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final update for Python 2.7 version of Karoo

Python-2.7-archive
kstaats 2019-05-18 09:46:11 -07:00
parent 262261c530
commit f347f5c629
3 changed files with 53 additions and 31 deletions
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22
RELEASE_NOTES.txt
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@ -1,7 +1,25 @@
2019 05/18
"My apology to the users of Karoo GP for the incredible gap between the most recent and this update. My research and
work have for the past year taken me in other directions. This update marks my return to an active effort in working on
Karoo, and GP applied to a growing research space. I appreciate your patience and understanding." --kai
NOTE: this is the *final* update to the Python 2.7 version of Karoo. All future development will be in Python 3.x.
And now, the updates:
Fixed the Test function to evaluate the Tree selected by the user.
Modified the menu option from 't'est to 'e'valulate from the Pause menu.
Added a user defined (m)in node count, (f)ull set of features, or (b)oth as the gene_pool regulator. For more about
the "full set of features" see 2018 04/19 and the new 'swim' variable.
2018 05/18
Fixed a bug which automically re-engaged the run after selecting a pause menu option if mid generation. Now, it
correctly again require an ENTER before continuing.
Fixed a bug which automically re-engaged the run after selecting a pause menu option if mid generation. Now, it again
requires ENTER before continuing.
2018 05/10

50
modules/karoo_gp_base_class.py
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@ -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 2.1.2
# version 2.1.3
'''
A NOTE TO THE NEWBIE, EXPERT, AND BRAVE
@ -253,7 +253,10 @@ class Base_GP(object):
menu_dict = menu.pause(menu_dict) # call the external function menu.pause
### 2) unpack values returned from menu.pause ###
input_a = menu_dict['input_a']
input_b = menu_dict['input_b']
self.display = menu_dict['display']
self.tree_depth_min = menu_dict['tree_depth_min']
self.gen_max = menu_dict['gen_max']
@ -263,50 +266,51 @@ class Base_GP(object):
self.evolve_branch = menu_dict['evolve_branch']
self.evolve_cross = menu_dict['evolve_cross']
### 3) execute the user queries returned from menu.pause ###
if menu_dict['input_a'] == 'esc': return 0 # ENTER enables next step in generational, interactive, and debug display
elif menu_dict['input_a'] == 'test': # evaluate a Tree against the TEST data
expr = str(self.algo_sym) # might change this to algo_raw for more correct expression evaluation
result = self.fx_fitness_eval(expr, self.data_test, get_pred_labels = True)
print '\n\t\033[36mTree', menu_dict['input_b'], 'yields (raw):', self.algo_raw, '\033[0;0m'
print '\t\033[36mTree', menu_dict['input_b'], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m\n'
### 3) execute the user queries returned from menu.pause ###
if input_a == 'esc': return 0 # ENTER enables next step in generational, interactive, and debug display
elif input_a == 'eval': # evaluate a Tree against the TEST data
self.fx_eval_poly(self.population_b[input_b]) # generate the raw and sympified expression for the given Tree using SymPy
#print '\n\t\033[36mTree', input_b, 'yields (raw):', self.algo_raw, '\033[0;0m' # print the raw expression
print '\t\033[36mTree', input_b, 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m\n' # print the sympified expression
result = self.fx_fitness_eval(str(self.algo_sym), self.data_test, get_pred_labels = True) # might change to algo_raw evaluation
if self.kernel == 'c': self.fx_fitness_test_classify(result) # TF tested 2017 02/02
elif self.kernel == 'r': self.fx_fitness_test_regress(result)
elif self.kernel == 'm': self.fx_fitness_test_match(result)
# elif self.kernel == '[other]': self.fx_fitness_test_[other](result)
elif menu_dict['input_a'] == 'print_a': # print a Tree from population_a
self.fx_display_tree(self.population_a[menu_dict['input_b']])
elif input_a == 'print_a': # print a Tree from population_a
self.fx_display_tree(self.population_a[input_b])
elif menu_dict['input_a'] == 'print_b': # print a Tree from population_b
self.fx_display_tree(self.population_b[menu_dict['input_b']])
elif input_a == 'print_b': # print a Tree from population_b
self.fx_display_tree(self.population_b[input_b])
elif menu_dict['input_a'] == 'pop_a': # list all Trees in population_a
elif input_a == 'pop_a': # list all Trees in population_a
print ''
for tree_id in range(1, len(self.population_a)):
self.fx_eval_poly(self.population_a[tree_id]) # extract the expression
print '\t\033[36m Tree', self.population_a[tree_id][0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m'
elif menu_dict['input_a'] == 'pop_b': # list all Trees in population_b
elif input_a == 'pop_b': # list all Trees in population_b
print ''
for tree_id in range(1, len(self.population_b)):
self.fx_eval_poly(self.population_b[tree_id]) # extract the expression
print '\t\033[36m Tree', self.population_b[tree_id][0][1], 'yields (sym):\033[1m', self.algo_sym, '\033[0;0m'
elif menu_dict['input_a'] == 'load': # load population_s to replace population_a
elif input_a == 'load': # load population_s to replace population_a
self.fx_data_recover(self.filename['s']) # NEED TO replace 's' with a user defined filename
elif menu_dict['input_a'] == 'write': # write the evolving population_b to disk
elif input_a == 'write': # write the evolving population_b to disk
self.fx_data_tree_write(self.population_b, 'b')
print '\n\t All current members of the evolving population_b saved to karoo_gp/runs/[date-time]/population_b.csv'
elif menu_dict['input_a'] == 'cont': # check for added generations, then exit fx_karoo_pause and continue the run
if menu_dict['input_b'] > 0: self.gen_max = self.gen_max + menu_dict['input_b']
elif input_a == 'cont': # check for added generations, then exit fx_karoo_pause and continue the run
if input_b > 0: self.gen_max = self.gen_max + input_b
else: pass
elif menu_dict['input_a'] == 'quit': # quit and save run-time parameters to disk
elif input_a == 'quit': # quit and save run-time parameters to disk
self.fx_data_params_write('Desktop')
print '\n\t \033[32mYour Trees and runtime parameters are archived in karoo_gp/runs/[date-time]/\033[0;0m'
if pause == 0: sys.exit() # force quit due to being one level below the while loop
@ -596,7 +600,7 @@ class Base_GP(object):
# test the most fit Tree and write to the .txt log
self.fx_eval_poly(self.population_b[int(fittest_tree)]) # generate the raw and sympified equation for the given Tree using SymPy
self.fx_eval_poly(self.population_b[int(fittest_tree)]) # generate the raw and sympified expression for the given Tree using SymPy
expr = str(self.algo_sym) # get simplified expression and process it by TF - tested 2017 02/02
result = self.fx_fitness_eval(expr, self.data_test, get_pred_labels = True)
@ -2515,7 +2519,7 @@ class Base_GP(object):
ind = ind + '\t'
print ''
self.fx_eval_poly(tree) # generate the raw and sympified equation for the entire Tree
self.fx_eval_poly(tree) # generate the raw and sympified expression 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'
@ -2557,7 +2561,7 @@ class Base_GP(object):
ind = ind + '\t'
print ''
self.fx_eval_poly(tree) # generate the raw and sympified equation for the entire Tree
self.fx_eval_poly(tree) # generate the raw and sympified expression 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'

12
modules/karoo_gp_pause.py
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@ -1,7 +1,7 @@
# Karoo GP Pause Menu
# A text-based user interface for mid-run parameter configuration and population studies
# by Kai Staats, MSc; see LICENSE.md
# version 2.1.2
# version 2.1.3
def pause(menu_dict):
@ -13,7 +13,7 @@ def pause(menu_dict):
Arguments required: menu_dict
'''
options = ['','?','help','i','m','g','s','db','ts','min','bal','l','pop','t','p','id','dir','load','w','cont','q']
options = ['','?','help','i','m','g','s','db','ts','min','bal','l','pop','e','p','id','dir','load','w','cont','q']
while True:
try:
@ -40,7 +40,7 @@ def pause(menu_dict):
print ''
print '\t\033[36m\033[1m l \t\033[0;0m list Trees with leading fitness scores'
print '\t\033[36m\033[1m pop \t\033[0;0m list Trees in current population'
print '\t\033[36m\033[1m t \t\033[0;0m evaluate a single Tree against the test data'
print '\t\033[36m\033[1m e \t\033[0;0m evaluate a single Tree against the test data'
print '\t\033[36m\033[1m p \t\033[0;0m print a single Tree to screen'
print ''
print '\t\033[36m\033[1m id \t\033[0;0m display current generation ID'
@ -159,16 +159,16 @@ def pause(menu_dict):
if menu_dict['gen_id'] == 1: menu_dict['input_a'] = 'pop_a'
else: menu_dict['input_a'] = 'pop_b'
elif menu == 't': # evaluate a Tree against the TEST data
elif menu == 'e': # evaluate a Tree against the TEST data
if menu_dict['gen_id'] == 1: print '\n\t\033[32m You cannot evaluate the foundation population. Be patient ...\033[0;0m'
else: # gen_id > 1
while True:
try:
query = raw_input('\n\t Select a Tree to test: ')
query = raw_input('\n\t Select a Tree to evaluate: ')
if query not in str(range(1, menu_dict['pop_b_len'])) or query == '0': raise ValueError()
elif query == '': break
else: menu_dict['input_a'] = 'test'; menu_dict['input_b'] = int(query); break
else: menu_dict['input_a'] = 'eval'; menu_dict['input_b'] = int(query); break
except ValueError: print '\n\t\033[32m Enter a number from 1 including %s. Try again ...\033[0;0m' %str(menu_dict['pop_b_len'] - 1)
except KeyboardInterrupt: print '\n\t\033[32m Enter q to quit\033[0;0m'