KNN Regression results in zero MSE on training set (sklearn)

Question

Using sklearn and trying to evaluate a KNN regression function with the below code:

def cross_validate(X,y,n_neighbors, test_size=0.20):
    training_mses = []
    test_mses = []

    n = X.shape[ 0]
    test_n = int( np.round( test_size * n, 0))

    indices = np.arange(n)
    random.shuffle( indices)

    test_indices = indices[ 0:test_n]
    training_indices = indices[test_n:]


    X_test, y_test = X[test_indices], y[test_indices]
    X_train,y_train = X[training_indices], y[training_indices]

    knn = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors, weights = "distance",
                                    algorithm = 'brute')
    model = knn.fit(X_train,y_train)
    y_hat = model.predict( X_train)
    training_mse = mse( y_train - y_hat)

    model2 = knn.fit(X_test,y_test)
    y_hat = model2.predict( X_test)
    test_mse = mse( y_test - y_hat)

    return training_mse, test_mse 

I did something similar with linear regression. The difference I have found is that when I run it on KNN regression, the training_mse and test_mse are both 0. If I use the test data on the model fitted with the training set, it gives me an mse value that is non-zero. But I just don't believe that the fitted values for the training and test set are the same as the observed values. What am I doing wrong? The function I was trying to emulate is below and gives non-zero values for mse:

def cross_validate( formula, data, test_size=0.20):
    training_mses = []
    test_mses = []

    n = data.shape[ 0]
    test_n = int( np.round( test_size * n, 0))

    indices = deepcopy( data.index).values
    random.shuffle( indices)

    test_indices = indices[ 0:test_n]
    training_indices = indices[test_n:]

    test_set = data.ix[ test_indices]
    training_set = data.ix[ training_indices]

    y, X = patsy.dmatrices( formula, training_set, return_type="matrix")
    model = linear.LinearRegression( fit_intercept=False).fit( X, y)
    y_hat = model.predict( X)

    training_mse = mse( y - y_hat)

    y, X = patsy.dmatrices( formula, test_set, return_type="matrix")
    y_hat = model.predict( X)

    test_mse = mse( y - y_hat)

    return training_mse, test_mse