support for argparse added
Kai Staats
parent
6d1358fb5d
commit
caafb5660e
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@ -2,7 +2,7 @@
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# Define the methods and global variables used by Karoo GP
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# by Kai Staats, MSc UCT / AIMS
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# Much thanks to Emmanuel Dufourq and Arun Kumar for their support, guidance, and free psychotherapy sessions
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# version 0.9.1.5
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# version 0.9.1.6
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'''
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A NOTE TO THE NEWBIE, EXPERT, AND BRAVE
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@ -16,7 +16,6 @@ import os
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import sys
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import time
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import argparse
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import numpy as np
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import pprocess as pp
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import sklearn.metrics as skm
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@ -36,7 +35,8 @@ class Base_GP(object):
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will 'print' to screen.
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The categories (denoted by #+++++++ banners) are as follows:
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'fx_karoo_' Methods to Run Karoo GP (with the exception of top-level 'karoo_gp' itself)
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'karoo_gp' A single, top-level method which conducts an entire run. Used by karoo_gp_server.py
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'fx_karoo_' Methods to Run Karoo GP
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'fx_gen_' Methods to Generate a Tree
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'fx_eval_' Methods to Evaluate a Tree
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'fx_fitness_' Methods to Evaluate Tree Fitness
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@ -114,6 +114,7 @@ class Base_GP(object):
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self.fittest_dict = {} # temp store all Trees which share the best fitness score
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self.gene_pool = [] # temp store all Tree IDs for use by Tournament
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self.core_count = pp.get_number_of_cores() # pprocess
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self.class_labels = 0 # temp set a variable which will be assigned the number of class labels (data_y)
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return
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@ -122,20 +123,19 @@ class Base_GP(object):
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# Methods to Run Karoo GP |
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#++++++++++++++++++++++++++++++++++++++++++
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def karoo_gp(self, run, tree_type, tree_depth_base):
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def karoo_gp(self, tree_type, tree_depth_base, filename):
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'''
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This is single method enables the engagement of the entire Karoo GP application. It is used by
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karoo_gp_server.py and the future, single command line executable, but not by karoo_gp_main.py which engages
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each of the included functions sequentially.
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This single method enables the engagement of the entire Karoo GP application. It is used by karoo_gp_server.py
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for both scripted and command line execution, but not by karoo_gp_main.py.
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Arguments required: run, tree_type, tree_depth_base
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Arguments required: tree_type, tree_depth_base
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'''
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self.karoo_banner(run)
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self.karoo_banner()
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# construct first generation of Trees
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self.fx_karoo_data_load()
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self.fx_karoo_data_load(tree_type, tree_depth_base, filename)
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self.generation_id = 1 # set initial generation ID
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self.population_a = ['Karoo GP by Kai Staats, Generation ' + str(self.generation_id)] # a list which will store all Tree arrays, one generation at a time
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self.fx_karoo_construct(tree_type, tree_depth_base) # construct the first population of Trees
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@ -167,16 +167,12 @@ class Base_GP(object):
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return
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def karoo_banner(self, run):
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def karoo_banner(self):
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'''
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This method makes Karoo GP look old-school cool!
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While the banner remains the same, it presents a configuration request unique to a 'server' run. At the time
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of this writing, the only options are 'server' or 'main' where 'main' defaults to requests for feedback based
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upon the display mode selected by the user. See 'fx_karoo_construct' for examples.
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Arguments required: run
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Arguments required: none
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'''
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os.system('clear')
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@ -191,20 +187,12 @@ class Base_GP(object):
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print '\t ** ** ** ** ** ** ** ** ** ** ** ** **'
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print '\t ** ** ** ** ** ** ****** ****** ****** **'
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print '\033[0;0m'
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print '\t\033[36m Genetic Programming in Python - by Kai Staats, version 0.9\033[0;0m'
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if run == 'server':
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print '\n\t Type \033[1m?\033[0;0m to configure Karoo GP before your run, or \033[1mENTER\033[0;0m to continue.\033[0;0m'
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self.fx_karoo_pause(0)
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elif run == 'main': pass
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else: pass
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print '\t\033[36m Genetic Programming in Python - by Kai Staats, version 0.9.1.6\033[0;0m'
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return
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def fx_karoo_data_load(self):
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def fx_karoo_data_load(self, tree_type, tree_depth_base, filename):
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'''
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The data and function .csv files are loaded according to the fitness function kernel selected by the user. An
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@ -221,9 +209,10 @@ class Base_GP(object):
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func_dict = {'b':'files/functions_BOOL.csv', 'c':'files/functions_CLASSIFY.csv', 'r':'files/functions_REGRESS.csv', 'm':'files/functions_MATCH.csv', 'p':'files/functions_PLAY.csv'}
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fitt_dict = {'b':'max', 'c':'max', 'r':'min', 'm':'max', 'p':''}
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if len(sys.argv) == 1: # load data in the karoo_gp/files/ directory
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if len(sys.argv) == 1: # load data from the default karoo_gp/files/ directory
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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
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data_y = np.loadtxt(data_dict[self.kernel], skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels)
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self.class_labels = len(np.unique(data_y))
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header = open(data_dict[self.kernel],'r')
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self.terminals = header.readline().split(','); self.terminals[-1] = self.terminals[-1].replace('\n','') # load the variables across the top of the .csv
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@ -232,9 +221,9 @@ class Base_GP(object):
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self.fitness_type = fitt_dict[self.kernel]
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elif len(sys.argv) == 2: # load an external data file
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print '\n\t\033[36m You have opted to load an alternative dataset:', sys.argv[1], '\033[0;0m'
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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
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data_y = np.loadtxt(sys.argv[1], skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels)
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self.class_labels = len(np.unique(data_y))
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header = open(sys.argv[1],'r')
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self.terminals = header.readline().split(','); self.terminals[-1] = self.terminals[-1].replace('\n','') # load the variables across the top of the .csv
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@ -242,7 +231,17 @@ class Base_GP(object):
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self.functions = np.loadtxt(func_dict[self.kernel], delimiter=',', skiprows=1, dtype = str) # load the user defined functions (operators)
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self.fitness_type = fitt_dict[self.kernel]
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else: print '\n\t\033[31mERROR! You have assigned too many command line arguments at launch. Try again ...\033[0;0m'; sys.exit()
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elif len(sys.argv) > 2: # receive filename and additional flags from karoo_gp_server.py via argparse
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data_x = np.loadtxt(filename, skiprows = 1, delimiter = ',', dtype = float); data_x = data_x[:,0:-1] # load all but the right-most column
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data_y = np.loadtxt(filename, skiprows = 1, usecols = (-1,), delimiter = ',', dtype = float) # load only right-most column (class labels)
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self.class_labels = len(np.unique(data_y))
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header = open(filename,'r')
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self.terminals = header.readline().split(','); self.terminals[-1] = self.terminals[-1].replace('\n','') # load the variables across the top of the .csv
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self.functions = np.loadtxt(func_dict[self.kernel], delimiter=',', skiprows=1, dtype = str) # load the user defined functions (operators)
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self.fitness_type = fitt_dict[self.kernel]
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### 2) from the dataset, generate TRAINING and TEST data ###
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@ -356,7 +355,7 @@ class Base_GP(object):
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def fx_karoo_construct(self, tree_type, tree_depth_base):
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'''
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As used by the method 'fx_karoo_gp', this method constructs the initial population based upon the user-defined
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As used by the method 'karoo_gp', this method constructs the initial population based upon the user-defined
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Tree type and initial, maximum Tree depth. "Ramped half/half" is currently not ramped, rather split 50/50
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Full/Grow. This will be updated with a future version of Karoo GP.
