else:
res = model.predict(X)
return res
if __name__ == "__main__":
data = pd.read_csv('training.csv')
test_data = pd.read_csv('testing.csv')
X, test_X, y = preprocessing(data, test_data)
# print(X.shape, test_X.shape)
if Use_library:
model = build_model()
model = fit_model(model, X, y)
res = predict(model, test_X)
else:
model = LogisticRegression(X,
y,
alpha=0.1,
num_iters=50,
regularized=True,
normalization='l2')
params = model.train(X, y, np.unique(y))
classifedLabels = []
for eachData in test_X:
classifedLabels.append(model.classify(eachData, params))
res = np.array(classifedLabels)
if save_res:
np.save('result', res)
npy2csv('result.npy',
"result" + str(np.random.randint(0, 1000)) + ".csv")
