def user_format(userPath,userSavePath):
'''给粉丝|关注|微博数加性别标签'''
print '''读取用户列表'''
userlist =[]
gender =[]
t=[]
userf = codecs.open(userPath)
for line in userf.readlines():
usertemp = line.strip().split(',')
userlist.append([tk for tk in usertemp[:]])
userlist = np.array(userlist)
data = userlist.T[4:].T
genderlabel = userlist.T[1].T
for i in range(0,len(genderlabel)):
if genderlabel[i] =='女':
gender.append(0)
else:
gender.append(1)
t.append([double(tk) for tk in data[i][:]])
data = np.array(t)
genderlabel = np.array(gender)
print len(data[0])
print len(genderlabel)
dump_svmlight_file(data, genderlabel,userSavePath,zero_based=False)
userf.close()
def gender_label_format(userPath,contentPath,DataPath,WritePath):
'''加性别标签'''
data = []
label = []
userlist =[]
imagename =[]
contentlist= []
print'''导入数据'''
img = codecs.open(DataPath)
for line in img.readlines():
datatemp = line.strip().split(',')
imagename.append(datatemp[1])
data.append([double(tk) for tk in datatemp[2:]])
img.close()
imagename = np.array(imagename)
data = np.array(data)
print'''导入用户信息'''
userf = codecs.open(userPath)
for line in userf.readlines():
usertemp = line.strip().split(',')
#print usertemp[1]
userlist.append([tk for tk in usertemp[:]])
userf.close()
print'''导入图片信息'''
contentf = codecs.open(contentPath)
for line in contentf.readlines():
contenttemp = line.strip().split(',')
#print contenttemp[1]
contentlist.append([tk for tk in contenttemp[:]])
contentf.close()
print '''填入标签'''
for i in range(0,len(imagename)):
name = imagename[i]
print name
flag = 0
for li in contentlist:
#print li
if(name == li[1]):
for user in userlist:
#print user
if(user[0] == li [0]):
flag = 1
print user[1]
if(user[1] == '女'):
label.append(0)
else:
label.append(1)
break
break
#print genderlabel
if(flag == 0):
print i
print(name+"没有对应的标签")
np.delete(data, i, 0)#删除对应的数据
label = np.array(label)
print label
print len(data)
print len(label)
dump_svmlight_file(data, label,WritePath,zero_based=False)
def tovw(x, y=None, sample_weight=None):
""" Convert array or sparse matrix to Vowpal Wabbit format
Parameters
----------
x : {array-like, sparse matrix}, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : {array-like}, shape (n_samples,), optional
Target vector relative to X.
sample_weight : {array-like}, shape (n_samples,), optional
sample weight vector relative to X.
Returns
-------
out : {array-like}, shape (n_samples, 1)
Training vectors in VW string format
"""
use_truth = y is not None
use_weight = sample_weight is not None
# convert to numpy array if needed
if not isinstance(x, (np.ndarray, csr_matrix)):
x = np.array(x)
if not isinstance(y, np.ndarray):
y = np.array(y)
# make sure this is a 2d array
if x.ndim == 1:
x = x.reshape(1, -1)
if y.ndim == 0:
y = y.reshape(1)
rows, cols = x.shape
# check for invalid characters if array has string values
if x.dtype.char == 'S':
for row in rows:
for col in cols:
x[row, col] = INVALID_CHARS.sub('.', x[row, col])
# convert input to svmlight format
s = StringIO.StringIO()
dump_svmlight_file(x, np.zeros(rows), s)
# parse entries to construct VW format
rows = s.getvalue().split('\n')[:-1]
out = []
for idx, row in enumerate(rows):
truth = y[idx] if use_truth else 1
weight = sample_weight[idx] if use_weight else 1
features = row.split('0 ', 1)[1]
# only using a single namespace and no tags
out.append(('{y} {w} |{ns} {x}'.format(y=truth, w=weight, ns=DEFAULT_NS, x=features)))
s.close()
return out
def tovw(x, y=None, sample_weight=None):
"""Convert array or sparse matrix to Vowpal Wabbit format
Parameters
----------
x : {array-like, sparse matrix}, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : {array-like}, shape (n_samples,), optional
Target vector relative to X.
sample_weight : {array-like}, shape (n_samples,), optional
sample weight vector relative to X.
