return krr_model.predict(array)
# dimension of the ambient space
dim = X_scaled.shape[1]
# the example to explain
xi = X_scaled[0, :]
# grid of bandwidths
n_nu = 50
nu_min = 0.001
nu_max = 4
nu_grid = np.linspace(nu_min, nu_max, num=n_nu)
# get the summary of the training data
my_stats = get_training_data_stats(X_scaled, p)
coef_store = np.zeros((n_nu, dim + 1))
std_store = np.zeros((n_nu, dim + 1))
beta_emp_store = np.zeros((n_nu, n_exp, dim + 1))
# main loop: for each bandwidth obtain many LIME explanations
for i_nu in range(n_nu):
nu = nu_grid[i_nu]
s_nu = "bandwidth: {}".format(nu)
print(s_nu)
# creating the explainer
explainer = lime.lime_tabular.LimeTabularExplainer(
X_orig,
# the example to explain
xi = np.random.uniform(-10,10,(dim,))
# training set
n_train = 1000
train = np.random.uniform(-10,10,(n_train,dim))
# bandwidth parameter
nu = 10
# number of bins along each dimension
p = 10
# creating the discretizer
my_stats = get_training_data_stats(train,p)
# creating the explainer
explainer = lime.lime_tabular.LimeTabularExplainer(train,
mode='regression',
feature_selection='none',
training_data_stats=my_stats,
kernel_width=nu)
beta_emp_store = np.zeros((n_exp,dim+1))
for i_exp in range(n_exp):
if np.mod(i_exp + 1,10) == 0:
s_exp = "Experiment {} / {} is running...".format(i_exp + 1,n_exp)
print(s_exp)
# the example to explain
xi = np.random.uniform(-10, 10, (dim, ))
# training set: uniform on [-10,10]^dim
n_train = 1000
train_set = np.random.uniform(-10, 10, (n_train, dim))
# bandwidth parameter
nu = 10
# number of bins
p = 4
# creating the discretizer
my_stats = get_training_data_stats(train_set, p)
# creating the explainer
explainer = lime.lime_tabular.LimeTabularExplainer(
train_set,
mode='regression',
feature_selection='none',
training_data_stats=my_stats,
kernel_width=nu)
beta_emp_store_1 = np.zeros((n_exp, dim + 1))
beta_emp_store_2 = np.zeros((n_exp, dim + 1))
beta_emp_store_sum = np.zeros((n_exp, dim + 1))
for i_exp in range(n_exp):
if np.mod(i_exp + 1, 10) == 0: