tf.string), swap_memory = True) + + skew = (self.class_labels / 2) - 1 + + rule11 = tf.equal(solution, 0) + rule12 = tf.less_equal(result, 0 - skew) + rule13 = tf.logical_and(rule11, rule12) + + rule21 = tf.equal(solution, self.class_labels - 1) + rule22 = tf.greater(result, solution - 1 - skew) + rule23 = tf.logical_and(rule21, rule22) + + rule31 = tf.less(solution - 1 - skew, result) + rule32 = tf.less_equal(result, solution - skew) + rule33 = tf.logical_and(rule31, rule32) + + pairwise_fitness = tf.cast(tf.logical_or(tf.logical_or(rule13, rule23), rule33), tf.int32) + + + elif self.kernel == 'r': # REGRESSION kernel + + ''' + A very, very basic REGRESSION kernel which is not designed to perform well in the real world. It requires + that you raise the minimum node count to keep it from converging on the value of '1'. Consider writing or + integrating a more sophisticated kernel. + ''' + + pairwise_fitness = tf.abs(solution - result) + + + elif self.kernel == 'm': # MATCH kernel + + ''' + This is used for demonstration purposes only. + ''' + + # pairwise_fitness = tf.cast(tf.equal(solution, result), tf.int32) # breaks due to floating points + RTOL, ATOL = 1e-05, 1e-08 # fixes above issue by checking if a float value lies within a range of values + pairwise_fitness = tf.cast(tf.less_equal(tf.abs(solution - result), ATOL + RTOL * tf.abs(result)), tf.int32) + + # elif self.kernel == '[other]': # [OTHER] kernel + # pairwise_fitness = tf.cast(tf.___(solution, result) + + else: raise Exception('Kernel type is wrong or missing. You entered {}'.format(self.kernel)) + + fitness = tf.reduce_sum(pairwise_fitness) + + # Process TF graph and collect the results + result, pred_labels, solution, fitness, pairwise_fitness = sess.run([result, pred_labels, solution, fitness, pairwise_fitness]) + + return {'result': result, 'pred_labels': pred_labels, 'solution': solution, 'fitness': float(fitness), 'pairwise_fitness': pairwise_fitness} + + + def fx_fitness_expr_parse(self, expr, tensors): + + ''' + Extract expression tree from the string algo_sym and transform into TensorFlow (TF) graph. + + Called by: + + Arguments required: expr, tensors + ''' + + tree = ast.parse(expr, mode='eval').body + + return self.fx_fitness_node_parse(tree, tensors) + + + def fx_fitness_chain_bool(self, values, operation, tensors): + + ''' + Chains a sequence of boolean operations (e.g. 'a and b and c') into a single TensorFlow (TF) sub graph. + + Called by: + + Arguments required: values, operation, tensors + ''' + + x = tf.cast(self.fx_fitness_node_parse(values[0], tensors), tf.bool) + if len(values) > 1: + return operation(x, self.fx_fitness_chain_bool(values[1:], operation, tensors)) + else: + return x + + + def fx_fitness_chain_compare(self, comparators, ops, tensors): + + ''' + Chains a sequence of comparison operations (e.g. 'a > b < c') into a single TensorFlow (TF) sub graph. + + Called by: + + Arguments required: comparators, ops, tensors + ''' + + x = self.fx_fitness_node_parse(comparators[0], tensors) + y = self.fx_fitness_node_parse(comparators[1], tensors) + if len(comparators) > 2: + return tf.logical_and(operators[type(ops[0])](x, y), self.fx_fitness_chain_compare(comparators[1:], ops[1:], tensors)) + else: + return operators[type(ops[0])](x, y) + + + def fx_fitness_node_parse(self, node, tensors): + + ''' + Recursively transforms parsed expression tree into TensorFlow (TF) graph. + + Called by: + + Arguments required: node, tensors + ''' + + if isinstance(node, ast.Name): # + return tensors[node.id] + + elif isinstance(node, ast.Num): # + shape = tensors[tensors.keys()[0]].get_shape() + return tf.constant(node.n, shape=shape, dtype=tf.float32) + + elif isinstance(node, ast.BinOp): # , e.g., x + y + return operators[type(node.op)](self.fx_fitness_node_parse(node.left, tensors), self.fx_fitness_node_parse(node.right, tensors)) + + elif isinstance(node, ast.UnaryOp): # e.g., -1 + return operators[type(node.op)](self.fx_fitness_node_parse(node.operand, tensors)) + + elif isinstance(node, ast.Call): # () e.g., sin(x) + return operators[node.func.id](*[self.fx_fitness_node_parse(arg, tensors) for arg in node.args]) + + elif isinstance(node, ast.BoolOp): # e.g. x or y + return self.fx_fitness_chain_bool(node.values, operators[type(node.op)], tensors) + + elif isinstance(node, ast.Compare): # e.g., a > z + return self.fx_fitness_chain_compare([node.left] + node.comparators, node.ops, tensors) + + else: raise TypeError(node) + + + def fx_fitness_labels_map(self, result): + + ''' + For the CLASSIFY kernel, creates a TensorFlow (TF) sub-graph defined as a sequence of boolean conditions based upon + the quantity of true class labels provided in the data .csv. Outputs an array of tuples containing the predicted + labels based upon the result and corresponding boolean condition triggered. + + For comparison, the original (pre-TensorFlow) cod follows: + + skew = (self.class_labels / 2) - 1 # '-1' keeps a binary classification splitting over the origin + if solution == 0 and result <= 0 - skew; fitness = 1: # check for first class (the left-most bin) + elif solution == self.class_labels - 1 and result > solution - 1 - skew; fitness = 1: # check for last class (the right-most bin) + elif solution - 1 - skew < result <= solution - skew; fitness = 1: # check for class bins between first and last + else: fitness = 0 # no class match + + Called by: + + Arguments required: result + ''' + + skew = (self.class_labels / 2) - 1 + label_rules = {self.class_labels - 1: (tf.constant(self.class_labels - 1), tf.constant(' > {}'.format(self.class_labels - 2 - skew)))} + + for class_label in range(self.class_labels - 2, 0, -1): + cond = (class_label - 1 - skew < result) & (result <= class_label - skew) + label_rules[class_label] = tf.cond(cond, lambda: (tf.constant(class_label), tf.constant(' <= {}'.format(class_label - skew))), lambda: label_rules[class_label + 1]) + + pred_label = tf.cond(result <= 0 - skew, lambda: (tf.constant(0), tf.constant(' <= {}'.format(0 - skew))), lambda: label_rules[1]) + + return pred_label + + + def fx_fitness_store(self, tree, fitness): + + ''' + Records the fitness and length of the raw algorithm (multivariate expression) to the Numpy array. Parsimony can + be used to apply pressure to the evolutionary process to select from a set of trees with the same fitness function + the one(s) with the simplest (shortest) multivariate expression. + + Called by: + + Arguments required: tree, fitness + ''' + + fitness = float(fitness) + fitness = round(fitness, self.precision) + + tree[12][1] = fitness # store the fitness with each tree + tree[12][2] = len(str(self.algo_raw)) # store the length of the raw algo for parsimony + # if len(tree[3]) > 4: # if the Tree array is wide enough -- SEE SCRATCHPAD + + return + + + def fx_fitness_tournament(self, tourn_size): + + ''' + Multiple contenders ('tourn_size') are randomly selected and then compared for their respective fitness, as + determined in 'fx_fitness_gym'. The tournament is engaged to select a single Tree for each invocation of the + genetic operators: reproduction, mutation (point, branch), and crossover (sexual reproduction). + + The original Tournament Selection drew directly from the foundation generation (gp.generation_a). However, + with the introduction of a minimum number of nodes as defined by the user ('gp.tree_depth_min'), + 'gp.gene_pool' limits the Trees to those which meet all criteria. + + Stronger boundary parameters (a reduced gap between the min and max number of nodes) may invoke more compact + solutions, but also runs the risk of elitism, even total population die-off where a healthy population once existed. + + Called by: + + Arguments required: tourn_size + ''' + + tourn_test = 0 + # short_test = 0 # an incomplete parsimony test (seeking shortest solution) + + if self.display == 'i': print '\n\tEnter the tournament ...' + + for n in range(tourn_size): + # tree_id = np.random.randint(1, self.tree_pop_max + 1) # former method of selection from the unfiltered population + rnd = np.random.randint(len(self.gene_pool)) # select one Tree at random from the gene pool + tree_id = int(self.gene_pool[rnd]) + + fitness = float(self.population_a[tree_id][12][1]) # extract the fitness from the array + fitness = round(fitness, self.precision) # force 'result' and 'solution' to the same number of floating points + + if self.fitness_type == 'max': # if the fitness function is Maximising + + # first time through, 'tourn_test' will be initialised below + + if fitness > tourn_test: # if the current Tree's 'fitness' is greater than the priors' + if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '>', tourn_test, 'and leads\033[0;0m' + tourn_lead = tree_id # set 'TREE_ID' for the new leader + tourn_test = fitness # set 'fitness' of the new leader + # short_test = int(self.population_a[tree_id][12][2]) # set len(algo_raw) of new leader + + elif fitness == tourn_test: # if the current Tree's 'fitness' is