def confusion_matrix(k):
t = int(500 / k) #number of documents in a fold
print('number of documents in a fold=', t)
dataset = {}
true_predicted = {}
cmatrix = {}
for category in categories:
system("ls ./webpages/" + category + ">.tmp")
a = open(".tmp")
files = a.read()
a.close()
files = files.split('\n')
files.pop()
dataset[category] = files
true_predicted[category] = 0
cmatrix[category] = {}
for c in categories:
cmatrix[category][c] = 0
for i in range(0, k):
print('i=', i)
train_set = {}
test_set = {}
database = {}
for category in categories:
train_set[category] = list(dataset[category][0:i * t] +
dataset[category][(i + 1) * t:500])
test_set[category] = list(dataset[category][i * t:(i + 1) * t])
database[category] = {}
#print('train-set\n',train_set)
#print('test-set\n',test_set)
for category in categories:
for file in train_set[category]:
freq = getList("./webpages/" + category + "/" + file)
for word in freq:
if word in database[category]:
database[category][word] += freq[word]
else:
database[category][word] = freq[word]
make_training_set(database)
print('database created')
for category in categories:
for file in test_set[category]:
freq = getList("./webpages/" + category + "/" + file)
p_cat = naive_bayes(freq)
if p_cat == category:
true_predicted[category] += 1
cmatrix[category][p_cat] += 1
print('k=', k, 'i=', i, 'category=', category, 'true_predicted\n',
true_predicted)
print(true_predicted)
for actual in categories:
for predicted in categories:
print('cmatrix[', actual, '][', predicted, ']=',
cmatrix[actual][predicted])
def confusion_matrix(k):
t=int(500/k) #number of documents in a fold
print('number of documents in a fold=',t)
dataset={}
true_predicted={}
cmatrix={}
for category in categories:
system("ls ./webpages/"+category+">.tmp")
a=open(".tmp")
files=a.read()
a.close()
files=files.split('\n')
files.pop()
dataset[category]=files
true_predicted[category]=0
cmatrix[category]={}
for c in categories:
cmatrix[category][c]=0
for i in range(0,k):
print('i=',i)
train_set={}
test_set={}
database={}
for category in categories:
train_set[category]=list(dataset[category][0:i*t]+dataset[category][(i+1)*t:500])
test_set[category]=list(dataset[category][i*t:(i+1)*t])
database[category]={}
#print('train-set\n',train_set)
#print('test-set\n',test_set)
for category in categories:
for file in train_set[category]:
freq=getList("./webpages/"+category+"/"+file)
for word in freq:
if word in database[category]:
database[category][word]+=freq[word]
else:
database[category][word]=freq[word]
make_training_set(database)
print('database created')
for category in categories:
for file in test_set[category]:
freq=getList("./webpages/"+category+"/"+file)
p_cat=naive_bayes(freq)
if p_cat==category:
true_predicted[category]+=1
cmatrix[category][p_cat]+=1;
print('k=',k,'i=',i,'category=',category,'true_predicted\n',true_predicted)
print(true_predicted)
for actual in categories:
for predicted in categories:
print('cmatrix[',actual,'][',predicted,']=',cmatrix[actual][predicted])
def k_fold_accuracy(k):
t=int(1000/k) #number of documents in a fold
print('number of documents in a fold=',t)
dataset={}
true_predicted={}
for category in categories:
system("ls ./dataset/"+category+">.tmp")
a=open(".tmp")
files=a.read()
a.close()
files=files.split('\n')
files.pop()
dataset[category]=files
true_predicted[category]=0
for i in range(0,k):
print('i=',i)
train_set={}
test_set={}
database={}
for category in categories:
train_set[category]=list(dataset[category][0:i*t]+dataset[category][(i+1)*t:])
test_set[category]=list(dataset[category][i*t:(i+1)*t])
database[category]={}
#print('train-set\n',train_set)
#print('test-set\n',test_set)
for category in categories:
for file in train_set[category]:
freq=getList("./dataset/"+category+"/"+file)
for word in freq:
if word in database[category]:
database[category][word]+=freq[word]
else:
database[category][word]=freq[word]
print('database created')
for category in categories:
for file in test_set[category]:
freq=getList("./dataset/"+category+"/"+file)
p_cat=naive_bayes(freq,database)
if p_cat==category:
true_predicted[category]+=1
print('k=',k,'i=',i,'category=',category,'true_predicted\n',true_predicted)
output={}
for category in categories:
output[category]=true_predicted[category]*100/1000.0
return output
def k_fold_accuracy(k):
t=int(1000/k) #number of documents in a fold
print('number of documents in a fold=',t)
