# 3. Explaining Machine Learning¶

Interpretability of Machine Learning models has recently become a relevant research direction to more thoroughly address and mitigate the issues of adversarial examples and to better understand the potential flows of the most recent algorithm such as Deep Neural Networks.

In this tutorial, we explore different methods that SecML provides to compute post-hoc explanations, which consist on analyzing a trained model to understand which components such as features or training prototypes are more relevant during the decision (classification) phase.

## 3.1. Feature-based explanations¶

Feature-based explanation methods assign a value to each feature of an input sample depending on how relevant it is towards the classification decision. These relevance values are often called attributions.

In this tutorial, we are going to test the following gradient-based explanation methods:

[baehrens2010explain] D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, K.-R.Muller, “How to explain individual classification decisions”, in: J. Mach. Learn. Res. 11 (2010) 1803-1831

[shrikumar2016not] A. Shrikumar, P. Greenside, A. Shcherbina, A. Kundaje, “Not just a blackbox: Learning important features through propagating activation differences”, 2016 arXiv:1605.01713.

[melis2018explaining] M. Melis, D. Maiorca, B. Biggio, G. Giacinto and F. Roli, “Explaining Black-box Android Malware Detection,” 2018 26th European Signal Processing Conference (EUSIPCO), Rome, 2018, pp. 524-528.

[sundararajan2017axiomatic] Sundararajan, Mukund, Ankur Taly, and Qiqi Yan. “Axiomatic Attribution for Deep Networks.” Proceedings of the 34th International Conference on Machine Learning, Volume 70, JMLR. org, 2017, pp. 3319-3328.

### 3.1.1. Training of the classifier¶

First, we load the MSNIT dataset and we train an SVM classifier with RBF kernel.

[1]:

random_state = 999

n_tr = 500  # Number of training set samples
n_ts = 500  # Number of test set samples

# Normalize the features in [0, 1]
tr.X /= 255
ts.X /= 255

from secml.ml.classifiers.multiclass import CClassifierMulticlassOVA
from secml.ml.classifiers import CClassifierSVM
from secml.ml.kernels import CKernelRBF

clf = CClassifierMulticlassOVA( CClassifierSVM, kernel=CKernelRBF(gamma=1e-2))

print("Training of classifier...")
clf.fit(tr.X, tr.Y)

# Compute predictions on a test set
y_pred = clf.predict(ts.X)

# Metric to use for performance evaluation
from secml.ml.peval.metrics import CMetricAccuracy
metric = CMetricAccuracy()

# Evaluate the accuracy of the classifier
acc = metric.performance_score(y_true=ts.Y, y_pred=y_pred)

print("Accuracy on test set: {:.2%}".format(acc))


Training of classifier...
Accuracy on test set: 83.80%


### 3.1.2. Compute the explanations¶

The secml.explanation package provides different explanation methods as subclasses of CExplainer. Each explainer requires as input a trained classifier.

To compute the explanation on a sample, the .explain() method should be used. For gradient-based methods, the label y of the class wrt the explanation should be computed is required.

The .explain() method will return the relevance value associated to each feature of the input sample.

[2]:

from secml.explanation import \

explainers = (
{
},
{
},
{
},
)


[3]:

i = 123  # Test sample on which explanations should be computed
x, y = ts[i, :].X, ts[i, :].Y

print("Explanations for sample {:} (true class: {:})".format(i, y.item()))

from secml.array import CArray

for expl in explainers:

print("Computing explanations using '{:}'...".format(
expl['exp'].__class__.__name__))

# Compute explanations (attributions) wrt each class
attr = CArray.empty(shape=(tr.num_classes, x.size))
for c in tr.classes:

attr_c = expl['exp'].explain(x, y=c)
attr[c, :] = attr_c

expl['attr'] = attr

Explanations for sample 123 (true class: 6)


### 3.1.3. Visualize results¶

We now visualize the explanations computed using the different methods, in rows. In columns, we show the explanations wrt each different class.

Above the original tested sample, its true class label is shown.

Red (blue) pixels denote positive (negative) relevance of the corresponding feature wrt the specific class.

[4]:

from secml.figure import CFigure
# Only required for visualization in notebooks
%matplotlib inline

fig = CFigure(height=4.5, width=14, fontsize=13)

for i, expl in enumerate(explainers):

sp_idx = i * (tr.num_classes+1)

# Original image
fig.subplot(len(explainers), tr.num_classes+1, sp_idx+1)

if i == 0:  # For the first row only
fig.sp.title("Origin y: {:}".format(y.item()))

fig.sp.ylabel(expl['label'])  # Label of the explainer

fig.sp.yticks([])
fig.sp.xticks([])

# Threshold to plot positive and negative relevance values symmetrically
th = max(abs(expl['attr'].min()), abs(expl['attr'].max()))

# Plot explanations
for c in tr.classes:

fig.subplot(len(explainers), tr.num_classes+1, sp_idx+2+c)
cmap='seismic', vmin=-1*th, vmax=th)

fig.sp.yticks([])
fig.sp.xticks([])

if i == 0:  # For the first row only
fig.sp.title("c: {:}".format(c))

fig.title("Explanations", x=0.55)
fig.tight_layout(rect=[0, 0.003, 1, 0.94])
fig.show()


For both gradient*input and integrated gradients methods we can observe a well defined area of positive (red) relevance for the explanation computed wrt the digit 6. This is expected as the true class of the tested sample is in fact 6. Moreover, a non-zero relevance value is mainly assigned to the features which are present in the tested sample, which is an expected behavior of these explanation methods.

Conversely, the gradient method assigns relevance to a wider area of the image, even external to the actual digit. This leads to explanations which are in many cases difficult to interpret. For this reason, more advanced explanation methods are often favored.

## 3.2. Prototype-based explanation¶

Prototype-based explanation methods select specifc samples from the training dataset to explain the behavior of machine learning models.

In this tutorial, we are going to test the explanation method proposed in:

[koh2017understanding] Koh, Pang Wei, and Percy Liang, “Understanding black-box predictions via influence functions”, in: Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.

### 3.2.1. Training of the classifier¶

As our implementation of the prototype-based explanation methods currently only supports binary classifiers, we load the 2-classes MNIST59 dataset and then we train a SVM classifier with RBF kernel.

[5]:

n_tr = 100  # Number of training set samples
n_ts = 500  # Number of test set samples

digits = (5, 9)

# Normalize the features in [0, 1]
tr.X /= 255
ts.X /= 255

clf = CClassifierSVM(kernel=CKernelRBF(gamma=1e-2))

print("Training of classifier...")
clf.fit(tr.X, tr.Y)

# Compute predictions on a test set
y_pred = clf.predict(ts.X)

# Metric to use for performance evaluation
metric = CMetricAccuracy()

# Evaluate the accuracy of the classifier
acc = metric.performance_score(y_true=ts.Y, y_pred=y_pred)

print("Accuracy on test set: {:.2%}".format(acc))

Training of classifier...
Accuracy on test set: 96.20%


### 3.2.2. Compute the influential training prototypes¶

The CExplainerInfluenceFunctions class provides the influence functions prototype-based method described previously. It requires as input the classifier to explain and its training set. It also requires the identifier of the loss used to train the classier. In the case of SVM, it is the hinge loss.

To compute the influence of each training sample wrt the test samples, the .explain() method should be used.

[6]:

from secml.explanation import CExplainerInfluenceFunctions

explanation = CExplainerInfluenceFunctions(
clf, tr, outer_loss_idx='hinge')  # SVM loss is 'hinge'

print("Computing influence of each training prototype on test samples...")

infl = explanation.explain(ts.X, ts.Y)

print("Done.")

Computing influence of each training prototype on test samples...
Done.


### 3.2.3. Visualize results¶

We now visualize, wrt each class, the 3 most influential training prototypes for two different test samples. Above each training sample, the influence value is shown.

In addition, above the original tested samples, the true class label is shown.

[7]:

fig = CFigure(height=3.5, width=9, fontsize=13)

n_xc = 3  # Number of tr prototypes to plot per class

ts_list = (50, 100)  # Test samples to evaluate

infl_argsort = infl.argsort(axis=1)  # Sort influence values

for i, ts_idx in enumerate(ts_list):

sp_idx = i * (n_xc*tr.num_classes+1)

x, y = ts[ts_idx, :].X, ts[ts_idx, :].Y

# Original image
fig.subplot(len(ts_list), n_xc*tr.num_classes+1, sp_idx+1)

fig.sp.yticks([])
fig.sp.xticks([])

tr_top = infl_argsort[ts_idx, :n_xc]
tr_top = tr_top.append(infl_argsort[ts_idx, -n_xc:])

# Plot top influential training prototypes
for j, tr_idx in enumerate(tr_top):
fig.subplot(len(ts_list), n_xc*tr.num_classes+1, sp_idx+2+j)