equal to the priors' + if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '=', tourn_test, 'and leads\033[0;0m' + tourn_lead = tree_id # in case there is no variance in this tournament + # tourn_test remains unchanged + + # NEED TO ADD: option for parsimony + # if int(self.population_a[tree_id][12][2]) < short_test: + # short_test = int(self.population_a[tree_id][12][2]) # set len(algo_raw) of new leader + # print '\t\033[36m with improved parsimony score of:\033[1m', short_test, '\033[0;0m' + + elif fitness < tourn_test: # if the current Tree's 'fitness' is less than the priors' + if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '<', tourn_test, 'and is ignored\033[0;0m' + # tourn_lead remains unchanged + # tourn_test remains unchanged + + else: print '\n\t\033[31m ERROR! In fx_fitness_tournament: fitness =', fitness, 'and tourn_test =', tourn_test, '\033[0;0m'; self.fx_karoo_pause(0) + + + elif self.fitness_type == 'min': # if the fitness function is Minimising + + if tourn_test == 0: # first time through, 'tourn_test' is given a baseline value + tourn_test = fitness + + if fitness < tourn_test: # if the current Tree's 'fitness' is less than the priors' + if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '<', tourn_test, 'and leads\033[0;0m' + tourn_lead = tree_id # set 'TREE_ID' for the new leader + tourn_test = fitness # set 'fitness' of the new leader + + elif fitness == tourn_test: # if the current Tree's 'fitness' is equal to the priors' + if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '=', tourn_test, 'and leads\033[0;0m' + tourn_lead = tree_id # in case there is no variance in this tournament + # tourn_test remains unchanged + + elif fitness > tourn_test: # if the current Tree's 'fitness' is greater than the priors' + if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has fitness', fitness, '>', tourn_test, 'and is ignored\033[0;0m' + # tourn_lead remains unchanged + # tourn_test remains unchanged + + else: print '\n\t\033[31m ERROR! In fx_fitness_tournament: fitness =', fitness, 'and tourn_test =', tourn_test, '\033[0;0m'; self.fx_karoo_pause(0) + + + tourn_winner = np.copy(self.population_a[tourn_lead]) # copy full Tree so as to not inadvertantly modify the original tree + + if self.display == 'i': print '\n\t\033[36mThe winner of the tournament is Tree:\033[1m', tourn_winner[0][1], '\033[0;0m' + + return tourn_winner + + + def fx_fitness_gene_pool(self): + + ''' + The gene pool was introduced as means by which advanced users could define additional constraints on the evolved + functions, in an effort to guide the evolutionary process. The first constraint introduced is the 'mininum number + of nodes' parameter (gp.tree_depth_min). This defines the minimum number of nodes (in the context of Karoo, this + refers to both functions (operators) and terminals (operands)). + + When the minimum node count is human guided, it can keep the solution from defaulting to a local minimum, as with + 't/t' in the Kepler problem, by forcing a more complex solution. If you find that when engaging the Regression + kernel you are met with a solution which is too simple (eg: linear instead of non-linear), try increasing the + minimum number of nodes (with the launch of Karoo, or mid-stream by way of the pause menu). + + What's more, you can add additional constraints to the Gene Pool, thereby customizing how the next generation is + selected. + + At this time, the gene pool does *not* limit the number of times any given Tree may be selected for mutation or + reproduction nor does it take into account parsimony (seeking the simplest multivariate expression). + + This method is automatically invoked with every Tournament Selection ('fx_fitness_tournament'). + + Called by: + + Arguments required: none + ''' + + self.gene_pool = [] + if self.display == 'i': print '\n Prepare a viable gene pool ...'; self.fx_karoo_pause(0) + + for tree_id in range(1, len(self.population_a)): + + self.fx_eval_poly(self.population_a[tree_id]) # extract the expression + + if len(self.population_a[tree_id][3])-1 >= self.tree_depth_min and self.algo_sym != 1: # check if Tree meets the requirements + if self.display == 'i': print '\t\033[36m Tree', tree_id, 'has >=', self.tree_depth_min, 'nodes and is added to the gene pool\033[0;0m' + self.gene_pool.append(self.population_a[tree_id][0][1]) + + if len(self.gene_pool) > 0 and self.display == 'i': print '\n\t The total population of the gene pool is', len(self.gene_pool); self.fx_karoo_pause(0) + + elif len(self.gene_pool) <= 0: # the evolutionary constraints were too tight, killing off the entire population + # self.generation_id = self.generation_id - 1 # revert the increment of the 'generation_id' + # self.generation_max = self.generation_id # catch the unused "cont" values in the 'fx_karoo_pause' method + print "\n\t\033[31m\033[3m 'They're dead Jim. They're all dead!'\033[0;0m There are no Trees in the gene pool. You should archive your populations and (q)uit."; self.fx_karoo_pause(0) + + return + + + def fx_fitness_test_classify(self, result): + + ''' + Print the Precision-Recall and Confusion Matrix for a CLASSIFICATION run against the test data. + + From scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html + Precision (P) = true_pos / true_pos + false_pos + Recall (R) = true_pos / true_pos + false_neg + harmonic mean of Precision and Recall (F1) = 2(P x R) / (P + R) + + From scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html + y_pred = result, the predicted labels generated by Karoo GP + y_true = solution, the true labels associated with the data + + Called by: + + Arguments required: result + ''' + + for i in range(len(result['result'])): + print '\t\033[36m Data row {} predicts class:\033[1m {} ({} True)\033[0;0m\033[36m as {:.2f}{}\033[0;0m'.format(i, int(result['pred_labels'][0][i]), int(result['solution'][i]), result['result'][i], result['pred_labels'][1][i]) + + print '\n Fitness score: {}'.format(result['fitness']) + print '\n Precision-Recall report:\n', skm.classification_report(result['solution'], result['pred_labels'][0]) + print ' Confusion matrix:\n', skm.confusion_matrix(result['solution'], result['pred_labels'][0]) + + return + + + def fx_fitness_test_regress(self, result): + + ''' + Print the Fitness score and Mean Squared Error for a REGRESSION run against the test data. + + Called by: + + Arguments required: result + + ''' + + for i in range(len(result['result'])): + print '\t\033[36m Data row {} predicts value:\033[1m {:.2f} ({:.2f} True)\033[0;0m'.format(i, result['result'][i], result['solution'][i]) + + MSE, fitness = skm.mean_squared_error(result['result'], result['solution']), result['fitness'] + print '\n\t Regression fitness score: {}'.format(fitness) + print '\t Mean Squared Error: {}'.format(MSE) + + return + + + def fx_fitness_test_match(self, result): + + ''' + Print the accuracy for a MATCH kernel run against the test data. + + Called by: + + Arguments required: result + ''' + + for i in range(len(result['result'])): + print '\t\033[36m Data row {} predicts match:\033[1m {:.2f} ({:.2f} True)\033[0;0m'.format(i, result['result'][i], result['solution'][i]) + + print '\n\tMatching fitness score: {}'.format(result['fitness']) + + return + + + # def fx_fitness_test_[other](self, result): + + # ''' + # Print the [statistical measure] for a [OTHER] kernel run against the test data. + # ''' + + # for i in range(len(result['result'])): + # print '\t\033[36m Data row {} predicts value:\033[1m {} ({} label)\033[0;0m'.format(i, int(result['result'][i]), int(result['solution'][i])) + + # print '\n\tFitness score: {}'.format(result['fitness']) + + # Called by: + + # Arguments required: result + + # return + + + #+++++++++++++++++++++++++++++++++++++++++++++ + # Methods to Construct the next Generation | + #+++++++++++++++++++++++++++++++++++++++++++++ + + def fx_nextgen_reproduce(self): + + ''' + Through tournament selection, a single Tree from the prior generation is copied without mutation to the next + generation. This is analogous to a member of the prior generation directly entering the gene pool of the + subsequent (younger) generation. + + Called by: fx_karoo_gp, fx_karoo_continue + + Arguments required: none + ''' + + if self.display != 's': + if self.display == 'i': print '' + print ' Perform', self.evolve_repro, 'Reproductions ...' + if self.display == 'i': self.fx_karoo_pause(0) + + for n in range(self.evolve_repro): # quantity of Trees to be copied without mutation + tourn_winner = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for each reproduction + tourn_winner = self.fx_evolve_fitness_wipe(tourn_winner) # wipe fitness data + self.population_b.append(tourn_winner) # append array to next generation population of Trees + + return + + + def fx_nextgen_point_mutate(self): + + ''' + Through tournament selection, a copy of a Tree from the prior generation mutates before being added to the + next generation. In this method, a single