def x_2func(traindir,testdir):
'''
train = nb.read('train_nb_eventmodel')
sta = nb.sta_count(train)
category_tokens = get_category_tokens()
token_x = x_2(train,sta,category_tokens)
nb.write(token_x,'token_x')
'''
category = nb.read('category_nb_eventmodel')
tokens_x = nb.read('token_x')
category_convert = nb.convert(category)
tokens_all_x = []
x_category = {}
for i in range(10):
tokens = sorted(tokens_x[i],key=tokens_x[i].get,reverse = True)
x_category[i] = tokens[:100]
'''
x_category[i] = []
for word in tokens:
if tokens_x[i][word] >10.83:
x_category[i].append(word)
else:
break
'''
print len(x_category[i])
tokens_all_x = set(tokens_all_x)|set(x_category[i])
print len(tokens_all_x)
nb.write(x_category,'x_category')
nb.write(tokens_all_x,'tokens_all_x')
def logistic_l1():
traindir ='./data/training'
testdir = './data/test'
tokens = list(nb.read('tokens'))
train_x,train_y,category = vec.func2(traindir,tokens)
train_x = np.array(train_x)
train_y = np.array(train_y)
print train_x.shape
clf = LogisticRegression(penalty='l2')
clf.fit(train_x,train_y)
category = nb.read('category')
result,test_x,test_file= vec.func3(testdir,tokens,category)
test_x = np.array(test_x)
print test_x.shape
predict = np.array(clf.predict(test_x))
test_file = np.array(test_file)
predict = np.column_stack((test_file,predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict,category_convert,result,path)
def logistic_l1():
traindir = './data/training'
testdir = './data/test'
tokens = list(nb.read('tokens'))
train_x, train_y, category = vec.func2(traindir, tokens)
train_x = np.array(train_x)
train_y = np.array(train_y)
print train_x.shape
clf = LogisticRegression(penalty='l2')
clf.fit(train_x, train_y)
category = nb.read('category')
result, test_x, test_file = vec.func3(testdir, tokens, category)
test_x = np.array(test_x)
print test_x.shape
predict = np.array(clf.predict(test_x))
test_file = np.array(test_file)
predict = np.column_stack((test_file, predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict, category_convert, result, path)
def logistic_own():
train_x = nb.read('train_x')
train_y = nb.read('train_y')
category = nb.read('category')
result =nb.read('result')
test_x = nb.read('test_x')
test_file = nb.read('test_file')
m,n=train_x.shape
temp = np.ones((m,1))
train_x = np.column_stack((temp,train_x))
temp = np.ones((len(test_x),1))
test_x = np.column_stack((temp,test_x))
predict = np.zeros((len(test_x),1))
train_x = np.mat(train_x)
train_y = np.mat(train_y).transpose()
test_x = np.mat(test_x)
#由于要实现多分类，我们可以通过多个二分类来实现预测
for i in range(10):
binary_y = np.mat(np.zeros((m,1)).astype(int))
for index in range(len(train_y)):
if train_y[index]==i:
binary_y[index]=1
else:
binary_y[index]=0
weight = np.mat(np.ones((n+1,1)))
alpha = 0.0001
maxitem = 100
for k in range(maxitem):
h = sigmoid(train_x*weight)
#我们在计算代价函数的时候,不能简单的用公式实现,应当进行判断
J = calj(binary_y,h,m)
#J = 1.0/m*(-binary_y.transpose()*np.log2(h)-(1-binary_y.transpose())*np.log2(1-h))
error = h-binary_y
weight -= alpha*(train_x.transpose()*error)
binary_predict = test_x*weight
for index in range(len(binary_predict)):
if binary_predict[index]>0:
predict[index]=i
predict = np.array(predict).astype(int)
test_file = np.array(test_file)
predict = np.column_stack((test_file,predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict,category_convert,result,path)
def logistic_own():
train_x = nb.read('train_x')
train_y = nb.read('train_y')
category = nb.read('category')
result = nb.read('result')
test_x = nb.read('test_x')
test_file = nb.read('test_file')
m, n = train_x.shape
temp = np.ones((m, 1))
train_x = np.column_stack((temp, train_x))
temp = np.ones((len(test_x), 1))
test_x = np.column_stack((temp, test_x))
predict = np.zeros((len(test_x), 1))
train_x = np.mat(train_x)
train_y = np.mat(train_y).transpose()
test_x = np.mat(test_x)
#由于要实现多分类，我们可以通过多个二分类来实现预测
for i in range(10):
binary_y = np.mat(np.zeros((m, 1)).astype(int))
for index in range(len(train_y)):
if train_y[index] == i:
binary_y[index] = 1
else:
binary_y[index] = 0
weight = np.mat(np.ones((n + 1, 1)))
alpha = 0.0001
maxitem = 100
for k in range(maxitem):
h = sigmoid(train_x * weight)
#我们在计算代价函数的时候,不能简单的用公式实现,应当进行判断
J = calj(binary_y, h, m)
#J = 1.0/m*(-binary_y.transpose()*np.log2(h)-(1-binary_y.transpose())*np.log2(1-h))
error = h - binary_y
weight -= alpha * (train_x.transpose() * error)
binary_predict = test_x * weight
for index in range(len(binary_predict)):
if binary_predict[index] > 0:
predict[index] = i
predict = np.array(predict).astype(int)
test_file = np.array(test_file)
predict = np.column_stack((test_file, predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict, category_convert, result, path)
def logistic_x():
train_x,train_y,category,result,test_x,test_file = preprocess()
clf = LogisticRegression(penalty='l1')
clf.fit(train_x,train_y)
predict = clf.predict(test_x)
predict = np.array(predict)
predict = np.column_stack((test_file,predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict,category_convert,result,path)
def logistic_x():
train_x, train_y, category, result, test_x, test_file = preprocess()
clf = LogisticRegression(penalty='l1')
clf.fit(train_x, train_y)
predict = clf.predict(test_x)
predict = np.array(predict)
predict = np.column_stack((test_file, predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict, category_convert, result, path)
def sto_logistic():
train_x = nb.read('train_x')
train_y = nb.read('train_y')
category = nb.read('category')
result =nb.read('result')
test_x = nb.read('test_x')
test_file = nb.read('test_file')
m,n=train_x.shape
temp = np.ones((m,1))
train_x = np.column_stack((temp,train_x))
temp = np.ones((len(test_x),1))
test_x = np.column_stack((temp,test_x))
predict = np.zeros((len(test_x),1))
train_x = np.mat(train_x)
train_y = np.mat(train_y).transpose()
test_x = np.mat(test_x)
#由于要实现多分类，我们可以通过多个二分类来实现预测
for i in range(10):
binary_y = np.mat(np.zeros((m,1)).astype(int))
for index in range(len(train_y)):
if train_y[index]==i:
binary_y[index]=1
else:
binary_y[index]=0
weight = np.mat(np.ones((n+1,1)))
alpha = 0.001
maxitem =5000
for k in range(maxitem):
index = random.randrange(m)
h = sigmoid(train_x[index]*weight)
error = h - binary_y[index]
weight -= alpha*(train_x[index].transpose()*error)
binary_predict = test_x*weight
for index in range(len(binary_predict)):
if binary_predict[index]>0:
predict[index]=i
predict = np.array(predict).astype(int)
test_file = np.array(test_file)
predict = np.column_stack((test_file,predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict,category_convert,result,path)
def sto_logistic():
train_x = nb.read('train_x')
train_y = nb.read('train_y')
category = nb.read('category')
result = nb.read('result')
test_x = nb.read('test_x')
test_file = nb.read('test_file')
m, n = train_x.shape
temp = np.ones((m, 1))
train_x = np.column_stack((temp, train_x))
temp = np.ones((len(test_x), 1))
test_x = np.column_stack((temp, test_x))
predict = np.zeros((len(test_x), 1))
train_x = np.mat(train_x)
train_y = np.mat(train_y).transpose()
test_x = np.mat(test_x)
#由于要实现多分类，我们可以通过多个二分类来实现预测
for i in range(10):
binary_y = np.mat(np.zeros((m, 1)).astype(int))
for index in range(len(train_y)):
if train_y[index] == i:
binary_y[index] = 1
else:
binary_y[index] = 0
weight = np.mat(np.ones((n + 1, 1)))
alpha = 0.001
maxitem = 5000
for k in range(maxitem):
index = random.randrange(m)
h = sigmoid(train_x[index] * weight)
error = h - binary_y[index]
weight -= alpha * (train_x[index].transpose() * error)
binary_predict = test_x * weight
for index in range(len(binary_predict)):
if binary_predict[index] > 0:
predict[index] = i
predict = np.array(predict).astype(int)
test_file = np.array(test_file)
predict = np.column_stack((test_file, predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict, category_convert, result, path)
def gifunc():
train = nb.read('train_nb_eventmodel')
sta = nb.sta_count(train)
all_tokens = get_all_tokens()
tokens_gi = gi(train,sta,all_tokens)
nb.write(tokens_gi,'tokens_gi')
category = nb.read('category_nb_eventmodel')
tokens_gi = nb.read('tokens_gi')
category_convert = nb.convert(category)
tokens_all_gi = []
gi_category = {}
for i in range(10):
tokens = sorted(tokens_gi,key=tokens_gi.get,reverse=True)
gi_category[i] = tokens[:100]
tokens_all_gi = set(tokens_all_gi)|set(gi_category[i])
print tokens_all_gi
nb.write(gi_category,'gi_category')
nb.write(tokens_all_gi,'tokens_all_gi')
def dffunc():
#这里先使用文档频率，需要统计在某一类别下词t在多少个文档中出现
'''
train = nb.read('train_nb_eventmodel')
sta = nb.sta_count(train)
all_tokens = get_all_tokens()
tokens_df = df(train,sta,all_tokens)
nb.write(tokens_df,'tokens_df')
'''
category = nb.read('category_nb_eventmodel')
tokens_df = nb.read('tokens_df')
category_convert = nb.convert(category)
tokens_all_df = []
df_category = {}
for i in range(10):
tokens = sorted(tokens_df,key=tokens_df.get,reverse = True)
df_category[i] = tokens[:200]
tokens_all_df = set(tokens_all_df)|set(df_category[i])
print tokens_all_df
nb.write(df_category,'df_category')
nb.write(tokens_all_df,'tokens_all_df')
def mi_func():
'''
train = nb.read('train_nb_eventmodel')
sta = nb.sta_count(train)
category_tokens = get_category_tokens()
token_mi = mi(train,sta,category_tokens)
nb.write(token_mi,'token_mi')
'''
category = nb.read('category_nb_eventmodel')
token_mi = nb.read('token_mi')
category_convert = nb.convert(category)
tokens_all_mi = []
mi_category = {}
for i in range(10):
tokens = sorted(token_mi[i],key = token_mi[i].get,reverse = True)
mi_category[i] = tokens[:500]
tokens_all_mi = set(tokens_all_mi)|set(mi_category[i])
print len(tokens_all_mi)
nb.write(mi_category,'mi_category')
nb.write(tokens_all_mi,'tokens_all_mi')
'''
