def main():
meta = 'df_OSU_LMS_METASTATIC.csv'
osu = 'LMS_OSU_MUTATION.csv'
tcga = 'LMS_TCGA_MUTATION.csv'
netA = []
netB = []
df = pd.read_csv(meta)
netA = process(df, rb1=True)
netB = process(df)
df = pd.read_csv(osu)
b = bae.BayesClassifier()
b.fit(netA, netB, df)
print(b.attributes)
df = pd.read_csv(tcga)
b.predict(netA, netB, df)
df = pd.read_csv(tcga)
be = bae.BayesClassifier()
be.fit(netA, netB, df)
print(be.attributes)
df = pd.read_csv(osu)
be.predict(netA, netB, df)
def run_bayes(data_path):
training_path = os.path.join(data_path,"TRAINING")
classifications = [x for x in os.listdir(training_path)
if os.path.isdir(os.path.join(training_path,x))]
classifier = BayesClassifier.BayesClassifier()
train_bayes(classifier, classifications, training_path)
testing_path = os.path.join(data_path,"TESTING")
print "Running on Training Data (asterisk means incorrect)..."
test_bayes(classifier, classifications, training_path)
print "Running on Testing Data (asterisk means incorrect)..."
test_bayes(classifier, classifications, testing_path)
def eval(sText):
totalaccuracy_numer = 0
totalaccuracy_denom = 0
for test in range(0, 10):
thisaccuracy_numer = 0
thisaccuracy_denom = 0
split(sText, "output")
for doc in range(0, 5):
print "i is: ", doc
totaldic = defaultdict(lambda: 0)
totalcorrectdic = defaultdict(lambda: 0)
bc = BayesClassifier()
bc.train("output.train{0}".format(doc % 5))
bc.train("output.train{0}".format((doc + 1) % 5))
bc.train("output.train{0}".format((doc + 2) % 5))
bc.train("output.train{0}".format((doc + 3) % 5))
reader = DataReader("output.train{0}".format((doc + 4) % 5))
correct = 0
total = 0
hold = 0
for label, tokens, company, date, price, risklength in reader:
print label
tokenstring = " "
tokenstring = tokenstring.join(tokens)
print date
if risklength == 1:
print "invalid document; ignore"
elif bc.classify(tokenstring, risklength, date) == "HOLD":
#elif bc.classify(tokenstring, risklength) == "HOLD":
hold += 1
else:
totaldic[label] += 1
total += 1
if bc.classify(tokenstring, risklength, date) == label:
#if bc.classify(tokenstring, risklength) == label:
correct += 1
totalcorrectdic[label] += 1
print "Holds: ", hold
print "Accuracy:", correct / float(total)
thisaccuracy_numer += correct / float(total)
thisaccuracy_denom += 1
for key in totaldic:
print totalcorrectdic[key], totaldic[key]
print key, " precision: ", totalcorrectdic[key] / float(
totaldic[key])
print "This Round Accuracy: ", thisaccuracy_numer / thisaccuracy_denom
totalaccuracy_numer += thisaccuracy_numer
totalaccuracy_denom += thisaccuracy_denom
print "Total Accuracy: ", totalaccuracy_numer / totalaccuracy_denom
def testClassifier(outputLabel):
bc = BayesClassifier()
bc.train(outputLabel + ".train")
reader = DataReader(outputLabel + ".test")
correctLabel = {}
numberGuess = {}
correct = 0.0
total = 0.0
for label, tokens in reader:
if not label in correctLabel:
correctLabel[label] = 0.0
guess = bc.classify(" ".join(tokens))
if not guess in numberGuess:
numberGuess[guess] = 0.0
if guess == label:
correctLabel[guess] += 1
correct += 1
numberGuess[guess] += 1
total += 1
for label in correctLabel:
print "Correct " + label, "-", correctLabel[label] / numberGuess[label]
print "Total accuracy -", correct / total
dc = DC.DistanceClassifier()
dc.train(trainInput, trainOutput, 'euclidean')
resultsDcTrain = dc.test(trainInput, trainOutput, 'euclidean')
resultsDcTest = dc.test(testInput, testOutput, 'euclidean')
print("Distance classifier accuracy for train set : " + str(np.sum([x[0] for x in resultsDcTrain[0]])/np.sum([x[1] for x in resultsDcTrain[0]])))
print("Distance classifier accuracy for test set : " + str(np.sum([x[0] for x in resultsDcTest[0]])/np.sum([x[1] for x in resultsDcTest[0]])))
