# print(v)
# print(doc.class_name)
if class_name in v:
class_label = k
train_dict.update({(docid + class_name): [class_label, {term_id: term_val}]})
# write to file
with open(training_file_tfidf, "w") as train_obj:
for doc, val in train_dict.items():
x = ''
classid = str(train_dict[doc][0])
for i in val[1:]:
for k, v in i.items():
x = x + " " + str(k) + ":" + str(v)
tfidfdata = classid + "\t" + x + "\n"
train_obj.write(tfidfdata)
print("training data file.tfidf generated successfully")
if __name__ == '__main__':
'''class_defn_file("class_sample_file")
index_obj = InvertedIndex()
iindex = index_obj.indexingCranfield("sample_newsgroup")
feature_defn_file(iindex,"feature_sample_file")
training_file_idf("feature_sample_file", "class_sample_file", "training_sample_file.tf", "training_sample_file.idf", "training_sample_file.tfidf", iindex)'''
class_defn_file("class_definition_file")
index_obj = InvertedIndex()
iindex = index_obj.indexingCranfield("mini_newsgroups")
feature_defn_file(iindex, "feature_definition_file")
training_file("feature_definition_file", "class_definition_file", "training_data_file.tf", "training_data_file.idf", "training_data_file.tfidf", iindex)
def extractfeature(self, directoryOfNewsgroup, featureDefinitionFile,
classDefinitionFile, trainingDataFile):
iindexObject = InvertedIndex()
invertedIndex = iindexObject.indexingCranfield(directoryOfNewsgroup)
f = open(featureDefinitionFile, "w")
counter = 0
for x in invertedIndex.items.keys():
counter = counter + 1
formattedData = str(counter) + " " + x + "\n"
f.write(formattedData)
self.termIdLookup[x] = counter
f.close()
#as per the proejct requirement hardcoding the class files here and outputting
classDefinitiontuple = ("1 comp.graphics", "1 comp.os.ms-windows.misc",
"1 comp.sys.ibm.pc.hardware",
"1 comp.sys.mac.hardware", "1 comp.windows.x",
"2 rec.autos", "2 rec.motorcycles",
"2 rec.sport.baseball", "2 rec.sport.hockey",
"3 sci.crypt", "3 sci.electronics",
"3 sci.med", "3 sci.space", "4 misc.forsale",
"5 talk.politics.misc", "5 talk.politics.guns",
"5 talk.politics.mideast",
"6 talk.religion.misc", "6 alt.atheism",
"6 soc.religion.christian")
classfile = open(classDefinitionFile, "w")
for x in classDefinitiontuple:
classfile.write(x + "\n")
classfile.close()
#end of hardcoded class files
print('tf start')
libsvmtf = {}
if os.path.exists("training_data_file.TF"):
os.remove("training_data_file.TF")
newsgroup = self.getNewsGroupFile(directoryOfNewsgroup)
for x in invertedIndex.items.keys():
for postingobject in invertedIndex.items.get(x).posting.keys():
#libsvmtf.setdefault(invertedIndex.items.get(x).posting.get(postingobject).docID, []).append('\t')
libsvmtf.setdefault(
invertedIndex.items.get(x).posting.get(
postingobject).docID,
[]).append(self.getKeysByValue(self.termIdLookup, x))
libsvmtf.setdefault(
invertedIndex.items.get(x).posting.get(
postingobject).docID, []).append(':')
#libsvmtf.setdefault(invertedIndex.items.get(x).posting.get(postingobject).docID, []).append('\t')
libsvmtf.setdefault(
invertedIndex.items.get(x).posting.get(
postingobject).docID, []).append(
round(
invertedIndex.items.get(x).posting.get(
postingobject).termfreq, 5))
# libsvmtf.setdefault(invertedIndex.items.get(x).posting.get(postingobject).docID, []).append('\t')
for x in libsvmtf:
tfdata = ''
libsvmtffile = open("training_data_file.TF", "a")
if x in newsgroup.class1items1:
classid = 1
if x in newsgroup.class1items2:
classid = 2
if x in newsgroup.class1items3:
classid = 3
if x in newsgroup.class1items4:
classid = 4
if x in newsgroup.class1items5:
classid = 5
if x in newsgroup.class1items6:
classid = 6
# print('\t '.join(libsvmtf))
#tfdata = str(x) +" : "+str(''.join(str(libsvmtf[x]).split(",")))[1:-1] + "\n"
# for row in reader: # read a row as {column1: value1, column2: value2,...}
# for (k, v) in row.items(): # go over each column name and value
# columns[k].append(v) # append the value into the appropriate list
# saved_column = df.column_name # you can also use df['column_name']
#print (str(tempstr).split("'",""))
tfdata = str(classid) + " " + str(''.join(
