def prediksi_list(x):
new_x = list()
#print("Praproses ->")
for ix, i in enumerate(x):
new_x.append(pps.praposes(i))
if ix % 100 == 0 and ix != 0:
print(ix, end=" ")
else:
print(".", end="")
print("|")
print("<<Masuk Proses Prediksi>>")
hasil_prediksi_list = SVM.predict(tfidf.transform(new_x).toarray())
dixt = {"prediksi": list(hasil_prediksi_list), "komentar": new_x}
return pd.DataFrame.from_dict(dixt)
# def prediksi_SVM_list_nopps(x):
# # new_x = list()
# # #print("Praproses ->")
# # for ix, i in enumerate(x):
# # new_x.append(pps.praposes(i))
# # if ix%100==0 and ix != 0:
# # print(ix, end=" ")
# # else:
# # print(".", end="")
# print("|")
# print("<<Masuk Proses Prediksi>>")
# hasil_prediksi_list = SVM.predict(tfidf.transform(x).toarray())
# dixt = {
# "prediksi":list(hasil_prediksi_list),
# "komentar":x
# }
return pd.DataFrame.from_dict(dixt)
def prediksi(x, normalisasi = True):
if type(x) is str:
return CNB.predict(tfidf.transform([pps.praposes(x, normalisasi = normalisasi)]).toarray())[0]
elif type(x) is list:
new_x = list()
#print("Praproses ->")
for ix, i in enumerate(x):
new_x.append(pps.praposes(i, normalisasi = normalisasi))
if ix%100==0 and ix != 0:
print(ix, end=" ")
else:
print(".", end="")
print("|")
print("<<Masuk Proses Prediksi>>")
hasil_prediksi_list = CNB.predict(tfidf.transform(new_x).toarray())
dixt = {
"prediksi":list(hasil_prediksi_list),
"komentar":new_x
}
return pd.DataFrame.from_dict(dixt)
else:
return "Masukan harus bertipe string atau list (array)"
def prediksi_list(x, normalisasi=True):
new_x = list()
#print("Praproses ->")
for ix, i in enumerate(x):
new_x.append(pps.praposes(i, normalisasi=normalisasi))
if ix % 100 == 0 and ix != 0:
print(ix, end=" ")
else:
print(".", end="")
print("|")
print("<<Masuk Proses Prediksi>>")
hasil_prediksi_list = GNB.predict(tfidf.transform(new_x).toarray())
dixt = {"prediksi": list(hasil_prediksi_list), "komentar": new_x}
return pd.DataFrame.from_dict(dixt)
def score(x, y):
if type(x) is list:
new_x = list()
#print("Praproses ->")
for ix, i in enumerate(x):
new_x.append(pps.praposes(i))
if ix%100==0 and ix != 0:
print(ix, end=" ")
else:
print(".", end="")
print("|")
print("<<Masuk Proses Prediksi>>")
hasil_prediksi_list = CNB.predict(tfidf.transform(new_x).toarray(), y)
dixt = {
"prediksi":list(hasil_prediksi_list),
"komentar":new_x
}
return pd.DataFrame.from_dict(dixt)
else:
return "Inputan harus bertype list dan memiliki label"
def prediksi_single(x, normalisasi = True):
return CNB.predict(tfidf.transform([pps.praposes(x, normalisasi = normalisasi)]).toarray())[0]
def save_kata ():
pps.savek()
# def prediksi_SVM_list_nopps(x):
# # new_x = list()
# # #print("Praproses ->")
# # for ix, i in enumerate(x):
# # new_x.append(pps.praposes(i))
# # if ix%100==0 and ix != 0:
# # print(ix, end=" ")
# # else:
# # print(".", end="")
# print("|")
# print("<<Masuk Proses Prediksi>>")
# hasil_prediksi_list = SVM.predict(tfidf.transform(x).toarray())
# dixt = {
# "prediksi":list(hasil_prediksi_list),
# "komentar":x
# }
# return pd.DataFrame.from_dict(dixt)
# def prediksi_cnb_single(x):
# return cnb.predict(tfidf_cnb.transform([pps.praposes(x)]).toarray())[0]
# def prediksi_cnb_list(x):
# new_x = list()
# #print("Praproses ->")
# for ix, i in enumerate(x):
# new_x.append(pps.praposes(i))
# if ix%100==0 and ix != 0:
# print(ix, end=" ")
# else:
# print(".", end="")
# print("|")
# print("<<Masuk Proses Prediksi>>")
# hasil_prediksi_list = cnb.predict(tfidf.transform(new_x).toarray())
# dixt = {
# "prediksi":list(hasil_prediksi_list),
# "komentar":new_x
# }
# return pd.DataFrame.from_dict(dixt)
# def prediksi2_single(x):
# return SVM2.predict(tfidf2.transform([pps.praposes(x)]).toarray())[0]
# def prediksi2_list(x):
# new_x = list()
# #print("Praproses ->")
# for ix, i in enumerate(x):
# new_x.append(pps.praposes(i))
# if ix%100==0 and ix != 0:
# print(ix, end=" ")
# else:
# print(".", end="")
# print("|")
# print("<<Masuk Proses Prediksi>>")
# hasil_prediksi_list = SVM2.predict(tfidf2.transform(new_x).toarray())
# dixt = {
# "prediksi":list(hasil_prediksi_list),
# "komentar":new_x
# }
# return pd.DataFrame.from_dict(dixt)
def prediksi_single(x):
return GNB.predict(tfidf.transform([pps.praposes(x)]).toarray())[0]
def prediksi2_single(x):
return SVM2.predict(tfidf2.transform([pps.praposes(x)]).toarray())[0]
def save_kata():
pps.savek()
def prediksi(x, normalisasi=True):
if type(x) is str:
return GNB.predict(
tfidf.transform([pps.praposes(x, normalisasi=normalisasi)
]).toarray())[0]
elif type(x) is list:
new_x = list()
#print("Praproses ->")
for ix, i in enumerate(x):
new_x.append(pps.praposes(i))
if ix % 100 == 0 and ix != 0:
print(ix, end=" ")
else:
print(".", end="")
print("|")
print("<<Masuk Proses Prediksi>>")
hasil_prediksi_list = GNB.predict(tfidf.transform(new_x).toarray())
dixt = {"prediksi": list(hasil_prediksi_list), "komentar": new_x}
return pd.DataFrame.from_dict(dixt)
else:
return "Masukan harus bertipe string atau list (array)"
# def prediksi_SVM_list_nopps(x):
# # new_x = list()
# # #print("Praproses ->")
# # for ix, i in enumerate(x):
# # new_x.append(pps.praposes(i))
# # if ix%100==0 and ix != 0:
# # print(ix, end=" ")
# # else:
# # print(".", end="")
# print("|")
# print("<<Masuk Proses Prediksi>>")
# hasil_prediksi_list = SVM.predict(tfidf.transform(x).toarray())
# dixt = {
# "prediksi":list(hasil_prediksi_list),
# "komentar":x
# }
# return pd.DataFrame.from_dict(dixt)
# def prediksi_cnb_single(x):
# return cnb.predict(tfidf_cnb.transform([pps.praposes(x)]).toarray())[0]
# def prediksi_cnb_list(x):
# new_x = list()
# #print("Praproses ->")
# for ix, i in enumerate(x):
# new_x.append(pps.praposes(i))
# if ix%100==0 and ix != 0:
# print(ix, end=" ")
# else:
# print(".", end="")
# print("|")
# print("<<Masuk Proses Prediksi>>")
# hasil_prediksi_list = cnb.predict(tfidf.transform(new_x).toarray())
# dixt = {
# "prediksi":list(hasil_prediksi_list),
# "komentar":new_x
# }
# return pd.DataFrame.from_dict(dixt)
# def prediksi2_single(x):
# return SVM2.predict(tfidf2.transform([pps.praposes(x)]).toarray())[0]
# def prediksi2_list(x):
# new_x = list()
# #print("Praproses ->")
# for ix, i in enumerate(x):
# new_x.append(pps.praposes(i))
# if ix%100==0 and ix != 0:
# print(ix, end=" ")
# else:
# print(".", end="")
# print("|")
# print("<<Masuk Proses Prediksi>>")
# hasil_prediksi_list = SVM2.predict(tfidf2.transform(new_x).toarray())
# dixt = {
# "prediksi":list(hasil_prediksi_list),
# "komentar":new_x
# }
# return pd.DataFrame.from_dict(dixt)