point is selected for mutation while maintaining function nodes as + functions (operators) and terminal nodes as terminals (variables). The size and shape of the Tree will remain + identical. + + Called by: fx_karoo_gp, fx_karoo_continue + + Arguments required: none + ''' + + if self.display != 's': + if self.display == 'i': print '' + print ' Perform', self.evolve_point, 'Point Mutations ...' + if self.display == 'i': self.fx_karoo_pause(0) + + for n in range(self.evolve_point): # quantity of Trees to be generated through mutation + tourn_winner = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for each mutation + tourn_winner, node = self.fx_evolve_point_mutate(tourn_winner) # perform point mutation; return single point for record keeping + self.population_b.append(tourn_winner) # append array to next generation population of Trees + + return + + + def fx_nextgen_branch_mutate(self): + + ''' + Through tournament selection, a copy of a Tree from the prior generation mutates before being added to the + next generation. Unlike Point Mutation, in this method an entire branch is selected. If the evolutionary run is + designated as Full, the size and shape of the Tree will remain identical, each node mutated sequentially, where + functions remain functions and terminals remain terminals. If the evolutionary run is designated as Grow or + Ramped Half/Half, the size and shape of the Tree may grow smaller or larger, but it may not exceed + tree_depth_max as defined by the user. + + Called by: fx_karoo_gp, fx_karoo_continue + + Arguments required: none + ''' + + if self.display != 's': + if self.display == 'i': print '' + print ' Perform', self.evolve_branch, 'Full or Grow Mutations ...' + if self.display == 'i': self.fx_karoo_pause(0) + + for n in range(self.evolve_branch): # quantity of Trees to be generated through mutation + tourn_winner = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for each mutation + branch = self.fx_evolve_branch_select(tourn_winner) # select point of mutation and all nodes beneath + + # TEST & DEBUG: comment the top or bottom to force all Full or all Grow methods + + if tourn_winner[1][1] == 'f': # perform Full method mutation on 'tourn_winner' + tourn_winner = self.fx_evolve_full_mutate(tourn_winner, branch) + + elif tourn_winner[1][1] == 'g': # perform Grow method mutation on 'tourn_winner' + tourn_winner = self.fx_evolve_grow_mutate(tourn_winner, branch) + + self.population_b.append(tourn_winner) # append array to next generation population of Trees + + return + + + def fx_nextgen_crossover(self): + + ''' + Through tournament selection, two trees are selected as parents to produce two offspring. Within each parent + Tree a branch is selected. Parent A is copied, with its selected branch deleted. Parent B's branch is then + copied to the former location of Parent A's branch and inserted (grafted). The size and shape of the child + Tree may be smaller or larger than either of the parents, but may not exceed 'tree_depth_max' as defined + by the user. + + This process combines genetic code from two parent Trees, both of which were chosen by the tournament process + as having a higher fitness than the average population. Therefore, there is a chance their offspring will + provide an improvement in total fitness. In most GP applications, Crossover is the most commonly applied + evolutionary operator (~70-80%). + + For those who like to watch, select 'db' (debug mode) at the launch of Karoo GP or at any (pause). + + Called by: fx_karoo_gp, fx_karoo_continue + + Arguments required: none + ''' + + if self.display != 's': + if self.display == 'i': print '' + print ' Perform', self.evolve_cross, 'Crossovers ...' + if self.display == 'i': self.fx_karoo_pause(0) + + for n in range(self.evolve_cross / 2): # quantity of Trees to be generated through Crossover, accounting for 2 children each + + parent_a = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for 'parent_a' + branch_a = self.fx_evolve_branch_select(parent_a) # select branch within 'parent_a', to copy to 'parent_b' and receive a branch from 'parent_b' + + parent_b = self.fx_fitness_tournament(self.tourn_size) # perform tournament selection for 'parent_b' + branch_b = self.fx_evolve_branch_select(parent_b) # select branch within 'parent_b', to copy to 'parent_a' and receive a branch from 'parent_a' + + parent_c = np.copy(parent_a); branch_c = np.copy(branch_a) # else the Crossover mods affect the parent Trees, due to how Python manages '=' + parent_d = np.copy(parent_b); branch_d = np.copy(branch_b) # else the Crossover mods affect the parent Trees, due to how Python manages '=' + + offspring_1 = self.fx_evolve_crossover(parent_a, branch_a, parent_b, branch_b) # perform Crossover + self.population_b.append(offspring_1) # append the 1st child to next generation of Trees + + offspring_2 = self.fx_evolve_crossover(parent_d, branch_d, parent_c, branch_c) # perform Crossover + self.population_b.append(offspring_2) # append the 2nd child to next generation of Trees + + return + + + #+++++++++++++++++++++++++++++++++++++++++++++ + # Methods to Evolve a Population | + #+++++++++++++++++++++++++++++++++++++++++++++ + + def fx_evolve_point_mutate(self, tree): + + ''' + Mutate a single point in any Tree (Grow or Full). + + Called by: + + Arguments required: tree + ''' + + node = np.random.randint(1, len(tree[3])) # randomly select a point in the Tree (including root) + if self.display == 'i': print '\t\033[36m with', tree[5][node], 'node\033[1m', tree[3][node], '\033[0;0m\033[36mchosen for mutation\n\033[0;0m' + elif self.display == 'db': print '\n\n\033[33m *** Point Mutation *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree + + if tree[5][node] == 'root': + rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators + tree[6][node] = self.functions[rnd][0] # replace function (operator) + + elif tree[5][node] == 'func': + rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators + tree[6][node] = self.functions[rnd][0] # replace function (operator) + + elif tree[5][node] == 'term': + rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals + tree[6][node] = self.terminals[rnd] # replace terminal (variable) + + else: print '\n\t\033[31m ERROR! In fx_evolve_point_mutate, node_type =', tree[5][node], '\033[0;0m'; self.fx_karoo_pause(0) + + tree = self.fx_evolve_fitness_wipe(tree) # wipe fitness data + + if self.display == 'db': print '\n\033[36m This is tourn_winner after node\033[1m', node, '\033[0;0m\033[36mmutation and updates:\033[0;0m\n', tree; self.fx_karoo_pause(0) + + return tree, node # 'node' is returned only to be assigned to the 'tourn_trees' record keeping + + + def fx_evolve_full_mutate(self, tree, branch): + + ''' + Mutate a branch of a Full method Tree. + + The full mutate method is straight-forward. A branch was generated and passed to this method. As the size and + shape of the Tree must remain identical, each node is mutated sequentially (copied from the new Tree to replace + the old, node for node), where functions remain functions and terminals remain terminals. + + Called by: + + Arguments required: tree, branch + ''' + + if self.display == 'db': print '\n\n\033[33m *** Full Mutation: function to function *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree + + for n in range(len(branch)): + + # 'root' is not made available for Full mutation as this would build an entirely new Tree + + if tree[5][branch[n]] == 'func': + if self.display == 'i': print '\t\033[36m from\033[1m', tree[5][branch[n]], '\033[0;0m\033[36mto\033[1m func \033[0;0m' + + rnd = np.random.randint(0, len(self.functions[:,0])) # call the previously loaded .csv which contains all operators + tree[6][branch[n]] = self.functions[rnd][0] # replace function (operator) + + elif tree[5][branch[n]] == 'term': + if self.display == 'i': print '\t\033[36m from\033[1m', tree[5][branch[n]], '\033[0;0m\033[36mto\033[1m term \033[0;0m' + + rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals + tree[6][branch[n]] = self.terminals[rnd] # replace terminal (variable) + + tree = self.fx_evolve_fitness_wipe(tree) # wipe fitness data + + if self.display == 'db': print '\n\033[36m This is tourn_winner after nodes\033[1m', branch, '\033[0;0m\033[36mwere mutated and updated:\033[0;0m\n', tree; self.fx_karoo_pause(0) + + return tree + + + def fx_evolve_grow_mutate(self, tree, branch): + + ''' + Mutate a branch of a Grow method Tree. + + A branch is selected within a given tree. + + If the point of mutation ('branch_top') resides at 'tree_depth_max', we do not need to grow a new tree. As the + methods for building trees always assume root (node 0) to be a function, we need only mutate this terminal node + to another terminal node, and this branch mutate method is complete. + + If the top of that branch is a terminal which does not reside at 'tree_depth_max', then it may either remain a + terminal (in which case a new value is randomly assigned) or it may mutate into a function. If it becomes a + function, a new branch (mini-tree) is generated to be appended to that nodes current location. The same is true + for function-to-function mutation. Either way, the new branch will be only as deep as allowed by the distance + from it's branch_top to the bottom of the tree. + + If however a function mutates into a terminal, the entire branch beneath the function is deleted from the array + and the entire array is updated, to fix parent/child links, associated arities, and node IDs. + + Called by: + + Arguments required: tree, branch + ''' + + branch_top = int(branch[0]) # replaces 2 instances, below; tested 2016 07/09 + branch_depth = self.tree_depth_max - int(tree[4][branch_top]) # 'tree_depth_max' - depth at 'branch_top' to set max potential size of new branch - 2016 07/10 + + if branch_depth < 0: # this has never occured ... yet + print '\n\t\033[31m ERROR! In fx_evolve_grow_mutate: branch_depth < 0\033[0;0m' + print '\t branch_depth =', branch_depth; self.fx_karoo_pause(0) + + elif branch_depth == 0: # the point of mutation ('branch_top') chosen resides at the maximum allowable depth, so mutate term to term + + if self.display == 'i': print '\t\033[36m max depth branch node\033[1m', tree[3][branch_top], '\033[0;0m\033[36mmutates from \033[1mterm\033[0;0m \033[36mto \033[1mterm\033[0;0m\n' + if self.display == 'db': print '\n\n\033[33m *** Grow Mutation: terminal to terminal *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree + + rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals + tree[6][branch_top] = self.terminals[rnd] # replace terminal (variable) + + if self.display == 'db': print '\n\033[36m This is tourn_winner after terminal\033[1m', branch_top, '\033[0;0m\033[36mmutation, branch deletion, and updates:\033[0;0m\n', tree; self.fx_karoo_pause(0) + + else: # the point of mutation ('branch_top') chosen is at least one degree of depth from the maximum allowed + + # type_mod = '[func or term]' # TEST & DEBUG: force to 'func' or 'term' and comment the next 3 lines + rnd = np.random.randint(2) + if rnd == 0: type_mod = 'func' # randomly selected as Function + elif rnd == 1: type_mod = 'term' # randomly selected as Terminal + + if type_mod == 'term': # mutate 'branch_top' to a terminal and delete all nodes beneath (no subsequent nodes are added to this branch) + + if self.display == 'i': print '\t\033[36m branch node\033[1m', tree[3][branch_top], '\033[0;0m\033[36mmutates from\033[1m', tree[5][branch_top], '\033[0;0m\033[36mto\033[1m term \n\033[0;0m' + if self.display == 'db': print '\n\n\033[33m *** Grow Mutation: branch_top to terminal *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree + + rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals + tree[5][branch_top] = 'term' # replace type ('func' to 'term' or 'term' to 'term') + tree[6][branch_top] = self.terminals[rnd] # replace label + + tree = np.delete(tree, branch[1:], axis = 1) # delete all nodes beneath point of mutation ('branch_top') + tree = self.fx_evolve_node_arity_fix(tree) # fix all node arities + tree = self.fx_evolve_child_link_fix(tree) # fix all child links + tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's + + if self.display == 'db': print '\n\033[36m This is tourn_winner after terminal\033[1m', branch_top, '\033[0;0m\033[36mmutation, branch deletion, and updates:\033[0;0m\n', tree; self.fx_karoo_pause(0) + + + if type_mod == 'func': # mutate 'branch_top' to a function (a new 'gp.tree' will be copied, node by node, into 'tourn_winner') + + if self.display == 'i': print '\t\033[36m branch node\033[1m', tree[3][branch_top], '\033[0;0m\033[36mmutates from\033[1m', tree[5][branch_top], '\033[0;0m\033[36mto\033[1m func \n\033[0;0m' + if self.display == 'db': print '\n\n\033[33m *** Grow Mutation: branch_top to function *** \033[0;0m\n\n\033[36m This is the unaltered tourn_winner:\033[0;0m\n', tree + + self.fx_init_tree_build('mutant', self.pop_tree_type, branch_depth) # build new Tree ('gp.tree') with a maximum depth which matches 'branch' + + if self.display == 'db': print '\n\033[36m This is the new Tree to be inserted at node\033[1m', branch_top, '\033[0;0m\033[36min tourn_winner:\033[0;0m\n', self.tree; self.fx_karoo_pause(0) + + # because we already know the maximum depth to which this branch can grow, there is no need to prune after insertion + tree = self.fx_evolve_branch_top_copy(tree, branch) # copy root of new 'gp.tree' to point of mutation ('branch_top') in 'tree' ('tourn_winner') + tree = self.fx_evolve_branch_body_copy(tree) # copy remaining nodes in new 'gp.tree' to 'tree' ('tourn_winner') + + tree = self.fx_evolve_fitness_wipe(tree) # wipe fitness data + + return tree + + + def fx_evolve_crossover(self, parent, branch_x, offspring, branch_y): + + ''' + Refer to the method 'fx_nextgen_crossover' for a full description of the genetic operator Crossover. + + This method is called twice to produce 2 offspring per pair of parent Trees. Note that in the method + 'karoo_fx_crossover' the parent/branch relationships are swapped from the first run to the second, such that + this method receives swapped components to produce the alternative offspring. Therefore 'parent_b' is first + passed to 'offspring' which will receive 'branch_a'. With the second run, 'parent_a' is passed to 'offspring' which + will receive 'branch_b'. + + Called by: + + Arguments required: parent, branch_x, offspring, branch_y (parents_a / _b, branch_a / _b from 'fx_nextgen_crossover') + ''' + + crossover = int(branch_x[0]) # pointer to the top of the 1st parent branch passed from 'fx_nextgen_crossover' + branch_top = int(branch_y[0]) # pointer to the top of the 2nd parent branch passed from 'fx_nextgen_crossover' + + if self.display == 'db': print '\n\n\033[33m *** Crossover *** \033[0;0m' + + if len(branch_x) == 1: # if the branch from the parent contains only one node (terminal) + + if self.display == 'i': print '\t\033[36m terminal crossover from \033[1mparent', parent[0][1], '\033[0;0m\033[36mto \033[1moffspring', offspring[0][1], '\033[0;0m\033[36mat node\033[1m', branch_top, '\033[0;0m' + + if self.display == 'db': + print '\n\033[36m In a copy of one parent:\033[0;0m\n', offspring + print '\n\033[36m ... we remove nodes\033[1m', branch_y, '\033[0;0m\033[36mand replace node\033[1m', branch_top, '\033[0;0m\033[36mwith a terminal from branch_x\033[0;0m'; self.fx_karoo_pause(0) + + offspring[5][branch_top] = 'term' # replace type + offspring[6][branch_top] = parent[6][crossover] # replace label with that of a particular node in 'branch_x' + offspring[8][branch_top] = 0 # set terminal arity + + offspring = np.delete(offspring, branch_y[1:], axis = 1) # delete all nodes beneath point of mutation ('branch_top') + offspring = self.fx_evolve_child_link_fix(offspring) # fix all child links + offspring = self.fx_evolve_node_renum(offspring) # renumber all 'NODE_ID's + + if self.display == 'db': print '\n\033[36m This is the resulting offspring:\033[0;0m\n', offspring; self.fx_karoo_pause(0) + + + else: # we are working with a branch from 'parent' >= depth 1 (min 3 nodes) + + if self.display == 'i': print '\t\033[36m branch crossover from \033[1mparent', parent[0][1], '\033[0;0m\033[36mto \033[1moffspring', offspring[0][1], '\033[0;0m\033[36mat node\033[1m', branch_top, '\033[0;0m' + + # self.fx_init_tree_build('test', 'f', 2) # TEST & DEBUG: disable the next 'self.tree ...' line + self.tree = self.fx_evolve_branch_copy(parent, branch_x) # generate stand-alone 'gp.tree' with properties of 'branch_x' + + if self.display == 'db': + print '\n\033[36m From one parent:\033[0;0m\n', parent + print '\n\033[36m ... we copy branch_x\033[1m', branch_x, '\033[0;0m\033[36mas a new, sub-tree:\033[0;0m\n', self.tree; self.fx_karoo_pause(0) + + if self.display == 'db': + print '\n\033[36m ... and insert it into a copy of the second parent in place of the selected branch\033[1m', branch_y,':\033[0;0m\n', offspring; self.fx_karoo_pause(0) + + offspring = self.fx_evolve_branch_top_copy(offspring, branch_y) # copy root of 'branch_y' ('gp.tree') to 'offspring' + offspring = self.fx_evolve_branch_body_copy(offspring) # copy remaining nodes in 'branch_y' ('gp.tree') to 'offspring' + offspring = self.fx_evolve_tree_prune(offspring, self.tree_depth_max) # prune to the max Tree depth + adjustment - tested 2016 07/10 + + offspring = self.fx_evolve_fitness_wipe(offspring) # wipe fitness data + + return offspring + + + def fx_evolve_branch_select(self, tree): + + ''' + Select all nodes in the 'tourn_winner' Tree at and below the randomly selected starting point. + + While Grow mutation uses this method to select a region of the 'tourn_winner' to delete, Crossover uses this + method to select a region of the 'tourn_winner' which is then converted to a stand-alone tree. As such, it is + imperative that the nodes be in the correct order, else all kinds of bad things happen. + + Called by: + + Arguments required: tree + ''' + + branch = np.array([]) # the array is necessary in order to len(branch) when 'branch' has only one element + branch_top = np.random.randint(2, len(tree[3])) # randomly select a non-root node + branch_eval = self.fx_eval_id(tree, branch_top) # generate tuple of 'branch_top' and subseqent nodes + branch_symp = sympify(branch_eval) # convert string into something useful + branch = np.append(branch, branch_symp) # append list to array + + branch = np.sort(branch) # sort nodes in branch for Crossover. + + if self.display == 'i': print '\t \033[36mwith nodes\033[1m', branch, '\033[0;0m\033[36mchosen for mutation\033[0;0m' + + return branch + + + def fx_evolve_branch_top_copy(self, tree, branch): + + ''' + Copy the point of mutation ('branch_top') from 'gp.tree' to 'tree'. + + This method works with 3 inputs: local 'tree' is being modified; local 'branch' is a section of 'tree' which + will be removed; and global 'gp.tree' (recycling from initial population generation) is the new Tree to be + copied into 'tree', replacing 'branch'. + + This method is used in both Grow Mutation and Crossover. + + Called by: + + Arguments required: tree, branch + ''' + + branch_top = int(branch[0]) + + tree[5][branch_top] = 'func' # update type ('func' to 'term' or 'term' to 'term'); this modifies gp.tree[5[1] from 'root' to 'func' + tree[6][branch_top] = self.tree[6][1] # copy node_label from new tree + tree[8][branch_top] = self.tree[8][1] # copy node_arity from new tree + + tree = np.delete(tree, branch[1:], axis = 1) # delete all nodes beneath point of mutation ('branch_top') + + c_buffer = self.fx_evolve_c_buffer(tree, branch_top) # generate c_buffer for point of mutation ('branch_top') + tree = self.fx_evolve_child_insert(tree, branch_top, c_buffer) # insert new nodes + tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's + + if self.display == 'db': + print '\n\t ... inserted node 1 of', len(self.tree[3])-1 + print '\n\033[36m This is the Tree after a new node is inserted:\033[0;0m\n', tree; self.fx_karoo_pause(0) + + return tree + + + def fx_evolve_branch_body_copy(self, tree): + + ''' + Copy the body of 'gp.tree' to 'tree', one node at a time. + + This method works with 3 inputs: local 'tree' is being modified; local 'branch' is a section of 'tree' which + will be removed; and global 'gp.tree' (recycling from initial population generation) is the new Tree to be + copied into 'tree', replacing 'branch'. + + This method is used in both Grow Mutation and Crossover. + + Called by: + + Arguments required: tree + ''' + + node_count = 2 # set node count for 'gp.tree' to 2 as the new root has already replaced 'branch_top' in 'fx_evolve_branch_top_copy' + + while node_count < len(self.tree[3]): # increment through all nodes in the new Tree ('gp.tree'), starting with node 2 + + for j in range(1, len(tree[3])): # increment through all nodes in tourn_winner ('tree') + + if self.display == 'db': print '\tScanning tourn_winner node_id:', j + + if tree[5][j] == '': + tree[5][j] = self.tree[5][node_count] # copy 'node_type' from branch to tree + tree[6][j] = self.tree[6][node_count] # copy 'node_label' from branch to tree + tree[8][j] = self.tree[8][node_count] # copy 'node_arity' from branch to tree + + if tree[5][j] == 'term': + tree = self.fx_evolve_child_link_fix(tree) # fix all child links + tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's + + if tree[5][j] == 'func': + c_buffer = self.fx_evolve_c_buffer(tree, j) # generate 'c_buffer' for point of mutation ('branch_top') + tree = self.fx_evolve_child_insert(tree, j, c_buffer) # insert new nodes + tree = self.fx_evolve_child_link_fix(tree) # fix all child links + tree = self.fx_evolve_node_renum(tree) # renumber all 'NODE_ID's + + if self.display == 'db': + print '\n\t ... inserted node', node_count, 'of', len(self.tree[3])-1 + print '\n\033[36m This is the Tree after a new node is inserted:\033[0;0m\n', tree; self.fx_karoo_pause(0) + + node_count = node_count + 1 # exit loop when 'node_count' reaches the number of columns in the array 'gp.tree' + + return tree + + + def fx_evolve_branch_copy(self, tree, branch): + + ''' + This method prepares a stand-alone Tree as a copy of the given branch. + + This method is used with Crossover. + + Called by: + + Arguments required: tree, branch + ''' + + new_tree = np.array([ ['TREE_ID'],['tree_type'],['tree_depth_base'],['NODE_ID'],['node_depth'],['node_type'],['node_label'],['node_parent'],['node_arity'],['node_c1'],['node_c2'],['node_c3'],['fitness'] ]) + + # tested 2015 06/08 + for n in range(len(branch)): + + node = branch[n] + branch_top = int(branch[0]) + + TREE_ID = 'copy' + tree_type = tree[1][1] + tree_depth_base = int(tree[4][branch[-1]]) - int(tree[4][branch_top]) # subtract depth of 'branch_top' from the last in 'branch' + NODE_ID = tree[3][node] + node_depth = int(tree[4][node]) - int(tree[4][branch_top]) # subtract the depth of 'branch_top' from the current node depth + node_type = tree[5][node] + node_label = tree[6][node] + node_parent = '' # updated by 'fx_evolve_parent_link_fix', below + node_arity = tree[8][node] + node_c1 = '' # updated by 'fx_evolve_child_link_fix', below + node_c2 = '' + node_c3 = '' + fitness = '' + + new_tree = np.append(new_tree, [ [TREE_ID],[tree_type],[tree_depth_base],[NODE_ID],[node_depth],[node_type],[node_label],[node_parent],[node_arity],[node_c1],[node_c2],[node_c3],[fitness] ], 1) + + new_tree = self.fx_evolve_node_renum(new_tree) + new_tree = self.fx_evolve_child_link_fix(new_tree) + new_tree = self.fx_evolve_parent_link_fix(new_tree) + new_tree = self.fx_data_tree_clean(new_tree) + + return new_tree + + + def fx_evolve_c_buffer(self, tree, node): + + ''' + This method serves the very important function of determining the links from parent to child for any given + node. The single, simple formula [parent_arity_sum + prior_sibling_arity - prior_siblings] perfectly determines + the correct position of the child node, already in place or to be inserted, no matter the depth nor complexity + of the tree. + + This method is currently called from the evolution methods, but will soon (I hope) be called from the first + generation Tree generation methods (above) such that the same method may be used repeatedly. + + Called by: + + Arguments required: tree, node + ''' + + parent_arity_sum = 0 + prior_sibling_arity = 0 + prior_siblings = 0 + + for n in range(1, len(tree[3])): # increment through all nodes (exclude 0) in array 'tree' + + if int(tree[4][n]) == int(tree[4][node])-1: # find parent nodes at the prior depth + if tree[8][n] != '': parent_arity_sum = parent_arity_sum + int(tree[8][n]) # sum arities of all parent nodes at the prior depth + + if int(tree[4][n]) == int(tree[4][node]) and int(tree[3][n]) < int(tree[3][node]): # find prior siblings at the current depth + if tree[8][n] != '': prior_sibling_arity = prior_sibling_arity + int(tree[8][n]) # sum prior sibling arity + prior_siblings = prior_siblings + 1 # sum quantity of prior siblings + + c_buffer = node + (parent_arity_sum + prior_sibling_arity - prior_siblings) # One algo to rule the world! + + return c_buffer + + + def fx_evolve_child_link(self, tree, node, c_buffer): + + ''' + Link each parent node to its children. + + Called by: + + Arguments required: tree, node, c_buffer + ''' + + if int(tree[3][node]) == 1: c_buffer = c_buffer + 1 # if root (node 1) is passed through this method + + if tree[8][node] != '': + + if int(tree[8][node]) == 0: # if arity = 0 + tree[9][node] = '' + tree[10][node] = '' + tree[11][node] = '' + + elif int(tree[8][node]) == 1: # if arity = 1 + tree[9][node] = c_buffer + tree[10][node] = '' + tree[11][node] = '' + + elif int(tree[8][node]) == 2: # if arity = 2 + tree[9][node] = c_buffer + tree[10][node] = c_buffer + 1 + tree[11][node] = '' + + elif int(tree[8][node]) == 3: # if arity = 3 + tree[9][node] = c_buffer + tree[10][node] = c_buffer + 1 + tree[11][node] = c_buffer + 2 + + else: print '\n\t\033[31m ERROR! In fx_evolve_child_link: node', node, 'has arity', tree[8][node]; self.fx_karoo_pause(0) + + return tree + + + def fx_evolve_child_link_fix(self, tree): + + ''' + In a given Tree, fix 'node_c1', 'node_c2', 'node_c3' for all nodes. + + This is required anytime the size of the array 'gp.tree' has been modified, as with both Grow and Full mutation. + + Called by: + + Arguments required: tree + ''' + + # tested 2015 06/04 + for node in range(1, len(tree[3])): + + c_buffer = self.fx_evolve_c_buffer(tree, node) # generate c_buffer for each node + tree = self.fx_evolve_child_link(tree, node, c_buffer) # update child links for each node + + return tree + + + def fx_evolve_child_insert(self, tree, node, c_buffer): + + ''' + Insert child nodes. + + Called by: + + Arguments required: tree, node, c_buffer + ''' + + if int(tree[8][node]) == 0: # if arity = 0 + print '\n\t\033[31m ERROR! In fx_evolve_child_insert: node', node, 'has arity 0\033[0;0m'; self.fx_karoo_pause(0) + + elif int(tree[8][node]) == 1: # if arity = 1 + tree = np.insert(tree, c_buffer, '', axis=1) # insert