def explain(self, options, instance=None):
initjs()
background_data = self._kmeans(options['kmeans_count']) \
if options['background_data'] == 'kmeans' else options['data']
nsamples = 'auto' if options['auto_nsamples'] else options['nsamples']
explainer = KernelExplainer(model=self.predict_function,
data=background_data,
link=options['link'])
# create sample from train data
data = self.X_train[np.random.choice(
self.X_train.shape[0], size=options['sample_size'], replace=False
), :]
shap_values = explainer.shap_values(X=data,
nsamples=nsamples,
l1_reg=options['l1_reg'])
# limit to only selected class (if any was selected)
if 'class_to_explain' in options and options['class_to_explain'] != -1:
shap_values = shap_values[options['class_to_explain']]
summary_plot(shap_values=shap_values,
features=data,
feature_names=self.feature_names,
plot_type='bar',
class_names=self.class_names)
def main():
n_features = 100
random_state = 0
X_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'), categories=None).data
X_train = preprocess_data(X_train)
print('qui')
stc = generate_synthetic_text_classifier(X_train, n_features=n_features, random_state=random_state)
predict = stc['predict']
predict_proba = stc['predict_proba']
words = stc['words_vec']
vectorizer = stc['vectorizer']
nbr_terms = stc['nbr_terms']
X_test = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'), categories=None).data
X_test = preprocess_data(X_test)
X_test = vectorizer.transform(X_test).toarray()
print('quo')
# reference = np.zeros(nbr_terms)
# explainer = KernelExplainer(predict_proba, np.reshape(reference, (1, len(reference))))
sentences_length = list()
for x in X_test:
sentences_length.append(len(np.where(x != 0)[0]))
print('qua')
avg_nbr_words = np.mean(sentences_length)
std_nbr_words = np.std(sentences_length)
words_with_weight = np.where(words != 0)[0]
print(avg_nbr_words, std_nbr_words)
print(words_with_weight)
reference = list()
for i in range(10):
nbr_words_in_sentence = int(np.random.normal(avg_nbr_words, std_nbr_words))
selected_words = np.random.choice(range(nbr_terms), size=nbr_words_in_sentence, replace=False)
print(i, nbr_words_in_sentence, len(set(selected_words) & set(words_with_weight)))
while len(set(selected_words) & set(words_with_weight)) > 0:
nbr_words_in_sentence = int(np.random.normal(avg_nbr_words, std_nbr_words))
selected_words = np.random.choice(range(nbr_terms), size=nbr_words_in_sentence, replace=False)
print(i, nbr_words_in_sentence, len(set(selected_words) & set(words_with_weight)))
sentence = np.zeros(nbr_terms)
sentence[selected_words] = 1.0
reference.append(sentence)
print('')
reference = np.array(reference)
print(reference)
explainer = KernelExplainer(predict_proba, reference) #X_test[:10])
for x in X_test[:10]:
expl_val = explainer.shap_values(x, l1_reg='bic')[1]
gt_val = get_word_importance_explanation(x, stc)
wbs = word_based_similarity(expl_val, gt_val, use_values=False)
# print(expl_val)
# print(gt_val)
print(wbs, word_based_similarity(expl_val, gt_val, use_values=True))
print('')
def LIME_graphSHAP(X, y, feature_names, ylabels, clf, xs_toexplain, labels_toexplain, ax=None, subplots=False, plotlime=True):
## Plot explanations on feature space
if ax is None:
if subplots:
if len(xs_toexplain)>=5:
nrows = int(len(xs_toexplain)/5)
fig, axs = plt.subplots(nrows=nrows, ncols=int(len(xs_toexplain)/nrows), figsize=(15,3*nrows))
axs = axs.flatten()
else:
fig, axs = plt.subplots(nrows=1, ncols=len(xs_toexplain), figsize=(15,3))
else:
fig, ax = plt.subplots()
# Plot LIME result - loop if several
for i in range(len(xs_toexplain)):
if subplots:
plt.sca(axs[i])
ax = axs[i]
if i==0 or subplots:
# Plot contour of black-box predictions
plot_classification_contour(X, clf, ax)
# Plot training set
plot_training_set(X, y, ax)
ylim_bak = ax.get_ylim()
xlim_bak = ax.get_xlim()
#color_palette = sns.color_palette("bright", n_colors=len(xs_toexplain))
color_palette = ['lime' for _ in range(len(xs_toexplain))]
## LIME - Generate explanations
explainer = lime_assessment.lime_tabular.LimeTabularExplainer(X, feature_names=feature_names, class_names=ylabels, discretize_continuous=False, kernel_width=None)
exp = explainer.explain_instance(xs_toexplain[i], clf.predict_proba, num_features=2, top_labels=len(ylabels), labels=range(len(ylabels)))
shap_explainer = KernelExplainer(clf.predict_proba, X, nsamples=10000)
e = shap_explainer.explain(np.reshape(xs_toexplain[i], (1, X.shape[1])))
# Plot LIME regression
if plotlime == True:
plot_lime_regression(X, exp, xs_toexplain[i], labels_toexplain[i], ax, color_palette[i], exp.points_to_plot)
x_ridge = [-10, 10]
row = 0
y_shap = [(0.5 - e.effects[0, row] * x - e.base_value[row])/e.effects[1, row] for x in x_ridge]
# Plot LIME linear regression
plt.sca(ax)
plt.plot(x_ridge, y_shap, color='red', linestyle=':', linewidth=4, label="other shap regression")
plt.scatter(xs_toexplain[i][0], xs_toexplain[i][1], color='lime', marker='8', linewidth=4)
plt.ylim(ylim_bak)
plt.xlim(xlim_bak)
def fit(self,
explainer,
new_observation,
shap_explainer_type=None,
**kwargs):
"""Calculate the result of explanation
Fit method makes calculations in place and changes the attributes.
