def myclassify_practice_set(numfiers,xtrain,ytrain,xtltrain,xtltest,xtest,ytarget=None,testing=False,grids='ABCDEFGHI'):
#NOTE we might not need xtltrain
# xtrain and ytrain are your training set. xtltrain is the indices of corresponding recordings in xtrain and ytrain. these will always be present
#xtest is your testing set. xtltest is the corresponding indices of the recording. for the practice set xtltest = xtrunclength
# ytest is optional and depends on if you are using a testing set or the practice set
# remove NaN, Inf, and -Inf values from the xtest feature matrix
xtest,xtltest,ytarget = removeNanAndInf(xtest,xtltest,ytarget)
# print 'finished removal of Nans'
ytrain = np.ravel(ytrain)
ytarget = np.ravel(ytarget)
#if xtest is NxM matrix, returns Nxnumifiers matrix where each column corresponds to a classifiers prediction vector
count = 0
# print numfiers
predictionMat = np.empty((xtest.shape[0],numfiers))
predictionStringMat = []
finalPredMat = []
targetStringMat = []
targets1 = []
predictions1 = []
# svc1 = SVC()
# svc1.fit(xtrain,ytrain)
# ytest = svc1.predict(xtest)
# predictionMat[:,count] = ytest
# count+=1
if count < numfiers:
# votingClassifiers combine completely different machine learning classifiers and use a majority vote
clff1 = SVC()
clff2 = RFC(bootstrap=False)
clff3 = ETC()
clff4 = neighbors.KNeighborsClassifier()
clff5 = quadda()
eclf = VotingClassifier(estimators = [('svc',clff1),('rfc',clff2),('etc',clff3),('knn',clff4),('qda',clff5)])
eclf = eclf.fit(xtrain,ytrain)
#print(eclf.score(xtest,ytest))
# for claf, label in zip([clff1,clff2,clff3,clff4,clff5,eclf],['SVC','RFC','ETC','KNN','QDA','Ensemble']):
# cla
# scores = crossvalidation.cross_val_score(claf,xtrain,ytrain,scoring='accuracy')
# print ()
ytest = eclf.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
bagging2 = BaggingClassifier(ETC(),bootstrap=False,bootstrap_features=False)
bagging2.fit(xtrain,ytrain)
#print bagging2.score(xtest,ytest)
ytest = bagging2.predict(xtest)
predictionMat[:,count] = ytest
count += 1
if count < numfiers:
tree2 = ETC()
tree2.fit(xtrain,ytrain)
ytest = tree2.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
bagging1 = BaggingClassifier(ETC())
bagging1.fit(xtrain,ytrain)
#print bagging1.score(xtest,ytest)
ytest = bagging1.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
svc1 = SVC()
svc1.fit(xtrain,ytrain)
ytest = svc1.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
# Quadradic discriminant analysis - classifier with quadratic decision boundary -
qda = quadda()
qda.fit(xtrain,ytrain)
#print(qda.score(xtest,ytest))
ytest = qda.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
tree1 = DTC()
tree1.fit(xtrain,ytrain)
ytest = tree1.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
knn1 = neighbors.KNeighborsClassifier() # this classifies based on the #k nearest neighbors, where k is definted by the user.
knn1.fit(xtrain,ytrain)
ytest = knn1.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
# linear discriminant analysis - classifier with linear decision boundary -
lda = linda()
lda.fit(xtrain,ytrain)
ytest = lda.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
tree3 = RFC()
tree3.fit(xtrain,ytrain)
ytest = tree3.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
bagging3 = BaggingClassifier(RFC(),bootstrap=False,bootstrap_features=False)
bagging3.fit(xtrain,ytrain)
ytest = bagging3.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
bagging4 = BaggingClassifier(SVC(),bootstrap=False,bootstrap_features=False)
bagging4.fit(xtrain,ytrain)
ytest = bagging4.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
tree4 = RFC(bootstrap=False)
tree4.fit(xtrain,ytrain)
ytest = tree4.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
tree6 = GBC()
tree6.fit(xtrain,ytrain)
ytest = tree6.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
knn2 = neighbors.KNeighborsClassifier(n_neighbors = 10)
knn2.fit(xtrain,ytrain)
ytest = knn2.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
knn3 = neighbors.KNeighborsClassifier(n_neighbors = 3)
knn3.fit(xtrain,ytrain)
ytest = knn3.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
knn4 = neighbors.KNeighborsClassifier(algorithm = 'ball_tree')
knn4.fit(xtrain,ytrain)