Returns
-------
out : {array-like}, shape (n_samples, 1)
Training vectors in VW string format
"""
use_truth = y is not None
use_weight = sample_weight is not None
# convert to numpy array if needed
if not isinstance(x, (np.ndarray, csr_matrix)):
x = np.array(x)
if not isinstance(y, np.ndarray):
y = np.array(y)
# make sure this is a 2d array
if x.ndim == 1:
x = x.reshape(1, -1)
if y.ndim == 0:
y = y.reshape(1)
rows, cols = x.shape
# check for invalid characters if array has string values
if x.dtype.char == 'S':
for row in rows:
for col in cols:
x[row, col] = INVALID_CHARS.sub('.', x[row, col])
# convert input to svmlight format
s = StringIO.StringIO()
dump_svmlight_file(x, np.zeros(rows), s)
# parse entries to construct VW format
rows = s.getvalue().split('\n')[:-1]
out = []
for idx, row in enumerate(rows):
truth = y[idx] if use_truth else 1
weight = sample_weight[idx] if use_weight else 1
features = row.split('0 ', 1)[1]
# only using a single namespace and no tags
out.append(('{y} {w} |{ns} {x}'.format(y=truth, w=weight, ns=DEFAULT_NS, x=features)))
s.close()
return out
def write_to_file(X,K):
from scipy import io
from sklearn.datasets.svmlight_format import dump_svmlight_file
dd_path = olfaction_prediction_path + '/data/derived/'
if not os.path.isdir(dd_path):
os.mkdir(dd_path)
np.savetxt(dd_path + 'nspdk_r3_d4_unaug_gramian.mtx.gz', K)
# Write features in standard libSVM format:
dump_svmlight_file(X,np.zeros(X.shape[0]),dd_path + 'nspdk_r3_d4_unaug.svm')
def write_to_file(X, K):
from scipy import io
from sklearn.datasets.svmlight_format import dump_svmlight_file
dd_path = olfaction_prediction_path + '/data/derived/'
if not os.path.isdir(dd_path):
os.mkdir(dd_path)
np.savetxt(dd_path + 'nspdk_r3_d4_unaug_gramian.mtx.gz', K)
# Write features in standard libSVM format:
dump_svmlight_file(X, np.zeros(X.shape[0]),
dd_path + 'nspdk_r3_d4_unaug.svm')
def term_label_format(imagePath,termPath,termWritePath):
''''加终端标签'''
data = []
termlabel = []
termlist = []
imagename =[]
contentlist= []
img = codecs.open(imagePath)
for line in img.readlines():
datatemp = line.strip().split(',')
imagename.append(datatemp[1])
data.append([double(tk) for tk in datatemp[2:]])
img.close()
imagename = np.array(imagename)
data = np.array(data)
comf = codecs.open(termPath)
for line in comf.readlines():
termlisttemp = line.strip().split(",")
termlist.append(termlisttemp)
comf.close()
contentf = codecs.open(contentPath)
for line in contentf.readlines():
contenttemp = line.strip().split(',')
#print contenttemp[1]
contentlist.append([tk for tk in contenttemp[:]])
contentf.close()
print '''填入标签'''
for i in range(0,len(imagename)):
name = imagename[i]
flag = 0
for li in contentlist:
#print li
if(name == li[1]):
for term in termlist:
if(term[0] == li [6]):
flag = 1
termlabel.append(int(term[1]))
print i,name,term[0],term[1]
break
break
if(flag == 0):
print i
print(name+"没有对应的标签")
del data[i]#删除对应的数据
termlabel = np.array(termlabel)
print termlabel
print len(data)
print len(termlabel)
dump_svmlight_file(data, termlabel,termWritePath,zero_based=False)
def cutUselessFeatures(Path,delline,writePath):
"""
把区分度不大的特征删除
"""
from sklearn.datasets import load_svmlight_file
data, target = load_svmlight_file(Path)
deldata=data.toarray()
for i in delline:
print i
deldata = np.delete(deldata, i, 1)#删除第i列的元素值
dump_svmlight_file(deldata, target,writePath,zero_based=False)
def transform_features(self):
totransform = []
for index, item in enumerate(self.feat_head):
field = item[0]
func_name = item[1]
transform = item[2]
is_enable = item[3]
if is_enable:
if not field in self.stumble_data.get_features():
print 'field not in feature..generating:' + field
func_name(field)
totransform.append((field, transform))
if len(totransform):
mapper = DataFrameMapper(totransform)
mapper.fit(self.stumble_data.all_pd[:self.stumble_data.len_train])
#
X_transformed_train = mapper.transform(
self.stumble_data.all_pd[:self.stumble_data.len_train])
X_transformed_test = mapper.transform(
self.stumble_data.all_pd[self.stumble_data.len_train:])
for index, item in enumerate(self.feat_head):
field = item[0]
is_enable = item[3]
if is_enable and field in self.stumble_data.get_features():
del self.stumble_data.all_pd[field]
import pdb
pdb.set_trace()
from scipy.sparse import hstack
X_train = X_transformed_train
X_test = X_transformed_test
y_train = self.stumble_data.all_pd[:self.stumble_data.
len_train]['label']
# print 'Dumping train in SVMLight.'
dump_svmlight_file(X_train, y_train, output_train_libsvm_file)
# print 'Dumping test in SVMLight.'
# dump_svmlight_file(X_test, pred, output_test_libsvm_file )
else:
X_train = X_train.as_matrix()
X_test = X_test.as_matrix()
return X_train, y_train, X_test
def transform_features(self):
totransform = []
for index, item in enumerate(self.feat_head):
field = item[0]
func_name = item[1]
transform = item[2]
is_enable = item[3]
if is_enable:
if not field in self.stumble_data.get_features():
print 'field not in feature..generating:' + field
func_name(field)
totransform.append((field, transform))
if len(totransform):
mapper = DataFrameMapper(totransform)
mapper.fit(self.stumble_data.all_pd[:self.stumble_data.len_train])
#
X_transformed_train = mapper.transform(
self.stumble_data.all_pd[:self.stumble_data.len_train])
X_transformed_test = mapper.transform(
self.stumble_data.all_pd[self.stumble_data.len_train:])
for index, item in enumerate(self.feat_head):
field = item[0]
is_enable = item[3]
if is_enable and field in self.stumble_data.get_features():
del self.stumble_data.all_pd[field]
import pdb
pdb.set_trace()
from scipy.sparse import hstack
X_train = X_transformed_train
X_test = X_transformed_test
y_train = self.stumble_data.all_pd[:self.stumble_data.len_train]['label']
# print 'Dumping train in SVMLight.'
dump_svmlight_file(X_train, y_train, output_train_libsvm_file )
# print 'Dumping test in SVMLight.'
# dump_svmlight_file(X_test, pred, output_test_libsvm_file )
else:
X_train = X_train.as_matrix()
X_test = X_test.as_matrix()
return X_train, y_train, X_test
def sina_content_format(contentPath,userPath,contentSavePath):
'''图片微博加标签'''
data = []
userlist =[]
contentlist =[]
genderlabel= []
contentf = codecs.open(contentPath)
print '''读取发图微博'''
for line in contentf.readlines():
contenttemp = line.strip().split(',')
#print contenttemp[1]
contentlist.append([tk for tk in contenttemp[:]])
contentf.close()
print '''读取用户列表'''
userf = codecs.open(userPath)
for line in userf.readlines():
usertemp = line.strip().split(',')
#print usertemp[1]
userlist.append([tk for tk in usertemp[:]])
userf.close()
print '''填入数据和标签'''
for i in range(len(contentlist)):
temp = []
temp=[int(tk) for tk in contentlist[i][2:5]]
s = contentlist[i][5].split(" ")
t = s[1].split(":")
temp.append(int(t[0]))
temp.append(int(contentlist[i][7]))
#填入数据
data.append(temp)
#填入标签
for user in userlist:
if(user[0]==contentlist[i][0]):
if(user[1] == '女'):
genderlabel.append(0)
else:
genderlabel.append(1)
break
data = np.array(data)
genderlabel = np.array(genderlabel)
print len(data)
print len(genderlabel)
dump_svmlight_file(data, genderlabel,contentSavePath,zero_based=False)
# category
vec = DictVectorizer()
print 'Transforming to dict.'