Parameters
-----------
explainer : Explainer object
Model wrapper created using the Explainer class.
new_observation : pd.Series or np.ndarray
An observation for which a prediction needs to be explained.
shap_explainer_type : {'TreeExplainer', 'DeepExplainer', 'GradientExplainer', 'LinearExplainer', 'KernelExplainer'}
String name of the Explainer class (default is `None`, which automatically
chooses an Explainer to use).
kwargs: dict
Keyword parameters passed to the `shapley_values` method.
Returns
-----------
None
"""
from shap import TreeExplainer, DeepExplainer, GradientExplainer, LinearExplainer, KernelExplainer
checks.check_compatibility(explainer)
shap_explainer_type = checks.check_shap_explainer_type(
shap_explainer_type, explainer.model)
if self.type == 'predict_parts':
new_observation = checks.check_new_observation_predict_parts(
new_observation, explainer)
if shap_explainer_type == "TreeExplainer":
try:
self.shap_explainer = TreeExplainer(explainer.model,
explainer.data.values)
except: # https://github.com/ModelOriented/DALEX/issues/371
self.shap_explainer = TreeExplainer(explainer.model)
elif shap_explainer_type == "DeepExplainer":
self.shap_explainer = DeepExplainer(explainer.model,
explainer.data.values)
elif shap_explainer_type == "GradientExplainer":
self.shap_explainer = GradientExplainer(explainer.model,
explainer.data.values)
elif shap_explainer_type == "LinearExplainer":
self.shap_explainer = LinearExplainer(explainer.model,
explainer.data.values)
elif shap_explainer_type == "KernelExplainer":
self.shap_explainer = KernelExplainer(
lambda x: explainer.predict(x), explainer.data.values)
self.result = self.shap_explainer.shap_values(new_observation.values,
**kwargs)
self.new_observation = new_observation
self.shap_explainer_type = shap_explainer_type
def main(model, i):
with open(f'{model}-models.pkl', 'rb') as fh:
models = pickle.load(fh)
sampled = np.load(f'{model}-selected.npy', allow_pickle=True)
sampled_indices = sampled[-1]
def model_wrapper(X, i=0):
mu, var = predict_coregionalized(models[0], X, int(i))
return mu.flatten()
shap0 = KernelExplainer(partial(model_wrapper, i=int(i)),
shap.kmeans(X, 40))
shap0_values = shap0.shap_values(X)
np.save(f'{model}-shap-{i}', shap0_values)
def explain(self, options, instance=None):
if instance is None:
raise ValueError("Instance was not provided")
initjs()
instance = instance.to_numpy()
data = self._kmeans(options['kmeans_count']) \
if options['background_data'] == 'kmeans' else options['data']
nsamples = 'auto' if options['auto_nsamples'] else options['nsamples']
explainer = KernelExplainer(model=self.predict_function,
data=data,
link=options['link'])
shap_values = explainer.shap_values(X=instance,
nsamples=nsamples,
l1_reg=options['l1_reg'])
if self.is_classification:
shap_values = shap_values[options['class_to_explain']]
base_value = explainer.expected_value[[
options['class_to_explain']
]]
else:
base_value = explainer.expected_value
if options['plot_type'] == 'force' or options['plot_type'] == 'both':
display(
force_plot(base_value=base_value,
shap_values=shap_values,
features=instance,
feature_names=self.feature_names,
show=True,
link=options['link']))
if options['plot_type'] == 'decision' or options['plot_type'] == 'both':
decision_plot(base_value=base_value,
shap_values=shap_values,
features=instance,
feature_names=list(self.feature_names),
show=True,
color_bar=True,
link=options['link'])
def test_front_page_model_agnostic():
from shap import KernelExplainer, DenseData, visualize, initjs
from sklearn import datasets,neighbors
from numpy import random, arange
# print the JS visualization code to the notebook
initjs()
# train a k-nearest neighbors classifier on a random subset
iris = datasets.load_iris()
random.seed(2)
inds = arange(len(iris.target))
random.shuffle(inds)
knn = neighbors.KNeighborsClassifier()
knn.fit(iris.data, iris.target == 0)
# use Shap to explain a single prediction
background = DenseData(iris.data[inds[:100],:], iris.feature_names) # name the features
explainer = KernelExplainer(knn.predict, background, nsamples=100)#knn.predict
x = iris.data[inds[102:103],:]
visualize(explainer.explain(x))
def _explain_other_models(
model: Model,
transformed_data: Table,
transformed_reference_data: Table,
progress_callback: Callable,
) -> Tuple[List[np.ndarray], np.ndarray, np.ndarray]:
"""
Computes SHAP values for any learner with KernelExplainer.
