Resolves Issue kstaats#23: "On unary operators"

rll2021 · Feb 9, 2021 · 742e3b9 · 742e3b9
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diff --git a/RELEASE_NOTES.txt b/RELEASE_NOTES.txt
@@ -1,3 +1,7 @@
+20210209
+
+This update permits unary math operators to be specified and treated as unary. Miscellaneous bugs also fixed.
+
 20210125
 
 This relatively minor update to the code enables compatibility with Python 3.8 and Tensorflow v2.4.1. In the base_class the 'branch' array is now defined as an integer to comply with what appears to be a change in Numpy. And finally, the director structure is modified in preparation for delivering Karoo GP as a proper pip package.

diff --git a/RELEASE_NOTES.txt.orig b/RELEASE_NOTES.txt.orig
diff --git a/karoo-gp.py b/karoo-gp.py
@@ -194,9 +194,9 @@
 	### additional configuration parameters ###
 
 	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_point = int(0.1 * 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
+	evolve_cross = int(0.6 * tree_pop_max) # quantity of a population generated through Crossover
 	filename = '' # not required unless an external file is referenced
 	precision = 6 # number of floating points for the round function in 'fx_fitness_eval'
 	swim = 'p' # require (p)artial or (f)ull set of features (operators) for each Tree entering the gene_pool
@@ -219,9 +219,9 @@
 	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 = '[7 for each 100] recommended tournament size')
 	ap.add_argument('-evr', action = 'store', dest = 'evo_r', default = 0.1, help = '[0.0-1.0] decimal percent of pop generated through Reproduction')
-	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')
+	ap.add_argument('-evp', action = 'store', dest = 'evo_p', default = 0.1, help = '[0.0-1.0] decimal percent of pop generated through Point Mutation')
 	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')
-	ap.add_argument('-evc', action = 'store', dest = 'evo_c', default = 0.7, help = '[0.0-1.0] decimal percent of pop generated through Crossover')
+	ap.add_argument('-evc', action = 'store', dest = 'evo_c', default = 0.6, help = '[0.0-1.0] decimal percent of pop generated through Crossover')
 	ap.add_argument('-fil', action = 'store', dest = 'filename', default = '', help = '/path/to_your/[data].csv')
 
 	args = ap.parse_args()

diff --git a/karoo-gp.py.orig b/karoo-gp.py.orig
@@ -0,0 +1,256 @@
+#!/bin/python3
+# Karoo GP (desktop + server combined)
+# Use Genetic Programming for Classification and Symbolic Regression
+# by Kai Staats, MSc with TensorFlow support provided by Iurii Milovanov; see LICENSE.md
+# pip install package preparation by Antonio Spadaro and Ezio Melotti
+# version 2.4 for Python 3.8
+
+'''
+A word to the newbie, expert, and brave--
+Even if you are highly experienced in Genetic Programming, it is recommended that you review the 'Karoo User Guide' 
+before running 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 likely find more enjoyment of this particular flavour of GP with a little understanding 
+of its intent and design.
+
+Without any command line arguments, Karoo GP relies upon user settings and the datasets located in karoo_gp/files/.
+
+	$ python karoo_gp_main.py
+	
+
+If you include the path to an external dataset, it will auto-load at launch:
+
+	$ python karoo_gp_main.py /[path]/[to_your]/[filename].csv
+	
+
+If you include one or more additional arguments, they will override the default values, as follows:
+
+	-ker [r,c,m]			fitness function: (r)egression, (c)lassification, or (m)atching
+	-typ [f,g,r]			Tree type: (f)ull, (g)row, or (r)amped half/half
+	-bas [3...10]			maximum Tree depth for initial population
+	-max [3...10]			maximum Tree depth for entire run
+	-min [3 to 2^(bas +1) - 1]	minimum number of nodes
+	-pop [10...1000]		number of trees in each generational population
+	-gen [1...100]			number of generations
+	-tor [7 per 100]		number of trees selected for tournament
+	-evr [0.0...1.0]  		decimal percent of pop generated through Reproduction
+	-evp [0.0...1.0]  		decimal percent of pop generated through Point Mutation
+	-evb [0.0...1.0]  		decimal percent of pop generated through Branch Mutation
+	-evc [0.0...1.0]  		decimal percent of pop generated through Crossover
+	
+If you include any of the above flags, then you *must* also include a flag to load an external dataset.
+
+	-fil [path]/[to]/[data].csv	an external dataset
+
+
+An example is given, as follows:
+
+	$ python karoo_gp_server.py -ker c -typ r -bas 4 -fil [path]/[to]/[data].csv
+
+'''
+
+import os
+import sys
+import argparse
+from karoo_gp import base_class, __version__
+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 with TensorFlow - by Kai Staats, version {}\033[0;0m'.format(__version__))
+print ('')
+
+
+#++++++++++++++++++++++++++++++++++++++++++
+#   User Interface for Configuation       |
+#++++++++++++++++++++++++++++++++++++++++++
+
+if len(sys.argv) < 3: # either no command line argument, or only a filename is provided
+
+	while True:
+		try:
+			query = input('\t Select (c)lassification, (r)egression, (m)atching, or (p)lay (default m): ')
+			if query in ['c','r','m','p','']: kernel = query or 'm'; break
+			else: raise ValueError()
+		except ValueError: print ('\t\033[32m Select from the options given. Try again ...\n\033[0;0m')
+		except KeyboardInterrupt: sys.exit()
+
+	if kernel == 'p': # play mode
+		while True:
+			try:
+				query = input('\t Select (f)ull or (g)row (default g): ')
+				if query in ['f','g','']: tree_type = query or 'f'; break
+				else: raise ValueError()
+			except ValueError: print ('\t\033[32m Select from the options given. Try again ...\n\033[0;0m')
+			except KeyboardInterrupt: sys.exit()
+
+		while True:
+			try:
+				query = input('\t Enter the depth of the Tree (default 1): ')
+				if query == '': tree_depth_base = 1; break
+				elif int(query) in list(range(1,11)): tree_depth_base = int(query); break
+				else: raise ValueError()
+			except ValueError: print ('\t\033[32m Enter a number from 1 including 10. Try again ...\n\033[0;0m')
+			except KeyboardInterrupt: sys.exit()
+
+		tree_depth_max = tree_depth_base
+		tree_depth_min = 3
+		tree_pop_max = 1
+		gen_max = 1
+		tourn_size = 0
+		display = 'm'
+		#	evolve_repro, evolve_point, evolve_branch, evolve_cross, tourn_size, precision, filename are not required
+
+	else: # if any other kernel is selected
+
+		while True:
+			try:
+				query = input('\t Select (f)ull, (g)row, or (r)amped 50/50 method (default r): ')
+				if query in ['f','g','r','']: tree_type = query or 'r'; break
+				else: raise ValueError()
+			except ValueError: print ('\t\033[32m Select from the options given. Try again ...\n\033[0;0m')
+			except KeyboardInterrupt: sys.exit()
+
+		while True:
+			try:
+				query = input('\t Enter depth of the \033[3minitial\033[0;0m population of Trees (default 3): ')
+				if query == '': tree_depth_base = 3; break
+				elif int(query) in list(range(1,11)): tree_depth_base = int(query); break
+				else: raise ValueError()
+			except ValueError: print ('\t\033[32m Enter a number from 1 including 10. Try again ...\n\033[0;0m')
+			except KeyboardInterrupt: sys.exit()
+
+		while True:
+			try:
+				query = input('\t Enter maximum Tree depth (default %s): ' %str(tree_depth_base))
+				if query == '': tree_depth_max = tree_depth_base; break
+				elif int(query) in list(range(tree_depth_base,11)): tree_depth_max = int(query); break
+				else: raise ValueError()
+			except ValueError: print ('\t\033[32m Enter a number from %s including 10. Try again ...\n\033[0;0m' %str(tree_depth_base))
+			except KeyboardInterrupt: sys.exit()
+
+		max_nodes = 2**(tree_depth_base+1)-1 # calc the max number of nodes for the given depth
+
+		while True:
+			try:
+				query = input('\t Enter minimum number of nodes for any given Tree (default 3; max %s): ' %str(max_nodes))