X_2_cat_feat = vec.fit_transform(category_rows)
from scipy.sparse import hstack
Y_temp = hstack((X_2_cat_feat, X_1_norm_feat))
Y_temp_2 = hstack((Y_temp, X_0_text_feat_bus_name))
Y = Y_temp_2.tocsr()
dump_group_names(
vec.get_feature_names(),
feature_name_bus_name,
'bus_name',
float_header_list,
output_train_libsvm_file + '.grp',
y_shape,
)
print 'Dumping train in SVMLight.'
dump_svmlight_file(Y[0:len_train], rating_rows[0:len_train],
output_train_libsvm_file)
print 'Dumping test in SVMLight.'
dump_svmlight_file(Y[len_train:], rating_rows[len_train:],
output_test_libsvm_file)
print 'done... Dumping in SVMLight.'
# category
vec = DictVectorizer()
print 'Transforming to dict.'
X_2_cat_feat = vec.fit_transform(category_rows)
from scipy.sparse import hstack
if(len(X_1_norm_feat) > 0 ):
Y_temp = hstack((X_2_cat_feat,X_1_norm_feat))
else:
Y_temp = X_2_cat_feat
if ( len(text_rows) > 0 ):
Y_temp_2 = hstack((Y_temp,X_0_text_feat_bus_name))
else:
Y_temp_2 = Y_temp
Y = Y_temp_2.tocsr()
#dump_group_names(vec.get_feature_names(), feature_name_bus_name, 'bus_name',float_header_list, output_train_libsvm_file + '.grp', y_shape, )
print 'Dumping train in SVMLight.'
dump_svmlight_file(Y[0:len_train], rating_rows[0:len_train], output_train_libsvm_file )
print 'Dumping test in SVMLight.'
dump_svmlight_file(Y[len_train:], rating_rows[len_train:], output_test_libsvm_file )
print 'done... Dumping in SVMLight.'
def tovw(x, y=None, sample_weight=None):
"""Convert array or sparse matrix to Vowpal Wabbit format
Parameters
----------
x : {array-like, sparse matrix}, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : {array-like}, shape (n_samples,), optional
Target vector relative to X.
sample_weight : {array-like}, shape (n_samples,), optional
sample weight vector relative to X.
Returns
-------
out : {array-like}, shape (n_samples, 1)
Training vectors in VW string format
Examples
--------
>>> import pandas as pd
>>> from sklearn.feature_extraction.text import HashingVectorizer
>>> from vowpalwabbit.sklearn_vw import tovw
>>> X = pd.Series(['cat', 'dog', 'cat', 'cat'], name='catdog')
>>> y = pd.Series([-1, 1, -1, -1], name='label')
>>> hv = HashingVectorizer()
>>> hashed = hv.fit_transform(X)
>>> tovw(x=hashed, y=y)
"""
use_truth = y is not None
use_weight = sample_weight is not None
# convert to numpy array if needed
if not isinstance(x, (np.ndarray, csr_matrix)):
x = np.array(x)
if not isinstance(y, np.ndarray):
y = np.array(y)
# make sure this is a 2d array
if x.ndim == 1:
x = x.reshape(1, -1)
if y.ndim == 0:
y = y.reshape(1)
rows, cols = x.shape
# check for invalid characters if array has string values
if x.dtype.char == 'S':
for row in rows:
for col in cols:
x[row, col] = INVALID_CHARS.sub('.', x[row, col])
# convert input to svmlight format
s = io.BytesIO()
dump_svmlight_file(x, np.zeros(rows), s)
# parse entries to construct VW format
rows = s.getvalue().decode('ascii').split('\n')[:-1]
out = []
for idx, row in enumerate(rows):
truth = y[idx] if use_truth else 1
weight = sample_weight[idx] if use_weight else 1
features = row.split('0 ', 1)[1]