str(libsvmtf[x]).split(",")))[1:-1] + "\n"
tfdata = str.replace(tfdata, " ':' ", ":")
print(tfdata)
libsvmtffile.write(tfdata)
libsvmtffile.close()
print('tf complete')
print('idf start')
libsvmidf = {}
if os.path.exists("training_data_file.IDF"):
os.remove("training_data_file.IDF")
newsgroup = self.getNewsGroupFile(directoryOfNewsgroup)
for x in invertedIndex.items.keys():
for postingobject in invertedIndex.items.get(x).posting.keys():
# libsvmtf.setdefault(invertedIndex.items.get(x).posting.get(postingobject).docID, []).append('\t')
libsvmidf.setdefault(
invertedIndex.items.get(x).posting.get(
postingobject).docID,
[]).append(self.getKeysByValue(self.termIdLookup, x))
libsvmidf.setdefault(
invertedIndex.items.get(x).posting.get(
postingobject).docID, []).append(':')
# libsvmtf.setdefault(invertedIndex.items.get(x).posting.get(postingobject).docID, []).append('\t')
libsvmidf.setdefault(
invertedIndex.items.get(x).posting.get(
postingobject).docID,
[]).append(invertedIndex.items.get(x).idf)
# libsvmtf.setdefault(invertedIndex.items.get(x).posting.get(postingobject).docID, []).append('\t')
for x in libsvmidf:
idfdata = ''
libsvmidffile = open("training_data_file.IDF", "a")
if x in newsgroup.class1items1:
classid = 1
if x in newsgroup.class1items2:
classid = 2
if x in newsgroup.class1items3:
classid = 3
if x in newsgroup.class1items4:
classid = 4
if x in newsgroup.class1items5:
classid = 5
if x in newsgroup.class1items6:
classid = 6
# print('\t '.join(libsvmtf))
# tfdata = str(x) +" : "+str(''.join(str(libsvmtf[x]).split(",")))[1:-1] + "\n"
# for row in reader: # read a row as {column1: value1, column2: value2,...}
# for (k, v) in row.items(): # go over each column name and value
# columns[k].append(v) # append the value into the appropriate list
# saved_column = df.column_name # you can also use df['column_name']
idfdata = str(classid) + " " + str(''.join(
str(libsvmidf[x]).split(",")))[1:-1] + "\n"
idfdata = str.replace(idfdata, " ':' ", ":")
# print(idfdata)
libsvmidffile.write(idfdata)
libsvmidffile.close()
print('idf complete')
print('TF-idf start')
libsvmtfidf = {}
if os.path.exists("training_data_file.TFIDF"):
os.remove("training_data_file.TFIDF")
newsgroup = self.getNewsGroupFile(directoryOfNewsgroup)
for x in invertedIndex.items.keys():
for postingobject in invertedIndex.items.get(x).posting.keys():
# libsvmtf.setdefault(invertedIndex.items.get(x).posting.get(postingobject).docID, []).append('\t')
libsvmtfidf.setdefault(
invertedIndex.items.get(x).posting.get(
postingobject).docID,
[]).append(self.getKeysByValue(self.termIdLookup, x))
libsvmtfidf.setdefault(
invertedIndex.items.get(x).posting.get(
postingobject).docID, []).append(':')
# libsvmtf.setdefault(invertedIndex.items.get(x).posting.get(postingobject).docID, []).append('\t')
libsvmtfidf.setdefault(
invertedIndex.items.get(x).posting.get(
postingobject).docID, []).append(
invertedIndex.items.get(x).posting.get(
postingobject).termfreq *
invertedIndex.items.get(x).idf)
# libsvmtf.setdefault(invertedIndex.items.get(x).posting.get(postingobject).docID, []).append('\t')
for x in libsvmtfidf:
tfidfdata = ''
libsvmtfidffile = open("training_data_file.TFIDF", "a")
if x in newsgroup.class1items1:
classid = 1
if x in newsgroup.class1items2:
classid = 2
if x in newsgroup.class1items3:
classid = 3
if x in newsgroup.class1items4:
classid = 4
if x in newsgroup.class1items5:
classid = 5
if x in newsgroup.class1items6:
classid = 6
# print('\t '.join(libsvmtf))
# tfdata = str(x) +" : "+str(''.join(str(libsvmtf[x]).split(",")))[1:-1] + "\n"
# for row in reader: # read a row as {column1: value1, column2: value2,...}
# for (k, v) in row.items(): # go over each column name and value
# columns[k].append(v) # append the value into the appropriate list
# saved_column = df.column_name # you can also use df['column_name']
tfidfdata = str(classid) + " " + str(''.join(
str(libsvmtfidf[x]).split(",")))[1:-1] + "\n"
tfidfdata = str.replace(tfidfdata, " ':' ", ":")
# print(tfidfdata)
libsvmtfidffile.write(tfidfdata)
libsvmtfidffile.close()
print('TF-idf complete')