node for 'node_c1' + tree[3][c_buffer] = c_buffer # node ID + tree[4][c_buffer] = int(tree[4][node]) + 1 # node_depth + tree[7][c_buffer] = int(tree[3][node]) # parent ID + + elif int(tree[8][node]) == 2: # if arity = 2 + tree = np.insert(tree, c_buffer, '', axis=1) # insert node for 'node_c1' + tree[3][c_buffer] = c_buffer # node ID + tree[4][c_buffer] = int(tree[4][node]) + 1 # node_depth + tree[7][c_buffer] = int(tree[3][node]) # parent ID + + tree = np.insert(tree, c_buffer + 1, '', axis=1) # insert node for 'node_c2' + tree[3][c_buffer + 1] = c_buffer + 1 # node ID + tree[4][c_buffer + 1] = int(tree[4][node]) + 1 # node_depth + tree[7][c_buffer + 1] = int(tree[3][node]) # parent ID + + elif int(tree[8][node]) == 3: # if arity = 3 + tree = np.insert(tree, c_buffer, '', axis=1) # insert node for 'node_c1' + tree[3][c_buffer] = c_buffer # node ID + tree[4][c_buffer] = int(tree[4][node]) + 1 # node_depth + tree[7][c_buffer] = int(tree[3][node]) # parent ID + + tree = np.insert(tree, c_buffer + 1, '', axis=1) # insert node for 'node_c2' + tree[3][c_buffer + 1] = c_buffer + 1 # node ID + tree[4][c_buffer + 1] = int(tree[4][node]) + 1 # node_depth + tree[7][c_buffer + 1] = int(tree[3][node]) # parent ID + + tree = np.insert(tree, c_buffer + 2, '', axis=1) # insert node for 'node_c3' + tree[3][c_buffer + 2] = c_buffer + 2 # node ID + tree[4][c_buffer + 2] = int(tree[4][node]) + 1 # node_depth + tree[7][c_buffer + 2] = int(tree[3][node]) # parent ID + + else: print '\n\t\033[31m ERROR! In fx_evolve_child_insert: node', node, 'arity > 3\033[0;0m'; self.fx_karoo_pause(0) + + return tree + + + def fx_evolve_parent_link_fix(self, tree): + + ''' + In a given Tree, fix 'parent_id' for all nodes. + + This is automatically handled in all mutations except with Crossover due to the need to copy branches 'a' and + 'b' to their own trees before inserting them into copies of the parents. + + Technically speaking, the 'node_parent' value is not used by any methods. The parent ID can be completely out + of whack and the expression will work perfectly. This is maintained for the sole purpose of granting the user + a friendly, makes-sense interface which can be read in both directions. + + Called by: + + Arguments required: tree + ''' + + ### THIS METHOD MAY NOT BE REQUIRED AS SORTING 'branch' SEEMS TO HAVE FIXED 'parent_id' ### + + # tested 2015 06/05 + for node in range(1, len(tree[3])): + + if tree[9][node] != '': + child = int(tree[9][node]) + tree[7][child] = node + + if tree[10][node] != '': + child = int(tree[10][node]) + tree[7][child] = node + + if tree[11][node] != '': + child = int(tree[11][node]) + tree[7][child] = node + + return tree + + + def fx_evolve_node_arity_fix(self, tree): + + ''' + In a given Tree, fix 'node_arity' for all nodes labeled 'term' but with arity 2. + + This is required after a function has been replaced by a terminal, as may occur with both Grow mutation and + Crossover. + + Called by: + + Arguments required: tree + ''' + + # tested 2015 05/31 + for n in range(1, len(tree[3])): # increment through all nodes (exclude 0) in array 'tree' + + if tree[5][n] == 'term': # check for discrepency + tree[8][n] = '0' # set arity to 0 + tree[9][n] = '' # wipe 'node_c1' + tree[10][n] = '' # wipe 'node_c2' + tree[11][n] = '' # wipe 'node_c3' + + return tree + + + def fx_evolve_node_renum(self, tree): + + ''' + Renumber all 'NODE_ID' in a given tree. + + This is required after a new generation is evolved as the NODE_ID numbers are carried forward from the previous + generation but are no longer in order. + + Called by: + + Arguments required: tree + ''' + + for n in range(1, len(tree[3])): + + tree[3][n] = n # renumber all Trees in given population + + return tree + + + def fx_evolve_fitness_wipe(self, tree): + + ''' + Remove all fitness data from a given tree. + + This is required after a new generation is evolved as the fitness of the same Tree prior to its mutation will + no longer apply. + + Called by: + + Arguments required: tree + ''' + + tree[12][1:] = '' # wipe fitness data + + return tree + + + def fx_evolve_tree_prune(self, tree, depth): + + ''' + This method reduces the depth of a Tree. Used with Crossover, the input value 'branch' can be a partial Tree + (branch) or a full tree, and it will operate correctly. The input value 'depth' becomes the new maximum depth, + where depth is defined as the local maximum + the user defined adjustment. + + Called by: + + Arguments required: tree, depth + ''' + + nodes = [] + + # tested 2015 06/08 + for n in range(1, len(tree[3])): + + if int(tree[4][n]) == depth and tree[5][n] == 'func': + rnd = np.random.randint(0, len(self.terminals) - 1) # call the previously loaded .csv which contains all terminals + tree[5][n] = 'term' # mutate type 'func' to 'term' + tree[6][n] = self.terminals[rnd] # replace label + + elif int(tree[4][n]) > depth: # record nodes deeper than the maximum allowed Tree depth + nodes.append(n) + + else: pass # as int(tree[4][n]) < depth and will remain untouched + + tree = np.delete(tree, nodes, axis = 1) # delete nodes deeper than the maximum allowed Tree depth + tree = self.fx_evolve_node_arity_fix(tree) # fix all node arities + + return tree + + + def fx_evolve_tree_renum(self, population): + + ''' + Renumber all 'TREE_ID' in a given population. + + This is required after a new generation is evolved as the TREE_ID numbers are carried forward from the previous + generation but are no longer in order. + + Called by: + + Arguments required: population + ''' + + for tree_id in range(1, len(population)): + + population[tree_id][0][1] = tree_id # renumber all Trees in given population + + return population + + + def fx_evolve_pop_copy(self, pop_a, title): + + ''' + Copy one population to another. + + Simply copying a list of arrays generates a pointer to the original list. Therefore we must append each array + to a new, empty array and then build a list of those new arrays. + + Called by: + + Arguments required: pop_a, title + ''' + + pop_b = [title] # an empty list stores a copy of the prior generation + + for tree in range(1, len(pop_a)): # increment through each Tree in the current population + + tree_copy = np.copy(pop_a[tree]) # copy each array in the current population + pop_b.append(tree_copy) # add each copied Tree to the new population list + + return pop_b + + + #+++++++++++++++++++++++++++++++++++++++++++++ + # Methods to Visualize a Tree | + #+++++++++++++++++++++++++++++++++++++++++++++ + + def fx_display_tree(self, tree): + + ''' + Display all or part of a Tree on-screen. + + This method displays all sequential node_ids from 'start' node through bottom, within the given tree. + + Called by: + + Arguments required: tree + ''' + + ind = '' + print '\n\033[1m\033[36m Tree ID', int(tree[0][1]), '\033[0;0m' + + for depth in range(0, self.tree_depth_max + 1): # increment through all possible Tree depths - tested 2016 07/09 + print '\n', ind,'\033[36m Tree Depth:', depth, 'of', tree[2][1], '\033[0;0m' + + for node in range(1, len(tree[3])): # increment through all nodes (redundant, I know) + if int(tree[4][node]) == depth: + print '' + print ind,'\033[1m\033[36m NODE:', tree[3][node], '\033[0;0m' + print ind,' type:', tree[5][node] + print ind,' label:', tree[6][node], '\tparent node:', tree[7][node] + print ind,' arity:', tree[8][node], '\tchild node(s):', tree[9][node], tree[10][node], tree[11][node] + + ind = ind + '\t' + + print '' + self.fx_eval_poly(tree) # generate the raw and sympified equation for the entire Tree + print '\t\033[36mTree', tree[0][1], 'yields (raw):', self.algo_raw, '\033[0;0m' + print '\t\033[36mTree', tree[0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' + + return + + + def fx_display_branch(self, tree, start): + + ''' + Display a Tree branch on-screen. + + This method displays all sequential node_ids from 'start' node through bottom, within the given branch. + + This method is not used by Karoo GP at this time. + + Called by: + + Arguments required: tree, start + ''' + + branch = np.array([]) # the array is necessary in order to len(branch) when 'branch' has only one element + branch_eval = self.fx_eval_id(tree, start) # generate tuple of given 'branch' + branch_symp = sympify(branch_eval) # convert string from tuple to list + branch = np.append(branch, branch_symp) # append list to array + ind = '' + + # for depth in range(int(tree[4][start]), int(tree[2][1]) + self.tree_depth_max + 1): # increment through all Tree depths - tested 2016 07/09 + for depth in range(int(tree[4][start]), self.tree_depth_max + 1): # increment through all Tree depths - tested 2016 07/09 + print '\n', ind,'\033[36m Tree Depth:', depth, 'of', tree[2][1], '\033[0;0m' + + for n in range(0, len(branch)): # increment through all nodes listed in the branch + node = branch[n] + + if int(tree[4][node]) == depth: + print '' + print ind,'\033[1m\033[36m NODE:', node, '\033[0;0m' + print ind,' type:', tree[5][node] + print