# only using a single namespace and no tags
out.append(('{y} {w} |{ns} {x}'.format(y=truth, w=weight, ns=DEFAULT_NS, x=features)))
s.close()
return out
def feature_format(sinadataPath,userPath,contentPath,sinaGenderPath):
'''性别标签格式化'''
imagename = []#每行数据所对应的图片么
data = []# 数据矩阵
contentlist = []#微博列表
genderlabel = []#性别标签
userlist = [] #用户列表
#注意读取的格式编码！！！，有中文时字符编码是uft-8的菜可以识别，
#可以在eclipse建立普通文件复制内容过来就可以解决
''''读取数据'''''
f = codecs.open(sinadataPath)
for line in f.readlines():
datatemp = line.strip().split(',')
imagename.append(datatemp[1])
data.append([double(tk) for tk in datatemp[2:]])
f.close()
imagename = np.array(imagename)
data = np.array(data)
#print imagename
#print data
print '''读取发图微博'''
contentf = codecs.open(contentPath)
for line in contentf.readlines():
contenttemp = line.strip().split(',')
#print contenttemp[1]
contentlist.append([tk for tk in contenttemp[:]])
contentf.close()
print '''读取用户列表'''
userf = codecs.open(userPath)
for line in userf.readlines():
usertemp = line.strip().split(',')
#print usertemp[1]
userlist.append([tk for tk in usertemp[:]])
userf.close()
print '''填入标签'''
for i in range(0,len(imagename)):
name = imagename[i]
print name
flag = 0
for li in contentlist:
#print li
if(name == li[1]):
for user in userlist:
#print user
if(user[0] == li [0]):
flag = 1
#print user[1]
if(user[1] == '女'):
genderlabel.append(0)
else:
genderlabel.append(1)
break
break
#print genderlabel
if(flag == 0):
print i
print(name+"没有对应的标签")
np.delete(data, i, 0)#删除对应的数据
genderlabel = np.array(genderlabel)
print genderlabel
print data.shape[0]
print genderlabel.shape[0]
print ''''构建libsvm数据'''
dump_svmlight_file(data, genderlabel,sinaGenderPath,zero_based=False)
def wechat_fomat(dataPath,labelPath,writeGenderPath,writeLocPath):
'''微信数据格式化'''
imagename = []#每行数据所对应的图片么
data = []# 数据矩阵
genderlabel = []#性别标签
loclabel = []#位置标签
labelfile = []
#注意读取的格式编码！！！，有中文时字符编码是uft-8的菜可以识别，
#可以在eclipse建立普通文件复制内容过来就可以解决
''''读取数据'''''
f = codecs.open(dataPath)
for line in f.readlines():
tokens = line.strip().split(' ')
imagename.append(tokens[0])
data.append([double(tk) for tk in tokens[1:]])
f.close()
imagename = np.array(imagename)
data = np.array(data)
print imagename
'''''读取标签'''
labelf = codecs.open(labelPath)
for line in labelf.readlines():
tokens = line.strip().split(' ')
labelfile.append([tk for tk in tokens[:]])
# print labelfile
flag = 0
'''填入标签'''''
for i in range(0,len(imagename)):
name = imagename[i]
flag = 0
for li in labelfile:
# print li[3]
if(name == li[3]):
flag = 1
if(li[1] == '女'):
genderlabel.append(0)
else:
genderlabel.append(1)
if(li[5] == '2'):
loclabel.append(0)
else:
loclabel.append(1)
if(flag == 0):
print i
print(name+"没有对应的标签")
np.delete(data, i, 0)#删除对应的数据
# print loclabel
# label = np.array(label)
labelf.close()
''''稀疏矩阵化数据'''
data = np.array(data)
genderlabel = np.array(genderlabel)
loclabel = np.array(loclabel)
'''查看数据是否一致大小
如果结果不一致说明标签和数据不匹配.
'''
print data.shape[0]
print genderlabel.shape[0]
print loclabel.shape[0]
''''将libsvm格式数据写到文件'''
dump_svmlight_file(data, genderlabel,writeGenderPath,zero_based=False)
dump_svmlight_file(data, loclabel,writeLocPath,zero_based=False)
print ("Wechat format End!")