254 lines
12 KiB
Python
254 lines
12 KiB
Python
# Karoo GP (desktop + server combined)
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# Use Genetic Programming for Classification and Symbolic Regression
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# by Kai Staats, MSc with TensorFlow support provided by Iurii Milovanov; see LICENSE.md
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# version 2.3 for Python 3.6
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'''
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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 User Guide'
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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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Without any command line arguments, Karoo GP relies upon user settings and the datasets located in karoo_gp/files/.
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$ python 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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If you include one or more additional arguments, they will 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 initial population
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-max [3...10] maximum Tree depth for entire run
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-min [3 to 2^(bas +1) - 1] minimum number of nodes
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-pop [10...1000] number of trees in each generational population
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-gen [1...100] number of generations
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-tor [7 per 100] number of trees selected for tournament
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-evr [0.0...1.0] decimal percent of pop generated through Reproduction
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-evp [0.0...1.0] decimal percent of pop generated through Point Mutation
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-evb [0.0...1.0] decimal percent of pop generated through Branch Mutation
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-evc [0.0...1.0] decimal percent of pop generated through Crossover
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If you include any of the above flags, then you *must* also include a flag to load an external dataset.
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-fil [path]/[to]/[data].csv an external dataset
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An example is given, as follows:
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$ python karoo_gp_server.py -ker c -typ r -bas 4 -fil [path]/[to]/[data].csv
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'''
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import os
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import sys; sys.path.append('modules/') # add 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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os.system('clear')
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print ('\n\033[36m\033[1m')
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print ('\t ** ** ****** ***** ****** ****** ****** ******')
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print ('\t ** ** ** ** ** ** ** ** ** ** ** ** **')
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print ('\t ** ** ** ** ** ** ** ** ** ** ** ** **')
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print ('\t **** ******** ****** ** ** ** ** ** *** *******')
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print ('\t ** ** ** ** ** ** ** ** ** ** ** ** **')
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print ('\t ** ** ** ** ** ** ** ** ** ** ** ** **')
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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 with TensorFlow - by Kai Staats, version 2.3\033[0;0m')
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print ('')
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#++++++++++++++++++++++++++++++++++++++++++
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# User Interface for Configuation |
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#++++++++++++++++++++++++++++++++++++++++++
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if len(sys.argv) < 3: # either no command line argument, or only a filename is provided
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while True:
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try:
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query = input('\t Select (c)lassification, (r)egression, (m)atching, or (p)lay (default m): ')
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if query in ['c','r','m','p','']: kernel = query or 'm'; break
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else: raise ValueError()
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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 kernel == 'p': # play mode
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while True:
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try:
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query = input('\t Select (f)ull or (g)row (default g): ')
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if query in ['f','g','']: tree_type = query or 'f'; break
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else: raise ValueError()
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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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while True:
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try:
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query = input('\t Enter the depth of the Tree (default 1): ')
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if query == '': tree_depth_base = 1; break
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elif int(query) in list(range(1,11)): tree_depth_base = int(query); break
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else: raise ValueError()
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except ValueError: print ('\t\033[32m Enter a number from 1 including 10. Try again ...\n\033[0;0m')
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except KeyboardInterrupt: sys.exit()
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tree_depth_max = tree_depth_base
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tree_depth_min = 3
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tree_pop_max = 1
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gen_max = 1
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tourn_size = 0
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display = 'm'
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# evolve_repro, evolve_point, evolve_branch, evolve_cross, tourn_size, precision, filename are not required
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else: # if any other kernel is selected
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while True:
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try:
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query = input('\t Select (f)ull, (g)row, or (r)amped 50/50 method (default r): ')
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if query in ['f','g','r','']: tree_type = query or 'r'; break
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else: raise ValueError()
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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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while True:
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try:
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query = input('\t Enter depth of the \033[3minitial\033[0;0m population of Trees (default 3): ')
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if query == '': tree_depth_base = 3; break
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elif int(query) in list(range(1,11)): tree_depth_base = int(query); break
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else: raise ValueError()
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except ValueError: print ('\t\033[32m Enter a number from 1 including 10. Try again ...\n\033[0;0m')
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except KeyboardInterrupt: sys.exit()
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while True:
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try:
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query = input('\t Enter maximum Tree depth (default %s): ' %str(tree_depth_base))
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if query == '': tree_depth_max = tree_depth_base; break
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elif int(query) in list(range(tree_depth_base,11)): tree_depth_max = int(query); break
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else: raise ValueError()
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except ValueError: print ('\t\033[32m Enter a number from %s including 10. Try again ...\n\033[0;0m' %str(tree_depth_base))
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except KeyboardInterrupt: sys.exit()
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max_nodes = 2**(tree_depth_base+1)-1 # calc the max number of nodes for the given depth
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while True:
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try:
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query = input('\t Enter minimum number of nodes for any given Tree (default 3; max %s): ' %str(max_nodes))
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if query == '': tree_depth_min = 3; break
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elif int(query) in list(range(3,max_nodes + 1)): tree_depth_min = int(query); break
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else: raise ValueError()
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except ValueError: print ('\t\033[32m Enter a number from 3 including %s. Try again ...\n\033[0;0m' %str(max_nodes))