ytest = knn4.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
knn5 = neighbors.KNeighborsClassifier(algorithm = 'kd_tree')
knn5.fit(xtrain,ytrain)
ytest = knn5.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
ncc1 = NearestCentroid()
ncc1.fit(xtrain,ytrain)
ytest = ncc1.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
tree5 = ABC()
tree5.fit(xtrain,ytrain)
ytest = tree5.predict(xtest)
predictionMat[:,count] = ytest
count+=1
# print xtltest
# print len(ytest)
for colCount in range(predictionMat.shape[1]):
tempCol = predictionMat[:,colCount]
if testing:
modeCol = temppredWindowVecModeFinder(tempCol,xtltest,4,grids,isPrint=0)
else:
modeCol = predWindowVecModeFinder(tempCol,xtltest,4,isPrint=0)
ytarg = predWindowVecModeFinder(ytarget,xtltest,1,isPrint=0)
if testing:
modeStr = temppredVec2Str(modeCol,grids)
else:
modeStr = predVec2Str(modeCol)
modeStrans = predVec2Str(ytarg)
predictionStringMat.append(modeStr)
predictions1.append(modeCol)
finalPredMat += map(int,modeCol)
targetStringMat.append(modeStrans)
targets1.append(ytarg)
if testing == False:
if ytarget != None:
#print targets1
#print ""
#print predictions1
confusionme = confusion_matrix(targets1[0],predictions1[0])
#print "Confusion Matrix is: "
#print confusionme
return predictionStringMat, targetStringMat, finalPredMat
# In[18]:
# Nearest shrunken Centroid
for shrinkage in [None,0.05,0.1,0.2,0.3,0.4,0.5]:
ncc2 = NearestCentroid(shrink_threshold = shrinkage)
ncc2.fit(xtrain,ytrain1)
print(ncc2.score(xtest,ytest1))
# In[19]:
# linear discriminant analysis - classifier with linear decision boundary -
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as linda
lda = linda()
lda.fit(xtrain,ytrain1)
print(lda.score(xtest,ytest1))
# In[20]:
# Quadradic discriminant analysis - classifier with quadratic decision boundary -
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as quadda
qda = quadda()
qda.fit(xtrain,ytrain1)
print(qda.score(xtest,ytest1))
# might want to try normalizing stuff, or trying to fix rank deficiencies?
def myclassify(numfiers=5,xtrain=xtrain,ytrain=ytrain,xtest=xtest,ytest=ytest):
count = 0
bagging2 = BaggingClassifier(ETC(),bootstrap=False,bootstrap_features=False)
bagging2.fit(xtrain,ytrain)
#print bagging2.score(xtest,ytest)
count += 1
classifiers = [bagging2.score(xtest,ytest)]
if count < numfiers:
tree2 = ETC()
tree2.fit(xtrain,ytrain)
#print tree2.fit(xtrain,ytrain)
#print tree2.score(xtest,ytest)
count+=1
classifiers = np.append(classifiers,tree2.score(xtest,ytest))
print "1"
print tree2.score(xtest,ytest)
if count < numfiers:
bagging1 = BaggingClassifier(ETC())
bagging1.fit(xtrain,ytrain)
#print bagging1.score(xtest,ytest)
count+=1
classifiers = np.append(classifiers,bagging1.score(xtest,ytest))
print "2"
print bagging1.score(xtest,ytest)
# if count < numfiers:
# # votingClassifiers combine completely different machine learning classifiers and use a majority vote
# clff1 = SVC()
# clff2 = RFC(bootstrap=False)
# clff3 = ETC()
# clff4 = neighbors.KNeighborsClassifier()
# clff5 = quadda()
# print"3"
# eclf = VotingClassifier(estimators = [('svc',clff1),('rfc',clff2),('etc',clff3),('knn',clff4),('qda',clff5)])
# eclf = eclf.fit(xtrain,ytrain)
# #print(eclf.score(xtest,ytest))
# # for claf, label in zip([clff1,clff2,clff3,clff4,clff5,eclf],['SVC','RFC','ETC','KNN','QDA','Ensemble']):
# # cla
# # scores = crossvalidation.cross_val_score(claf,xtrain,ytrain,scoring='accuracy')
# # print ()
# count+=1
# classifiers = np.append(classifiers,eclf.score(xtest,ytest))
# if count < numfiers:
# svc1 = SVC()
# svc1.fit(xtrain,ytrain)
# dec = svc1.score(xtest,ytest)
# count+=1
# classifiers = np.append(classifiers,svc1.score(xtest,ytest))
# print "3"
if count < numfiers:
# Quadradic discriminant analysis - classifier with quadratic decision boundary -
qda = quadda()
qda.fit(xtrain,ytrain)
#print(qda.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,qda.score(xtest,ytest))
print "4"
if count < numfiers:
tree1 = DTC()
tree1.fit(xtrain,ytrain)
#print tree1.fit(xtrain,ytrain)
#print tree1.score(xtest,ytest)
count+=1
classifiers = np.append(classifiers,tree1.score(xtest,ytest))
if count < numfiers:
knn1 = neighbors.KNeighborsClassifier() # this classifies based on the #k nearest neighbors, where k is definted by the user.
knn1.fit(xtrain,ytrain)
#print(knn1.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,knn1.score(xtest,ytest))
if count < numfiers:
# linear discriminant analysis - classifier with linear decision boundary -
lda = linda()
lda.fit(xtrain,ytrain)
#print(lda.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,lda.score(xtest,ytest))
if count < numfiers:
tree3 = RFC()
tree3.fit(xtrain,ytrain)
#print tree3.score(xtest,ytest)
count+=1
classifiers = np.append(classifiers,tree3.score(xtest,ytest))
if count < numfiers:
bagging3 = BaggingClassifier(RFC(),bootstrap=False,bootstrap_features=False)
bagging3.fit(xtrain,ytrain)
#print bagging3.score(xtest,ytest)
count+=1
classifiers = np.append(classifiers,bagging3.score(xtest,ytest))
if count < numfiers:
bagging4 = BaggingClassifier(SVC(),bootstrap=False,bootstrap_features=False)
bagging4.fit(xtrain,ytrain)
#print bagging4.score(xtest,ytest)
count+=1
classifiers = np.append(classifiers,bagging4.score(xtest,ytest))
if count < numfiers:
tree4 = RFC(bootstrap=False)
tree4.fit(xtrain,ytrain)
#print tree4.score(xtest,ytest)
count+=1
classifiers = np.append(classifiers,tree4.score(xtest,ytest))
if count < numfiers:
tree6 = GBC()
tree6.fit(xtrain,ytrain)
#print(tree6.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,tree6.score(xtest,ytest))
if count < numfiers:
knn2 = neighbors.KNeighborsClassifier(n_neighbors = 10)
knn2.fit(xtrain,ytrain)
#print(knn2.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,knn2.score(xtest,ytest))
if count < numfiers:
knn3 = neighbors.KNeighborsClassifier(n_neighbors = 3)
knn3.fit(xtrain,ytrain)
#print(knn3.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,knn3.score(xtest,ytest))
if count < numfiers:
knn4 = neighbors.KNeighborsClassifier(algorithm = 'ball_tree')
knn4.fit(xtrain,ytrain)
#print(knn4.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,knn4.score(xtest,ytest))
if count < numfiers:
knn5 = neighbors.KNeighborsClassifier(algorithm = 'kd_tree')
knn5.fit(xtrain,ytrain)
#print(knn5.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,knn5.score(xtest,ytest))
if count < numfiers:
ncc1 = NearestCentroid()
ncc1.fit(xtrain,ytrain)
#print (ncc1.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,ncc1.score(xtest,ytest))
if count < numfiers:
# Nearest shrunken Centroid
for shrinkage in [None,0.05,0.1,0.2,0.3,0.4,0.5]:
ncc2 = NearestCentroid(shrink_threshold = shrinkage)
ncc2.fit(xtrain,ytrain)
#print(ncc2.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,ncc2.score(xtest,ytest))
if count < numfiers:
tree5 = ABC()
tree5.fit(xtrain,ytrain)
#print(tree5.score(xtest,ytest))
count+=1
classifiers = np.append(classifiers,tree5.score(xtest,ytest))
classifierlabel = ["BaggingETC (with bootstraps set to false)","ETC","BaggingETC","Voting Classifier","svm","QDA","DTC","KNN (default)","LDA","RFC",
"BaggingRFC (with bootstraps set to false)","BaggingSVC (with bootstraps set to false)","RFC (bootstrap false)","GBC",
"knn (n_neighbors = 10)","knn (n_neighbors = 3)","knn (ball tree algorithm)","knn (kd_tree algorithm)",
"Nearest Centroid","Shrunken Centroid?","ABC"]
classifierlabel = classifierlabel[:len(classifiers)]
#print len(classifiers)
#print classifiers
for i in range(len(classifiers)):
print ("{} classifier has percent correct {}".format(classifierlabel[i],classifiers[i]))
def myclassify_AudPow(numfiers,xtrain_1,xtrain_2,ytrain_1,ytrain_2,xtest):
# remove NaN, Inf, and -Inf values from the xtest feature matrix
xtest = xtest[~np.isnan(xtest).any(axis=1),:]
xtest = xtest[~np.isinf(xtest).any(axis=1),:]
xtrain = np.append(xtrain_1,xtrain_2,0)
ytrain = np.append(ytrain_1,ytrain_2)
ytrain = np.ravel(ytrain)
xtrunclength = sio.loadmat('../Files/xtrunclength.mat')
xtrunclength = xtrunclength['xtrunclength'][0]
#if xtest is NxM matrix, returns Nxnumifiers matrix where each column corresponds to a classifiers prediction vector
count = 0
# print numfiers
predictionMat = np.empty((xtest.shape[0],numfiers))
predictionStringMat = []
finalPredMat = []
bagging2 = BaggingClassifier(ETC(),bootstrap=False,bootstrap_features=False)
bagging2.fit(xtrain,ytrain)
#print bagging2.score(xtest,ytest)
ytest = bagging2.predict(xtest)
predictionMat[:,count] = ytest
count += 1
if count < numfiers:
tree2 = ETC()
tree2.fit(xtrain,ytrain)
ytest = tree2.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
bagging1 = BaggingClassifier(ETC())
bagging1.fit(xtrain,ytrain)
#print bagging1.score(xtest,ytest)
ytest = bagging1.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
# votingClassifiers combine completely different machine learning classifiers and use a majority vote
clff1 = SVC()
clff2 = RFC(bootstrap=False)
clff3 = ETC()
clff4 = neighbors.KNeighborsClassifier()
clff5 = quadda()
eclf = VotingClassifier(estimators = [('svc',clff1),('rfc',clff2),('etc',clff3),('knn',clff4),('qda',clff5)])
eclf = eclf.fit(xtrain,ytrain)
#print(eclf.score(xtest,ytest))
# for claf, label in zip([clff1,clff2,clff3,clff4,clff5,eclf],['SVC','RFC','ETC','KNN','QDA','Ensemble']):
# cla
# scores = crossvalidation.cross_val_score(claf,xtrain,ytrain,scoring='accuracy')
# print ()
ytest = eclf.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
svc1 = SVC()
svc1.fit(xtrain,ytrain)
ytest = svc1.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
# Quadradic discriminant analysis - classifier with quadratic decision boundary -
qda = quadda()
qda.fit(xtrain,ytrain)
#print(qda.score(xtest,ytest))
ytest = qda.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
tree1 = DTC()
tree1.fit(xtrain,ytrain)
ytest = tree1.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
knn1 = neighbors.KNeighborsClassifier() # this classifies based on the #k nearest neighbors, where k is definted by the user.
knn1.fit(xtrain,ytrain)
ytest = knn1.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
# linear discriminant analysis - classifier with linear decision boundary -
lda = linda()
lda.fit(xtrain,ytrain)
ytest = lda.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
tree3 = RFC()
tree3.fit(xtrain,ytrain)
ytest = tree3.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
bagging3 = BaggingClassifier(RFC(),bootstrap=False,bootstrap_features=False)
bagging3.fit(xtrain,ytrain)
ytest = bagging3.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
bagging4 = BaggingClassifier(SVC(),bootstrap=False,bootstrap_features=False)
bagging4.fit(xtrain,ytrain)
ytest = bagging4.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
tree4 = RFC(bootstrap=False)
tree4.fit(xtrain,ytrain)
ytest = tree4.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
tree6 = GBC()
tree6.fit(xtrain,ytrain)
ytest = tree6.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
knn2 = neighbors.KNeighborsClassifier(n_neighbors = 10)
knn2.fit(xtrain,ytrain)
ytest = knn2.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
knn3 = neighbors.KNeighborsClassifier(n_neighbors = 3)
knn3.fit(xtrain,ytrain)
ytest = knn3.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
knn4 = neighbors.KNeighborsClassifier(algorithm = 'ball_tree')
knn4.fit(xtrain,ytrain)
ytest = knn4.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
knn5 = neighbors.KNeighborsClassifier(algorithm = 'kd_tree')
knn5.fit(xtrain,ytrain)
ytest = knn5.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
ncc1 = NearestCentroid()
ncc1.fit(xtrain,ytrain)
ytest = ncc1.predict(xtest)
predictionMat[:,count] = ytest
count+=1
if count < numfiers:
tree5 = ABC()
tree5.fit(xtrain,ytrain)
ytest = tree5.predict(xtest)
predictionMat[:,count] = ytest
count+=1
for colCount in range(predictionMat.shape[1]):
tempCol = predictionMat[:,colCount]
modeCol = predWindowVecModeFinder(tempCol,xtrunclength)
modeStr = predVec2Str(modeCol)
predictionStringMat.append(modeStr)
finalPredMat += map(int,modeCol)
return predictionStringMat,finalPredMat