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@ -366,11 +365,7 @@ class Base_GP(object):
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if self.display == 'i' or self.display == 'g':
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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'
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self.fx_karoo_pause(0)
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if self.display == 's':
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print '\n\t Type \033[1m?\033[0;0m to configure Karoo GP before your run, or \033[1mENTER\033[0;0m to continue.\033[0;0m'
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self.fx_karoo_pause(0)
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if tree_type == 'r': # Ramped 50/50
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for TREE_ID in range(1, int(self.tree_pop_max / 2) + 1):
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self.fx_gen_tree_build(TREE_ID, 'f', tree_depth_base) # build 1/2 of the 1st generation of Trees as Full
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@ -537,7 +532,7 @@ class Base_GP(object):
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print '\t\033[36m\033[1m i \t\033[0;0m interactive display mode'
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print '\t\033[36m\033[1m m \t\033[0;0m minimal display mode'
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print '\t\033[36m\033[1m g \t\033[0;0m generation display mode'
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print '\t\033[36m\033[1m s \t\033[0;0m silent (server) display mode'
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print '\t\033[36m\033[1m s \t\033[0;0m silent display mode'
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print '\t\033[36m\033[1m db \t\033[0;0m debug display mode'
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print '\t\033[36m\033[1m t \t\033[0;0m timer display mode'
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print ''
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@ -566,7 +561,7 @@ class Base_GP(object):
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elif pause == 'i': self.display = 'i'; print '\t Interactive mode engaged (for control freaks)'
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elif pause == 'm': self.display = 'm'; print '\t Minimal mode engaged (for recovering control freaks)'
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elif pause == 'g': self.display = 'g'; print '\t Generation mode engaged (for GP gurus)'
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elif pause == 's': self.display = 's'; print '\t Server mode engaged (for zen masters)'
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# elif pause == 's': self.display = 's'; print '\t Server mode engaged (for zen masters)'
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elif pause == 'db': self.display = 'db'; print '\t Debug mode engaged (for vouyers)'
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elif pause == 't': self.display = 't'; print '\t Timer mode engaged (for managers)'
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@ -2,16 +2,36 @@
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# Use Genetic Programming for Classification and Symbolic Regression
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# by Kai Staats, MSc UCT / AIMS
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# Much thanks to Emmanuel Dufourq and Arun Kumar for their support, guidance, and free psychotherapy sessions
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# version 0.9.1.5
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# version 0.9.1.6
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'''
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A NOTE TO THE NEWBIE, EXPERT, AND BRAVE
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Even if you are highly experienced in Genetic Programming, it is recommended that you review the 'Karoo Quick Start' before running
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this application. While your computer will not burst into flames nor will the sun collapse into a black hole if you do not, you will
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likely find more enjoyment of this particular flavour of GP with a little understanding of its intent and design.
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A word to the newbie, expert, and brave--
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Even if you are highly experienced in Genetic Programming, it is recommended that you review the 'Karoo Quick Start'
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before running this application. While your computer will not burst into flames nor will the sun collapse into a black
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hole if you do not, you will likely find more enjoyment of this particular flavour of GP with a little understanding
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of its intent and design.
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KAROO GP DESKTOP
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This is the Karoo GP desktop application. It presents a simple yet functional user interface for configuring each
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Karoo GP run. While this can be launched on a remote server, you may find that once you get the hang of using Karoo,
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and are in more of a production mode than one of experimentation, using karoo_gp_server.py is more to your liking as
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it provides both a scripted and/or command-line launch vehicle.
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To launch Karoo GP desktop:
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$ python karoo_gp_main.py
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(or from iPython)
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$ run karoo_gp_main.py
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If you include the path to an external dataset, it will auto-load at launch:
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$ python karoo_gp_main.py /[path]/[to_your]/[filename].csv
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'''
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import sys # sys.path.append('modules/') # add the directory 'modules' to the current path
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import sys # sys.path.append('modules/') to add the directory 'modules' to the current path
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import karoo_gp_base_class; gp = karoo_gp_base_class.Base_GP()
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#++++++++++++++++++++++++++++++++++++++++++
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@ -26,7 +46,7 @@ Future versions will enable all of these parameters to be configured via an exte
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command-line arguments passed at launch.