ind,' label:', tree[6][node], '\tparent node:', tree[7][node] + print ind,' arity:', tree[8][node], '\tchild node(s):', tree[9][node], tree[10][node], tree[11][node] + + ind = ind + '\t' + + print '' + self.fx_eval_poly(tree) # generate the raw and sympified equation for the entire Tree + print '\t\033[36mTree', tree[0][1], 'yields (raw):', self.algo_raw, '\033[0;0m' + print '\t\033[36mTree', tree[0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m' + + return diff --git a/karoo_gp_main.py b/karoo_gp_main.py index b2a8c2a..e4e58f5 100644 --- a/karoo_gp_main.py +++ b/karoo_gp_main.py @@ -1,7 +1,7 @@ # Karoo GP Main (desktop) # Use Genetic Programming for Classification and Symbolic Regression # by Kai Staats, MSc; see LICENSE.md -# version 1.0.8 +# version 1.1 ''' A word to the newbie, expert, and brave-- @@ -31,37 +31,44 @@ If you include the path to an external dataset, it will auto-load at launch: ''' import sys; sys.path.append('modules/') # add directory 'modules' to the current path -import time - +import os import karoo_gp_base_class; gp = karoo_gp_base_class.Base_GP() +os.system('clear') +print '\n\033[36m\033[1m' +print '\t ** ** ****** ***** ****** ****** ****** ******' +print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' +print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' +print '\t **** ******** ****** ** ** ** ** ** *** *******' +print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' +print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' +print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' +print '\t ** ** ** ** ** ** ****** ****** ****** **' +print '\033[0;0m' +print '\t\033[36m Genetic Programming in Python - by Kai Staats, version 1.1\033[0;0m' +print '' + #++++++++++++++++++++++++++++++++++++++++++ # User Defined Configuration | #++++++++++++++++++++++++++++++++++++++++++ ''' -Karoo GP queries the user for key parameters, some of which may be adjusted during run-time -at user invoked pauses. See the User Guide for meaning and value of each of the following parameters. - -Future versions will enable all of these parameters to be configured via an external configuration file and/or -command-line arguments passed at launch. +Karoo GP queries the user for key parameters, some of which may be adjusted during run-time at user invoked pauses. +See the User Guide for meaning and value of each of the following parameters. The server version of Karoo enables +all parameters to be configured via command-line arguments. ''' -gp.karoo_banner() - -print '' - menu = ['c','r','m','p',''] while True: try: - gp.kernel = raw_input('\t Select (c)lassification, (r)egression, (m)atching, or (p)lay (default m): ') - if gp.kernel not in menu: raise ValueError() - gp.kernel = gp.kernel or 'm'; break + kernel = raw_input('\t Select (c)lassification, (r)egression, (m)atching, or (p)lay (default m): ') + if kernel not in menu: raise ValueError() + kernel = kernel or 'm'; break except ValueError: print '\t\033[32m Select from the options given. Try again ...\n\033[0;0m' except KeyboardInterrupt: sys.exit() -if gp.kernel == 'p': +if kernel == 'p': menu = ['f','g',''] while True: @@ -94,24 +101,30 @@ while True: except KeyboardInterrupt: sys.exit() -if gp.kernel == 'p': # if the Play kernel is selected - gp.tree_depth_max = tree_depth_base - gp.tree_pop_max = 1 - gp.display = 'm' - +if kernel == 'p': # if the Play kernel is selected + tree_depth_max = tree_depth_base + tree_depth_min = 0 + tree_pop_max = 1 + generation_max = 1 + display = 'm' +# evolve_repro = evolve_point = evolve_branch = evolve_cross = '' +# tourn_size = '' +# precision = '' +# filename = '' + else: # if any other kernel is selected - if tree_type == 'f': gp.tree_depth_max = tree_depth_base + if tree_type == 'f': tree_depth_max = tree_depth_base else: # if type is Full, the maximum Tree depth for the full run is equal to the initial population menu = range(tree_depth_base,11) while True: try: - gp.tree_depth_max = raw_input('\t Enter maximum Tree depth (default %i): ' %tree_depth_base) - if gp.tree_depth_max not in str(menu): raise ValueError() - elif gp.tree_depth_max == '': gp.tree_depth_max = tree_depth_base - gp.tree_depth_max = int(gp.tree_depth_max) - if gp.tree_depth_max < tree_depth_base: raise ValueError() # an ugly exception to the norm 20170918 + tree_depth_max = raw_input('\t Enter maximum Tree depth (default %i): ' %tree_depth_base) + if tree_depth_max not in str(menu): raise ValueError() + elif tree_depth_max == '': tree_depth_max = tree_depth_base + tree_depth_max = int(tree_depth_max) + if tree_depth_max < tree_depth_base: raise ValueError() # an ugly exception to the norm 20170918 else: break except ValueError: print '\t\033[32m Enter a number >= the initial Tree depth. Try again ...\n\033[0;0m' except KeyboardInterrupt: sys.exit() @@ -119,147 +132,56 @@ else: # if any other kernel is selected menu = range(3,101) while True: try: - gp.tree_depth_min = raw_input('\t Enter minimum number of nodes for any given Tree (default 3): ') - if gp.tree_depth_min not in str(menu) or gp.tree_depth_min == '0': raise ValueError() - elif gp.tree_depth_min == '': gp.tree_depth_min = 3 - gp.tree_depth_min = int(gp.tree_depth_min); break + tree_depth_min = raw_input('\t Enter minimum number of nodes for any given Tree (default 3): ') + if tree_depth_min not in str(menu) or tree_depth_min == '0': raise ValueError() + elif tree_depth_min == '': tree_depth_min = 3 + tree_depth_min = int(tree_depth_min); break except ValueError: print '\t\033[32m Enter a number from 3 to 2^(depth + 1) - 1 including 100. Try again ...\n\033[0;0m' except KeyboardInterrupt: sys.exit() menu = range(10,1001) while True: try: - gp.tree_pop_max = raw_input('\t Enter number of Trees in each population (default 100): ') - if gp.tree_pop_max not in str(menu) or gp.tree_pop_max == '0': raise ValueError() - elif gp.tree_pop_max == '': gp.tree_pop_max = 100 - gp.tree_pop_max = int(gp.tree_pop_max); break + tree_pop_max = raw_input('\t Enter number of Trees in each population (default 100): ') + if tree_pop_max not in str(menu) or tree_pop_max == '0': raise ValueError() + elif tree_pop_max == '': tree_pop_max = 100 + tree_pop_max = int(tree_pop_max); break except ValueError: print '\t\033[32m Enter a number from 10 including 1000. Try again ...\n\033[0;0m' except KeyboardInterrupt: sys.exit() menu = range(1,101) while True: try: - gp.generation_max = raw_input('\t Enter max number of generations (default 10): ') - if gp.generation_max not in str(menu) or gp.generation_max == '0': raise ValueError() - elif gp.generation_max == '': gp.generation_max = 10 - gp.generation_max = int(gp.generation_max); break + generation_max = raw_input('\t Enter max number of generations (default 10): ') + if generation_max not in str(menu) or generation_max == '0': raise ValueError() + elif generation_max == '': generation_max = 10 + generation_max = int(generation_max); break except ValueError: print '\t\033[32m Enter a number from 1 including 100. Try again ...\n\033[0;0m' except KeyboardInterrupt: sys.exit() menu = ['i','g','m','s','db',''] while True: try: - gp.display = raw_input('\t Display (i)nteractive, (g)eneration, (m)iminal, (s)ilent, or (d)e(b)ug (default m): ') - if gp.display not in menu: raise ValueError() - gp.display = gp.display or 'm'; break + display = raw_input('\t Display (i)nteractive, (g)eneration, (m)iminal, (s)ilent, or (d)e(b)ug (default m): ') + if display not in menu: raise ValueError() + display = display or 'm'; break except ValueError: print '\t\033[32m Select from the options given. Try again ...\n\033[0;0m' except KeyboardInterrupt: sys.exit() # define the ratio between types of mutation, where all sum to 1.0; can be adjusted in 'i'nteractive mode -gp.evolve_repro = int(0.1 * gp.tree_pop_max) # quantity of a population generated through Reproduction -gp.evolve_point = int(0.0 * gp.tree_pop_max) # quantity of a population generated through Point Mutation -gp.evolve_branch = int(0.2 * gp.tree_pop_max) # quantity of a population generated through Branch Mutation -gp.evolve_cross = int(0.7 * gp.tree_pop_max) # quantity of a population generated through Crossover +evolve_repro = int(0.1 * tree_pop_max) # quantity of a population generated through Reproduction +evolve_point = int(0.0 * tree_pop_max) # quantity of a population generated through Point Mutation +evolve_branch = int(0.2 * tree_pop_max) # quantity of a population generated through Branch Mutation +evolve_cross = int(0.7 * tree_pop_max) # quantity of a population generated through Crossover -gp.tourn_size = 7 # qty of individuals entered into each tournament (standard 10); can be adjusted in 'i'nteractive mode -gp.precision = 6 # the number of floating points for the round function in 'fx_fitness_eval'; hard coded +tourn_size = 7 # qty of individuals entered into each tournament (standard = 7%); can be adjusted in 'i'nteractive mode +precision = 6 # the number of floating points for the round function in 'fx_fitness_eval'; hard coded +filename = '' # not required unless an external file is referenced +# pass all user defined settings to the base_class and launch Karoo GP +gp.fx_karoo_gp(kernel, tree_type, tree_depth_base, tree_depth_max, tree_depth_min, tree_pop_max, generation_max, tourn_size, filename, evolve_repro, evolve_point, evolve_branch, evolve_cross, display, precision, 'm') -#++++++++++++++++++++++++++++++++++++++++++ -# Construct First Generation of Trees | -#++++++++++++++++++++++++++++++++++++++++++ - -''' -Karoo GP constructs the first generation of Trees. All subsequent generations evolve from priors, with no new Trees -constructed from scratch. All parameters which define the Trees were set by the user in the previous section. - -If the user has selected 'Play' mode, this is the only generation to be constructed, and then GP Karoo terminates. -''' - -start = time.time() # start the clock for the timer - -filename = '' # temp place holder - -gp.fx_karoo_data_load(filename) - -gp.generation_id = 1 # set initial generation ID - -gp.population_a = ['Karoo GP by Kai Staats, Generation ' + str(gp.generation_id)] # an empty list which will store all Tree arrays, one generation at a time - -gp.fx_karoo_construct(tree_type, tree_depth_base) # construct the first population of Trees - -if gp.kernel != 'p': print '\n We have constructed a population of', gp.tree_pop_max,'Trees for Generation 1\n' - -else: # EOL for Play mode - gp.fx_display_tree(gp.tree) # print the current Tree - gp.fx_archive_tree_write(gp.population_a, 'a') # save this one Tree to disk - sys.exit() - - -#++++++++++++++++++++++++++++++++++++++++++ -# Evaluate First Generation of Trees | -#++++++++++++++++++++++++++++++++++++++++++ - -''' -Karoo GP evaluates the first generation of Trees. This process flattens each GP Tree into a standard -equation by means of a recursive algorithm and subsequent processing by the SymPy library which -simultaneously evaluates the Tree for its results, returns null for divide by zero, reorganises -and then rewrites the expression in its simplest form. - -If the user has defined only 1 generation, then this is the end of the run. Else, Karoo GP -continues into multi-generational evolution. -''' - -if gp.display != 's': - print ' Evaluate the first generation of Trees ...' - if gp.display == 'i': gp.fx_karoo_pause(0) - -gp.fx_fitness_gym(gp.population_a) # generate expression, evaluate fitness, compare fitness -gp.fx_archive_tree_write(gp.population_a, 'a') # save the first generation of Trees to disk - -# no need to continue if only 1 generation or fewer than 10 Trees were designated by the user -if gp.tree_pop_max < 10 or gp.generation_max == 1: - gp.fx_archive_params_write('Desktop') # save run-time parameters to disk - gp.fx_karoo_eol() - sys.exit() - - -#++++++++++++++++++++++++++++++++++++++++++ -# Evolve Multiple Generations | -#++++++++++++++++++++++++++++++++++++++++++ - -''' -Karoo GP moves into multi-generational evolution. - -In the following four evolutionary methods, the global list of arrays 'gp.population_a' is repeatedly recycled as -the prior generation from which the local list of arrays 'gp.population_b' is created, one array at a time. The ratio of -invocation of the four evolutionary processes for each generation is set by the parameters in the 'User Defined -Configuration' (top). -''' - -for gp.generation_id in range(2, gp.generation_max + 1): # loop through 'generation_max' - - print '\n Evolve a population of Trees for Generation', gp.generation_id, '...' - gp.population_b = ['Karoo GP by Kai Staats, Evolving Generation'] # initialise population_b to host the next generation - - gp.fx_fitness_gene_pool() # generate the viable gene pool (compares against gp.tree_depth_min) - gp.fx_karoo_reproduce() # method 1 - Reproduction - gp.fx_karoo_point_mutate() # method 2 - Point Mutation - gp.fx_karoo_branch_mutate() # method 3 - Branch Mutation - gp.fx_karoo_crossover() # method 4 - Crossover Reproduction - gp.fx_eval_generation() # evaluate all Trees in a single generation - - gp.population_a = gp.fx_evolve_pop_copy(gp.population_b, ['Karoo GP by Kai Staats, Generation ' + str(gp.generation_id)]) - - -#++++++++++++++++++++++++++++++++++++++++++ -# "End of line, man!" --CLU | -#++++++++++++++++++++++++++++++++++++++++++ - -print '\n \033[36m Karoo GP has an ellapsed time of \033[0;0m\033[31m%f\033[0;0m' % (time.time() - start), '\033[0;0m' - -gp.fx_archive_tree_write(gp.population_b, 'f') # save the final generation of Trees to disk -gp.fx_karoo_eol() - +print 'You seem to have found your way back to the Desktop. Huh.' +sys.exit() diff --git a/karoo_gp_server.py b/karoo_gp_server.py index 3425999..cf95ea7 100644 --- a/karoo_gp_server.py +++ b/karoo_gp_server.py @@ -1,7 +1,7 @@ # Karoo GP Server # Use Genetic Programming for Classification and Symbolic Regression # by Kai Staats, MSc; see LICENSE.md -# version 1.0.8 +# version 1.1 ''' A word to the newbie, expert, and brave-- @@ -52,9 +52,24 @@ An example is given, as follows: ''' import sys; sys.path.append('modules/') # to add the directory 'modules' to the current path +import os import argparse import karoo_gp_base_class; gp = karoo_gp_base_class.Base_GP() +os.system('clear') +print '\n\033[36m\033[1m' +print '\t ** ** ****** ***** ****** ****** ****** ******' +print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' +print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' +print '\t **** ******** ****** ** ** ** ** ** *** *******' +print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' +print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' +print '\t ** ** ** ** ** ** ** ** ** ** ** ** **' +print '\t ** ** ** ** ** ** ****** ****** ****** **' +print '\033[0;0m' +print '\t\033[36m Genetic Programming in Python - by Kai Staats, version 1.1\033[0;0m' +print '' + ap = argparse.ArgumentParser(description = 'Karoo GP Server') ap.add_argument('-ker', action = 'store', dest = 'kernel', default = 'c', help = '[c,r,m] fitness function: (r)egression, (c)lassification, or (m)atching') ap.add_argument('-typ', action = 'store', dest = 'type', default = 'r', help = '[f,g,r] Tree type: (f)ull, (g)row, or (r)amped half/half') @@ -64,30 +79,31 @@ ap.add_argument('-min', action = 'store', dest = 'depth_min', default = 3, help ap.add_argument('-pop', action = 'store', dest = 'pop_max', default = 100, help = '[10...1000] maximum population') ap.add_argument('-gen', action = 'store', dest = 'gen_max', default = 10, help = '[1...100] number of generations') ap.add_argument('-tor', action = 'store', dest = 'tor_size', default = 7, help = '[1...max pop] tournament size') -ap.add_argument('-fil', action = 'store', dest = 'filename', default = 'files/data_MATCH.csv', help = '/path/to_your/[data].csv') +ap.add_argument('-fil', action = 'store', dest = 'filename', default = '', help = '/path/to_your/[data].csv') args = ap.parse_args() # pass the argparse defaults and/or user inputs to the required variables -gp.kernel = str(args.kernel) +kernel = str(args.kernel) tree_type = str(args.type) tree_depth_base = int(args.depth_base) -gp.tree_depth_max = int(args.depth_max) -gp.tree_depth_min = int(args.depth_min) -gp.tree_pop_max = int(args.pop_max) -gp.generation_max = int(args.gen_max) -gp.tourn_size = int(args.tor_size) +tree_depth_max = int(args.depth_max) +tree_depth_min = int(args.depth_min) +tree_pop_max = int(args.pop_max) +generation_max = int(args.gen_max) +tourn_size = int(args.tor_size) filename = str(args.filename) -gp.evolve_repro = int(0.1 * gp.tree_pop_max) # quantity of a population generated through Reproduction -gp.evolve_point = int(0.0 * gp.tree_pop_max) # quantity of a population generated through Point Mutation -gp.evolve_branch = int(0.2 * gp.tree_pop_max) # quantity of a population generated through Branch Mutation -gp.evolve_cross = int(0.7 * gp.tree_pop_max) # quantity of a population generated through Crossover +evolve_repro = int(0.1 * tree_pop_max) # quantity of a population generated through Reproduction +evolve_point = int(0.0 * tree_pop_max) # quantity of a population generated through Point Mutation +evolve_branch = int(0.2 * tree_pop_max) # quantity of a population generated through Branch Mutation +evolve_cross = int(0.7 * tree_pop_max) # quantity of a population generated through Crossover -gp.display = 's' # display mode is set to (s)ilent -gp.precision = 6 # the number of floating points for the round function in 'fx_fitness_eval' +display = 's' # display mode is set to (s)ilent +precision = 6 # the number of floating points for the round function in 'fx_fitness_eval' -# run Karoo GP -gp.karoo_gp(tree_type, tree_depth_base, filename) +# pass all user defined settings to the base_class and launch Karoo GP +gp.fx_karoo_gp(kernel, tree_type, tree_depth_base, tree_depth_max, tree_depth_min, tree_pop_max, generation_max, tourn_size, filename, evolve_repro, evolve_point, evolve_branch, evolve_cross, display, precision, 's') +sys.exit()