def __init__(self, TM, recreate = False, predict_ratio = 1.0):
"""Create object from dataset using TM as trustmetric.
predict_ratio is the part of the edges that will randomly be
picked for prediction.
NB: The save format for wiki, is different from the save format
for Advogato, and other datasets"""
Network.__init__(self, make_base_path = False,silent=TM.dataset.silent)
self.name = 'WikiCalcGraph'
self.TM = TM
self.dataset = dataset = TM.dataset
self.predict_ratio = predict_ratio
self._cachedict = {'network':'Pred'+self.dataset._name(),'date':self.dataset.date,'lang':self.dataset.lang}
self.start_time = time.time()
if hasattr(dataset, "filepath"):
self.path = os.path.join(os.path.split(dataset.filepath)[0],
path_name(TM))
tp=relative_path(self.path,'datasets' )
if tp:
self.path = os.path.join( os.path.split(tp[0])[0] ,'shared_datasets',tp[1])
if hasattr(TM,"noneToValue") and TM.noneToValue:
self.path = os.path.join(self.path,'noneTo'+TM.defaultPredict)
if not os.path.exists(self.path):
mkpath(self.path)
self.__set_filepath()
self.basePath,self.relpath = relative_path( self.filepath, 'shared_datasets' )
self.url = os.path.join( 'http://www.trustlet.org/trustlet_dataset_svn/', os.path.split( self.relpath )[0] )
self.filename = os.path.split(self.filepath)[1]
if not recreate and os.path.exists(self.filepath):
#if in cache file doesn't exist the dataset with the right keys
#create it and save it
if not self._readCache(self.filepath):
sys.stderr.write( "I can't find dataset with threshold "+self.dataset.threshold+" and bots set to"+self.dataset.bots+"\n" )
graph = self._generate()
self._writeCache(graph)
else:
graph = self._generate()
self._writeCache(graph)
self._set_arrays()
self._prepare()
if hasattr(self.TM, 'rescale') and self.TM.rescale:
self._rescale()
if not self.silent:
print "Init took", hms(time.time() - self.start_time)
def __init__(self, TM, recreate = False, predict_ratio = 1.0):
"""Create object from dataset using TM as trustmetric.
predict_ratio is the part of the edges that will randomly be
picked for prediction."""
Network.__init__(self, make_base_path = False,silent=TM.dataset.silent)
self.name = 'CalcGraph'
self.TM = TM
self.dataset = dataset = TM.dataset
self.predict_ratio = predict_ratio
self._cachedict = {'network':'Pred'+TM.dataset._name(), 'date':TM.dataset.date}
self.start_time = time.time()
if hasattr(dataset, "filepath"):
path = os.path.join(os.path.split(dataset.filepath)[0], path_name(TM))
if dataset._name() == 'Weighted' or self._name() == '' or self._name() == 'Dummy' or self._name() =='Dummyweighted':
self.path = path
else:
(home,rel)=relative_path(path,'datasets' )
self.path = os.path.join( os.path.split(home)[0] ,'shared_datasets',rel)
#not necessary
#if hasattr(TM,"noneToValue") and TM.noneToValue:
# self.path = os.path.join(self.path,'noneTo'+TM.defaultPredict)
mkpath(self.path)
self.filepath = os.path.join(self.path,
get_name(self) + '.dot')
#splits path, PGPath is the path to c2 dataset, relative to datasets folder,
#relpath is the absolutepath to datasets folder
if 'shared_datasets' in self.filepath:
self.basePath,relpath = relative_path( self.filepath, 'shared_datasets' )
elif 'datasets' in self.filepath:
self.basePath,relpath = relative_path( self.filepath, 'datasets' )
else:
raise Exception("Malformed path ("+self.filepath+")! it must contain 'datasets' or 'shared_datasets' folder")
self.relpath = relpath #path to dataset relative to svn directory
self.url = os.path.join( 'http://www.trustlet.org/trustlet_dataset_svn/', os.path.split( relpath )[0] )
self.filename = os.path.split(self.filepath)[1]
if not recreate and (os.path.exists(self.filepath) or os.path.exists(self.filepath+'.bz2')):
self._read_dot(self.filepath)
else:
graph = self._generate()
self._write_pred_graph_dot(graph)
self._set_arrays()
self._prepare()
if hasattr(self.TM, 'rescale') and self.TM.rescale:
self._rescale()
if not self.dataset.silent:
print "Init took", hms(time.time() - self.start_time)
def __init__(self, TM, recreate=False, predict_ratio=1.0):
"""Create object from dataset using TM as trustmetric.
predict_ratio is the part of the edges that will randomly be
picked for prediction.
NB: The save format for wiki, is different from the save format
for Advogato, and other datasets"""
Network.__init__(self, make_base_path=False, silent=TM.dataset.silent)
self.name = 'WikiCalcGraph'
self.TM = TM
self.dataset = dataset = TM.dataset
self.predict_ratio = predict_ratio
self._cachedict = {
'network': 'Pred' + self.dataset._name(),
'date': self.dataset.date,
'lang': self.dataset.lang
}
self.start_time = time.time()
if hasattr(dataset, "filepath"):
self.path = os.path.join(
os.path.split(dataset.filepath)[0], path_name(TM))
tp = relative_path(self.path, 'datasets')
if tp:
self.path = os.path.join(
os.path.split(tp[0])[0], 'shared_datasets', tp[1])
if hasattr(TM, "noneToValue") and TM.noneToValue:
self.path = os.path.join(self.path,
'noneTo' + TM.defaultPredict)
if not os.path.exists(self.path):
mkpath(self.path)
self.__set_filepath()
self.basePath, self.relpath = relative_path(
self.filepath, 'shared_datasets')
self.url = os.path.join(
'http://www.trustlet.org/trustlet_dataset_svn/',
os.path.split(self.relpath)[0])
self.filename = os.path.split(self.filepath)[1]
if not recreate and os.path.exists(self.filepath):
#if in cache file doesn't exist the dataset with the right keys
#create it and save it
if not self._readCache(self.filepath):
sys.stderr.write("I can't find dataset with threshold " +
self.dataset.threshold +
" and bots set to" + self.dataset.bots +
"\n")
graph = self._generate()
self._writeCache(graph)
else:
graph = self._generate()
self._writeCache(graph)
self._set_arrays()
self._prepare()
if hasattr(self.TM, 'rescale') and self.TM.rescale:
self._rescale()
if not self.silent:
print "Init took", hms(time.time() - self.start_time)
def __init__(self, TM, recreate=False, predict_ratio=1.0):
"""Create object from dataset using TM as trustmetric.
predict_ratio is the part of the edges that will randomly be
picked for prediction."""
Network.__init__(self, make_base_path=False, silent=TM.dataset.silent)
self.name = 'CalcGraph'
self.TM = TM
self.dataset = dataset = TM.dataset
self.predict_ratio = predict_ratio
self._cachedict = {
'network': 'Pred' + TM.dataset._name(),
'date': TM.dataset.date
}
self.start_time = time.time()
if hasattr(dataset, "filepath"):
path = os.path.join(
os.path.split(dataset.filepath)[0], path_name(TM))
if dataset._name() == 'Weighted' or self._name(
) == '' or self._name() == 'Dummy' or self._name(
) == 'Dummyweighted':
self.path = path
else:
(home, rel) = relative_path(path, 'datasets')
self.path = os.path.join(
os.path.split(home)[0], 'shared_datasets', rel)
#not necessary
#if hasattr(TM,"noneToValue") and TM.noneToValue:
# self.path = os.path.join(self.path,'noneTo'+TM.defaultPredict)
mkpath(self.path)
self.filepath = os.path.join(self.path, get_name(self) + '.dot')
#splits path, PGPath is the path to c2 dataset, relative to datasets folder,
#relpath is the absolutepath to datasets folder
if 'shared_datasets' in self.filepath:
self.basePath, relpath = relative_path(self.filepath,
'shared_datasets')
elif 'datasets' in self.filepath:
self.basePath, relpath = relative_path(self.filepath,
'datasets')
else:
raise Exception(
"Malformed path (" + self.filepath +
")! it must contain 'datasets' or 'shared_datasets' folder"
)
self.relpath = relpath #path to dataset relative to svn directory
self.url = os.path.join(
'http://www.trustlet.org/trustlet_dataset_svn/',
os.path.split(relpath)[0])
self.filename = os.path.split(self.filepath)[1]
if not recreate and (os.path.exists(self.filepath)
or os.path.exists(self.filepath + '.bz2')):
self._read_dot(self.filepath)
else:
graph = self._generate()
self._write_pred_graph_dot(graph)
self._set_arrays()
self._prepare()
if hasattr(self.TM, 'rescale') and self.TM.rescale:
self._rescale()
if not self.dataset.silent:
print "Init took", hms(time.time() - self.start_time)