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'''
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gp.karoo_banner('main')
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gp.karoo_banner()
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print ''
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@ -39,17 +59,6 @@ while True:
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except ValueError: print '\t\033[32m Select from the options given. Try again ...\n\033[0;0m'
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except KeyboardInterrupt: sys.exit()
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if gp.kernel == 'c': # if the Classification kernel is selected (above)
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menu = range(1,101)
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while True:
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try:
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gp.class_labels = raw_input('\t Enter the number of class labels (default 3): ')
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if gp.class_labels not in str(menu) or gp.class_labels == '0': raise ValueError()
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gp.class_labels = gp.class_labels or 3; gp.class_labels = int(gp.class_labels); break
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except ValueError: print '\t\033[32m Select from the options given. Try again ...\n\033[0;0m'
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except KeyboardInterrupt: sys.exit()
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menu = ['f','g','r','']
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while True:
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try:
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@ -117,10 +126,10 @@ else: # if any other kernel is selected
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except ValueError: print '\t\033[32m Enter a number from 1 including 100. Try again ...\n\033[0;0m'
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except KeyboardInterrupt: sys.exit()
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menu = ['i','m','g','s','db','t','']
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menu = ['i','g','m','s','db','t','']
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while True:
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try:
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gp.display = raw_input('\t Display (i)nteractive, (m)iminal, (g)eneration, or (s)ilent (default m): ')
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gp.display = raw_input('\t Display (i)nteractive, (g)eneration, (m)iminal, or (s)ilent (default m): ')
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if gp.display not in menu: raise ValueError()
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gp.display = gp.display or 'm'; break
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except ValueError: print '\t\033[32m Select from the options given. Try again ...\n\033[0;0m'
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@ -128,18 +137,15 @@ else: # if any other kernel is selected
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# define the ratio between types of mutation, where all sum to 1.0; can be adjusted in 'i'nteractive mode
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gp.evolve_repro = int(0.0 * gp.tree_pop_max) # percentage of subsequent population to be generated through Reproduction
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gp.evolve_point = int(0.0 * gp.tree_pop_max) # percentage of subsequent population to be generated through Point Mutation
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gp.evolve_branch = int(0.0 * gp.tree_pop_max) # percentage of subsequent population to be generated through Branch Mutation
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gp.evolve_cross = int(1.0 * gp.tree_pop_max) # percentage of subsequent population to be generated through Crossover Reproduction
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gp.evolve_repro = int(0.1 * gp.tree_pop_max) # percentage of subsequent population to be generated through Reproduction
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gp.evolve_point = int(0.1 * gp.tree_pop_max) # percentage of subsequent population to be generated through Point Mutation
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gp.evolve_branch = int(0.2 * gp.tree_pop_max) # percentage of subsequent population to be generated through Branch Mutation
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gp.evolve_cross = int(0.6 * gp.tree_pop_max) # percentage of subsequent population to be generated through Crossover
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gp.tourn_size = 10 # qty of individuals entered into each tournament (standard 10); can be adjusted in 'i'nteractive mode
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gp.cores = 1 # replace '1' with 'int(gp.core_count)' to auto-set to max; can be adjusted in 'i'nteractive mode
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gp.precision = 4 # the number of floating points for the round function in 'fx_fitness_eval'; hard coded
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# if len(sys.argv) == 2: # look for an argument when Karoo GP is launched
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# gp.data_load = int(sys.argv[1]) # assign file for the data load method in karoo_base_class
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#++++++++++++++++++++++++++++++++++++++++++
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# Construct First Generation of Trees |
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@ -152,7 +158,8 @@ constructed from scratch. All parameters which define the Trees were set by the
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If the user has selected 'Play' mode, this is the only generation to be constructed, and then GP Karoo terminates.
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'''
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gp.fx_karoo_data_load()
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filename = '' # temp place holder
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gp.fx_karoo_data_load(tree_type, tree_depth_base, filename)
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gp.generation_id = 1 # set initial generation ID
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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
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Binary file not shown.
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@ -2,37 +2,88 @@
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# Use Genetic Programming for Classification and Symbolic Regression
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# by Kai Staats, MSc UCT / AIMS
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# Much thanks to Emmanuel Dufourq and Arun Kumar for their support, guidance, and free psychotherapy sessions
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# version 0.9.1.5
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# version 0.9.1.6
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'''
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A NOTE TO THE NEWBIE, EXPERT, AND BRAVE
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Even if you are highly experienced in Genetic Programming, it is recommended that you review the 'Karoo Quick Start' before running
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this application. While your computer will not burst into flames nor will the sun collapse into a black hole if you do not, you will
|
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likely find more enjoyment of this particular flavour of GP with a little understanding of its intent and design.
|
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A word to the newbie, expert, and brave--
|
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Even if you are highly experienced in Genetic Programming, it is recommended that you review the 'Karoo Quick Start'
|
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before running this application. While your computer will not burst into flames nor will the sun collapse into a black
|
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hole if you do not, you will likely find more enjoyment of this particular flavour of GP with a little understanding
|
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of its intent and design.
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KAROO GP SERVER
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This is the Karoo GP server application. It can be internally scripted, fully command-line configured, or a combination
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of both. If this is your first time using Karoo GP, please run the desktop application karoo_gp_main.py first in order
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that you come to understand its full functionality.
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To launch Karoo GP server:
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$ python karoo_gp_server.py
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(or from iPython)
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$ run karoo_gp_server.py
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Without any arguments, Karoo GP relies entirely upon the scripted settings and the datasets located in karoo_gp/files/.