"""
# 1000 is a number that for normal data and model do not take so long
data_sample, sample_mask = _subsample_data(transformed_data, 1000)
try:
ref = kmeans(transformed_reference_data.X, k=10)
except ValueError:
# k-means fails with value error when it cannot produce enough clusters
# in this case we will use sample instead of clusters
ref = sample(transformed_reference_data.X, nsamples=100)
explainer = KernelExplainer(
lambda x:
(model(x)
if model.domain.class_var.is_continuous else model(x, model.Probs)),
ref,
)
shap_values = []
for i, row in enumerate(data_sample.X):
progress_callback(i / len(data_sample))
shap_values.append(
explainer.shap_values(row,
nsamples=100,
silent=True,
l1_reg="num_features(90)"))
return (
_join_shap_values(shap_values),
sample_mask,
explainer.expected_value,
)
class ShapExplainer(Explainer):
def __init__(self, model, training_data):
super(ShapExplainer, self).__init__(model, training_data)
data = self.preprocessor.transform(training_data)
background_data = kmeans(data, 10)
self.explainer = KernelExplainer(model=self.model.predict_proba,
data=background_data)
def explain(self, instance, budget):
instance = self.preprocessor.transform([instance])[0]
values = self.explainer.shap_values(X=instance,
nsamples="auto",
l1_reg='num_features(%d)' %
budget)[1]
pairs = sorted(zip(self.feature_names, values),
key=lambda x: abs(x[1]),
reverse=True)
return pairs[:budget]
def main():
n_features = (20, 20)
img_size = (32, 32, 3)
cell_size = (4, 4)
colors_p = np.array([0.15, 0.7, 0.15])
p_border = 1.0
# img_draft = np.array([ # ['k', 'k', 'k', 'k', 'k', 'k', 'k', 'k'],
# ['k', 'k', 'k', 'k', 'k', 'g', 'r', 'k'],
# ['g', 'k', 'k', 'k', 'k', 'k', 'k', 'g'],
# ['k', 'g', 'k', 'k', 'k', 'b', 'k', 'k'],
# ['k', 'g', 'k', 'k', 'g', 'g', 'k', 'b'],
# ['k', 'k', 'k', 'k', 'g', 'k', 'k', 'g'],
# ['g', 'k', 'k', 'k', 'k', 'k', 'k', 'k'],
# ['k', 'k', 'k', 'k', 'k', 'k', 'k', 'k'],
# ['k', 'k', 'k', 'k', 'g', 'k', 'k', 'k'],
#
# ])
# img = generate_img_defined(img_draft, img_size=img_size, cell_size=cell_size)
# plt.imshow(img)
# plt.xticks(())
# plt.yticks(())
# # plt.savefig('../fig/pattern.png', format='png', bbox_inches='tight')
# plt.show()
pattern_draft = np.array([ # ['k', 'k', 'k', 'k', 'k', 'k', 'k', 'k'],
['k', 'k', 'k', 'k', 'k'],
['k', 'k', 'k', 'b', 'k'],
['k', 'k', 'g', 'g', 'k'],
['k', 'k', 'g', 'k', 'k'],
['k', 'k', 'k', 'k', 'k'],
])
pattern = generate_img_defined(pattern_draft,
img_size=(20, 20, 3),
cell_size=cell_size)
sic = generate_synthetic_image_classifier(img_size=img_size,
cell_size=cell_size,
n_features=n_features,
p_border=p_border,
pattern=pattern)
pattern = sic['pattern']
predict = sic['predict']
predict_proba = sic['predict_proba']
plt.imshow(pattern)
plt.xticks(())
plt.yticks(())
# plt.savefig('../fig/pattern.png', format='png', bbox_inches='tight')
plt.show()
X_test = generate_random_img_dataset(pattern,
nbr_images=1000,
pattern_ratio=0.4,
img_size=img_size,
cell_size=cell_size,
min_nbr_cells=0.1,
max_nbr_cells=0.3,
colors_p=colors_p)
Y_test = predict(X_test)
idx = np.where(Y_test == 1)[0][0]
# x = X_test[idx]
img_draft = np.array([ # ['k', 'k', 'k', 'k', 'k', 'k', 'k', 'k'],
['k', 'k', 'k', 'k', 'k', 'g', 'r', 'k'],
['g', 'k', 'k', 'k', 'k', 'k', 'k', 'g'],
['k', 'g', 'k', 'k', 'k', 'b', 'k', 'k'],
['k', 'g', 'k', 'k', 'g', 'g', 'k', 'b'],
['k', 'k', 'k', 'k', 'g', 'k', 'k', 'g'],
['g', 'k', 'k', 'k', 'k', 'k', 'k', 'k'],
['k', 'k', 'k', 'k', 'k', 'k', 'k', 'k'],
['k', 'k', 'k', 'k', 'g', 'k', 'k', 'k'],
])
x = generate_img_defined(img_draft, img_size=img_size, cell_size=cell_size)
plt.imshow(x)
plt.xticks(())
plt.yticks(())
# plt.savefig('../fig/image.png', format='png', bbox_inches='tight')
plt.show()
gt_val = get_pixel_importance_explanation(x, sic)
max_val = np.nanpercentile(np.abs(gt_val), 99.9)
plt.imshow(np.reshape(gt_val, img_size[:2]),
cmap='RdYlBu',
vmin=-max_val,
vmax=max_val,
alpha=0.7)
plt.xticks(())
plt.yticks(())
# plt.savefig('../fig/saliencymap.png', format='png', bbox_inches='tight')
plt.show()
# plt.imshow(x)
# plt.imshow(np.reshape(gt_val, img_size[:2]), cmap='RdYlBu', vmin=-max_val, vmax=max_val, alpha=0.7)
# plt.xticks(())
# plt.yticks(())
# plt.savefig('../fig/saliencymap2.png', format='png', bbox_inches='tight')
# plt.show()
lime_explainer = LimeImageExplainer()
segmenter = SegmentationAlgorithm('quickshift',
kernel_size=1,
max_dist=10,
ratio=0.5)
tot_num_features = img_size[0] * img_size[1]
lime_exp = lime_explainer.explain_instance(x,
predict_proba,
top_labels=2,
hide_color=0,
num_samples=10000,
segmentation_fn=segmenter)
_, lime_expl_val = lime_exp.get_image_and_mask(
1,
positive_only=True,
num_features=tot_num_features,
hide_rest=False,
min_weight=0.0)
max_val = np.nanpercentile(np.abs(lime_expl_val), 99.9)
plt.imshow(lime_expl_val,
cmap='RdYlBu',
vmin=-max_val,
vmax=max_val,
alpha=0.7)
plt.xticks(())
plt.yticks(())
plt.title('lime', fontsize=20)
plt.savefig('../fig/saliencymap_lime.png',
format='png',
bbox_inches='tight')
plt.show()
background = np.array([np.zeros(img_size).ravel()] * 10)
shap_explainer = KernelExplainer(predict_proba, background)
shap_expl_val = shap_explainer.shap_values(x.ravel(), l1_reg='bic')[1]
shap_expl_val = np.sum(np.reshape(shap_expl_val, img_size), axis=2)
tmp = np.zeros(shap_expl_val.shape)
tmp[np.where(shap_expl_val > 0.0)] = 1.0
shap_expl_val = tmp
max_val = np.nanpercentile(np.abs(shap_expl_val), 99.9)
plt.imshow(shap_expl_val,
cmap='RdYlBu',
vmin=-max_val,
vmax=max_val,
alpha=0.7)
plt.xticks(())
plt.yticks(())
plt.title('shap', fontsize=20)
plt.savefig('../fig/saliencymap_shap.png',
format='png',
bbox_inches='tight')
plt.show()
nbr_records = 10
Xm_test = np.array([x.ravel() for x in X_test[:nbr_records]])
maple_explainer = MAPLE(Xm_test,
Y_test[:nbr_records],
Xm_test,
Y_test[:nbr_records],
n_estimators=5,
max_features=0.5,
min_samples_leaf=5)
maple_exp = maple_explainer.explain(x)
maple_expl_val = maple_exp['coefs'][:-1]
maple_expl_val = np.sum(np.reshape(maple_expl_val, img_size), axis=2)
tmp = np.zeros(maple_expl_val.shape)
tmp[np.where(maple_expl_val > 0.0)] = 1.0
maple_expl_val = tmp
max_val = np.nanpercentile(np.abs(shap_expl_val), 99.9)
plt.imshow(maple_expl_val,
cmap='RdYlBu',
vmin=-max_val,
vmax=max_val,
alpha=0.7)
plt.xticks(())
plt.yticks(())
plt.title('maple', fontsize=20)
plt.savefig('../fig/saliencymap_maple.png',
format='png',
bbox_inches='tight')
plt.show()
lime_f1, lime_pre, lime_rec = pixel_based_similarity(lime_expl_val.ravel(),
gt_val,
ret_pre_rec=True)
shap_f1, shap_pre, shap_rec = pixel_based_similarity(shap_expl_val.ravel(),
gt_val,
ret_pre_rec=True)
maple_f1, maple_pre, maple_rec = pixel_based_similarity(
maple_expl_val.ravel(), gt_val, ret_pre_rec=True)
print(lime_f1, lime_pre, lime_rec)
print(shap_f1, shap_pre, shap_rec)
print(maple_f1, maple_pre, maple_rec)
class ShapWrapper:
"""Explanation wrapper for the 'shap' package
This object uses the shap package to create the model explanation.
See ttps://github.com/slundberg/shap
Parameters
----------
type : {'predict_parts', 'model_parts'}
Attributes
----------
result : list or numpy.ndarray
Calculated shap values for `new_observation` data.
shap_explainer : {shap.TreeExplainer, shap.DeepExplainer,
shap.GradientExplainer, shap.LinearExplainer, shap.KernelExplainer}
Explainer object from the 'shap' package.
shap_explainer_type : {'TreeExplainer', 'DeepExplainer',
'GradientExplainer', 'LinearExplainer', 'KernelExplainer'}
String name of the Explainer class.
new_observation : pandas.Series or pandas.DataFrame
Observations for which the shap values will be calculated
(later stored in `result`).
type : {'predict_parts', 'model_parts'}
Notes
----------
https://github.com/slundberg/shap
"""
def __init__(self, type):
self.shap_explainer = None
self.type = type
self.result = None
self.new_observation = None
self.shap_explainer_type = None
def _repr_html_(self):
return self.result._repr_html_()
def fit(self,
explainer,
new_observation,
shap_explainer_type=None,
**kwargs):
"""Calculate the result of explanation
Fit method makes calculations in place and changes the attributes.
Parameters
-----------
explainer : Explainer object
Model wrapper created using the Explainer class.
new_observation : pd.Series or np.ndarray
An observation for which a prediction needs to be explained.
shap_explainer_type : {'TreeExplainer', 'DeepExplainer',
'GradientExplainer', 'LinearExplainer', 'KernelExplainer'}
String name of the Explainer class (default is None, which automatically
chooses an Explainer to use).
Returns
-----------
None
"""
from shap import TreeExplainer, DeepExplainer, GradientExplainer, LinearExplainer, KernelExplainer
check_compatibility(explainer)
shap_explainer_type = check_shap_explainer_type(
shap_explainer_type, explainer.model)
if self.type == 'predict_parts':
new_observation = check_new_observation_predict_parts(
new_observation, explainer)
if shap_explainer_type == "TreeExplainer":
self.shap_explainer = TreeExplainer(explainer.model,
explainer.data.values)
elif shap_explainer_type == "DeepExplainer":
self.shap_explainer = DeepExplainer(explainer.model,
explainer.data.values)
elif shap_explainer_type == "GradientExplainer":
self.shap_explainer = GradientExplainer(explainer.model,
explainer.data.values)
elif shap_explainer_type == "LinearExplainer":
self.shap_explainer = LinearExplainer(explainer.model,
explainer.data.values)
elif shap_explainer_type == "KernelExplainer":
self.shap_explainer = KernelExplainer(
lambda x: explainer.predict(x), explainer.data.values)
self.result = self.shap_explainer.shap_values(new_observation.values,
**kwargs)
self.new_observation = new_observation
self.shap_explainer_type = shap_explainer_type
def plot(self, **kwargs):
"""Plot the Shap Wrapper
Parameters
----------
kwargs :
Keyword arguments passed to one of the:
- shap.force_plot when type is 'predict_parts'
- shap.summary_plot when type is 'model_parts'
Exceptions are: `base_value`, `shap_values`,
`features` and `feature_names`.
Other parameters: https://github.com/slundberg/shap
Returns
-----------
None
Notes
--------
https://github.com/slundberg/shap
"""
from shap import force_plot, summary_plot
if self.type == 'predict_parts':
if isinstance(self.shap_explainer.expected_value,
(np.ndarray, list)):
base_value = self.shap_explainer.expected_value[1]
else:
base_value = self.shap_explainer.expected_value
shap_values = self.result[1] if isinstance(self.result,
list) else self.result
force_plot(base_value=base_value,
shap_values=shap_values,
features=self.new_observation.values,
feature_names=self.new_observation.columns,
matplotlib=True,
**kwargs)
elif self.type == 'model_parts':
summary_plot(shap_values=self.result,
features=self.new_observation,
**kwargs)
def run(black_box, n_records, img_size, cell_size, n_features, p_border,
colors_p, random_state, filename):
sic = generate_synthetic_image_classifier(img_size=img_size,
cell_size=cell_size,
n_features=n_features,
p_border=p_border,
random_state=random_state)
pattern = sic['pattern']
predict = sic['predict']
predict_proba = sic['predict_proba']
X_test = generate_random_img_dataset(pattern,
nbr_images=n_records,
pattern_ratio=0.5,
img_size=img_size,
cell_size=cell_size,
min_nbr_cells=0.1,
max_nbr_cells=0.3,
colors_p=colors_p)
Y_test_proba = predict_proba(X_test)
Y_test = predict(X_test)
lime_explainer = LimeImageExplainer()
segmenter = SegmentationAlgorithm('quickshift',
kernel_size=1,
max_dist=10,
ratio=0.5)
tot_num_features = img_size[0] * img_size[1]
background = np.array([np.zeros(img_size).ravel()] * 10)
shap_explainer = KernelExplainer(predict_proba, background)
nbr_records_explainer = 10
idx_records_train_expl = np.random.choice(range(len(X_test)),
size=nbr_records_explainer,
replace=False)
idx_records_test_expl = np.random.choice(range(len(X_test)),
size=nbr_records_explainer,
replace=False)
Xm_train = np.array([x.ravel() for x in X_test[idx_records_train_expl]])
Xm_test = np.array([x.ravel() for x in X_test[idx_records_test_expl]])
print(datetime.datetime.now(), 'build maple')
maple_explainer = MAPLE(Xm_train,
Y_test_proba[idx_records_train_expl][:, 1],
Xm_test,
Y_test_proba[idx_records_test_expl][:, 1],
n_estimators=100,
max_features=0.5,
min_samples_leaf=2)
print(datetime.datetime.now(), 'build maple done')
idx = 0
results = list()
for x, y in zip(X_test, Y_test):
print(datetime.datetime.now(),
'seneca - image',
'black_box %s' % black_box,
'n_features %s' % str(n_features),
'rs %s' % random_state,
'%s/%s' % (idx, n_records),
end=' ')
gt_val = get_pixel_importance_explanation(x, sic)
lime_exp = lime_explainer.explain_instance(x,
predict_proba,
top_labels=2,
hide_color=0,
num_samples=10000,
segmentation_fn=segmenter)
_, lime_expl_val = lime_exp.get_image_and_mask(
y,
positive_only=True,
num_features=tot_num_features,
hide_rest=False,
min_weight=0.0)
shap_expl_val = shap_explainer.shap_values(x.ravel(), l1_reg='bic')[1]
shap_expl_val = np.sum(np.reshape(shap_expl_val, img_size), axis=2)
tmp = np.zeros(shap_expl_val.shape)
tmp[np.where(shap_expl_val > 0.0)] = 1.0
shap_expl_val = tmp
maple_exp = maple_explainer.explain(x)
maple_expl_val = maple_exp['coefs'][:-1]
maple_expl_val = np.sum(np.reshape(maple_expl_val, img_size), axis=2)
tmp = np.zeros(maple_expl_val.shape)
tmp[np.where(maple_expl_val > 0.0)] = 1.0
maple_expl_val = tmp
lime_f1, lime_pre, lime_rec = pixel_based_similarity(
lime_expl_val.ravel(), gt_val, ret_pre_rec=True)
shap_f1, shap_pre, shap_rec = pixel_based_similarity(
shap_expl_val.ravel(), gt_val, ret_pre_rec=True)
maple_f1, maple_pre, maple_rec = pixel_based_similarity(
maple_expl_val.ravel(), gt_val, ret_pre_rec=True)
res = {
'black_box': black_box,
'n_records': n_records,
'img_size': '"%s"' % str(img_size),
'cell_size': '"%s"' % str(cell_size),
'n_features': '"%s"' % str(n_features),
'random_state': random_state,
'idx': idx,
'lime_f1': lime_f1,
'lime_pre': lime_pre,
'lime_rec': lime_rec,
'shap_f1': shap_f1,
'shap_pre': shap_pre,
'shap_rec': shap_rec,
'maple_f1': maple_f1,
'maple_pre': maple_pre,
'maple_rec': maple_rec,
'p_border': p_border
}
results.append(res)
print('lime %.2f' % lime_f1, 'shap %.2f' % shap_f1,
'maple %.2f' % maple_f1)
idx += 1
df = pd.DataFrame(data=results)
df = df[[
'black_box', 'n_records', 'img_size', 'cell_size', 'n_features',
'random_state', 'idx', 'lime_f1', 'lime_pre', 'lime_rec', 'shap_f1',
'shap_pre', 'shap_rec', 'maple_f1', 'maple_pre', 'maple_rec',
'p_border'
]]
# print(df.head())
if not os.path.isfile(filename):
df.to_csv(filename, index=False)
else:
df.to_csv(filename, mode='a', index=False, header=False)
def run(black_box, n_records, n_features, random_state, filename):
X_train = fetch_20newsgroups(subset='train',
remove=('headers', 'footers', 'quotes'),
categories=None).data
X_train = preprocess_data(X_train)
stc = generate_synthetic_text_classifier(X_train,
n_features=n_features,
random_state=random_state)
predict_proba = stc['predict_proba']
vectorizer = stc['vectorizer']
nbr_terms = stc['nbr_terms']
X_test = fetch_20newsgroups(subset='test',
remove=('headers', 'footers', 'quotes'),
categories=None).data
X_test = preprocess_data(X_test)
X_test_nbrs = vectorizer.transform(X_test).toarray()
Y_test = predict_proba(X_test_nbrs)
lime_explainer = LimeTextExplainer(class_names=[0, 1])
print(datetime.datetime.now(), 'build shap')
reference = get_reference4shap(X_test_nbrs,
stc['words_vec'],
nbr_terms,
nbr_references=10)
shap_explainer = KernelExplainer(predict_proba, reference)
print(datetime.datetime.now(), 'build shap done')
# print(idx_records_train_expl)
# print(X_test_nbrs[idx_records_train_expl])
# print(Y_test[idx_records_train_expl][:, 1])
# print(np.any(np.isnan(X_test_nbrs[idx_records_train_expl])))
# print(np.any(np.isnan(X_test_nbrs[idx_records_test_expl])))
# print(np.any(np.isnan(Y_test[idx_records_train_expl][:, 1])))
# print(np.any(np.isnan(Y_test[idx_records_test_expl][:, 1])))
nbr_records_explainer = 100
idx_records_train_expl = np.random.choice(range(len(X_test)),
size=nbr_records_explainer,
replace=False)
idx_records_test_expl = np.random.choice(range(len(X_test)),
size=nbr_records_explainer,
replace=False)
print(datetime.datetime.now(), 'build maple')
maple_explainer = MAPLE(X_test_nbrs[idx_records_train_expl],
Y_test[idx_records_train_expl][:, 1],
X_test_nbrs[idx_records_test_expl],
Y_test[idx_records_test_expl][:, 1],
n_estimators=100,
max_features=0.5,
min_samples_leaf=2)
print(datetime.datetime.now(), 'build maple done')
results = list()
explained = 0
for idx, x in enumerate(X_test):
x_nbrs = X_test_nbrs[idx]
print(datetime.datetime.now(),
'seneca - text',
'black_box %s' % black_box,
'n_features %s' % n_features,
'rs %s' % random_state,
'%s/%s' % (idx, n_records),
end=' ')
gt_val_text = get_word_importance_explanation_text(x, stc)
gt_val = get_word_importance_explanation(x_nbrs, stc)
try:
lime_exp = lime_explainer.explain_instance(x,
predict_proba,
num_features=n_features)
lime_expl_val = {e[0]: e[1] for e in lime_exp.as_list()}
shap_expl_val = shap_explainer.shap_values(x_nbrs, l1_reg='bic')[1]
maple_exp = maple_explainer.explain(x_nbrs)
maple_expl_val = maple_exp['coefs'][:-1]
except ValueError:
print(datetime.datetime.now(), 'Error in explanation')
continue
lime_cs = word_based_similarity_text(lime_expl_val,
gt_val_text,
use_values=True)
lime_f1, lime_pre, lime_rec = word_based_similarity_text(
lime_expl_val, gt_val_text, use_values=False, ret_pre_rec=True)
shap_cs = word_based_similarity(shap_expl_val, gt_val, use_values=True)
shap_f1, shap_pre, shap_rec = word_based_similarity(shap_expl_val,
gt_val,
use_values=False,
ret_pre_rec=True)
maple_cs = word_based_similarity(maple_expl_val,
gt_val,
use_values=True)
maple_f1, maple_pre, maple_rec = word_based_similarity(
maple_expl_val, gt_val, use_values=False, ret_pre_rec=True)
# print(gt_val)
# print(lime_expl_val)
# print(shap_expl_val)
# print(maple_expl_val)
res = {
'black_box': black_box,
'n_records': n_records,
'nbr_terms': nbr_terms,
'n_features': n_features,
'random_state': random_state,
'idx': idx,
'lime_cs': lime_cs,
'lime_f1': lime_f1,
'lime_pre': lime_pre,
'lime_rec': lime_rec,
'shap_cs': shap_cs,
'shap_f1': shap_f1,
'shap_pre': shap_pre,
'shap_rec': shap_rec,
'maple_cs': maple_cs,
'maple_f1': maple_f1,
'maple_pre': maple_pre,
'maple_rec': maple_rec,
}
results.append(res)
print('lime %.2f %.2f' % (lime_cs, lime_f1),
'shap %.2f %.2f' % (shap_cs, shap_f1),
'maple %.2f %.2f' % (maple_cs, maple_f1))
explained += 1
if explained >= n_records:
break
df = pd.DataFrame(data=results)
df = df[[
'black_box',
'n_records',
'nbr_terms',
'n_features',
'random_state',
'idx',
'lime_cs',
'lime_f1',
'lime_pre',
'lime_rec',
'shap_cs',
'shap_f1',
'shap_pre',
'shap_rec',
'maple_cs',
'maple_f1',
'maple_pre',
'maple_rec',
]]
# print(df.head())
if not os.path.isfile(filename):
df.to_csv(filename, index=False)
else:
df.to_csv(filename, mode='a', index=False, header=False)
# Set this flag to True and re-run the notebook to see the SHAP plots
shap_enabled = True
# COMMAND ----------
if shap_enabled:
from shap import KernelExplainer, summary_plot
# Sample background data for SHAP Explainer. Increase the sample size to reduce variance.
train_sample = X_train.sample(n=min(100, len(X_train.index)))
# Sample a single example from the validation set to explain. Increase the sample size and rerun for more thorough results.
example = X_val.sample(n=1)
# Use Kernel SHAP to explain feature importance on the example from the validation set.
predict = lambda x: model.predict(pd.DataFrame(x, columns=X_train.columns))
explainer = KernelExplainer(predict, train_sample, link="identity")
shap_values = explainer.shap_values(example, l1_reg=False)
summary_plot(shap_values, example)
# COMMAND ----------
# MAGIC %md
# MAGIC # Hyperopt
# MAGIC
# MAGIC Hyperopt is a Python library for "serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions"... In simpler terms, its a python library for hyperparameter tuning.
# MAGIC
# MAGIC There are two main ways to scale hyperopt with Apache Spark:
# MAGIC * Use single-machine hyperopt with a distributed training algorithm (e.g. MLlib)
# MAGIC * Use distributed hyperopt with single-machine training algorithms with the SparkTrials class.
# MAGIC
# MAGIC Resources:
def __init__(self, model, training_data):
super(ShapExplainer, self).__init__(model, training_data)
data = self.preprocessor.transform(training_data)
background_data = kmeans(data, 10)
self.explainer = KernelExplainer(model=self.model.predict_proba,
data=background_data)
def run(black_box, n_records, n_all_features, n_features, n_coefficients,
random_state, filename):
n = n_records
m = n_all_features
slc = generate_synthetic_linear_classifier2(n_features=n_features,
n_all_features=n_all_features,
n_coefficients=n_coefficients,
random_state=random_state)
feature_names = slc['feature_names']
class_values = slc['class_values']
predict_proba = slc['predict_proba']
# predict = slc['predict']
X_test = np.random.uniform(size=(n, n_all_features))
Xz = list()
for x in X_test:
nz = np.random.randint(0, n_features)
zeros_idx = np.random.choice(np.arange(n_features),
size=nz,
replace=False)
x[zeros_idx] = 0.0
Xz.append(x)
X_test = np.array(Xz)
Y_test = predict_proba(X_test)[:, 1]
lime_explainer = LimeTabularExplainer(X_test,
feature_names=feature_names,
class_names=class_values,
discretize_continuous=False,
discretizer='entropy')
reference = np.zeros(m)
shap_explainer = KernelExplainer(
predict_proba, np.reshape(reference, (1, len(reference))))
maple_explainer = MAPLE(X_test, Y_test, X_test, Y_test)
results = list()
for idx, x in enumerate(X_test):
gt_val = get_feature_importance_explanation2(x,
slc,
n_features,
n_all_features,
get_values=True)
gt_val_bin = get_feature_importance_explanation2(x,
slc,
n_features,
n_all_features,
get_values=False)
lime_exp = lime_explainer.explain_instance(x,
predict_proba,
num_features=m)
lime_expl_val = np.array([e[1] for e in lime_exp.as_list()])
tmp = np.zeros(lime_expl_val.shape)
tmp[np.where(lime_expl_val != 0.0)] = 1.0
lime_expl_val_bin = tmp
shap_expl_val = shap_explainer.shap_values(x, l1_reg='bic')[1]
tmp = np.zeros(shap_expl_val.shape)
tmp[np.where(shap_expl_val != 0.0)] = 1.0
shap_expl_val_bin = tmp
maple_exp = maple_explainer.explain(x)
maple_expl_val = maple_exp['coefs'][:-1]
tmp = np.zeros(maple_expl_val.shape)
tmp[np.where(maple_expl_val != 0.0)] = 1.0
maple_expl_val_bin = tmp
lime_fis = feature_importance_similarity(lime_expl_val, gt_val)
shap_fis = feature_importance_similarity(shap_expl_val, gt_val)
maple_fis = feature_importance_similarity(maple_expl_val, gt_val)
lime_rbs = rule_based_similarity(lime_expl_val_bin, gt_val_bin)
shap_rbs = rule_based_similarity(shap_expl_val_bin, gt_val_bin)
maple_rbs = rule_based_similarity(maple_expl_val_bin, gt_val_bin)
# print(gt_val)
# print(lime_expl_val)
# print(shap_expl_val)
# print(maple_expl_val)
res = {
'black_box': black_box,
'n_records': n_records,
'n_all_features': n_all_features,
'n_features': n_features,
'n_coefficients': n_coefficients,
'random_state': random_state,
'idx': idx,
'lime_cs': lime_fis,
'shap_cs': shap_fis,
'maple_cs': maple_fis,
'lime_f1': lime_rbs,
'shap_f1': shap_rbs,
'maple_f1': maple_rbs,
}
results.append(res)
print(datetime.datetime.now(), 'syege - tlsb2',
'black_box %s' % black_box, 'n_all_features %s' % n_all_features,
'n_features %s' % n_features,
'n_coefficients % s' % n_coefficients, 'rs %s' % random_state,
'%s %s' % (idx, n_records),
'lime %.2f %.2f' % (lime_fis, lime_rbs),
'shap %.2f %.2f' % (shap_fis, shap_rbs),
'maple %.2f %.2f' % (maple_fis, maple_rbs))
df = pd.DataFrame(data=results)
df = df[[
'black_box', 'n_records', 'n_all_features', 'n_features',
'n_coefficients', 'random_state', 'idx', 'lime_cs', 'shap_cs',
'maple_cs', 'lime_f1', 'shap_f1', 'maple_f1'
]]
# print(df.head())
if not os.path.isfile(filename):
df.to_csv(filename, index=False)
else:
df.to_csv(filename, mode='a', index=False, header=False)
def run(black_box, n_records, n_all_features, n_features, random_state, filename):
n = n_records
m = n_all_features
p_binary = 0.7
p_parenthesis = 0.3
slc = generate_synthetic_linear_classifier(expr=None, n_features=n_features, n_all_features=m,
random_state=random_state,
p_binary=p_binary, p_parenthesis=p_parenthesis)
expr = slc['expr']
X = slc['X']
feature_names = slc['feature_names']
class_values = slc['class_values']
predict_proba = slc['predict_proba']
# predict = slc['predict']
X_test = np.random.uniform(np.min(X), np.max(X), size=(n, m))
Y_test = predict_proba(X_test)[:, 1]
lime_explainer = LimeTabularExplainer(X_test, feature_names=feature_names, class_names=class_values,
discretize_continuous=False, discretizer='entropy')
reference = np.zeros(m)
shap_explainer = KernelExplainer(predict_proba, np.reshape(reference, (1, len(reference))))
maple_explainer = MAPLE(X_test, Y_test, X_test, Y_test)
results = list()
for idx, x in enumerate(X_test):
gt_val = get_feature_importance_explanation(x, slc, n_features, get_values=True)
lime_exp = lime_explainer.explain_instance(x, predict_proba, num_features=m)
# lime_exp_as_dict = {e[0]: e[1] for e in lime_exp.as_list()}
# lime_expl_val = np.asarray([lime_exp_as_dict.get('x%s' % i, .0) for i in range(m)])
lime_expl_val = np.array([e[1] for e in lime_exp.as_list()])
