Stacking scikit, Ensemble Machine Learning With Python

Question

I am trying to create a stack with various algorithms to compare their performances, taking into consideration the feature scales I created which are in dictionary format.

I ave the stack code here, which gives me this error: TypeError: 'numpy.float64' object is not callable

My X and y are respectively:

array([[  2.47475454,   0.40165523,   1.68081787, ...,  -6.59044146,
         -2.21290585,  -3.139579  ],
       [  0.84802507,   2.81841945,  -2.76008732, ...,   3.00844461,
          0.78661954,  -1.27681551],
       [ -1.90041246,  -0.56901823,  -1.76220236, ...,   3.37336417,
         -2.28613707,   1.90344983],

array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,
       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,
       0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0

This is the stacking code:

from sklearn.datasets import make_classification 
from sklearn.model_selection import RepeatedStratifiedKFold

# get the dataset
def get_dataset():
    return X, y

def get_models():
    models = dict()
    models['lr'] = LogisticRegression()
    models['knn'] = KNeighborsClassifier()
    models['cart'] = DecisionTreeClassifier()
    models['svm'] = SVC()
    models['bayes'] = GaussianNB()
    return (models)
    
def evaluate_model(model, X, y):
    cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
    scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')
    return scores

# define dataset
X, y = get_dataset()

# get the models to evaluate
models = get_models()

# evaluate the models and store results
results, names = list(), list()
for name, model in models.items():
    scores = evaluate_model(model, X, y)
    results.append(scores)
    names.append(name)
    print('>%s %.3f (%.3f)' % (names, mean(scores), std(scores)))
    
# plot model performance for comparison
pyplot.boxplot(results, labels=names, showmeans=True)
pyplot.show()

I would like to fix this error, which I dont understand why its being called... then I would like to somehow include the iteration on the feature scales for normalisation etc