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If you include the path to an external dataset, it will auto-load at launch:
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$ python karoo_gp_server.py /[path]/[to_your]/[filename].csv
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You can include a number of additional arguments which override the default values, as follows:
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-ker [r,c,m] fitness function: (r)egression, (c)lassification, or (m)atching
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-typ [f,g,r] Tree type: (f)ull, (g)row, or (r)amped half/half
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-bas [3...10] maximum Tree depth for the initial population
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-max [3...10] maximum Tree depth for the entire run
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-min [3...100] minimum number of nodes
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-pop [10...1000] maximum population
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-gen [1...100] number of generations
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Note that if you include any of the above flags, then you must also include a flag to load an external dataset:
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$ python karoo_gp_server.py -ker c -typ r -bas 4 -fil /[path]/[to_your]/[filename].csv
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'''
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import sys # sys.path.append('modules/') # add the directory 'modules' to the current path
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import sys # sys.path.append('modules/') to add the directory 'modules' to the current path
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import argparse
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import karoo_gp_base_class; gp = karoo_gp_base_class.Base_GP()
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# parameters configuration
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gp.kernel = 'c' # ['r','c','m'] fitness function: (r)egression, (c)lassification, or (m)atching
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gp.class_labels = 3 # [2,3, ...] number of class labels in the feature set
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tree_type = 'r' # ['f','g','r'] Tree (t)ype: (f)ull, (g)row, or (r)amped half/half
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tree_depth_base = 3 # [3,10] maximum Tree depth for the initial population, where nodes = 2^(depth + 1) - 1
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gp.tree_depth_max = 3 # [3,10] maximum Tree depth for the entire run; introduces potential bloat
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gp.tree_depth_min = 3 # [3,100] minimum number of nodes
|
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gp.tree_pop_max = 100 # [10,1000] maximum population
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gp.generation_max = 10 # [1,100] number of generations
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gp.display = 'm' # ['i','m','g','s'] display mode: (i)nteractive, (m)inimal, (g)enerational, or (s)erver
|
||||
ap = argparse.ArgumentParser(description = 'Karoo GP Server')
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ap.add_argument('-ker', action = 'store', dest = 'kernel', default = 'm', help = '[r,c,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')
|
||||
ap.add_argument('-bas', action = 'store', dest = 'depth_base', default = 3, help = '[3...10] maximum Tree depth for the initial population')
|
||||
ap.add_argument('-max', action = 'store', dest = 'depth_max', default = 3, help = '[3...10] maximum Tree depth for the entire run')
|
||||
ap.add_argument('-min', action = 'store', dest = 'depth_min', default = 3, help = '[3...100] minimum number of nodes')
|
||||
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('-fil', action = 'store', dest = 'filename', default = 'files/data_MATCH.csv', help = '/path/to_your/data.csv')
|
||||
|
||||
args = ap.parse_args()
|
||||
|
||||
# set the same parameters found in the Karoo GP desktop application, but potentially passed from the command line
|
||||
gp.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)
|
||||
filename = str(args.filename)
|
||||
|
||||
gp.display = 'n' # display mode is set to (s)ilent
|
||||
gp.evolve_repro = int(0.1 * gp.tree_pop_max) # percentage of subsequent population to be generated through Reproduction
|
||||
gp.evolve_point = int(0.1 * gp.tree_pop_max) # percentage of subsequent population to be generated through Point Mutation
|
||||
gp.evolve_branch = int(0.2 * gp.tree_pop_max) # percentage of subsequent population to be generated through Branch Mutation
|
||||
gp.evolve_cross = int(0.6 * gp.tree_pop_max) # percentage of subsequent population to be generated through Crossover Reproduction
|
||||
gp.evolve_cross = int(0.6 * gp.tree_pop_max) # percentage of subsequent population to be generated through Crossover
|
||||
|
||||
gp.tourn_size = 10 # qty of individuals entered into each tournament (standard 10); can be adjusted in 'i'nteractive mode
|
||||
gp.cores = 1 # replace '1' with 'int(gp.core_count)' to auto-set to max; can be adjusted in 'i'nteractive mode
|
||||
gp.precision = 4 # the number of floating points for the round function in 'fx_fitness_eval'; hard coded
|
||||
|
||||
# run Karoo GP
|
||||
gp.karoo_gp('server', tree_type, tree_depth_max)
|
||||
gp.karoo_gp(tree_type, tree_depth_base, filename)
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue