def __init__(self, context_n=3):
self.featureVector = FeatureVector()
self.phrase_dim = self.featureVector.dim()
self.phr_cox_dim = self.phrase_dim * 4
def __init__(self):
self.__featureVector = FeatureVector()
class Preprocessor():
def __init__(self, context_n=3):
self.featureVector = FeatureVector()
self.phrase_dim = self.featureVector.dim()
self.phr_cox_dim = self.phrase_dim * 4
def getDim(self):
return self.phrase_dim, self.phr_cox_dim
'''
Transforms words into vectors using FeatureVector and stacks vectors into matrix
'''
def concat(self, phrase, tags):
phrase = phrase.split()
ret_array = np.reshape(
self.featureVector.vectorise(phrase[0], tags[0]), (1, -1))
if len(phrase) > 1:
for i in range(len(phrase)):
ret_array = np.vstack([
ret_array,
self.featureVector.vectorise(phrase[i], tags[i])
])
return ret_array
'''
Returns concatenated vector of maximal and minimal features for given phrase vectors and context vectors
'''
def minMaxVector(self, phrase_vec, context_vec):
phrase_max_min = np.concatenate(
(phrase_vec.max(axis=0), phrase_vec.min(axis=0)))
context_max_min = np.concatenate(
(context_vec.max(axis=0), context_vec.min(axis=0)))
vector = np.concatenate((phrase_max_min, context_max_min))
return vector
'''
Main logic that for phrases, context and/or skills returns its vector values.
'''
def preprocess(self,
noun_phrases,
context,
np_tags,
context_tags,
skills=False):
phrases_vec = []
context_vec = []
phr_cox_vec = []
y = []
if skills != False:
skills = [x.lower() for x in skills]
for i in range(len(noun_phrases)):
current_phrase_vec = self.concat(noun_phrases[i], np_tags[i])
phrases_vec.append(current_phrase_vec)
current_context_vec = self.concat(context[i], context_tags[i])
context_vec.append(current_context_vec)
phr_cox_vec.append(
self.minMaxVector(current_phrase_vec, current_context_vec))
if skills != False:
if noun_phrases[i].lower() in skills:
y.append(1)
else:
y.append(0)
if skills != False:
return phrases_vec, context_vec, phr_cox_vec, y
return phrases_vec, context_vec, phr_cox_vec
class ArffHandler:
def __init__(self):
self.__featureVector = FeatureVector()
'''
def generateArffFile(filename, data): Generate sparse Arff files.
Inputs:
Filename: Any name you like. A file named "filename".arff will be created.
*** Data format must be ***
data = {"$PageId":
{
"class":$X,
"data":{$id1:$x1,$id2:$x2,$id3:$x3,$id4:$x4...}
}
}
example:
data = {5000:
{
"class":1,
"data":{"medicine":400,"cars":3,"health":9999,"Metallica":10...}
}
}
'''
def generateArffFile(self, filename, data):
self.__featureVector.createCountMap(data)
file = open(filename + ".arff", "w")
file.write("@RELATION " + filename + "\n")
sortedList = self.__featureVector.getSortedVocabularyMap()
#TWO FIRST ATTRIBUTES ARE RESERVED!
file.write("@ATTRIBUTE CLASS {0,1}\n")
file.write("@ATTRIBUTE CATEGORYNAME STRING\n")
for attribute in sortedList:
file.write("@ATTRIBUTE " + attribute[0] + "-"
+ str(attribute[1]) + " NUMERIC\n")
# file.write("@ATTRIBUTE name NUMERIC")
file.write("@DATA\n")
for categoryName, innerMap in data.iteritems():
catClass = innerMap["class"]
file.write("{0 " + catClass)
file.write(",1 " + categoryName)
for k, v in innerMap["data"].iteritems():
file.write("," + str(self.__featureVector.getIndex(k)) + " " + str(v))
file.write("}\n")
file.close()
logging.info("Created file on local drive: %s.arff", filename)
return filename + ".arff"
def readArffFile(self, filename):
featureVector = [] # list of maps
featureNameList = []
with open(filename, "r") as f:
for line in f:
if not line.strip().startswith('@') and len(line.strip()) > 0:
# start of actual data
pairs = line.strip(" {}\n").split(',')
featureMap = {}
for attr in pairs:
(key, value) = attr.split(' ', 1)
featureMap[int(key)] = value.strip().lower()
featureVector.append(featureMap)
else:
# feature definitions
if (not line.strip().lower().startswith('@data')) and (not line.lower().startswith('@relation')):
attrName = line.strip().split()[1]
if attrName.find('-') != -1:
(name, idx) = attrName.split('-')
featureNameList.append(name)
self.__featureVector.updateVocabularyMap(name, idx)
logging.debug("Updated %s with index %s", name, idx)
else:
featureNameList.append(attrName)
logging.info("Read arff file. Extracted feature vector")
return [featureNameList, featureVector]
def generateFeatureVector(self, data, categoryName):
return self.__featureVector.generateFeatureVector(data, categoryName)
