import predictor_knn as knn
import utilitaires as util
import best_formula as bf
func_train = util.adaptData
func_test = util.adaptData
param = util.getParam()
model = knn.model_knn
if __name__ == '__main__':
bf.best_formula(func_train,func_test,param,model)
cl2.fit(train[label], train["Survived"])
cl3.fit(train[label], train["Survived"])
cl4.fit(train[label], train["Survived"])
cl5.fit(train[label], train["Survived"])
cl6.fit(train[label], train["Survived"])
rf.fit(train[label], train["Survived"])
test_predict = pd.DataFrame.copy(test)
test_predict["Survived"] = rf.predict(test_predict[label])
return test_predict
func_train = util.adaptData
func_test = util.adaptData
na = util.getNumAdaptData()
if na == 1:
label = np.asarray(['Pclass', 'Sex', 'Age', 'SibSp', 'Parch'])
elif na == 2:
label = np.asarray(['Pclass', 'Sex', 'Age', 'Fare'])
elif na == 5:
label = np.asarray([
"Age", "Pclass", "Sex", "SibSp", "Parch", "Fare_bin", "EC", "EQ", "ES",
"Family", "Title", "Deck", "ASP"
])
else:
label = util.getParam()
model = model_ensemble
path = "../Predictions/ensemble.csv"
p.predictor(func_test, func_test, label, model, path)