shap_expl_val = shap_explainer.shap_values(x, l1_reg='bic')[1]
maple_exp = maple_explainer.explain(x)
maple_expl_val = maple_exp['coefs'][:-1]
lime_fis = feature_importance_similarity(lime_expl_val, gt_val)
shap_fis = feature_importance_similarity(shap_expl_val, gt_val)
maple_fis = feature_importance_similarity(maple_expl_val, gt_val)
# print(gt_val)
# print(lime_expl_val)
# print(shap_expl_val)
# print(maple_expl_val)
res = {
'black_box': black_box,
'n_records': n_records,
'n_all_features': n_all_features,
'n_features': n_features,
'random_state': random_state,
'idx': idx,
'lime': lime_fis,
'shap': shap_fis,
'maple': maple_fis,
'expr': expr,
}
results.append(res)
print(datetime.datetime.now(), 'syege - tlsb', 'black_box %s' % black_box,
'n_all_features %s' % n_all_features, 'n_features %s' % n_features, 'rs %s' % random_state,
'%s %s' % (idx, n_records), expr,
'lime %.2f' % lime_fis, 'shap %.2f' % shap_fis, 'maple %.2f' % maple_fis)
if idx > 0 and idx % 10 == 0:
df = pd.DataFrame(data=results)
df = df[['black_box', 'n_records', 'n_all_features', 'n_features', 'random_state', 'expr',
'idx', 'lime', 'shap', 'maple']]
# print(df.head())
if not os.path.isfile(filename):
df.to_csv(filename, index=False)
else:
df.to_csv(filename, mode='a', index=False, header=False)
results = list()
df = pd.DataFrame(data=results)
df = df[['black_box', 'n_records', 'n_all_features', 'n_features', 'random_state', 'expr',
'idx', 'lime', 'shap', 'maple']]
# print(df.head())
if not os.path.isfile(filename):
df.to_csv(filename, index=False)
else:
df.to_csv(filename, mode='a', index=False, header=False)
def data_shift_automatic(self,
fraction_shift=0.3,
fraction_points=0.1,
sample=True):
"""
Performing a dataset shift. For a subset of the instances, a subset of
the features is shifted upwards. The features to shift are selected
according to their Shapley value, so that the dataset shift will likely
impact the predictions of the model applied to the shifted data.
Parameters
----------
fraction_shift : float
The fraction of variables to shift (optional).
fraction_points : float
The fraction of points from the total dataset (so train and test
combined) to be shifted (optional).
sample : bool
Whether a subsample of the points should be used to select the most
important features in the dataset, to speed up calculations
(optional).
"""
# fit a linear regression to the data
reg = LinearRegression().fit(self.X, self.y)
# optionally take a sample of the points to speed up calculations
if sample:
X = self.X[np.random.randint(self.X.shape[0], size=100), :]
else:
X = self.X
# build a Shapley explainer on the regression and get SHAP values
explainer = KernelExplainer(reg.predict, X)
shap_values = explainer.shap_values(X, nsamples=20, l1_reg="aic")
# determine most important variables by SHAP value on average
avg_shap = np.average(np.absolute(shap_values), axis=0).flatten()
# get number of features to shift
shift_count = int(fraction_shift * self.X.shape[1])
# get indices of most important features
shift_fts = avg_shap.argsort()[::-1][:shift_count]
# new array for new data with shifted features
shifted_X = np.zeros_like(self.X)
# number of points to shift
ix = int((1 - fraction_points) * self.X.shape[0])
# shift feature by feature
for f_ix in range(self.X.shape[1]):
# place original feature in matrix
shifted_X[:, f_ix] = self.X[:, f_ix]
# check if feature has to be shifted
if f_ix in shift_fts:
# get feature from data
ft = self.X[ix:, f_ix]
# determine the maximum of this feature
max_f = np.max(ft)
# shift feature upward according to a Gaussian distribution
shifted_X[ix:, f_ix] = ft + np.random.normal(
max_f, abs(.1 * max_f), shifted_X[ix:, f_ix].shape[0])
# store the (partially) shifted features back
self.X = shifted_X
def main():
n_features = (8, 8)
img_size = (32, 32, 3)
cell_size = (4, 4)
colors_p = np.array([0.15, 0.7, 0.15])
p_border = 0.0
sic = generate_synthetic_image_classifier(img_size=img_size,
cell_size=cell_size,
n_features=n_features,
p_border=p_border)
pattern = sic['pattern']
predict = sic['predict']
predict_proba = sic['predict_proba']
plt.imshow(pattern)
plt.show()
X_test = generate_random_img_dataset(pattern,
nbr_images=1000,
pattern_ratio=0.4,
img_size=img_size,
cell_size=cell_size,
min_nbr_cells=0.1,
max_nbr_cells=0.3,
colors_p=colors_p)
Y_test = predict(X_test)
background = np.array([np.zeros(img_size).ravel()] * 10)
explainer = KernelExplainer(predict_proba, background)
x = X_test[-1]
plt.imshow(x)
plt.show()
print(Y_test[-1])
expl_val = explainer.shap_values(x.ravel())[1]
# expl_val = (expl_val - np.min(expl_val)) / (np.max(expl_val) - np.min(expl_val))
print(expl_val)
print(np.unique(expl_val, return_counts=True))
print(expl_val.shape)
sv = np.sum(np.reshape(expl_val, img_size), axis=2)
sv01 = np.zeros(sv.shape)
sv01[np.where(sv > 0.0)] = 1.0
# np.array([1.0 if v > 0.0 else 0.0 for v in expl_val])
sv = sv01
print(sv)
print(sv.shape)
max_val = np.nanpercentile(np.abs(sv), 99.9)
# plt.imshow(x)
plt.imshow(sv, cmap='RdYlBu', vmin=-max_val, vmax=max_val, alpha=0.7)
plt.show()
# shap.image_plot(expl_val, x)
gt_val = get_pixel_importance_explanation(x, sic)
print(gt_val.shape)
max_val = np.nanpercentile(np.abs(gt_val), 99.9)
# plt.imshow(x)
plt.imshow(np.reshape(gt_val, img_size[:2]),
cmap='RdYlBu',
vmin=-max_val,
vmax=max_val,
alpha=0.7)
plt.show()
print(pixel_based_similarity(sv.ravel(), gt_val))