+				if query == '': tree_depth_min = 3; break
+				elif int(query) in list(range(3,max_nodes + 1)): tree_depth_min = int(query); break
+				else: raise ValueError()
+			except ValueError: print ('\t\033[32m Enter a number from 3 including %s. Try again ...\n\033[0;0m' %str(max_nodes))
+			except KeyboardInterrupt: sys.exit()
+
+		#while True:
+			#try:
+				#query = input('\t Select (p)artial or (f)ull operator inclusion (default p): ')
+				#if query == '': swim = 'p'; break
+				#elif query in ['p','f']: swim = query; break
+				#else: raise ValueError()
+			#except ValueError: print ('\t\033[32m Select from the options given. Try again ...\n\033[0;0m')
+			#except KeyboardInterrupt: sys.exit()
+
+		while True:
+			try:
+				query = input('\t Enter number of Trees in each population (default 100): ')
+				if query == '': tree_pop_max = 100; break
+				elif int(query) in list(range(1,1001)): tree_pop_max = int(query); break
+				else: raise ValueError()
+			except ValueError: print ('\t\033[32m Enter a number from 1 including 1000. Try again ...\n\033[0;0m')
+			except KeyboardInterrupt: sys.exit()
+
+		# calculate the tournament size
+		tourn_size = int(tree_pop_max * 0.07) # default 7% can be changed by selecting (g)eneration and then 'ts'
+		if tourn_size < 2: tourn_size = 2 # forces some diversity for small populations
+		if tree_pop_max == 1: tourn_size = 1 # in theory, supports the evolution of a single Tree - NEED TO FIX 2018 04/19
+
+		while True:
+			try:
+				query = input('\t Enter max number of generations (default 10): ')
+				if query == '': gen_max = 10; break
+				elif int(query) in list(range(1,101)): gen_max = int(query); break
+				else: raise ValueError()
+			except ValueError: print ('\t\033[32m Enter a number from 1 including 100. Try again ...\n\033[0;0m')
+			except KeyboardInterrupt: sys.exit()
+
+		if gen_max > 1:
+			while True:
+				try:
+					query = input('\t Display (i)nteractive, (g)eneration, (m)iminal, (s)ilent, or (d)e(b)ug (default m): ')
+					if query in ['i','g','m','s','db','']: display = query or 'm'; break
+					else: raise ValueError()
+				except ValueError: print ('\t\033[32m Select from the options given. Try again ...\n\033[0;0m')
+				except KeyboardInterrupt: sys.exit()
+
+		else: display = 's' # display mode is not used, but a value must be passed
+
+	### additional configuration parameters ###
+
+	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
+	filename = '' # not required unless an external file is referenced
+	precision = 6 # number of floating points for the round function in 'fx_fitness_eval'
+	swim = 'p' # require (p)artial or (f)ull set of features (operators) for each Tree entering the gene_pool
+	mode = 'd' # pause at the (d)esktop when complete, awaiting further user interaction; or terminate in (s)erver mode
+
+
+#++++++++++++++++++++++++++++++++++++++++++
+#   Command Line for Configuation         |
+#++++++++++++++++++++++++++++++++++++++++++
+
+else: # 2 or more command line arguments are provided
+
+	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')
+	ap.add_argument('-bas', action = 'store', dest = 'depth_base', default = 4, help = '[3...10] maximum Tree depth for the initial population')
+	ap.add_argument('-max', action = 'store', dest = 'depth_max', default = 4, help = '[3...10] maximum Tree depth for the entire run')
+	ap.add_argument('-min', action = 'store', dest = 'depth_min', default = 3, help = 'minimum nodes, from 3 to 2^(base_depth +1) - 1')
+	ap.add_argument('-pop', action = 'store', dest = 'pop_max', default = 100, help = '[10...1000] number of trees per generation')
+	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 = '[7 for each 100] recommended tournament size')
+	ap.add_argument('-evr', action = 'store', dest = 'evo_r', default = 0.1, help = '[0.0-1.0] decimal percent of pop generated through Reproduction')
+	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')
+	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')
+	ap.add_argument('-evc', action = 'store', dest = 'evo_c', default = 0.7, help = '[0.0-1.0] decimal percent of pop generated through Crossover')
+	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
+	kernel = str(args.kernel)
+	tree_type = str(args.type)
+	tree_depth_base = int(args.depth_base)
+	tree_depth_max = int(args.depth_max)
+	tree_depth_min = int(args.depth_min)
+	tree_pop_max = int(args.pop_max)
+	gen_max = int(args.gen_max)
+	tourn_size = int(args.tor_size)
+	evolve_repro = int(float(args.evo_r) * tree_pop_max)
+	evolve_point = int(float(args.evo_p) * tree_pop_max)
+	evolve_branch = int(float(args.evo_b) * tree_pop_max)
+	evolve_cross = int(float(args.evo_c) * tree_pop_max)
+	filename = str(args.filename)
+
+	display = 's' # display mode is set to (s)ilent
+	precision = 6 # number of floating points for the round function in 'fx_fitness_eval'
+	swim = 'p' # require (p)artial or (f)ull set of features (operators) for each Tree entering the gene_pool
+	mode = 's' # pause at the (d)esktop when complete, awaiting further user interaction; or terminate in (s)erver mode
+
+
+#++++++++++++++++++++++++++++++++++++++++++
+#   Conduct the GP run                    |
+#++++++++++++++++++++++++++++++++++++++++++
+
+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)
+
+
diff --git a/karoo_gp/__init__.py b/karoo_gp/__init__.py
@@ -1 +1 @@
-__version__ = "2.4"
+__version__ = "2.5"
diff --git a/karoo_gp/__init__.py.orig b/karoo_gp/__init__.py.orig
@@ -0,0 +1 @@
+__version__ = "2.4"