IO.PlotCM(resultsDcTrain[1], save = True, fileName = "distanceConfusionTrain")
IO.PlotCM(resultsDcTest[1], save = True, fileName = "distanceConfusionTest")
'''
Assignment 3:
'''
bc = BC.BayesClassifier(7,5)
bc.train(trainInput, trainOutput)
resultsBcTrain = bc.test(trainInput, trainOutput)
resultsBcTest = bc.test(testInput, testOutput)
print("Bayes classifier accuracy for train set for 5 & 7 : " + str(resultsBcTrain))
print("Bayes classifier accuracy for test set for 5 & 7: " + str(resultsBcTest))
bc = BC.BayesClassifier(1,4)
bc.train(trainInput, trainOutput)
resultsBcTrain = bc.test(trainInput, trainOutput)
resultsBcTest = bc.test(testInput, testOutput)
print("Bayes classifier accuracy for train set for 1 & 4: " + str(resultsBcTrain))
def receive_server(socklink, address):
try:
with sem:
# 第一次从用户处接受将要发送字符串的长度
length = socketlink.recv(102400)
length = eval(length.decode())
totalData = [] # 记录用户发送的 byte 类型通信内容
current_length = 0
while current_length < length:
recv = socklink.recv(102400)
current_length += len(recv)
totalData.append(recv)
# 解码获得字典格式的通信内容
data = b''.join(totalData)
data.decode()
if not data: # 未接受到相关信息
print('No data received from ' + str(address) +
"! Request cancelled." + " " +
str(datetime.datetime.now().strftime('%F %T')))
raise Exception("No data received.")
else:
data_dic = eval(data) # 按约定好的通信格式，将字符串转换为字典变量
if data_dic['action'] == 'request-result':
# 在这里调用模型进行预测，然后返回一个结果（1（非垃圾）或-1（垃圾））
bayes = Classifier.BayesClassifier()
list = bayes.classify(data_dic['content'])
content = [] # 如果返回结果为 0 说明出现了错误
for result_item in list:
if result_item == 'ham':
content.append(1)
elif result_item == 'spam':
content.append(-1)
else:
content.append(0)
# 以下为服务器对于用户端的返回，返回预测结果
response_data = {
'action': 'response-reslut',
'content': content
}
socklink.sendall(repr(response_data).encode())
print('Request-result from ' + str(address) +
" handled successfully." + " " +
str(datetime.datetime.now().strftime('%F %T')))
elif data_dic['action'] == 'request-info':
# 在这里可以根据data['content']中的username(这里的username是用户的目前的邮箱)获得对应的配置规则
# 创建数据库操作对象
DB_operation = filter_rule_DB_operation.Filter_operation()
# 以下为服务器对于用户端的返回，返回对应的配置
response_data = DB_operation.search_owner(data_dic)
socklink.sendall(repr(response_data).encode())
print('Request-info from ' + str(address) +
" handled successfully." + " " +
str(datetime.datetime.now().strftime('%F %T')))
elif data_dic['action'] == 'post':
# 在这里需要写一个函数来接收data['content']中的配置规则,并返回一个值代表是否存储正确与否(1代表存储正确,-1代表存储失败)
DB_operation = filter_rule_DB_operation.Filter_operation()
# 以下为服务器对于用户端的返回，返回是否上传成功
response_data = DB_operation.add_one_rule(data_dic)
socklink.sendall(repr(response_data).encode())
print('post from ' + str(address) + " handled successfully." +
" " + str(datetime.datetime.now().strftime('%F %T')))
elif data_dic['action'] == 'delete':
# 收到一个删除过滤规则的请求
DB_operation = filter_rule_DB_operation.Filter_operation()
# 调用数据库规则删除函数，返回操作是否成功
response_data = DB_operation.delete_one_rule(data_dic)
socklink.sendall(repr(response_data).encode())
print('delete request from ' + str(address) +
" handled successfully." + " " +
str(datetime.datetime.now().strftime('%F %T')))
else:
# 如果以上格式均不符合，那么打印错误
print('Unknown request type from ' + str(address) +
'. Request cancelled' + " " +
str(datetime.datetime.now().strftime('%F %T')))
except Exception as error:
print('Error happened in processing thread. \nClient: ' +
str(address) + "\nError: " + str(error) + " " +
str(datetime.datetime.now().strftime('%F %T')))
traceback.print_exc()
finally:
socklink.close()
def bayesClassifier(x,y): clf = bc.BayesClassifier() return crossValidation(clf,x,y)
