From c6d40f68256a4d31587d38e939f1bcd338fe8131 Mon Sep 17 00:00:00 2001
From: VasilyShcherbinin <vasily.shcherbinin@outlook.com>
Date: Fri, 15 Feb 2019 21:59:54 +0000
Subject: [PATCH] Latest changes
---
UCS/RESULTS_FILE.txt | 14 ++++++++++----
UCS/UCS_Configuration_File.txt | 10 +++++-----
UCS/UCS_OutputFileManager.py | 2 --
UCS/UCS_Run.py | 3 +--
.../UCS_OutputFileManager.cpython-36.pyc | Bin 5819 -> 5772 bytes
5 files changed, 16 insertions(+), 13 deletions(-)
diff --git a/UCS/RESULTS_FILE.txt b/UCS/RESULTS_FILE.txt
index 73d0619..0ac008b 100644
--- a/UCS/RESULTS_FILE.txt
+++ b/UCS/RESULTS_FILE.txt
@@ -1,4 +1,10 @@
- Accuracy: 0.78125 Total time: 114.03 Rules: 360
- Total Av. Rules: 247.06666666666666
- Accuracy: 0.78125 Total time: 114.62 Rules: 365
- Total Av. Rules: 488.4
+ Accuracy: 0.90625 Total time: 33.04 Rules: 199
+ Accuracy: 0.921875 Total time: 34.97 Rules: 194
+ Accuracy: 0.859375 Total time: 35.2 Rules: 197
+ Accuracy: 0.859375 Total time: 36.26 Rules: 193
+ Accuracy: 0.890625 Total time: 34.05 Rules: 205
+ Accuracy: 0.875 Total time: 34.18 Rules: 201
+ Accuracy: 0.921875 Total time: 34.91 Rules: 218
+ Accuracy: 0.90625 Total time: 35.54 Rules: 202
+ Accuracy: 0.890625 Total time: 35.22 Rules: 205
+ Accuracy: 0.828125 Total time: 37.87 Rules: 216
diff --git a/UCS/UCS_Configuration_File.txt b/UCS/UCS_Configuration_File.txt
index 8654dc9..718466e 100644
--- a/UCS/UCS_Configuration_File.txt
+++ b/UCS/UCS_Configuration_File.txt
@@ -8,9 +8,9 @@ trainFile=6Multiplexer_Data_Complete.txt # FileName of training dataset
testFile=None # FileName of testing dataset. If no testing data available or desired, put 'None'.
outputDirectory=Local_Output # Output file directory
outputFile=ExampleRun # FileName of output files.
-learningIterations=20000 # Specify complete algorithm evaluation checkpoints and maximum number of learning iterations (e.g. 1000.2000.5000 = A maximum of 5000 learning iterations with evaluations at 1000, 2000, and 5000 iterations)
-N=1000 # Maximum size of the rule population (a.k.a. Micro-classifier population size, where N is the sum of the classifier numerosities in the population)
-p_spec=0.7 # The probability of specifying an attribute when covering. (1-p_spec = the probability of adding '#' in ternary rule representations). Greater numbers of attributes in a dataset will require lower values of p_spec.
+learningIterations=10000 # Specify complete algorithm evaluation checkpoints and maximum number of learning iterations (e.g. 1000.2000.5000 = A maximum of 5000 learning iterations with evaluations at 1000, 2000, and 5000 iterations)
+N=500 # Maximum size of the rule population (a.k.a. Micro-classifier population size, where N is the sum of the classifier numerosities in the population)
+p_spec=0.6 # The probability of specifying an attribute when covering. (1-p_spec = the probability of adding '#' in ternary rule representations). Greater numbers of attributes in a dataset will require lower values of p_spec.
kfold=5 # if not used, set to 0.
######--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
@@ -26,9 +26,9 @@ trackingFrequency=0 # Specifies the number of iterations before each esti
######--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
###### Supervised Learning Parameters - Generally just use default values.
######--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-nu=5 # (v) Power parameter used to determine the importance of high accuracy when calculating fitness. (typically set to 5, recommended setting of 1 in noisy data)
+nu=10 # (v) Power parameter used to determine the importance of high accuracy when calculating fitness. (typically set to 5, recommended setting of 1 in noisy data)
chi=0.8 # (X) The probability of applying crossover in the GA. (typically set to 0.5-1.0)
-upsilon=0.4 # (u) The probability of mutating an allele within an offspring.(typically set to 0.1-0.5)
+upsilon=0.5 # (u) The probability of mutating an allele within an offspring.(typically set to 0.1-0.5)
theta_GA=25 # The GA threshold; The GA is applied in a set when the average time since the last GA in the set is greater than theta_GA.
theta_del=20 # The deletion experience threshold; The calculation of the deletion probability changes once this threshold is passed.
theta_sub=20 # The subsumption experience threshold;
diff --git a/UCS/UCS_OutputFileManager.py b/UCS/UCS_OutputFileManager.py
index a1c9ed4..25bf7de 100644
--- a/UCS/UCS_OutputFileManager.py
+++ b/UCS/UCS_OutputFileManager.py
@@ -34,7 +34,6 @@ from UCS.UCS_Constants import *
class OutputFileManager:
totalPopulationSize = 0
- totalAverage = 0
def writePopStats(self, outFile, trainEval, testEval, exploreIter, pop, correct):
""" Makes output text file which includes all of the evaluation statistics for a complete analysis of all training and testing data on the current LCS rule population. """
@@ -140,7 +139,6 @@ class OutputFileManager:
# Write each classifier--------------------------------------------------------------------------------------------------------------------------------------
OutputFileManager.totalPopulationSize = len(pop.popSet)
- OutputFileManager.totalAverage += len(pop.popSet)
print(len(pop.popSet))
for cl in pop.popSet:
rulePopOut.write(str(cl.printClassifier()))
diff --git a/UCS/UCS_Run.py b/UCS/UCS_Run.py
index ae060b1..ee488d4 100644
--- a/UCS/UCS_Run.py
+++ b/UCS/UCS_Run.py
@@ -85,11 +85,10 @@ def mainRun():
print("Total run time in seconds: %.2f" % total)
f = open("RESULTS_FILE.txt", 'a')
f.write(" Accuracy: " + str(kfold_accuracy) + " Total time: " + str(total) + " Rules: " + str(OutputFileManager.totalPopulationSize) + "\n")
- f.write(" Total Av. Rules: " + str(OutputFileManager.totalAverage / 15) + "\n")
if __name__ == '__main__':
- for i in range(15):
+ for i in range(10):
mainRun()
diff --git a/UCS/__pycache__/UCS_OutputFileManager.cpython-36.pyc b/UCS/__pycache__/UCS_OutputFileManager.cpython-36.pyc
index 7847f6eeda339f59819a256be4dc5728d025d636..3ef47d470e1cc4d4c6426a9fab68a49f77aa6678 100644
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