from copy import deepcopy
if __name__ == '__main__':
timeStart = datetime.datetime.now()
(train_X, train_y, test_X, test_y) = mushrooms_bfgs.initialize(float(80)/100)
N = 22 # Number of Iterations at max can be equal to Number of features - 1
NumberOfFeaturesRemoved = 0
FeaturesRemoved = []
for i in xrange(1, N + 1):
# Maximum can remove all the features
importanceOfRandomForest = randomForest.netFeatureImportance(train_X, train_y, 15)
importanceOfSymbioticAlgorithm = Sampling.test_initialize()
#~ print "Feature Importance by Random Forest ", importanceOfRandomForest
#~ print "Feature Importance by Symbiotic Algorithm ", importanceOfSymbioticAlgorithm
if len(FeaturesRemoved) == 0 :
(train_X_Sym, test_X_Sym) = mushrooms_bfgs.featureRemoval(train_X, test_X, [importanceOfSymbioticAlgorithm[i - 1][0]])
(train_X_For, test_X_For) = mushrooms_bfgs.featureRemoval(train_X, test_X, [importanceOfRandomForest[i - 1][0]])
else:
trialFeatureSym = deepcopy(FeaturesRemoved)
trialFeatureFor = deepcopy(FeaturesRemoved)
trialFeatureSym.append(importanceOfSymbioticAlgorithm[i - 1][0])
trialFeatureFor.append(importanceOfRandomForest[i - 1][0])
print trialFeatureFor, trialFeatureSym
(train_X_Sym, test_X_Sym) = mushrooms_bfgs.featureRemoval(train_X, test_X, trialFeatureSym)
(train_X_For, test_X_For) = mushrooms_bfgs.featureRemoval(train_X, test_X, trialFeatureFor)
wilcoxonOut = WilcoxonTest.WilcoxonTest(train_X.ravel(), train_X_Sym.ravel() ,train_X_For.ravel())
if wilcoxonOut == 1:
if importanceOfRandomForest[i - 1][0] in FeaturesRemoved:
continue
FeaturesRemoved.append(importanceOfRandomForest[i - 1][0])
newData_Tr = deepcopy(train_X_For)
if __name__ == '__main__':
timeStart = datetime.datetime.now()
(train_X, train_y, test_X,
test_y) = mushrooms_bfgs.initialize(float(80) / 100)
N = 22 # Number of Iterations at max can be equal to Number of features - 1
NumberOfFeaturesRemoved = 0
FeaturesRemoved = []
for i in xrange(1, N + 1):
# Maximum can remove all the features
importanceOfRandomForest = randomForest.netFeatureImportance(
train_X, train_y, 15)
importanceOfSymbioticAlgorithm = Sampling.test_initialize()
#~ print "Feature Importance by Random Forest ", importanceOfRandomForest
#~ print "Feature Importance by Symbiotic Algorithm ", importanceOfSymbioticAlgorithm
if len(FeaturesRemoved) == 0:
(train_X_Sym, test_X_Sym) = mushrooms_bfgs.featureRemoval(
train_X, test_X, [importanceOfSymbioticAlgorithm[i - 1][0]])
(train_X_For, test_X_For) = mushrooms_bfgs.featureRemoval(
train_X, test_X, [importanceOfRandomForest[i - 1][0]])
else:
trialFeatureSym = deepcopy(FeaturesRemoved)
trialFeatureFor = deepcopy(FeaturesRemoved)
trialFeatureSym.append(importanceOfSymbioticAlgorithm[i - 1][0])
trialFeatureFor.append(importanceOfRandomForest[i - 1][0])
print trialFeatureFor, trialFeatureSym
(train_X_Sym, test_X_Sym) = mushrooms_bfgs.featureRemoval(
train_X, test_X, trialFeatureSym)
(train_X_For, test_X_For) = mushrooms_bfgs.featureRemoval(
train_X, test_X, trialFeatureFor)
wilcoxonOut = WilcoxonTest.WilcoxonTest(train_X.ravel(),
train_X_Sym.ravel(),
train_X_For.ravel())
z_stat_for_symbiotic, p_val_for_symbiotic = stats.ranksums(
Symbiotic_output, Original_input)
z_stat_for_GA, p_val_for_GA = stats.ranksums(Modified_GA_output,
Original_input)
print(p_val_for_symbiotic, p_val_for_GA)
if max(p_val_for_GA, p_val_for_symbiotic) < 1e-300:
return -1
if (p_val_for_GA > p_val_for_symbiotic):
print("Forest one is better")
return 1
else:
print("Symbiotic is better")
return 0
if __name__ == '__main__':
(train_X, train_y, test_X,
test_y) = mushrooms_bfgs.initialize(float(80) / 100)
(train_X_Sym,
test_X_Sym) = mushrooms_bfgs.featureRemoval(train_X, test_X, [3])
(train_X_For,
test_X_For) = mushrooms_bfgs.featureRemoval(train_X, test_X, [16])
#~ print train_X[0], train_X_Sym[0], train_X_For[0]
if WilcoxonTest(train_X.ravel(), train_X_Sym.ravel(), train_X_For.ravel()):
newData_Tr = train_X_For
newData_Ts = test_X_For
else:
newData_Tr = train_X_Sym
newData_Ts = test_X_Sym
#~ print WilcoxonTest([1,2,3,4], [1,2,3,5],[0,9,10,11])
Modified Genetic algorithm
Returns:
The best suited N-1 features for a given distribution or [-1]
'''
z_stat_for_symbiotic, p_val_for_symbiotic = stats.ranksums(
Symbiotic_output, Original_input)
z_stat_for_GA, p_val_for_GA = stats.ranksums(
Modified_GA_output, Original_input)
print p_val_for_symbiotic, p_val_for_GA
if max(p_val_for_GA , p_val_for_symbiotic) < 1e-300:
return -1
if (p_val_for_GA > p_val_for_symbiotic):
print "Forest one is better"
return 1
else:
print "Symbiotic is better"
return 0
if __name__ == '__main__':
(train_X, train_y, test_X, test_y) = mushrooms_bfgs.initialize(float(80)/100)
(train_X_Sym, test_X_Sym) = mushrooms_bfgs.featureRemoval(train_X, test_X, [3])
(train_X_For, test_X_For) = mushrooms_bfgs.featureRemoval(train_X, test_X, [16])
#~ print train_X[0], train_X_Sym[0], train_X_For[0]
if WilcoxonTest(train_X.ravel(), train_X_Sym.ravel() ,train_X_For.ravel()):
newData_Tr = train_X_For
newData_Ts = test_X_For
else:
newData_Tr = train_X_Sym
newData_Ts = test_X_Sym
#~ print WilcoxonTest([1,2,3,4], [1,2,3,5],[0,9,10,11])
