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mod to cwd path; split log into 2 files

pull/6/head
Kai Staats 2017-02-13 20:44:32 -07:00
parent 62699204c7
commit 1ddc7b0aeb
2 changed files with 28 additions and 8 deletions
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10
RELEASE_NOTES.txt
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@ -1,3 +1,13 @@
2017 02/13
Returned 'cwd = os.getcwd()' to 'cwd = os.path.dirname(full_path)' in fx_karoo_data_load in order to sustain chron
compliance (sorry Marco). Then split end-of-run logging into 2 files: runtime_config.txt and test.txt. Looking to the
future in which the test log, in particular, might be stored in such a way as to be readily parsed by an external
script for automated comparison of multiple runs.
Ideas are welcomed!
2017 02/09 2017 02/09
The User Guide has been updated to match the functions of Karoo GP v1.0.1. The User Guide has been updated to match the functions of Karoo GP v1.0.1.

26
karoo_gp_base_class.py
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@ -2,7 +2,7 @@
# Define the methods and global variables used by Karoo GP # Define the methods and global variables used by Karoo GP
# by Kai Staats, MSc; see LICENSE.md # 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 # Thanks to Emmanuel Dufourq and Arun Kumar for support during 2014-15 devel; TensorFlow support provided by Iurii Milovanov
# version 1.0.1 # version 1.0.2
''' '''
A NOTE TO THE NEWBIE, EXPERT, AND BRAVE A NOTE TO THE NEWBIE, EXPERT, AND BRAVE
@ -224,8 +224,8 @@ class Base_GP(object):
### 1) load the associated data set, operators, operands, fitness type, and coefficients ### ### 1) load the associated data set, operators, operands, fitness type, and coefficients ###
#full_path = os.path.realpath(__file__); cwd = os.path.dirname(full_path) full_path = os.path.realpath(__file__); cwd = os.path.dirname(full_path) # Good idea Marco :)
cwd = os.getcwd() # 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'} 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'}
@ -2596,7 +2596,7 @@ class Base_GP(object):
target.writerows([population[tree][row]]) target.writerows([population[tree][row]])
def fx_archive_params_write(self, app): # tested 2017 02/08 def fx_archive_params_write(self, app): # tested 2017 02/13
''' '''
Save run-time configuration parameters to disk. Save run-time configuration parameters to disk.
@ -2604,7 +2604,7 @@ class Base_GP(object):
Arguments required: none Arguments required: none
''' '''
file = open(self.path + '/parameters.txt', 'w') file = open(self.path + '/log_config.txt', 'w')
file.write('Karoo GP ' + app) file.write('Karoo GP ' + app)
file.write('\n launched: ' + str(self.datetime)) file.write('\n launched: ' + str(self.datetime))
@ -2625,12 +2625,22 @@ class Base_GP(object):
file.write('\n') file.write('\n')
file.write('\n tournament size: ' + str(self.tourn_size)) file.write('\n tournament size: ' + str(self.tourn_size))
file.write('\n population: ' + str(self.tree_pop_max)) file.write('\n population: ' + str(self.tree_pop_max))
file.write('\n number of generations: ' + str(self.generation_id)) file.write('\n number of generations: ' + str(self.generation_id))
file.write('\n\n')
file.close()
file = open(self.path + '/log_test.txt', 'w')
file.write('Karoo GP ' + app)
file.write('\n launched: ' + str(self.datetime))
file.write('\n dataset: ' + str(self.dataset))
file.write('\n')
# parse the 'l'ist for the highest numbered Tree
if len(self.fittest_dict) > 0: if len(self.fittest_dict) > 0:
file.write('\n\n The leading Trees and their associated expressions are:') file.write('\n The leading Trees and their associated expressions are:')
for item in sorted(self.fittest_dict): for item in sorted(self.fittest_dict):
file.write('\n\t ' + str(item) + ' : ' + str(self.fittest_dict[item])) file.write('\n\t ' + str(item) + ' : ' + str(self.fittest_dict[item]))