1.1.9

UrbsLab · May 10, 2020 · d286b32 · d286b32
1 parent 4722d51
commit d286b32
Show file tree

Hide file tree

Showing 6 changed files with 60 additions and 2,059 deletions.
diff --git a/eLCSPerformanceTests/eLCS Performance Comparison.ipynb b/eLCSPerformanceTests/eLCS Performance Comparison.ipynb
diff --git a/eLCSPerformanceTests/eLCS_Classifier.py b/eLCSPerformanceTests/eLCS_Classifier.py
@@ -315,7 +315,7 @@ def Mutation(self, state, phenotype):
         #-------------------------------------------------------
         for attRef in range(cons.env.formatData.numAttributes):  #Each condition specifies different attributes, so we need to go through all attributes in the dataset.
             attributeInfo = cons.env.formatData.attributeInfo[attRef]
-            if random.random() < cons.mu and state[attRef] != cons.labelMissingData:
+            if random.random() < cons.upsilon and state[attRef] != cons.labelMissingData:
                 #MUTATION--------------------------------------------------------------------------------------------------------------
                 if attRef not in self.specifiedAttList: #Attribute not yet specified
                     self.specifiedAttList.append(attRef)
@@ -372,7 +372,7 @@ def Mutation(self, state, phenotype):
     def discretePhenotypeMutation(self):
         """ Mutate this rule's discrete phenotype. """
         changed = False
-        if random.random() < cons.mu:
+        if random.random() < cons.upsilon:
             phenotypeList = copy.deepcopy(cons.env.formatData.phenotypeList)
             phenotypeList.remove(self.phenotype)
             newPhenotype = random.sample(phenotypeList,1)
@@ -385,7 +385,7 @@ def discretePhenotypeMutation(self):
     def continuousPhenotypeMutation(self, phenotype):
         """ Mutate this rule's continuous phenotype. """
         changed = False
-        if random.random() < cons.mu: #Mutate continuous phenotype
+        if random.random() < cons.upsilon: #Mutate continuous phenotype
             phenRange = self.phenotype[1] - self.phenotype[0]
             mutateRange = random.random()*0.5*phenRange
             tempKey = random.randint(0,2) #Make random choice between 3 scenarios, mutate minimums, mutate maximums, mutate both

diff --git a/eLCSPerformanceTests/eLCS_ClassifierSet.py b/eLCSPerformanceTests/eLCS_ClassifierSet.py
@@ -279,12 +279,12 @@ def runGA(self, exploreIter, state, phenotype):
         #-------------------------------------------------------
         if changed:
             cl1.setAccuracy((cl1.accuracy + cl2.accuracy)/2.0)
-            cl1.setFitness(cons.fitness_reduction * (cl1.fitness + cl2.fitness)/2.0)
+            cl1.setFitness(cons.fitnessReduction * (cl1.fitness + cl2.fitness)/2.0)
             cl2.setAccuracy(cl1.accuracy)
             cl2.setFitness(cl1.fitness)
         else:
-            cl1.setFitness(cons.fitness_reduction * cl1.fitness)
-            cl2.setFitness(cons.fitness_reduction * cl2.fitness)
+            cl1.setFitness(cons.fitnessReduction * cl1.fitness)
+            cl2.setFitness(cons.fitnessReduction * cl2.fitness)
 
         #-------------------------------------------------------
         # MUTATION OPERATOR 

diff --git a/eLCSPerformanceTests/eLCS_ParamParser.py b/eLCSPerformanceTests/eLCS_ParamParser.py
@@ -7,7 +7,7 @@
 
 class ParamParser:
     def __init__(self, dataFile, cv=False,learningIterations="10000", trackingFrequency=100000, N=1000,
-                 p_spec=0.5, discrete_attribute_limit=10,nu=5, chi=0.8, upsilon=0.04, theta_GA=25,
+                 p_spec=0.5, discreteAttributeLimit=10,nu=5, chi=0.8, upsilon=0.04, theta_GA=25,
                  theta_del=20, theta_sub=20, acc_sub=0.99, beta=0.2, delta=0.1, init_fit=0.01, fitnessReduction=0.1,
                  doSubsumption=True, selectionMethod='tournament', theta_sel=0.5,randomSeed = False,labelInstanceID='InstanceID',labelPhenotype="class",
                  labelMissingData="NA",doPopulationReboot=False,popRebootPath='ExampleRun_eLCS_50000'):
@@ -17,7 +17,7 @@ def __init__(self, dataFile, cv=False,learningIterations="10000", trackingFreque
         self.parameters['trackingFrequency'] = trackingFrequency
         self.parameters['N'] = N
         self.parameters['p_spec'] = p_spec
-        self.parameters['discrete_attribute_limit'] = discrete_attribute_limit
+        self.parameters['discreteAttributeLimit'] = discreteAttributeLimit
         self.parameters['nu'] = nu
         self.parameters['chi'] = chi
         self.parameters['upsilon'] = upsilon

diff --git a/setup.py b/setup.py
@@ -6,14 +6,14 @@
 setup(
   name = 'scikit-eLCS',
   packages = ['skeLCS'],
-  version = '1.1.8',
+  version = '1.1.9',
   license='License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
   description = 'Educational Learning Classifier System',
   long_description_content_type="text/markdown",
   author = 'Robert Zhang, Ryan J. Urbanowicz',
   author_email = 'robertzh@seas.upenn.edu,ryanurb@upenn.edu',
   url = 'https://github.com/UrbsLab/scikit-eLCS',
-  download_url = 'https://github.com/UrbsLab/scikit-eLCS/archive/v_1.1.8.tar.gz',
+  download_url = 'https://github.com/UrbsLab/scikit-eLCS/archive/v_1.1.9.tar.gz',
   keywords = ['machine learning','data analysis','data science','learning classifier systems'],
   install_requires=['numpy','pandas','scikit-learn'],
   classifiers=[

diff --git a/skeLCS/eLCS.py b/skeLCS/eLCS.py
@@ -264,9 +264,6 @@ def fit(self, X, y):
 
         Returns self
         """
-        #If trained already, raise Exception
-        if self.hasTrained:
-            raise Exception("Cannot train already trained model again")
 
         # Check if X and Y are numeric
         try: