def experiment():
percent_p = 3
path = r'C:\Users\yyveggie\Desktop\UCI\Conversion\mushroom.csv'
seed = 2019
est_error_upu = []
est_binary_upu = []
est_binary_pusb = []
est_error_pusb = []
est_error_drsb = []
est_binary_drsb = []
for k in range(10):
np.random.seed(seed)
pi = 0.6 # 类先验，表示U中P所占比例，现在的问题是，是否需要根据下文中比例进行计算，而不是提前随机指定
classifier = LogisticRegression(C=0.01, penalty='l2', solver='liblinear')
texts_1, texts_0 = CSV(path)
texts_1 = np.array_split(texts_1, 10) # 将类别为1的样本集分成十份
texts_0 = np.array_split(texts_0, 10) # 将类别为0的样本集分成十份
x_test = np.array(list(texts_1[k]) + list(texts_0[k])) # 测试集x，每一份每轮选一次
t_test = np.array(list(len(texts_1[k]) * [1]) + list(len(texts_0[k]) * [0])) # 测试集y，正例为1，负例为0
index_rest = sorted(set(range(10)) - set([k])) # 除了测试集剩下的索引
texts_1 = np.array(texts_1)
texts_0 = np.array(texts_0)
texts_1 = np.array([j for i in texts_1[index_rest] for j in i]) # p
texts_0 = np.array([j for i in texts_0[index_rest] for j in i]) # n
x = np.vstack((texts_1, texts_0)) # p和n组成训练集
one = np.ones((len(x), 1)) #
x_pn = np.concatenate([x, one], axis=1)
t = pd.Series([1] * len(texts_1) + [0] * len(texts_0))
classifier.fit(x_pn, t)
x_train = x
t_train = t
xp = x_train[t_train == 1]
one = np.ones((len(xp), 1))
xp_temp = np.concatenate([xp, one], axis=1)
xp_prob = classifier.predict_proba(xp_temp)[:, 1]
# xp_prob /= np.mean(xp_prob)
xp_prob = xp_prob ** 20
xp_prob /= np.max(xp_prob)
rand = np.random.uniform(size=len(xp))
temp = xp[xp_prob > rand]
pdata = int(percent_p / 10 * len(x)) # p样本数量，占了总数的3/10
while (len(temp) < pdata):
rand = np.random.uniform(size=len(xp))
temp = np.concatenate([temp, xp[xp_prob > rand]], axis=0)
xp = temp
perm = np.random.permutation(len(xp))
xp = xp[perm[:pdata]]
u = int(6 / 10 * len(x)) # u样本数量，占了总数的6/10
updata = np.int(u * pi) # U中P的数量 = U的数量 * 类先验
undata = u - updata # U中N的数量 = U的数量 - U中P的数量
xp_temp = x_train[t_train == 1]
xn_temp = x_train[t_train == 0]
perm = np.random.permutation(len(xp_temp))
xp_temp = xp_temp[perm[:updata]]
perm = np.random.permutation(len(xn_temp))
xn_temp = xn_temp[perm[:undata]]
xu = np.concatenate([xp_temp, xn_temp], axis=0)
x = np.concatenate([xp, xu], axis=0)
tp = np.ones(len(xp))
tu = np.zeros(len(xu))
t = np.concatenate([tp, tu], axis=0)
updata = np.int(1000 * pi)
undata = 1000 - updata
xp_test = x_test[t_test == 1]
perm = np.random.permutation(len(xp_test))
xp_test = xp_test[perm[:updata]]
xn_test = x_test[t_test == 0]
perm = np.random.permutation(len(xn_test))
xn_test = xn_test[perm[:undata]]
x_test = np.concatenate([xp_test, xn_test], axis=0)
tp = np.ones(len(xp_test))
tu = np.zeros(len(xn_test))
t_test = np.concatenate([tp, tu], axis=0)
pu = PU(pi=pi)
x_train = x
res, x_test_kernel = pu.optimize(x, t, x_test)
acc1, f1_binary1 = pu.test(x_test_kernel, res, t_test, quant=False)
acc2, f1_binary2 = pu.test(x_test_kernel, res, t_test, quant=True, pi=pi)
result = densratio(x_train[t == 1], x_train[t == 0])
r = result.compute_density_ratio(x_test)
temp = np.copy(r)
temp = np.sort(temp)
theta = temp[np.int(np.floor(len(x_test) * (1 - pi)))]
pred = np.zeros(len(x_test))
pred[r > theta] = 1
acc3 = np.mean(pred == t_test)
f1_binary3 = f1_score(t_test, pred, average='binary')
est_error_upu.append(acc1)
est_binary_upu.append(f1_binary1)
est_error_pusb.append(acc2)
est_binary_pusb.append(f1_binary2)
est_error_drsb.append(acc3)
est_binary_drsb.append(f1_binary3)
seed += 1
print("Iter：", k)
print("upu_accuracy ", acc1)
print("upu_f1_binary ", f1_binary1)
print("pusb_accuracy ", acc2)
print("pusb_f1_binary ", f1_binary2)
print("drsb_accuracy ", acc3)
print("drsb_f1_binary ", f1_binary3)
print("Accuracy for uPU：", np.mean(est_error_upu))
print("F1-Score for uPU：", np.mean(est_binary_upu))
print("Accuracy for PUSB：", np.mean(est_error_pusb))
print("F1-Score for PUSB：", np.mean(est_binary_pusb))
print("Accuracy for DRSB：", np.mean(est_error_drsb))
print("F1-Score for DRSB：", np.mean(est_binary_drsb))
'''CSV文件格式'''
from Conversion import CSV
from sklearn.svm import SVC
import numpy as np
import random
f1 = []
accuracy = []
path = r'C:\Users\yyveggie\Desktop\UCI\Conversion\mushroom.csv'
k = 5 # k近邻
T = 4.8 # 阈值
texts_1, texts_0 = CSV(path)
def SplitData(k, texts_1, texts_0): # 10折交叉
percent_p = 3 # p所占份数
texts_1 = np.array_split(texts_1, 10) # 将类别为1的样本集分成十份
texts_0 = np.array_split(texts_0, 10) # 将类别为0的样本集分成十份
test_x = list(texts_1[k]) + list(texts_0[k]) # 测试集x，每一份每轮选一次
test_y = list(len(texts_1[k]) * [1]) + list(len(texts_0[k]) * [0]) # 测试集y，正例为1，负例为0
index_rest = sorted(set(range(10)) - set([k])) # 除去测试集剩余索引
index_p = random.sample(index_rest, percent_p) # 随机选择percent_p个p索引
texts_1 = np.array(texts_1) # 转换为array格式可进行多维索引
p = texts_1[index_p] # p集合
index_except_p = sorted(set(range(10)) - set([k]) - set(index_p)) # 除去测试集和p集合剩余索引
texts_0 = np.array(texts_0) # 转换为array格式可进行多维索引
u = list(texts_1[index_except_p]) + list(texts_0[index_rest]) # u集合
def experiment():
percent_p = 3
path = r'C:\Users\yyveggie\Desktop\UCI\Conversion\mushroom.csv'
ite = 10
epoch = 100
batchsize = 1000
seed = 2020
gpu = True
acc_nnpu = []
f1_nnpu = []
acc_nnpusb = []
f1_nnpusb = []
for k in range(ite):
np.random.seed(seed)
#PN classification
texts_1, texts_0 = CSV(path)
texts_1 = np.array_split(texts_1, 10) # 将类别为1的样本集分成十份
texts_0 = np.array_split(texts_0, 10) # 将类别为0的样本集分成十份
x_test = np.array(list(texts_1[k]) + list(texts_0[k])) # 测试集x，每一份每轮选一次
t_test = np.array(list(len(texts_1[k]) * [1]) + list(len(texts_0[k]) * [0])) # 测试集y，正例为1，负例为0
index_rest = sorted(set(range(10)) - set([k]))
texts_1 = np.array(texts_1)
texts_0 = np.array(texts_0)
texts_1 = np.array([j for i in texts_1[index_rest] for j in i])
texts_0 = np.array([j for i in texts_0[index_rest] for j in i])
x = np.vstack((texts_1, texts_0))
t = pd.Series([1] * len(texts_1) + [0] * len(texts_0))
dim = x.shape[1]
print(x.shape)
x_train = x
t_train = pd.Series([1] * len(texts_1) + [0] * len(texts_0))
pi = np.mean(t_train)
model = MultiLayerPerceptron(dim)
optimizer = optimizers.Adam(1e-5)
optimizer.setup(model)
if gpu:
gpu_device = 0
cuda.get_device(gpu_device).use()
model.to_gpu(gpu_device)
xp = cuda.cupy
else:
xp = np
model, optimizer = train(x, t, epoch, model, optimizer, batchsize, xp)
x_p = x_train[t_train==1]
xp_prob = np.array([])
for j in six.moves.range(0, len(x_p), batchsize):
X = Variable(xp.array(x_p[j:j + batchsize], xp.float32))
g = chainer.cuda.to_cpu(model(X).data).T[0]
xp_prob = np.append(xp_prob, 1/(1+np.exp(-g)), axis=0)
xp_prob /= np.mean(xp_prob)
xp_prob = xp_prob
xp_prob /= np.max(xp_prob)
print(xp_prob)
rand = np.random.uniform(size=len(x_p))
x_p = x_p[xp_prob > rand]
perm = np.random.permutation(len(x_p))
pdata = int(percent_p / 10 * len(x)) # p样本数量，占了总数的3/10
x_p = x_p[perm[:pdata]]
tp = np.ones(len(x_p))
tu = np.zeros(len(x_train))
t_train = np.concatenate([tp, tu], axis=0)
x_train = np.concatenate([x_p, x_train], axis=0)
print(x_train.shape)
print(t_train.shape)
print(x_test.shape)
print(t_test.shape)
model = MultiLayerPerceptron(dim)
optimizer = optimizers.Adam(alpha=1e-5)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.005))
if gpu:
gpu_device = 0
cuda.get_device(gpu_device).use()
model.to_gpu(gpu_device)
xp = cuda.cupy
else:
xp = np
model, optimizer, acc1, acc2, f1_binary1, f1_binary2 = train_pu(x_train, t_train, x_test, t_test, pi, epoch, model, optimizer, batchsize, xp)
acc_nnpu.append(acc1)
f1_nnpu.append(f1_binary1)
acc_nnpusb.append(acc2)
f1_nnpusb.append(f1_binary2)
seed += 1
print("Iter：", k)
print("acc_nnpu：", acc1)
print("acc_nnpusb：", acc2)
print("f1_nnpu：", f1_binary1)
print("f1_nnpusb：", f1_binary2)
print("acc_nnpu_mean：", np.mean(acc_nnpu))
print("f1_nnpu_mean：", np.mean(f1_nnpu))
print("acc_nnpusb_mean：", np.mean(acc_nnpusb))
print("f1_nnpusb_mean：", np.mean(f1_nnpusb))