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[ ]:
if sys.argv[1] == "lr":
Traindata, TrainLabels, testdata, testlabels = convert_to_tfidf(
Traindata, TrainLabels, testdata, testlabels)
clf.lr(Traindata, TrainLabels, testdata, testlabels)
if sys.argv[1] == "svm":
Traindata, TrainLabels, testdata, testlabels = convert_to_tfidf(
Traindata, TrainLabels, testdata, testlabels)
clf.SVM_predict(Traindata, TrainLabels, testdata, testlabels)
if sys.argv[1] == "rfc":
Traindata, TrainLabels, testdata, testlabels = convert_to_tfidf(
Traindata, TrainLabels, testdata, testlabels)
clf.randomforest_predict(Traindata, TrainLabels, testdata, testlabels)
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)