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except KeyboardInterrupt: sys.exit()
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#while True:
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#try:
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#query = input('\t Select (p)artial or (f)ull operator inclusion (default p): ')
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#if query == '': swim = 'p'; break
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#elif query in ['p','f']: swim = query; break
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#else: raise ValueError()
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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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while True:
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try:
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query = input('\t Enter number of Trees in each population (default 100): ')
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if query == '': tree_pop_max = 100; break
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elif int(query) in list(range(1,1001)): tree_pop_max = int(query); break
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else: raise ValueError()
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except ValueError: print ('\t\033[32m Enter a number from 1 including 1000. Try again ...\n\033[0;0m')
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except KeyboardInterrupt: sys.exit()
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# calculate the tournament size
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tourn_size = int(tree_pop_max * 0.07) # default 7% can be changed by selecting (g)eneration and then 'ts'
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if tourn_size < 2: tourn_size = 2 # forces some diversity for small populations
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if tree_pop_max == 1: tourn_size = 1 # in theory, supports the evolution of a single Tree - NEED TO FIX 2018 04/19
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while True:
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try:
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query = input('\t Enter max number of generations (default 10): ')
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if query == '': gen_max = 10; break
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elif int(query) in list(range(1,101)): gen_max = int(query); break
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else: raise ValueError()
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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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if gen_max > 1:
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while True:
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try:
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query = input('\t Display (i)nteractive, (g)eneration, (m)iminal, (s)ilent, or (d)e(b)ug (default m): ')
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if query in ['i','g','m','s','db','']: display = query or 'm'; break
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else: raise ValueError()
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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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else: display = 's' # display mode is not used, but a value must be passed
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### additional configuration parameters ###
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evolve_repro = int(0.1 * tree_pop_max) # quantity of a population generated through Reproduction
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evolve_point = int(0.0 * tree_pop_max) # quantity of a population generated through Point Mutation
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evolve_branch = int(0.2 * tree_pop_max) # quantity of a population generated through Branch Mutation
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evolve_cross = int(0.7 * tree_pop_max) # quantity of a population generated through Crossover
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filename = '' # not required unless an external file is referenced
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precision = 6 # number of floating points for the round function in 'fx_fitness_eval'
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swim = 'p' # require (p)artial or (f)ull set of features (operators) for each Tree entering the gene_pool
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mode = 'd' # pause at the (d)esktop when complete, awaiting further user interaction; or terminate in (s)erver mode
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#++++++++++++++++++++++++++++++++++++++++++
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# Command Line for Configuation |
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#++++++++++++++++++++++++++++++++++++++++++
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else: # 2 or more command line arguments are provided
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ap = argparse.ArgumentParser(description = 'Karoo GP Server')
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ap.add_argument('-ker', action = 'store', dest = 'kernel', default = 'c', help = '[c,r,m] fitness function: (r)egression, (c)lassification, or (m)atching')
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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')
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ap.add_argument('-bas', action = 'store', dest = 'depth_base', default = 4, help = '[3...10] maximum Tree depth for the initial population')
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ap.add_argument('-max', action = 'store', dest = 'depth_max', default = 4, help = '[3...10] maximum Tree depth for the entire run')
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ap.add_argument('-min', action = 'store', dest = 'depth_min', default = 3, help = 'minimum nodes, from 3 to 2^(base_depth +1) - 1')
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ap.add_argument('-pop', action = 'store', dest = 'pop_max', default = 100, help = '[10...1000] number of trees per generation')
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ap.add_argument('-gen', action = 'store', dest = 'gen_max', default = 10, help = '[1...100] number of generations')
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ap.add_argument('-tor', action = 'store', dest = 'tor_size', default = 7, help = '[7 for each 100] recommended tournament size')
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ap.add_argument('-evr', action = 'store', dest = 'evo_r', default = 0.1, help = '[0.0-1.0] decimal percent of pop generated through Reproduction')
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ap.add_argument('-evp', action = 'store', dest = 'evo_p', default = 0.0, help = '[0.0-1.0] decimal percent of pop generated through Point Mutation')
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ap.add_argument('-evb', action = 'store', dest = 'evo_b', default = 0.2, help = '[0.0-1.0] decimal percent of pop generated through Branch Mutation')
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ap.add_argument('-evc', action = 'store', dest = 'evo_c', default = 0.7, help = '[0.0-1.0] decimal percent of pop generated through Crossover')
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ap.add_argument('-fil', action = 'store', dest = 'filename', default = '', help = '/path/to_your/[data].csv')
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args = ap.parse_args()
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# pass the argparse defaults and/or user inputs to the required variables
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kernel = str(args.kernel)
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tree_type = str(args.type)
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tree_depth_base = int(args.depth_base)
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tree_depth_max = int(args.depth_max)
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tree_depth_min = int(args.depth_min)
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tree_pop_max = int(args.pop_max)
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gen_max = int(args.gen_max)
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tourn_size = int(args.tor_size)
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evolve_repro = int(float(args.evo_r) * tree_pop_max)
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evolve_point = int(float(args.evo_p) * tree_pop_max)
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evolve_branch = int(float(args.evo_b) * tree_pop_max)
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evolve_cross = int(float(args.evo_c) * tree_pop_max)
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filename = str(args.filename)
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display = 's' # display mode is set to (s)ilent
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precision = 6 # number of floating points for the round function in 'fx_fitness_eval'
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swim = 'p' # require (p)artial or (f)ull set of features (operators) for each Tree entering the gene_pool
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mode = 's' # pause at the (d)esktop when complete, awaiting further user interaction; or terminate in (s)erver mode
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#++++++++++++++++++++++++++++++++++++++++++
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# Conduct the GP run |
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#++++++++++++++++++++++++++++++++++++++++++
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gp.fx_karoo_gp(kernel, tree_type, tree_depth_base, tree_depth_max, tree_depth_min, tree_pop_max, gen_max, tourn_size, filename, evolve_repro, evolve_point, evolve_branch, evolve_cross, display, precision, swim, mode)
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