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
X = [
list(map(int,
x.split(',')[:-1]))
for x in open('covtype.data').read().splitlines()[:SIZE_DATA]
]
_Y = [
x.split(',')[-1]
for x in open('covtype.data').read().splitlines()[:SIZE_DATA]
]
larg = largestClass(_Y)
# treat the largest class as positive, the rest as negative
Y = [1 if x == larg else -1 for x in _Y]
xTrain, xTest, yTrain, yTest = cv.train_test_split(X,
Y,
train_size=5000 / len(X))
# In[2]:
import Classifiers as clfs
clfs.KNN(xTrain, xTest, yTrain, yTest)
clfs.RandomForest(xTrain, xTest, yTrain, yTest)
clfs.BoostedDecisionTree(xTrain, xTest, yTrain, yTest)
clfs.NeuralNets(xTrain, xTest, yTrain, yTest)
#clfs.SVM(xTrain, xTest, yTrain, yTest)
clfs.linearSVC(xTrain, xTest, yTrain, yTest)
import Classifiers as clfs
clfs.XGBoost(xTrain, xTest, yTrain, yTest)
# In[ ]:
