def main(met_train, met_test, aqi_train, aqi_test, test):
while True:
ch = int(
input("\n\nchose among the following classifier\n"
"1.Rnadom Forrest\n"
"2.K-NN\n"
"3.SVM\n"
"4.Decision Tree\n"
"5.exit\n"))
if ch == 1:
model, accuracy = Classifiers.Random_Forest_Classifier(
met_train, met_test, aqi_train, aqi_test)
print(model.predict(test))
print(accuracy)
elif ch == 2:
model, accuracy = Classifiers.KNN(met_train, met_test, aqi_train,
aqi_test)
print(model.predict(test))
print(accuracy)
elif ch == 3:
model, accuracy = Classifiers.SVM(met_train, met_test, aqi_train,
aqi_test)
print(model.predict(test))
print(accuracy)
elif ch == 4:
model, accuracy = Classifiers.Decision_tree(
met_train, met_test, aqi_train, aqi_test)
print(model.predict(test))
print(accuracy)
elif ch == 5:
break
if sys.argv[1] == "gbc":
Traindata, TrainLabels, testdata, testlabels = convert_to_tfidf(
Traindata, TrainLabels, testdata, testlabels)
clf.XGBoost(Traindata, TrainLabels, testdata, testlabels)
if sys.argv[1] == "abc":
Traindata, TrainLabels, testdata, testlabels = convert_to_tfidf(
Traindata, TrainLabels, testdata, testlabels)
clf.ADABoost(Traindata, TrainLabels, testdata, testlabels)
if sys.argv[1] == "nn":
Traindata, TrainLabels, testdata, testlabels = convert_to_tfidf(
Traindata, TrainLabels, testdata, testlabels)
clf.NN(Traindata, TrainLabels, testdata, testlabels)
if sys.argv[1] == "dt":
Traindata, TrainLabels, testdata, testlabels = convert_to_tfidf(
Traindata, TrainLabels, testdata, testlabels)
clf.Decision_tree(Traindata, TrainLabels, testdata, testlabels)
if sys.argv[1] == "automl":
Traindata, TrainLabels, testdata, testlabels = convert_to_tfidf(
Traindata, TrainLabels, testdata, testlabels)
clf.autoML(Traindata, TrainLabels, testdata, testlabels)
if sys.argv[1] == "lstm":
Traindata, TrainLabels, testdata, testlabels, word2id_map, id2word_map = convert_to_word_embeddings(
Traindata, TrainLabels, testdata, testlabels)
W = get_embedding_weights("word2vec", word2id_map)
dl_models.LSTM_train(Traindata, TrainLabels, testdata, testlabels,
word2id_map, W)
if sys.argv[1] == "cnn":
Traindata, TrainLabels, testdata, testlabels, word2id_map, id2word_map = convert_to_word_embeddings(
Traindata, TrainLabels, testdata, testlabels)
W = get_embedding_weights("word2vec", word2id_map)
dl_models.CNN_train(Traindata, TrainLabels, testdata, testlabels,
