Using GridSearchCV with a set of multiple counter errors

Question

I try to use GridSearchCV to optimize the analysis that I do, and I read that it supports several evaluation methods , and I found an example of this method elsewhere ( example ), but when I try to run GridSearchCV with several evaluation indicators in several formats, which must be supported, this causes an error:

File "/home/graduate/adsherma/miniconda2/envs/testenv/lib/python2.7/site-packages/sklearn/model_selection/_validation.py", line 288, in _score
  score = scorer(estimator, X_test, y_test)
TypeError: 'dict' object is not callable

My source code for this:

DF = pd.read_pickle("OutPut/from_ntuples/nominal.pkl")
X = DF[RC.FittableParameters]
y = DF['isSignal']

pipe = Pipeline([
    ('reduce_dim', SelectKBest()),
    ('classify', AdaBoostClassifier())
])

BASE_ESTIMATORS = [DecisionTreeClassifier(max_depth=i) for i in range(1, 4)]
N_ESTIMATORS = range(100, 600, 100)

param_grid = [
    {
        'reduce_dim': [PCA()],
        'reduce_dim__n_components': [1,10,20,30,40,50],
        'classify__base_estimator': BASE_ESTIMATORS, 
        'classify__n_estimators': N_ESTIMATORS, 
    } ,
]
scoring = {'Precision': make_scorer(precision_score), 
    'Accuracy': make_scorer(accuracy_score)} #does not work
# scoring = ['accuracy', 'precision'] #Does not work
# scoring = 'accuracy' #works
# scoring = make_scorer(accuracy_score) #works
grid = GridSearchCV(pipe, cv=5, n_jobs=1, 
                    param_grid=param_grid, verbose=1,
                    scoring=scoring)
grid.fit(X, y)

The error will be the same if I try a list or tuple, but I complain that lists and tuples are not callable. This is basically a copy inserted from the link link above (cannot be attached because I don't have enough reputation), so I don’t understand a bit where to continue.