dataset={}
true_predicted={}
for category in categories:
system("ls ./dataset/"+category+">.tmp")
a=open(".tmp")
files=a.read()
a.close()
files=files.split('\n')
files.pop()
dataset[category]=files
true_predicted[category]=0
for i in range(0,k):
print('i=',i)
train_set={}
test_set={}
database={}
for category in categories:
train_set[category]=list(dataset[category][0:i*t]+dataset[category][(i+1)*t:])
test_set[category]=list(dataset[category][i*t:(i+1)*t])
database[category]={}
#print('train-set\n',train_set)
#print('test-set\n',test_set)
for category in categories:
for file in train_set[category]:
freq=getList("./dataset/"+category+"/"+file)
for word in freq:
if word in database[category]:
database[category][word]+=freq[word]
else:
database[category][word]=freq[word]
print('database created')
for category in categories:
for file in test_set[category]:
freq=getList("./dataset/"+category+"/"+file)
p_cat=naive_bayes(freq,database)
if p_cat==category:
true_predicted[category]+=1
print('k=',k,'i=',i,'category=',category,'true_predicted\n',true_predicted)
output={}
for category in categories:
output[category]=true_predicted[category]*100/1000.0
return output
def makedb(train_set):
database={}
for category in categories:
database[category]={}
for document in train_set[category]:
freq=getList('./dataset/'+category+'/'+document)
for word in freq:
if word not in database[category]:
database[category][word]=freq[word]
else:
database[category][word]+=freq[word]
return database
def accuracy_measure_n(n):
'''n->number of train documents'''
seed(0)
x=[]
y={}
documents={}
accuracy={}
for category in categories:
system("ls ./dataset/"+category+">.tmp")
a=open(".tmp")
files=a.read()
a.close()
files=files.split('\n')
files.pop()
documents[category]=files
accuracy[category]=0
for i in range(0,3):
print("n=",n,"i=",i)
train_set={}
test_set={}
for category in categories:
train_set[category]=[]
test_set[category]=[]
for j in range(0,n):
t=documents[category][randint(0,len(documents[category])-1)]
#print(type(t))
while t in train_set[category]:
t=documents[category][randint(0,len(documents[category])-1)]
train_set[category].append(t)
for d in documents[category]:
if d not in train_set[category]:
test_set[category].append(d)
print("Traing and test sets created")
'''#divide train set into 3
tset0={}
tset1={}
tset2={}
tset3={}
tset4={}
for category in train_set:
tset0[category]=train_set[category][0:int(n/5)]
tset1[category]=train_set[category][int(n/5):int(2*n/5)]
tset2[category]=train_set[category][int(2*n/5):int(3*n/5)]
tset3[category]=train_set[category][int(3*n/5):int(4*n/5)]
tset4[category]=train_set[category][int(4*n/5):]
#train the model
database0=makedb(tset0)
database1=makedb(tset1)
database2=makedb(tset2)
database3=makedb(tset3)
database4=makedb(tset4)
print("training completed n,i",n,i)
#test model
vp={0:1,1:1,2:1,3:1,4:1}
#number of train documents'''
database=makedb(train_set)
print(database)
pickle.dump(database,open('database.db','wb'))
exit()
for category in categories:
#vp={0:1,1:1,2:1,3:1,4:1}
p=0
j=0;
for document in test_set[category]:
j+=1
freq=getList('./dataset/'+category+'/'+document)
'''pc={}
cc={}
cc[0]=naive_bayes(freq,database0)
cc[1]=naive_bayes(freq,database1)
cc[2]=naive_bayes(freq,database2)
cc[3]=naive_bayes(freq,database3)
cc[4]=naive_bayes(freq,database4)
for l in range(0,5):
#print('classfier',l,'with power ',vp[l],'predicts',cc[l])
if cc[l] in pc:
pc[cc[l]]+=vp[l]
else:
pc[cc[l]]=vp[l]
#collect majority vote
p_cat=max(pc, key=pc.get)
#print('predicted category',p_cat)
if p_cat==category:
p+=1
#change voting power
for l in range(0,5):
if cc[l]==category:
if vp[l]<2:
vp[l]*=1.01
else:
if vp[l]>0.5:
vp[l]*=0.99
#print('n',n,'i',i,'j',j,'accuracy',(p/float(j))*100,'category',category)
#print("---------------------------------------------------")
'''
p_cat=naive_bayes(freq,database)
if p_cat==category:
p+=1
if p_cat=='Unable to decide':
print("unable to decide happens")
f=open("status.txt","a")
f.write('n='+str(n)+'round'+str(i)+'\n'+str(p)+'documents classified successfully out of '+str(j)+'documents in category'+category+'\n')
f.close()
accuracy[category]+=p*100/len(test_set[category])
f=open("status.txt","a")
for category in categories:
accuracy[category]=accuracy[category]/3
f.write("accuracy["+category+"]="+str(accuracy[category]));
f.close()
print(accuracy)
return accuracy