s_exp = "Experiment {} / {} is running...".format(i_exp + 1,n_exp)
print(s_exp)
# getting the explanation
exp = explainer.explain_instance(xi,
my_model,
num_samples=n_samples)
# getting the coefficients of the local model
beta_emp_store[i_exp,:] = format_coefs(exp)
# computing the theoretical values
beta_theo = compute_beta_indicator(xi,rect,nu,my_stats)
###########################################################################
# plotting the results
fig, ax = plt.subplots(figsize=(15,10))
plot_whisker_boxes(beta_emp_store,
ax,
theo=beta_theo)
# saving the fig
s_name = "results/indicator_explanation"
fig.savefig(s_name + '.pdf',format='pdf',bbox_inches = 'tight',pad_inches = 0)
s_exp = "Experiment {} / {} is running...".format(
i_exp + 1, n_exp)
print(s_exp)
# getting the explanation
exp = explainer.explain_instance(xi,
my_model,
num_samples=n_samples)
# getting the coefficients of the surrogate model
beta_emp_store[i_exp, :] = format_coefs(exp)
# getting the values given by theory
default_nu = np.sqrt(0.75 * dim)
beta_theo = compute_beta_linear(xi, f, default_nu, my_stats)
###################################################################
fig, ax = plt.subplots(figsize=(15, 10))
plot_whisker_boxes(
beta_emp_store,
ax,
theo=beta_theo,
title="Coefficients of surrogate model, $p={}$".format(p))
s_name = "results/cancellation_{}_boxes".format(p)
fig.savefig(s_name + '.pdf',
format='pdf',
bbox_inches='tight',
pad_inches=0)
xi = xi_list[i_xi]
ax.scatter(xi[0], xi[1], color='k')
s_xi = r"$\xi_" + str(i_xi + 1) + "$"
ax.annotate(s_xi, (xi[0] + 0.2, xi[1] + 0.2), fontsize=20)
ax.plot()
s_title = "2-D tree regressor"
ax.set_title(s_title, fontsize=40)
ax.tick_params(labelsize=30)
ax.set_xlabel(r"$x_1$", fontsize=30)
ax.set_ylabel(r"$x_2$", fontsize=30)
s_name = "results/general_situation"
fig.savefig(s_name + '.pdf',
format='pdf',
bbox_inches='tight',
pad_inches=0)
# plot empirical vs theory for all the xis
for i_xi in range(n_xi):
fig, ax = plt.subplots(figsize=(5, 10))
s_title = r"Explanation for $\xi_" + str(i_xi + 1) + "$"
plot_whisker_boxes(beta_emp_store[:, :, i_xi],
ax,
theo=beta_theo[:, i_xi],
title=s_title,
ylims=[-0.06, 0.15])
s_name = "results/explanation_xi_{}".format(i_xi + 1)
fig.savefig(s_name + '.pdf',
format='pdf',
bbox_inches='tight',
pad_inches=0)
if np.mod(i_exp + 1, 10) == 0:
s_exp = "Experiment {} / {} is running...".format(i_exp + 1, n_exp)
print(s_exp)
exp = explainer.explain_instance(xi,
tree_regressor.predict,
num_samples=n_samples)
beta_emp_store[i_exp, :] = format_coefs(exp)
# get the theory
# this can take a while if dim or depth are not small
print("Computing the theory...")
beta_theo = compute_beta_tree(xi, nu, tree_regressor, my_stats)
# #################################################################
# plot empirical vs theory
fig, ax = plt.subplots(figsize=(15, 10))
plot_whisker_boxes(
beta_emp_store,
ax,
theo=beta_theo,
title=r"Coefficients of the surrogate model for a CART tree")
s_name = "results/tree_explanation"
fig.savefig(s_name + '.pdf',
format='pdf',
bbox_inches='tight',
pad_inches=0)
matplotlib.rc('ytick', labelsize=15)
# plot the examples and superpixels
fig, axis = plt.subplots(1,2,figsize=(10,5))
digit = mnist.target[samp]
title_str = "Digit: {}".format(digit)
# left panel: digit + segmentation
plot_image_segmentation(axis[0],xi,segments=segments,
indices=indices,
title=title_str,
method="nearest",
out_size=299)
# right panel: empirical results + theory on top
plot_whisker_boxes(betahat_store,
axis[1],
title="Interpretable coefficients",
xlabel="superpixels",
theo=exp_theo,
rotate=False,
ylims=[-0.1,1.1],
feature_names=np.linspace(1,d,d,dtype=int),
color="red")
# save figure
s_name = fig_dir + "shape_detector_" + str(digit) + "_" + str(case)
plt.savefig(s_name + '.pdf', format='pdf', bbox_inches='tight', pad_inches=0, dpi=100)
print(s_exp)
# getting the explanation at xi
exp = explainer.explain_instance(xi,
my_model,
model_regressor=ridge_regressor,
num_samples=n_samples)
beta_emp_store[i_exp, :] = format_coefs(exp)
# getting the theoretical values
beta_theo = compute_beta_linear(xi, f, nu, my_stats)
###############################################################################
# plotting the result
fig, ax = plt.subplots(figsize=(15, 10))
plot_whisker_boxes(
beta_emp_store,
ax,
title="Coefficients of the surrogate model ($\lambda={}$)".format(
lbda),
theo=beta_theo,
ylims=ylims)
# and save it
s_name = "results/regularization_default_linear_lbda_{}".format(lbda)
fig.savefig(s_name + '.pdf',
format='pdf',
bbox_inches='tight',
pad_inches=0)
print(s_exp)
# getting the explanation
exp = explainer.explain_instance(xi, my_model, num_samples=n_samples)
# getting the coefficients of the local model
beta_emp_store[i_exp, :] = format_coefs(exp)
###########################################################################
# getting nicer feature names
#my_features = wine['feature_names']
my_features = [
'alcohol', 'Malic acid', 'ash', 'alcalinity', 'Mg', 'phenols',
'flavanoids', 'non-flavanoid phenol', 'proanthocyanins',
'color intensity', 'hue', 'part of diluted wines', 'proline'
]
# plotting the result
fig, ax = plt.subplots(figsize=(15, 10))
plot_whisker_boxes(beta_emp_store,
ax,
rotate=True,
feature_names=my_features)
s_name = "results/ignore_non_linear_default_weights"
fig.savefig(s_name + '.pdf',
format='pdf',
bbox_inches='tight',
pad_inches=0)
num_samples=n_samples)
# getting the coefficients of the local models
beta_emp_store_1[i_exp, :] = format_coefs(exp_1)
beta_emp_store_2[i_exp, :] = format_coefs(exp_2)
beta_emp_store_sum[i_exp, :] = format_coefs(exp_sum)
beta_mean_1 = np.mean(beta_emp_store_1, 0)
beta_mean_2 = np.mean(beta_emp_store_2, 0)
beta_mean_sum = np.mean(beta_emp_store_sum, 0)
###########################################################################
# plotting the interpretable coefficients for the three models
fig_1, ax_1 = plt.subplots(figsize=(15, 10))
plot_whisker_boxes(beta_emp_store_1, ax_1, theo=beta_mean_1, color="k")
fig_2, ax_2 = plt.subplots(figsize=(15, 10))
plot_whisker_boxes(beta_emp_store_2, ax_2, theo=beta_mean_2, color="k")
fig_sum, ax_sum = plt.subplots(figsize=(15, 10))
plot_whisker_boxes(beta_emp_store_sum,
ax_sum,
theo=beta_mean_sum,
color="red")
# saving the fig
fig_1.savefig("results/linearity_of_explanations_1.pdf",
format='pdf',
bbox_inches='tight',
pad_inches=0)