def PT_DeepLIFT(model, x, y_onthot, baseline=None, device='cuda:0', **kwargs):
input = torch.tensor(x, requires_grad=True).to(device)
model = model.to(device)
model.eval()
saliency = DeepLift(model)
target = torch.tensor(np.argmax(y_onthot, -1)).to(device)
if baseline is not None:
baseline = torch.tensor(baseline).to(device)
attribution_map = saliency.attribute(input,
target=target,
baselines=baseline)
return attribution_map.detach().cpu().numpy()
class DeepLiftExplainer:
def __init__(self, model, activation=torch.nn.Softmax(-1)):
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.base_model = model.to(self.device)
self.explainer = DeepLift(self.base_model)
self.activation = activation
def attribute(self, x, y, retrospective=False):
self.base_model.zero_grad()
if retrospective:
score = self.explainer.attribute(x,
target=y.long(),
baselines=(x * 0))
score = abs(score.detach().cpu().numpy())
else:
score = np.zeros(x.shape)
for t in range(1, x.shape[-1]):
x_in = x[:, :, :t + 1]
pred = self.activation(self.base_model(x_in))
if type(self.activation).__name__ == type(
torch.nn.Softmax(-1)).__name__:
target = torch.argmax(pred, -1)
imp = self.explainer.attribute(x_in,
target=target.long(),
baselines=(x[:, :, :t + 1] *
0))
score[:, :, t] = abs(imp.detach().cpu().numpy()[:, :, -1])
else:
#this works for multilabel and single prediction aka spike
n_labels = pred.shape[1]
if n_labels > 1:
imp = torch.zeros(list(x_in.shape) + [n_labels])
for l in range(n_labels):
target = (pred[:, l] > 0.5).float() #[:,0]
imp[:, :, :, l] = self.explainer.attribute(
x_in,
target=target.long(),
baselines=(x_in * 0))
score[:, :,
t] = (imp.detach().cpu().numpy()).max(3)[:, :,
-1]
else:
#this is for spike with just one label. and we will explain one cla
target = (pred > 0.5).float()[:, 0]
imp = self.explainer.attribute(
x_in,
target=target.long(),
baselines=(x[:, :, :t + 1] * 0))
score[:, :, t] = abs(imp.detach().cpu().numpy()[:, :,
-1])
return score
def test_sensitivity_additional_forward_args_multi_args(self) -> None:
model = BasicModel4_MultiArgs()
input1 = torch.tensor([[1.5, 2.0, 3.3]])
input2 = torch.tensor([[3.0, 3.5, 2.2]])
args = torch.tensor([[1.0, 3.0, 4.0]])
ig = DeepLift(model)
sensitivity1 = self.sensitivity_max_assert(
ig.attribute,
(input1, input2),
torch.zeros(1),
additional_forward_args=args,
n_perturb_samples=1,
max_examples_per_batch=1,
perturb_func=_perturb_func,
)
sensitivity2 = self.sensitivity_max_assert(
ig.attribute,
(input1, input2),
torch.zeros(1),
additional_forward_args=args,
n_perturb_samples=4,
max_examples_per_batch=2,
perturb_func=_perturb_func,
)
assertTensorAlmostEqual(self, sensitivity1, sensitivity2, 0.0)
def test_classification_sensitivity_tpl_target_w_baseline(self) -> None:
model = BasicModel_MultiLayer()
input = torch.arange(1.0, 13.0).view(4, 3)
baseline = torch.ones(4, 3)
additional_forward_args = (torch.arange(1, 13).view(4, 3).float(), True)
targets: List = [(0, 1, 1), (0, 1, 1), (1, 1, 1), (0, 1, 1)]
dl = DeepLift(model)
sens1 = self.sensitivity_max_assert(
dl.attribute,
input,
torch.tensor([0.01, 0.003, 0.001, 0.001]),
additional_forward_args=additional_forward_args,
baselines=baseline,
target=targets,
n_perturb_samples=10,
perturb_func=_perturb_func,
)
sens2 = self.sensitivity_max_assert(
dl.attribute,
input,
torch.zeros(4),
additional_forward_args=additional_forward_args,
baselines=baseline,
target=targets,
n_perturb_samples=10,
perturb_func=_perturb_func,
max_examples_per_batch=30,
)
assertTensorAlmostEqual(self, sens1, sens2)
def test_classification_infidelity_convnet_multi_targets(self) -> None:
model = BasicModel_ConvNet_One_Conv()
dl = DeepLift(model)
input = torch.stack([torch.arange(1, 17).float()] * 20, dim=0).view(20, 1, 4, 4)
self.infidelity_assert(
model,
dl.attribute(input, target=torch.tensor([1] * 20)) / input,
input,
torch.zeros(20),
target=torch.tensor([1] * 20),
multi_input=False,
n_perturb_samples=500,
max_batch_size=120,
)
def feature_importance(net,
graphs,
model_name,
n_graphs=50,
n_steps=2,
save=False):
n_graphs = min(n_graphs, len(graphs))
input_d = dgl.batch([graphs[i] for i in range(n_graphs)])
# ig = IntegratedGradients(net)
# ig_attr_test = ig.attribute(input_d.ndata[Constants.NODE_FEATURES_NAME],
# additional_forward_args=(dgl.batch([input_d] * n_steps)),
# target=1, n_steps=n_steps)
#
# ig_attr_test_sum = ig_attr_test.detach().numpy().sum(0)
# ig_attr_test_norm_sum = ig_attr_test_sum / np.linalg.norm(ig_attr_test_sum, ord=1)
dl = DeepLift(net)
dl_attr_test = dl.attribute(input_d.ndata[Constants.NODE_FEATURES_NAME],
additional_forward_args=(dgl.batch([input_d] *
2)),
target=1)
dl_attr_test_sum = dl_attr_test.detach().numpy().sum(0)
dl_attr_test_norm_sum = dl_attr_test_sum / np.linalg.norm(dl_attr_test_sum,
ord=1)
index_array = np.argsort(-np.absolute(dl_attr_test_norm_sum))
n = 25
names = np.array(Constants.FEATURE_NAMES)[index_array]
attrs = dl_attr_test_norm_sum[index_array]
for i in range(int(np.ceil(len(Constants.FEATURE_NAMES) / n))):
maxi = min((i + 1) * n, len(Constants.FEATURE_NAMES))
plt.figure()
plt.barh(names[maxi:i * n:-1], attrs[maxi:i * n:-1])
plt.title('Feature importance')
plt.tight_layout()
if save:
plt.savefig(
os.path.join(Constants.MODELS_PATH, model_name,
f'feature_importance_DL_{i}.png'))
else:
plt.show()
class Explainer():
def __init__(self, model):
self.model = model
self.explain = DeepLift(model)
def get_attribution_map(self, img, target=None):
if target is None:
target = torch.argmax(self.model(img), 1)
baseline_dist = torch.randn_like(img) * 0.001
attributions, delta = self.explain.attribute(
img, baseline_dist, target=target, return_convergence_delta=True)
return attributions
def find_genes_DeepLift(drug, ens, meta, test_tcga_expr):
from captum.attr import DeepLift
dl = DeepLift(ens)
x = torch.FloatTensor(test_tcga_expr.values)
attr = pd.DataFrame(dl.attribute(x).detach().numpy(),
index=test_tcga_expr.index,
columns=dataset.hgnc)
genes = get_ranked_list(attr)
out = pd.DataFrame(columns=['borda', 'mean'])
attr_mean = list(np.abs(attr).mean(axis=0).nlargest(200).index)
out['borda'] = genes
out['mean'] = attr_mean
out.to_csv(res_dir + drug + '/genes.csv', index=False)
fig, ax = plt.subplots(figsize=(15, 5))
for i in attr.index():
ax.plt(range(len(dataset.hgnc)), attr.loc[i], alpha=0.2)
plt.show()
def evaluation_ten_classes(initiate_or_load_model,
config_data,
singleton_scope=False,
reshape_size=None,
FIND_OPTIM_BRANCH_MODEL=False,
realtime_update=False,
ALLOW_ADHOC_NOPTIM=False):
from pipeline.training.training_utils import prepare_save_dirs
xai_mode = config_data['xai_mode']
MODEL_DIR, INFO_DIR, CACHE_FOLDER_DIR = prepare_save_dirs(config_data)
############################
VERBOSE = 0
############################
if not FIND_OPTIM_BRANCH_MODEL:
print(
'Using the following the model from (only) continuous training for xai evaluation [%s]'
% (str(xai_mode)))
net, evaluator = initiate_or_load_model(MODEL_DIR,
INFO_DIR,
config_data,
verbose=VERBOSE)
else:
BRANCH_FOLDER_DIR = MODEL_DIR[:MODEL_DIR.find('.model')] + '.%s' % (
str(config_data['branch_name_label']))
BRANCH_MODEL_DIR = os.path.join(
BRANCH_FOLDER_DIR,
'%s.%s.model' % (str(config_data['model_name']),
str(config_data['branch_name_label'])))
# BRANCH_MODEL_DIR = MODEL_DIR[:MODEL_DIR.find('.model')] + '.%s.model'%(str(config_data['branch_name_label']))
if ALLOW_ADHOC_NOPTIM: # this is intended only for debug runs
print('<< [EXY1] ALLOWING ADHOC NOPTIM >>')
import shutil
shutil.copyfile(BRANCH_MODEL_DIR, BRANCH_MODEL_DIR + '.noptim')
if os.path.exists(BRANCH_MODEL_DIR + '.optim'):
BRANCH_MODEL_DIR = BRANCH_MODEL_DIR + '.optim'
print(
' Using the OPTIMIZED branch model for [%s] xai evaluation: %s'
% (str(xai_mode), str(BRANCH_MODEL_DIR)))
elif os.path.exists(BRANCH_MODEL_DIR + '.noptim'):
BRANCH_MODEL_DIR = BRANCH_MODEL_DIR + '.noptim'
print(
' Using the partially optimized branch model for [%s] xai evaluation: %s'
% (str(xai_mode), str(BRANCH_MODEL_DIR)))
else:
raise RuntimeError(
'Attempting to find .optim or .noptim model, but not found.')
if VERBOSE >= 250:
print(
' """You may see a warning by pytorch for ReLu backward hook. It has been fixed externally, so you can ignore it."""'
)
net, evaluator = initiate_or_load_model(BRANCH_MODEL_DIR,
INFO_DIR,
config_data,
verbose=VERBOSE)
if xai_mode == 'Saliency': attrmodel = Saliency(net)
elif xai_mode == 'IntegratedGradients':
attrmodel = IntegratedGradients(net)
elif xai_mode == 'InputXGradient':
attrmodel = InputXGradient(net)
elif xai_mode == 'DeepLift':
attrmodel = DeepLift(net)
elif xai_mode == 'GuidedBackprop':
attrmodel = GuidedBackprop(net)
elif xai_mode == 'GuidedGradCam':
attrmodel = GuidedGradCam(net, net.select_first_layer()) # first layer
elif xai_mode == 'Deconvolution':
attrmodel = Deconvolution(net)
elif xai_mode == 'GradientShap':
attrmodel = GradientShap(net)
elif xai_mode == 'DeepLiftShap':
attrmodel = DeepLiftShap(net)
else:
raise RuntimeError('No valid attribution selected.')
if singleton_scope: # just to observe a single datapoint, mostly for debugging
singleton_scope_oberservation(net, attrmodel, config_data,
CACHE_FOLDER_DIR)
else:
aggregate_evaluation(net,
attrmodel,
config_data,
CACHE_FOLDER_DIR,
reshape_size=reshape_size,
realtime_update=realtime_update,
EVALUATE_BRANCH=FIND_OPTIM_BRANCH_MODEL)
def get_attribution(real_img,
fake_img,
real_class,
fake_class,
net_module,
checkpoint_path,
input_shape,
channels,
methods=["ig", "grads", "gc", "ggc", "dl", "ingrad", "random", "residual"],
output_classes=6,
downsample_factors=[(2,2), (2,2), (2,2), (2,2)]):
imgs = [image_to_tensor(normalize_image(real_img).astype(np.float32)),
image_to_tensor(normalize_image(fake_img).astype(np.float32))]
classes = [real_class, fake_class]
net = init_network(checkpoint_path, input_shape, net_module, channels, output_classes=output_classes,eval_net=True, require_grad=False,
downsample_factors=downsample_factors)
attrs = []
attrs_names = []
if "residual" in methods:
res = np.abs(real_img - fake_img)
res = res - np.min(res)
attrs.append(torch.tensor(res/np.max(res)))
attrs_names.append("residual")
if "random" in methods:
rand = np.abs(np.random.randn(*np.shape(real_img)))
rand = np.abs(scipy.ndimage.filters.gaussian_filter(rand, 4))
rand = rand - np.min(rand)
rand = rand/np.max(np.abs(rand))
attrs.append(torch.tensor(rand))
attrs_names.append("random")
if "gc" in methods:
net.zero_grad()
last_conv_layer = [(name,module) for name, module in net.named_modules() if type(module) == torch.nn.Conv2d][-1]
layer_name = last_conv_layer[0]
layer = last_conv_layer[1]
layer_gc = LayerGradCam(net, layer)
gc_real = layer_gc.attribute(imgs[0], target=classes[0])
gc_fake = layer_gc.attribute(imgs[1], target=classes[1])
gc_real = project_layer_activations_to_input_rescale(gc_real.cpu().detach().numpy(), (input_shape[0], input_shape[1]))
gc_fake = project_layer_activations_to_input_rescale(gc_fake.cpu().detach().numpy(), (input_shape[0], input_shape[1]))
attrs.append(torch.tensor(gc_real[0,0,:,:]))
attrs_names.append("gc_real")
attrs.append(torch.tensor(gc_fake[0,0,:,:]))
attrs_names.append("gc_fake")
# SCAM
gc_diff_0, gc_diff_1 = get_sgc(real_img, fake_img, real_class,
fake_class, net_module, checkpoint_path,
input_shape, channels, None, output_classes=output_classes,
downsample_factors=downsample_factors)
attrs.append(gc_diff_0)
attrs_names.append("gc_diff_0")
attrs.append(gc_diff_1)
attrs_names.append("gc_diff_1")
if "ggc" in methods:
net.zero_grad()
last_conv = [module for module in net.modules() if type(module) == torch.nn.Conv2d][-1]
guided_gc = GuidedGradCam(net, last_conv)
ggc_real = guided_gc.attribute(imgs[0], target=classes[0])
ggc_fake = guided_gc.attribute(imgs[1], target=classes[1])
attrs.append(ggc_real[0,0,:,:])
attrs_names.append("ggc_real")
attrs.append(ggc_fake[0,0,:,:])
attrs_names.append("ggc_fake")
net.zero_grad()
gbp = GuidedBackprop(net)
gbp_real = gbp.attribute(imgs[0], target=classes[0])
gbp_fake = gbp.attribute(imgs[1], target=classes[1])
attrs.append(gbp_real[0,0,:,:])
attrs_names.append("gbp_real")
attrs.append(gbp_fake[0,0,:,:])
attrs_names.append("gbp_fake")
ggc_diff_0 = gbp_real[0,0,:,:] * gc_diff_0
ggc_diff_1 = gbp_fake[0,0,:,:] * gc_diff_1
attrs.append(ggc_diff_0)
attrs_names.append("ggc_diff_0")
attrs.append(ggc_diff_1)
attrs_names.append("ggc_diff_1")
# IG
if "ig" in methods:
baseline = image_to_tensor(np.zeros(input_shape, dtype=np.float32))
net.zero_grad()
ig = IntegratedGradients(net)
ig_real, delta_real = ig.attribute(imgs[0], baseline, target=classes[0], return_convergence_delta=True)
ig_fake, delta_fake = ig.attribute(imgs[1], baseline, target=classes[1], return_convergence_delta=True)
ig_diff_0, delta_diff = ig.attribute(imgs[0], imgs[1], target=classes[0], return_convergence_delta=True)
ig_diff_1, delta_diff = ig.attribute(imgs[1], imgs[0], target=classes[1], return_convergence_delta=True)
attrs.append(ig_real[0,0,:,:])
attrs_names.append("ig_real")
attrs.append(ig_fake[0,0,:,:])
attrs_names.append("ig_fake")
attrs.append(ig_diff_0[0,0,:,:])
attrs_names.append("ig_diff_0")
attrs.append(ig_diff_1[0,0,:,:])
attrs_names.append("ig_diff_1")
# DL
if "dl" in methods:
net.zero_grad()
dl = DeepLift(net)
dl_real = dl.attribute(imgs[0], target=classes[0])
dl_fake = dl.attribute(imgs[1], target=classes[1])
dl_diff_0 = dl.attribute(imgs[0], baselines=imgs[1], target=classes[0])
dl_diff_1 = dl.attribute(imgs[1], baselines=imgs[0], target=classes[1])
attrs.append(dl_real[0,0,:,:])
attrs_names.append("dl_real")
attrs.append(dl_fake[0,0,:,:])
attrs_names.append("dl_fake")
attrs.append(dl_diff_0[0,0,:,:])
attrs_names.append("dl_diff_0")
attrs.append(dl_diff_1[0,0,:,:])
attrs_names.append("dl_diff_1")
# INGRAD
if "ingrad" in methods:
net.zero_grad()
saliency = Saliency(net)
grads_real = saliency.attribute(imgs[0],
target=classes[0])
grads_fake = saliency.attribute(imgs[1],
target=classes[1])
attrs.append(grads_real[0,0,:,:])
attrs_names.append("grads_real")
attrs.append(grads_fake[0,0,:,:])
attrs_names.append("grads_fake")
net.zero_grad()
input_x_gradient = InputXGradient(net)
ingrad_real = input_x_gradient.attribute(imgs[0], target=classes[0])
ingrad_fake = input_x_gradient.attribute(imgs[1], target=classes[1])
ingrad_diff_0 = grads_fake * (imgs[0] - imgs[1])
ingrad_diff_1 = grads_real * (imgs[1] - imgs[0])
attrs.append(torch.abs(ingrad_real[0,0,:,:]))
attrs_names.append("ingrad_real")
attrs.append(torch.abs(ingrad_fake[0,0,:,:]))
attrs_names.append("ingrad_fake")
attrs.append(torch.abs(ingrad_diff_0[0,0,:,:]))
attrs_names.append("ingrad_diff_0")
attrs.append(torch.abs(ingrad_diff_1[0,0,:,:]))
attrs_names.append("ingrad_diff_1")
attrs = [a.detach().cpu().numpy() for a in attrs]
attrs_norm = [a/np.max(np.abs(a)) for a in attrs]
return attrs_norm, attrs_names
def main(args):
train_loader, test_loader = data_generator(args.data_dir,1)
for m in range(len(models)):
model_name = "model_{}_NumFeatures_{}".format(models[m],args.NumFeatures)
model_filename = args.model_dir + 'm_' + model_name + '.pt'
pretrained_model = torch.load(open(model_filename, "rb"),map_location=device)
pretrained_model.to(device)
if(args.GradFlag):
Grad = Saliency(pretrained_model)
if(args.IGFlag):
IG = IntegratedGradients(pretrained_model)
if(args.DLFlag):
DL = DeepLift(pretrained_model)
if(args.GSFlag):
GS = GradientShap(pretrained_model)
if(args.DLSFlag):
DLS = DeepLiftShap(pretrained_model)
if(args.SGFlag):
Grad_ = Saliency(pretrained_model)
SG = NoiseTunnel(Grad_)
if(args.ShapleySamplingFlag):
SS = ShapleyValueSampling(pretrained_model)
if(args.GSFlag):
FP = FeaturePermutation(pretrained_model)
if(args.FeatureAblationFlag):
FA = FeatureAblation(pretrained_model)
if(args.OcclusionFlag):
OS = Occlusion(pretrained_model)
timeMask=np.zeros((args.NumTimeSteps, args.NumFeatures),dtype=int)
featureMask=np.zeros((args.NumTimeSteps, args.NumFeatures),dtype=int)
for i in range (args.NumTimeSteps):
timeMask[i,:]=i
for i in range (args.NumTimeSteps):
featureMask[:,i]=i
indexes = [[] for i in range(5,10)]
for i ,(data, target) in enumerate(test_loader):
if(target==5 or target==6 or target==7 or target==8 or target==9):
index=target-5
if(len(indexes[index])<1):
indexes[index].append(i)
for j, index in enumerate(indexes):
print(index)
# indexes = [[21],[17],[84],[9]]
for j, index in enumerate(indexes):
print("Getting Saliency for number", j+1)
for i, (data, target) in enumerate(test_loader):
if(i in index):
labels = target.to(device)
input = data.reshape(-1, args.NumTimeSteps, args.NumFeatures).to(device)
input = Variable(input, volatile=False, requires_grad=True)
baseline_single=torch.Tensor(np.random.random(input.shape)).to(device)
baseline_multiple=torch.Tensor(np.random.random((input.shape[0]*5,input.shape[1],input.shape[2]))).to(device)
inputMask= np.zeros((input.shape))
inputMask[:,:,:]=timeMask
inputMask =torch.Tensor(inputMask).to(device)
mask_single= torch.Tensor(timeMask).to(device)
mask_single=mask_single.reshape(1,args.NumTimeSteps, args.NumFeatures).to(device)
Data=data.reshape(args.NumTimeSteps, args.NumFeatures).data.cpu().numpy()
target_=int(target.data.cpu().numpy()[0])
plotExampleBox(Data,args.Graph_dir+'Sample_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.GradFlag):
attributions = Grad.attribute(input, \
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_Grad_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(Grad,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=None)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_Grad_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.IGFlag):
attributions = IG.attribute(input, \
baselines=baseline_single, \
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_IG_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(IG,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_single)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_IG_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.DLFlag):
attributions = DL.attribute(input, \
baselines=baseline_single, \
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_DL_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(DL,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_single)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_DL_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.GSFlag):
attributions = GS.attribute(input, \
baselines=baseline_multiple, \
stdevs=0.09,\
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_GS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(GS,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_multiple)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_GS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.DLSFlag):
attributions = DLS.attribute(input, \
baselines=baseline_multiple, \
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_DLS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(DLS,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_multiple)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_DLS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.SGFlag):
attributions = SG.attribute(input, \
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_SG_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(SG,input, args.NumFeatures,args.NumTimeSteps, labels)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_SG_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.ShapleySamplingFlag):
attributions = SS.attribute(input, \
baselines=baseline_single, \
target=labels,\
feature_mask=inputMask)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_SVS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(SS,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_single,hasFeatureMask=inputMask)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_SVS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
# if(args.FeaturePermutationFlag):
# attributions = FP.attribute(input, \
# target=labels),
# # perturbations_per_eval= 1,\
# # feature_mask=mask_single)
# saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
# plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_FP',greyScale=True)
if(args.FeatureAblationFlag):
attributions = FA.attribute(input, \
target=labels)
# perturbations_per_eval= input.shape[0],\
# feature_mask=mask_single)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_FA_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(FA,input, args.NumFeatures,args.NumTimeSteps, labels)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_FA_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.OcclusionFlag):
attributions = OS.attribute(input, \
sliding_window_shapes=(1,int(args.NumFeatures/10)),
target=labels,
baselines=baseline_single)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_FO_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(OS,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_single,hasSliding_window_shapes= (1,int(args.NumFeatures/10)))
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_FO_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
def extract_DL(self, X_test):
dl = DeepLift(self.net)
start = time.time()
dl_attr_test = dl.attribute(X_test.to(self.device))
print("temps train", time.time() - start)
return dl_attr_test.detach().cpu().numpy()
])
hypo_T = torch.from_numpy(hypo_T).float().to(device)
number_references = 10 # imp scores are averaged over 10 reference sequences
hypo_A_scores = []
hypo_C_scores = []
hypo_G_scores = []
hypo_T_scores = []
importance_scores = []
for ref in range(number_references):
seq_ref = seq[:, :, torch.randperm(seq.size()[2])]
# obtaining attribution scores
dl = DeepLift(model, multiply_by_inputs=False)
attributions, delta = dl.attribute(seq,
seq_ref,
target=JUND_index,
return_convergence_delta=True)
attribution_imp = (
seq - seq_ref) * attributions # obtaining importance scores
imp_score = torch.sum(attribution_imp,
axis=1) # summing across all 4 bases
importance_scores.append(imp_score)
# obtaining hypothetical importance scores for A,C,G,T
attributions_hypo_A = (hypo_A - seq_ref) * attributions
ig = IntegratedGradients(net)
nt = NoiseTunnel(ig)
attr_ig_nt = attribute_image_features(nt,
input,
baselines=input * 0,
nt_type='smoothgrad_sq',
n_samples=100,
stdevs=0.2)
attr_ig_nt = np.transpose(
attr_ig_nt.squeeze(0).cpu().detach().numpy(), (1, 2, 0))
# %% [markdown]
# Applies DeepLift on test image. Deeplift assigns attributions to each input pixel by looking at the differences of output and its reference in terms of the differences of the input from the reference.
# %%
dl = DeepLift(net)
attr_dl = attribute_image_features(dl, input, baselines=input * 0)
attr_dl = np.transpose(attr_dl.squeeze(0).cpu().detach().numpy(), (1, 2, 0))
# %% [markdown]
# In the cell below we will visualize the attributions for `Saliency Maps`, `DeepLift`, `Integrated Gradients` and `Integrated Gradients with SmoothGrad`.
# %%
print('Original Image')
print('Predicted:', classes[predicted[ind]], ' Probability:',
torch.max(F.softmax(outputs, 1)).item())
original_image = np.transpose((images[ind].cpu().detach().numpy() / 2) + 0.5,
(1, 2, 0))
_ = viz.visualize_image_attr(None,
def main(args):
warnings.filterwarnings("ignore")
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Executing on device:', device)
# --- Get Threshold ---
model = './model/base' + str(args.model_index) + '.pkl'
threshold, _ = get_threshold(model_dir=model,
use_cached=args.use_cached,
search_space=np.linspace(
0, 1, args.search_num))
# --- Load Model ---
model = torch.load(model, map_location=device).to(device)
model = model.eval()
# --- Fix Seed ---
torch.manual_seed(123)
np.random.seed(123)
# --- Label ---
label_dir = '../CheXpert-v1.0-small/valid.csv'
label = pd.read_csv(label_dir)
target_label = np.array(list(label.keys())[5:])
target_obsrv = np.array([8, 2, 6, 5, 10])
label = label.values
label_gd = label[:, 5:]
# --- Image ---
img_index = args.image_index
img_dir = '../'
transform = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
])
print('Patient:', label[img_index][0])
img = Image.open(os.path.join(img_dir, label[img_index][0])).convert('RGB')
img = transform(img)
img_transformed = img / 255
img_transformed = img_transformed.unsqueeze(0).to(device)
# --- Predict ---
pred = model(img_transformed)
print()
print('[Prediction]')
print('{:17s}|{:4s}|{:4s}|{:4s}|{:3s}'.format('Pathology', 'Prob', 'Thrs',
'Pred', 'Ans'))
for lbl, prd, thrsh, gd in zip(target_label[target_obsrv],
pred[0][target_obsrv],
threshold[target_obsrv],
label_gd[img_index][target_obsrv]):
print('{:17s}:{:4.2f} {:4.2f} {:4d} {:3d}'.format(
lbl, prd.item(), thrsh, int(prd.item() > thrsh), int(gd)))
print()
del pred
torch.cuda.empty_cache()
gc.collect()
model = model.to(torch.device('cpu'))
img_transformed = img_transformed.to(torch.device('cpu'))
# --- Visualization ---
pathology_input = input(
'Please enter which pathology to visualize:\n[0]Atelectasis\n[1]Cardiomegaly\n[2]Consolidation\n[3]Edema\n[4]Pleural Effusion\n[5]Exit\n'
)
if pathology_input == '0':
pathology = 8
print('Diagnosis on Atelectasis')
elif pathology_input == '1':
pathology = 2
print('Diagnosis on Cardiomegaly')
elif pathology_input == '2':
pathology = 6
print('Diagnosis on Consolidation')
elif pathology_input == '3':
pathology = 5
print('Diagnosis on Edema')
elif pathology_input == '4':
pathology = 10
print('Diagnosis on Pleural Effusion')
elif pathology_input == '5':
print('Exiting...')
return
else:
raise NotImplementedError('Only 0-5 are valid input values')
default_cmap = LinearSegmentedColormap.from_list('custom blue',
[(0, '#ffffff'),
(0.25, '#000000'),
(1, '#000000')],
N=256)
print()
method_input = input(
'Please enter which method to visualize:\n[0]GradientShap\n[1]DeepLift\n[2]Exit\n'
)
if method_input == '0':
print('Using GradientShap')
# --- Gradient Shap ---
gradient_shap = GradientShap(model)
# === baseline distribution ===
rand_img_dist = torch.cat([img_transformed * 0, img_transformed * 1])
attributions_gs = gradient_shap.attribute(img_transformed,
n_samples=50,
stdevs=0.0001,
baselines=rand_img_dist,
target=pathology)
_ = viz.visualize_image_attr_multiple(
np.transpose(attributions_gs.squeeze().cpu().detach().numpy(),
(1, 2, 0)),
np.transpose(img.squeeze().cpu().detach().numpy(), (1, 2, 0)),
["original_image", "heat_map"], ["all", "absolute_value"],
cmap=default_cmap,
show_colorbar=True)
del attributions_gs
elif method_input == '1':
print('Using DeepLIFT')
# --- Deep Lift ---
model = model_transform(model)
dl = DeepLift(model)
attr_dl = dl.attribute(img_transformed,
target=pathology,
baselines=img_transformed * 0)
_ = viz.visualize_image_attr_multiple(
np.transpose(attr_dl.squeeze().cpu().detach().numpy(), (1, 2, 0)),
np.transpose(img.squeeze().cpu().detach().numpy(), (1, 2, 0)),
["original_image", "heat_map"], ["all", "positive"],
cmap=default_cmap,
show_colorbar=True)
del attr_dl
elif method_input == '2':
print('Exiting...')
return
else:
raise NotImplementedError('Only 0-2 are valid input values')
"""
elif method_input == '2':
print('Using Integrated Gradients')
# --- Integrated Gradients ---
integrated_gradients = IntegratedGradients(model)
attributions_ig = integrated_gradients.attribute(img_transformed, target=pathology, n_steps=200)
_ = viz.visualize_image_attr_multiple(np.transpose(attributions_ig.squeeze().cpu().detach().numpy(), (1,2,0)),
np.transpose(img.squeeze().cpu().detach().numpy(), (1,2,0)),
method=["original_image", "heat_map"],
cmap=default_cmap,
show_colorbar=True,
sign=["all", "positive"])
del attributions_ig
elif method_input == '3':
print('Using Noise Tunnel')
# --- Noise Tunnel ---
integrated_gradients = IntegratedGradients(model)
noise_tunnel = NoiseTunnel(integrated_gradients)
attributions_ig_nt = noise_tunnel.attribute(img_transformed, n_samples=10, nt_type='smoothgrad_sq', target=pathology)
_ = viz.visualize_image_attr_multiple(np.transpose(attributions_ig_nt.squeeze().cpu().detach().numpy(), (1,2,0)),
np.transpose(img.squeeze().cpu().detach().numpy(), (1,2,0)),
["original_image", "heat_map"],
["all", "positive"],
cmap=default_cmap,
show_colorbar=True)
del attributions_ig_nt
"""
gc.collect()
return
def generate_saliency(model_path, saliency_path, saliency, aggregation):
checkpoint = torch.load(model_path,
map_location=lambda storage, loc: storage)
model_args = Namespace(**checkpoint['args'])
if args.model == 'lstm':
model = LSTM_MODEL(tokenizer,
model_args,
n_labels=checkpoint['args']['labels']).to(device)
model.load_state_dict(checkpoint['model'])
elif args.model == 'trans':
transformer_config = BertConfig.from_pretrained(
'bert-base-uncased', num_labels=model_args.labels)
model_cp = BertForSequenceClassification.from_pretrained(
'bert-base-uncased', config=transformer_config).to(device)
checkpoint = torch.load(model_path,
map_location=lambda storage, loc: storage)
model_cp.load_state_dict(checkpoint['model'])
model = BertModelWrapper(model_cp)
else:
model = CNN_MODEL(tokenizer,
model_args,
n_labels=checkpoint['args']['labels']).to(device)
model.load_state_dict(checkpoint['model'])
model.train()
pad_to_max = False
if saliency == 'deeplift':
ablator = DeepLift(model)
elif saliency == 'guided':
ablator = GuidedBackprop(model)
elif saliency == 'sal':
ablator = Saliency(model)
elif saliency == 'inputx':
ablator = InputXGradient(model)
elif saliency == 'occlusion':
ablator = Occlusion(model)
coll_call = get_collate_fn(dataset=args.dataset, model=args.model)
return_attention_masks = args.model == 'trans'
collate_fn = partial(coll_call,
tokenizer=tokenizer,
device=device,
return_attention_masks=return_attention_masks,
pad_to_max_length=pad_to_max)
test = get_dataset(path=args.dataset_dir,
mode=args.split,
dataset=args.dataset)
batch_size = args.batch_size if args.batch_size != None else \
model_args.batch_size
test_dl = DataLoader(batch_size=batch_size,
dataset=test,
shuffle=False,
collate_fn=collate_fn)
# PREDICTIONS
predictions_path = model_path + '.predictions'
if not os.path.exists(predictions_path):
predictions = defaultdict(lambda: [])
for batch in tqdm(test_dl, desc='Running test prediction... '):
if args.model == 'trans':
logits = model(batch[0],
attention_mask=batch[1],
labels=batch[2].long())
else:
logits = model(batch[0])
logits = logits.detach().cpu().numpy().tolist()
predicted = np.argmax(np.array(logits), axis=-1)
predictions['class'] += predicted.tolist()
predictions['logits'] += logits
with open(predictions_path, 'w') as out:
json.dump(predictions, out)
# COMPUTE SALIENCY
if saliency != 'occlusion':
embedding_layer_name = 'model.bert.embeddings' if args.model == \
'trans' else \
'embedding'
interpretable_embedding = configure_interpretable_embedding_layer(
model, embedding_layer_name)
class_attr_list = defaultdict(lambda: [])
token_ids = []
saliency_flops = []
for batch in tqdm(test_dl, desc='Running Saliency Generation...'):
if args.model == 'cnn':
additional = None
elif args.model == 'trans':
additional = (batch[1], batch[2])
else:
additional = batch[-1]
token_ids += batch[0].detach().cpu().numpy().tolist()
if saliency != 'occlusion':
input_embeddings = interpretable_embedding.indices_to_embeddings(
batch[0])
if not args.no_time:
high.start_counters([
events.PAPI_FP_OPS,
])
for cls_ in range(checkpoint['args']['labels']):
if saliency == 'occlusion':
attributions = ablator.attribute(
batch[0],
sliding_window_shapes=(args.sw, ),
target=cls_,
additional_forward_args=additional)
else:
attributions = ablator.attribute(
input_embeddings,
target=cls_,
additional_forward_args=additional)
attributions = summarize_attributions(
attributions, type=aggregation, model=model,
tokens=batch[0]).detach().cpu().numpy().tolist()
class_attr_list[cls_] += [[_li for _li in _l]
for _l in attributions]
if not args.no_time:
saliency_flops.append(
sum(high.stop_counters()) / batch[0].shape[0])
if saliency != 'occlusion':
remove_interpretable_embedding_layer(model, interpretable_embedding)
# SERIALIZE
print('Serializing...', flush=True)
with open(saliency_path, 'w') as out:
for instance_i, _ in enumerate(test):
saliencies = []
for token_i, token_id in enumerate(token_ids[instance_i]):
token_sal = {'token': tokenizer.ids_to_tokens[token_id]}
for cls_ in range(checkpoint['args']['labels']):
token_sal[int(
cls_)] = class_attr_list[cls_][instance_i][token_i]
saliencies.append(token_sal)
out.write(json.dumps({'tokens': saliencies}) + '\n')
out.flush()
return saliency_flops
def __init__(self, model):
self.model = model
self.explain = DeepLift(model)
def __init__(self, trainer):
CaptumDerivative.__init__(self, trainer)
DeepLift.__init__(self, self.trainer)
agg_w = learned_weights[0].T
for k in range(1, len(learned_weights)):
agg_w = np.dot(agg_w, learned_weights[k].T)
print(agg_w.shape)
np.save('agg_cancer_model_weights_sdae.npy', agg_w)
"""
print(
"< =============== Run DeepLIFT to measure gene contributions ====================== >"
)
dl_loader = DataLoader(x_tr, batch_size=x_tr.shape[0])
model.to(torch.device("cpu"))
deeplift = DeepLift(model)
deeplift_shap = DeepLiftShap(model)
deeplift_baseline = deeplift_baseline.resize(1, deeplift_baseline.shape[0])
attribution_brca = 0
attribution_ucec = 0
attribution_kirc = 0
attribution_luad = 0
attribution_skcm = 0
total = 0
for i, inputs in enumerate(dl_loader, 0):
attribution_brca = torch.abs(
deeplift.attribute(inputs, target=0, baselines=deeplift_baseline))
attribution_ucec = torch.abs(
def __init__(self, model, activation=torch.nn.Softmax(-1)):
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.base_model = model.to(self.device)
self.explainer = DeepLift(self.base_model)
self.activation = activation
def main(args, DatasetsTypes, DataGenerationTypes, models, device):
for m in range(len(models)):
for x in range(len(DatasetsTypes)):
for y in range(len(DataGenerationTypes)):
if (DataGenerationTypes[y] == None):
args.DataName = DatasetsTypes[x] + "_Box"
else:
args.DataName = DatasetsTypes[
x] + "_" + DataGenerationTypes[y]
Training = np.load(args.data_dir + "SimulatedTrainingData_" +
args.DataName + "_F_" +
str(args.NumFeatures) + "_TS_" +
str(args.NumTimeSteps) + ".npy")
TrainingMetaDataset = np.load(args.data_dir +
"SimulatedTrainingMetaData_" +
args.DataName + "_F_" +
str(args.NumFeatures) + "_TS_" +
str(args.NumTimeSteps) + ".npy")
TrainingLabel = TrainingMetaDataset[:, 0]
Testing = np.load(args.data_dir + "SimulatedTestingData_" +
args.DataName + "_F_" +
str(args.NumFeatures) + "_TS_" +
str(args.NumTimeSteps) + ".npy")
TestingDataset_MetaData = np.load(args.data_dir +
"SimulatedTestingMetaData_" +
args.DataName + "_F_" +
str(args.NumFeatures) +
"_TS_" +
str(args.NumTimeSteps) +
".npy")
TestingLabel = TestingDataset_MetaData[:, 0]
Training = Training.reshape(
Training.shape[0], Training.shape[1] * Training.shape[2])
Testing = Testing.reshape(Testing.shape[0],
Testing.shape[1] * Testing.shape[2])
scaler = MinMaxScaler()
scaler.fit(Training)
Training = scaler.transform(Training)
Testing = scaler.transform(Testing)
TrainingRNN = Training.reshape(Training.shape[0],
args.NumTimeSteps,
args.NumFeatures)
TestingRNN = Testing.reshape(Testing.shape[0],
args.NumTimeSteps,
args.NumFeatures)
train_dataRNN = data_utils.TensorDataset(
torch.from_numpy(TrainingRNN),
torch.from_numpy(TrainingLabel))
train_loaderRNN = data_utils.DataLoader(
train_dataRNN, batch_size=args.batch_size, shuffle=True)
test_dataRNN = data_utils.TensorDataset(
torch.from_numpy(TestingRNN),
torch.from_numpy(TestingLabel))
test_loaderRNN = data_utils.DataLoader(
test_dataRNN, batch_size=args.batch_size, shuffle=False)
modelName = "Simulated"
modelName += args.DataName
saveModelName = "../Models/" + models[m] + "/" + modelName
saveModelBestName = saveModelName + "_BEST.pkl"
pretrained_model = torch.load(saveModelBestName,
map_location=device)
Test_Acc = checkAccuracy(test_loaderRNN, pretrained_model,
args)
print('{} {} model BestAcc {:.4f}'.format(
args.DataName, models[m], Test_Acc))
if (Test_Acc >= 90):
if (args.GradFlag):
rescaledGrad = np.zeros((TestingRNN.shape))
Grad = Saliency(pretrained_model)
if (args.IGFlag):
rescaledIG = np.zeros((TestingRNN.shape))
IG = IntegratedGradients(pretrained_model)
if (args.DLFlag):
rescaledDL = np.zeros((TestingRNN.shape))
DL = DeepLift(pretrained_model)
if (args.GSFlag):
rescaledGS = np.zeros((TestingRNN.shape))
GS = GradientShap(pretrained_model)
if (args.DLSFlag):
rescaledDLS = np.zeros((TestingRNN.shape))
DLS = DeepLiftShap(pretrained_model)
if (args.SGFlag):
rescaledSG = np.zeros((TestingRNN.shape))
Grad_ = Saliency(pretrained_model)
SG = NoiseTunnel(Grad_)
if (args.ShapleySamplingFlag):
rescaledShapleySampling = np.zeros((TestingRNN.shape))
SS = ShapleyValueSampling(pretrained_model)
if (args.GSFlag):
rescaledFeaturePermutation = np.zeros(
(TestingRNN.shape))
FP = FeaturePermutation(pretrained_model)
if (args.FeatureAblationFlag):
rescaledFeatureAblation = np.zeros((TestingRNN.shape))
FA = FeatureAblation(pretrained_model)
if (args.OcclusionFlag):
rescaledOcclusion = np.zeros((TestingRNN.shape))
OS = Occlusion(pretrained_model)
idx = 0
mask = np.zeros((args.NumTimeSteps, args.NumFeatures),
dtype=int)
for i in range(args.NumTimeSteps):
mask[i, :] = i
for i, (samples, labels) in enumerate(test_loaderRNN):
print('[{}/{}] {} {} model accuracy {:.2f}'\
.format(i,len(test_loaderRNN), models[m], args.DataName, Test_Acc))
input = samples.reshape(-1, args.NumTimeSteps,
args.NumFeatures).to(device)
input = Variable(input,
volatile=False,
requires_grad=True)
batch_size = input.shape[0]
baseline_single = torch.from_numpy(
np.random.random(input.shape)).to(device)
baseline_multiple = torch.from_numpy(
np.random.random(
(input.shape[0] * 5, input.shape[1],
input.shape[2]))).to(device)
inputMask = np.zeros((input.shape))
inputMask[:, :, :] = mask
inputMask = torch.from_numpy(inputMask).to(device)
mask_single = torch.from_numpy(mask).to(device)
mask_single = mask_single.reshape(
1, args.NumTimeSteps, args.NumFeatures).to(device)
labels = torch.tensor(labels.int().tolist()).to(device)
if (args.GradFlag):
attributions = Grad.attribute(input, \
target=labels)
rescaledGrad[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.IGFlag):
attributions = IG.attribute(input, \
baselines=baseline_single, \
target=labels)
rescaledIG[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.DLFlag):
attributions = DL.attribute(input, \
baselines=baseline_single, \
target=labels)
rescaledDL[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.GSFlag):
attributions = GS.attribute(input, \
baselines=baseline_multiple, \
stdevs=0.09,\
target=labels)
rescaledGS[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.DLSFlag):
attributions = DLS.attribute(input, \
baselines=baseline_multiple, \
target=labels)
rescaledDLS[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.SGFlag):
attributions = SG.attribute(input, \
target=labels)
rescaledSG[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.ShapleySamplingFlag):
attributions = SS.attribute(input, \
baselines=baseline_single, \
target=labels,\
feature_mask=inputMask)
rescaledShapleySampling[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.FeaturePermutationFlag):
attributions = FP.attribute(input, \
target=labels,
perturbations_per_eval= input.shape[0],\
feature_mask=mask_single)
rescaledFeaturePermutation[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.FeatureAblationFlag):
attributions = FA.attribute(input, \
target=labels)
# perturbations_per_eval= input.shape[0],\
# feature_mask=mask_single)
rescaledFeatureAblation[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.OcclusionFlag):
attributions = OS.attribute(input, \
sliding_window_shapes=(1,args.NumFeatures),
target=labels,
baselines=baseline_single)
rescaledOcclusion[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
idx += batch_size
if (args.plot):
index = random.randint(0, TestingRNN.shape[0] - 1)
plotExampleBox(TestingRNN[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_sample',
flip=True)
print("Plotting sample", index)
if (args.GradFlag):
plotExampleBox(rescaledGrad[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_Grad',
greyScale=True,
flip=True)
if (args.IGFlag):
plotExampleBox(rescaledIG[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_IG',
greyScale=True,
flip=True)
if (args.DLFlag):
plotExampleBox(rescaledDL[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_DL',
greyScale=True,
flip=True)
if (args.GSFlag):
plotExampleBox(rescaledGS[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_GS',
greyScale=True,
flip=True)
if (args.DLSFlag):
plotExampleBox(rescaledDLS[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_DLS',
greyScale=True,
flip=True)
if (args.SGFlag):
plotExampleBox(rescaledSG[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_SG',
greyScale=True,
flip=True)
if (args.ShapleySamplingFlag):
plotExampleBox(
rescaledShapleySampling[index, :, :],
args.Saliency_Maps_graphs_dir + args.DataName +
"_" + models[m] + '_ShapleySampling',
greyScale=True,
flip=True)
if (args.FeaturePermutationFlag):
plotExampleBox(
rescaledFeaturePermutation[index, :, :],
args.Saliency_Maps_graphs_dir + args.DataName +
"_" + models[m] + '_FeaturePermutation',
greyScale=True,
flip=True)
if (args.FeatureAblationFlag):
plotExampleBox(
rescaledFeatureAblation[index, :, :],
args.Saliency_Maps_graphs_dir + args.DataName +
"_" + models[m] + '_FeatureAblation',
greyScale=True,
flip=True)
if (args.OcclusionFlag):
plotExampleBox(rescaledOcclusion[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_Occlusion',
greyScale=True,
flip=True)
if (args.save):
if (args.GradFlag):
print("Saving Grad", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_Grad_rescaled", rescaledGrad)
if (args.IGFlag):
print("Saving IG", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_IG_rescaled", rescaledIG)
if (args.DLFlag):
print("Saving DL", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_DL_rescaled", rescaledDL)
if (args.GSFlag):
print("Saving GS", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_GS_rescaled", rescaledGS)
if (args.DLSFlag):
print("Saving DLS", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_DLS_rescaled", rescaledDLS)
if (args.SGFlag):
print("Saving SG", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_SG_rescaled", rescaledSG)
if (args.ShapleySamplingFlag):
print("Saving ShapleySampling",
modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_ShapleySampling_rescaled",
rescaledShapleySampling)
if (args.FeaturePermutationFlag):
print("Saving FeaturePermutation",
modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_FeaturePermutation_rescaled",
rescaledFeaturePermutation)
if (args.FeatureAblationFlag):
print("Saving FeatureAblation",
modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_FeatureAblation_rescaled",
rescaledFeatureAblation)
if (args.OcclusionFlag):
print("Saving Occlusion",
modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_Occlusion_rescaled",
rescaledOcclusion)
else:
logging.basicConfig(filename=args.log_file,
level=logging.DEBUG)
logging.debug('{} {} model BestAcc {:.4f}'.format(
args.DataName, models[m], Test_Acc))
if not os.path.exists(args.ignore_list):
with open(args.ignore_list, 'w') as fp:
fp.write(args.DataName + '_' + models[m] + '\n')
else:
with open(args.ignore_list, "a") as fp:
fp.write(args.DataName + '_' + models[m] + '\n')
def __init__(self, model, train_data):
model.eval()
self.explainer = DeepLift(model)
self.model = model
def __init__(self, predictor: Predictor):
CaptumAttribution.__init__(self, 'deeplift', predictor)
self.submodel = self.predictor._model.captum_sub_model()
DeepLift.__init__(self, self.submodel)
def DeepLIFT(classifier_model, config, dataset_features, GNNgraph_list, current_fold, cuda=0):
'''
:param classifier_model: trained classifier model
:param config: parsed configuration file of config.yml
:param dataset_features: a dictionary of dataset features obtained from load_data.py
:param GNNgraph_list: a list of GNNgraphs obtained from the dataset
:param cuda: whether to use GPU to perform conversion to tensor
'''
# Initialise settings
config = config
interpretability_config = config["interpretability_methods"]["DeepLIFT"]
dataset_features = dataset_features
# Perform deeplift on the classifier model
dl = DeepLift(classifier_model)
output_for_metrics_calculation = []
output_for_generating_saliency_map = {}
# Obtain attribution score for use in qualitative metrics
tmp_timing_list = []
for GNNgraph in GNNgraph_list:
output = {'graph': GNNgraph}
for _, label in dataset_features["label_dict"].items():
# Relabel all just in case, may only relabel those that need relabelling
# if performance is poor
original_label = GNNgraph.label
GNNgraph.label = label
node_feat, n2n, subg = graph_to_tensor(
[GNNgraph], dataset_features["feat_dim"],
dataset_features["edge_feat_dim"], cuda)
start_generation = perf_counter()
attribution = dl.attribute(node_feat,
additional_forward_args=(n2n, subg, [GNNgraph]),
target=label)
tmp_timing_list.append(perf_counter() - start_generation)
attribution_score = torch.sum(attribution, dim=1).tolist()
attribution_score = standardize_scores(attribution_score)
GNNgraph.label = original_label
output[label] = attribution_score
output_for_metrics_calculation.append(output)
execution_time = sum(tmp_timing_list)/(len(tmp_timing_list))
# Obtain attribution score for use in generating saliency map for comparison with zero tensors
if interpretability_config["compare_with_zero_tensor"] is True:
if interpretability_config["sample_ids"] is not None:
if ',' in str(interpretability_config["sample_ids"]):
sample_graph_id_list = list(map(int, interpretability_config["sample_ids"].split(',')))
else:
sample_graph_id_list = [int(interpretability_config["sample_ids"])]
output_for_generating_saliency_map.update({"layergradcam_%s_%s" % (str(assign_type), str(label)): []
for _, label in dataset_features["label_dict"].items()})
for index in range(len(output_for_metrics_calculation)):
tmp_output = output_for_metrics_calculation[index]
tmp_label = tmp_output['graph'].label
if tmp_output['graph'].graph_id in sample_graph_id_list:
element_name = "layergradcam_%s_%s" % (str(assign_type), str(tmp_label))
output_for_generating_saliency_map[element_name].append(
(tmp_output['graph'], tmp_output[tmp_label]))
elif interpretability_config["number_of_zero_tensor_samples"] > 0:
# Randomly sample from existing list:
graph_idxes = list(range(len(output_for_metrics_calculation)))
random.shuffle(graph_idxes)
output_for_generating_saliency_map.update({"deeplift_zero_tensor_class_%s" % str(label): []
for _, label in dataset_features["label_dict"].items()})
# Begin appending found samples
for index in graph_idxes:
tmp_label = output_for_metrics_calculation[index]['graph'].label
element_name = "deeplift_zero_tensor_class_%s" % str(tmp_label)
if len(output_for_generating_saliency_map[element_name]) < interpretability_config["number_of_zero_tensor_samples"]:
output_for_generating_saliency_map[element_name].append(
(output_for_metrics_calculation[index]['graph'], output_for_metrics_calculation[index][tmp_label]))
# Obtain attribution score for use in generating saliency map for comparison with isomers
if interpretability_config["compare_with_isomorphic_samples"] is True:
if dataset_features["num_class"] != 2:
print("DeepLIFT.py: Comparing with isomorphic samples is only possible in binary classification tasks.")
else:
# Get all isomorphic pairs
class_0_graphs, class_1_graphs = get_isomorphic_pairs(
dataset_features["name"], GNNgraph_list, config["run"]["k_fold"], current_fold,
interpretability_config["number_of_isomorphic_sample_pairs"])
# Generate attribution scores for the isomorphic pairs
if class_0_graphs == None:
pass
elif len(class_0_graphs) == 0 or len(class_1_graphs) == 0:
print("DeepLIFT: No isomorphic pairs found for test dataset")
else:
output_for_generating_saliency_map["deeplift_isomorphic_class_0"] = []
output_for_generating_saliency_map["deeplift_isomorphic_class_1"] = []
for graph_0, graph_1 in zip(class_0_graphs, class_1_graphs):
node_feat_0, n2n, subg = graph_to_tensor(
[graph_0], dataset_features["feat_dim"],
dataset_features["edge_feat_dim"], cuda)
node_feat_1, _, _ = graph_to_tensor(
[graph_1], dataset_features["feat_dim"],
dataset_features["edge_feat_dim"], cuda)
attribution_0 = dl.attribute(node_feat_0,
additional_forward_args=(n2n, subg, [graph_0]),
baselines=node_feat_1,
target=graph_0.label)
attribution_1 = dl.attribute(node_feat_1,
additional_forward_args=(n2n, subg, [graph_1]),
baselines=node_feat_0,
target=graph_1.label)
attribution_score_0 = torch.sum(attribution_0, dim=1).tolist()
attribution_score_1 = torch.sum(attribution_1, dim=1).tolist()
attribution_score_0 = standardize_scores(attribution_score_0)
attribution_score_1 = standardize_scores(attribution_score_1)
output_for_generating_saliency_map["deeplift_isomorphic_class_0"].append(
(graph_0, attribution_score_0))
output_for_generating_saliency_map["deeplift_isomorphic_class_1"].append(
(graph_1, attribution_score_1))
return output_for_metrics_calculation, output_for_generating_saliency_map, execution_time
def test(cnn, valid_data, valid_loader, learning_file):
cnn.to("cpu")
cnn.eval()
#correct = 0
#prediction_positive = 0
'''
my_array = np.array(valid_data.data.values).astype(float)
print(type(my_array))
my_tensor = torch.tensor(my_array)
valid_x = torch.unsqueeze(my_tensor, dim = 1).type(torch.FloatTensor)
valid_y = valid_data.labels.numpy()
y_pre = cnn(valid_x)
_,pre_index = torch.max(y_pre, 1)
pre_index = pre_index.view(-1)
prediction = pre_index.numpy()
tp,fp,tn,fn, p,recall,F1,acc = Evaluate(prediction,valid_y)
logger.info( "TP:{:d},FP:{:d},TN:{:d},FN:{:d}".format(tp,fp,tn,fn))
logger.info( "precision:{:.2f},recall:{:.2f},F1:{:.2f},accuracy:{:.2f}".format(p,recall,F1,acc))
'''
precite = np.array([])
expected = np.array([])
attr_score = np.empty([40, 4], dtype=np.float64)
#ig = IntegratedGradients(cnn)
dl = DeepLift(cnn)
#t1 = time()
for i, data in enumerate(valid_loader):
# forward
inputs, labels = data
inputs = torch.unsqueeze(inputs, dim=1).type(torch.FloatTensor)
inputs = inputs #.to(device)
labels = labels #.to(device)
# for IntegratedGradients
#baseline = torch.zeros(inputs.shape)#.to(device)
baseline_np = np.array([[[0.3, 0.2, 0.2, 0.3]] * 40] * inputs.shape[0])
baseline = torch.from_numpy(baseline_np)
baseline = torch.unsqueeze(baseline, dim=1).type(torch.FloatTensor)
attributions, delta = dl.attribute(
inputs, baseline, target=0,
return_convergence_delta=True) #.to(device)
#logger.info('IG Attributions:' + str(attributions))
#logger.info('Convergence Delta:' + str(delta))
outputs = cnn(inputs)
attr_score += genScore(attributions)
#print('echo test:',i,time()-t1)
_, pre_index = torch.max(outputs.data, 1)
pre_index = pre_index.view(-1)
prediction = pre_index.numpy()
labels = labels.numpy()
precite = np.concatenate((precite, prediction))
expected = np.concatenate((expected, labels))
#prediction_positive += np.sum(prediction)
#correct += np.sum(prediction == labels)
attr_score_sum = np.sum(attr_score, axis=1)
attr_score = avgScore(attr_score, attr_score_sum)
np.save(
ATTR_PATH + 'attributions_Rep1_' + learning_file.split('_')[0][:-4] +
'.npy', attr_score)
cnn.to("cpu")
tp, fp, tn, fn, p, recall, F1, acc, mcc = Evaluate(precite, expected)
'''
logger.info( "TP:{:d},FP:{:d},TN:{:d},FN:{:d}".format(tp,fp,tn,fn))
logger.info( "precision:{:.2f},recall:{:.2f},F1:{:.2f},accuracy:{:.2f}".format(p,recall,F1,acc))
logger.info( "MCC:{:.2f}".format(mcc) )
'''
logger.info("TP:" + str(tp))
logger.info("FP:" + str(fp))
logger.info("TN:" + str(tn))
logger.info("FN:" + str(fn))
logger.info("Precision:{:.4f}".format(p))
logger.info("Recall:{:.4f}".format(recall))
logger.info("F1:{:.4f}".format(F1))
logger.info("Accuracy:{:.4f}".format(acc))
logger.info("MCC:{:.4f}".format(mcc))
#test_accuracy = correct / len(valid_data)
#print("Test accuracy: ", test_accuracy )
#logger.info( "Predicted positive: " + str( prediction_positive ) )
#logger.info( "Test accuracy: " + str( test_accuracy ) )
return tp, fp, tn, fn
def get_grad_imp(model,
X,
y=None,
mode='grad',
return_y=False,
clip=False,
baselines=None):
X.requires_grad_(True)
# X = X.cuda()
if mode in ['grad']:
logits = model(X)
if y is None:
y = logits.argmax(dim=1)
attributions = torch.autograd.grad(
logits[torch.arange(len(logits)), y].sum(), X)[0].detach()
else:
if y is None:
with torch.no_grad():
logits = model(X)
y = logits.argmax(dim=1)
if mode == 'deeplift':
dl = DeepLift(model)
attributions = dl.attribute(inputs=X, baselines=0., target=y)
attributions = attributions.detach()
# attributions = (attributions.detach() ** 2).sum(dim=1, keepdim=True)
# elif mode in ['deepliftshap', 'deepliftshap_mean']:
elif mode in ['deepliftshap']:
dl = DeepLiftShap(model)
attributions = []
for idx in range(0, len(X), 2):
the_x, the_y = X[idx:(idx + 2)], y[idx:(idx + 2)]
attribution = dl.attribute(inputs=the_x,
baselines=baselines,
target=the_y)
attributions.append(attribution.detach())
attributions = torch.cat(attributions, dim=0)
# attributions = dl.attribute(inputs=X, baselines=baselines, target=y).detach()
# if mode == 'deepliftshap':
# attributions = (attributions ** 2).sum(dim=1, keepdim=True)
# else:
# attributions = (attributions).mean(dim=1, keepdim=True)
elif mode in ['gradcam']:
orig_lgc = LayerGradCam(model, model.body[0])
attributions = orig_lgc.attribute(X, target=y)
attributions = F.interpolate(attributions,
size=X.shape[-2:],
mode='bilinear')
else:
raise NotImplementedError(f'${mode} is not specified.')
# Do clipping!
if clip:
attributions = myclip(attributions)
X.requires_grad_(False)
if not return_y:
return attributions
return attributions, y
# Occlusion
occlusion = Occlusion(model)
grads = occlusion.attribute(x, strides = (1, int(FS / 100)), target=labels[idx].item(), sliding_window_shapes=(1, int(FS / 10)), baselines=0)
grads_occ.append(grads.squeeze().cpu().detach().numpy())
# Integrated Gradients
integrated_gradients = IntegratedGradients(model)
grads = integrated_gradients.attribute(x, target=labels[idx].item(), n_steps=1000)
grads_igrad.append(grads.squeeze().cpu().detach().numpy())
# Gradient SHAP
gshap = GradientShap(model)
baseline_dist = torch.cat([x*0, x*1])
grads = gshap.attribute(x, n_samples=10, stdevs=0.1, baselines=baseline_dist, target=labels[idx].item())
grads_gshap.append(grads.squeeze().cpu().detach().numpy())
# DeepLIFT
dlift = DeepLift(model)
grads = dlift.attribute(x, x*0, target=labels[idx].item())
grads_dlift.append(grads.squeeze().cpu().detach().numpy())
signal.append(x.squeeze().cpu().detach().numpy())
with open(os.path.join('results', 'interp_' + os.path.basename(MODEL) + '.pk'), 'wb') as hf:
pk.dump({'x': signal, 'sal': grads_sal, 'occ': grads_occ, 'igrad': grads_igrad, 'gshap': grads_gshap, 'dlift': grads_dlift}, hf)
def run_saliency_methods(saliency_methods,
pretrained_model,
test_shape,
train_loader,
test_loader,
device,
model_type,
model_name,
saliency_dir,
tsr_graph_dir=None,
tsr_inputs_to_graph=()):
_, num_timesteps, num_features = test_shape
run_grad = "Grad" in saliency_methods
run_grad_tsr = "Grad_TSR" in saliency_methods
run_ig = "IG" in saliency_methods
run_ig_tsr = "IG_TSR" in saliency_methods
run_dl = "DL" in saliency_methods
run_gs = "GS" in saliency_methods
run_dls = "DLS" in saliency_methods
run_dls_tsr = "DLS_TSR" in saliency_methods
run_sg = "SG" in saliency_methods
run_shapley_sampling = "ShapleySampling" in saliency_methods
run_feature_permutation = "FeaturePermutation" in saliency_methods
run_feature_ablation = "FeatureAblation" in saliency_methods
run_occlusion = "Occlusion" in saliency_methods
run_fit = "FIT" in saliency_methods
run_ifit = "IFIT" in saliency_methods
run_wfit = "WFIT" in saliency_methods
run_iwfit = "IWFIT" in saliency_methods
if run_grad or run_grad_tsr:
Grad = Saliency(pretrained_model)
if run_grad:
rescaledGrad = np.zeros(test_shape)
if run_grad_tsr:
rescaledGrad_TSR = np.zeros(test_shape)
if run_ig or run_ig_tsr:
IG = IntegratedGradients(pretrained_model)
if run_ig:
rescaledIG = np.zeros(test_shape)
if run_ig_tsr:
rescaledIG_TSR = np.zeros(test_shape)
if run_dl:
rescaledDL = np.zeros(test_shape)
DL = DeepLift(pretrained_model)
if run_gs:
rescaledGS = np.zeros(test_shape)
GS = GradientShap(pretrained_model)
if run_dls or run_dls_tsr:
DLS = DeepLiftShap(pretrained_model)
if run_dls:
rescaledDLS = np.zeros(test_shape)
if run_dls_tsr:
rescaledDLS_TSR = np.zeros(test_shape)
if run_sg:
rescaledSG = np.zeros(test_shape)
Grad_ = Saliency(pretrained_model)
SG = NoiseTunnel(Grad_)
if run_shapley_sampling:
rescaledShapleySampling = np.zeros(test_shape)
SS = ShapleyValueSampling(pretrained_model)
if run_gs:
rescaledFeaturePermutation = np.zeros(test_shape)
FP = FeaturePermutation(pretrained_model)
if run_feature_ablation:
rescaledFeatureAblation = np.zeros(test_shape)
FA = FeatureAblation(pretrained_model)
if run_occlusion:
rescaledOcclusion = np.zeros(test_shape)
OS = Occlusion(pretrained_model)
if run_fit:
rescaledFIT = np.zeros(test_shape)
FIT = FITExplainer(pretrained_model, ft_dim_last=True)
generator = JointFeatureGenerator(num_features, data='none')
# TODO: Increase epochs
FIT.fit_generator(generator, train_loader, test_loader, n_epochs=300)
if run_ifit:
rescaledIFIT = np.zeros(test_shape)
if run_wfit:
rescaledWFIT = np.zeros(test_shape)
if run_iwfit:
rescaledIWFIT = np.zeros(test_shape)
idx = 0
mask = np.zeros((num_timesteps, num_features), dtype=int)
for i in range(num_timesteps):
mask[i, :] = i
for i, (samples, labels) in enumerate(test_loader):
input = samples.reshape(-1, num_timesteps, num_features).to(device)
input = Variable(input, volatile=False, requires_grad=True)
batch_size = input.shape[0]
baseline_single = torch.from_numpy(np.random.random(
input.shape)).to(device)
baseline_multiple = torch.from_numpy(
np.random.random((input.shape[0] * 5, input.shape[1],
input.shape[2]))).to(device)
inputMask = np.zeros((input.shape))
inputMask[:, :, :] = mask
inputMask = torch.from_numpy(inputMask).to(device)
mask_single = torch.from_numpy(mask).to(device)
mask_single = mask_single.reshape(1, num_timesteps,
num_features).to(device)
labels = torch.tensor(labels.int().tolist()).to(device)
if run_grad:
attributions = Grad.attribute(input, target=labels)
rescaledGrad[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_grad_tsr:
rescaledGrad_TSR[idx:idx + batch_size, :, :] = get_tsr_saliency(
Grad,
input,
labels,
graph_dir=tsr_graph_dir,
graph_name=f'{model_name}_{model_type}_Grad_TSR',
inputs_to_graph=tsr_inputs_to_graph,
cur_batch=i)
if run_ig:
attributions = IG.attribute(input,
baselines=baseline_single,
target=labels)
rescaledIG[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_ig_tsr:
rescaledIG_TSR[idx:idx + batch_size, :, :] = get_tsr_saliency(
IG,
input,
labels,
baseline=baseline_single,
graph_dir=tsr_graph_dir,
graph_name=f'{model_name}_{model_type}_IG_TSR',
inputs_to_graph=tsr_inputs_to_graph,
cur_batch=i)
if run_dl:
attributions = DL.attribute(input,
baselines=baseline_single,
target=labels)
rescaledDL[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_gs:
attributions = GS.attribute(input,
baselines=baseline_multiple,
stdevs=0.09,
target=labels)
rescaledGS[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_dls:
attributions = DLS.attribute(input,
baselines=baseline_multiple,
target=labels)
rescaledDLS[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_dls_tsr:
rescaledDLS_TSR[idx:idx + batch_size, :, :] = get_tsr_saliency(
DLS,
input,
labels,
baseline=baseline_multiple,
graph_dir=tsr_graph_dir,
graph_name=f'{model_name}_{model_type}_DLS_TSR',
inputs_to_graph=tsr_inputs_to_graph,
cur_batch=i)
if run_sg:
attributions = SG.attribute(input, target=labels)
rescaledSG[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_shapley_sampling:
attributions = SS.attribute(input,
baselines=baseline_single,
target=labels,
feature_mask=inputMask)
rescaledShapleySampling[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_feature_permutation:
attributions = FP.attribute(input,
target=labels,
perturbations_per_eval=input.shape[0],
feature_mask=mask_single)
rescaledFeaturePermutation[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_feature_ablation:
attributions = FA.attribute(input, target=labels)
# perturbations_per_eval= input.shape[0],\
# feature_mask=mask_single)
rescaledFeatureAblation[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_occlusion:
attributions = OS.attribute(input,
sliding_window_shapes=(1,
num_features),
target=labels,
baselines=baseline_single)
rescaledOcclusion[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_fit:
attributions = torch.from_numpy(FIT.attribute(input, labels))
rescaledFIT[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_ifit:
attributions = torch.from_numpy(
inverse_fit_attribute(input,
pretrained_model,
ft_dim_last=True))
rescaledIFIT[idx:idx + batch_size, :, :] = attributions
if run_wfit:
attributions = torch.from_numpy(
wfit_attribute(input,
pretrained_model,
N=test_shape[1],
ft_dim_last=True,
single_label=True))
rescaledWFIT[idx:idx + batch_size, :, :] = attributions
if run_iwfit:
attributions = torch.from_numpy(
wfit_attribute(input,
pretrained_model,
N=test_shape[1],
ft_dim_last=True,
single_label=True,
inverse=True))
rescaledIWFIT[idx:idx + batch_size, :, :] = attributions
idx += batch_size
if run_grad:
print("Saving Grad", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_Grad_rescaled",
rescaledGrad)
if run_grad_tsr:
print("Saving Grad_TSR", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type +
"_Grad_TSR_rescaled", rescaledGrad_TSR)
if run_ig:
print("Saving IG", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_IG_rescaled",
rescaledIG)
if run_ig_tsr:
print("Saving IG_TSR", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_IG_TSR_rescaled",
rescaledIG_TSR)
if run_dl:
print("Saving DL", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_DL_rescaled",
rescaledDL)
if run_gs:
print("Saving GS", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_GS_rescaled",
rescaledGS)
if run_dls:
print("Saving DLS", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_DLS_rescaled",
rescaledDLS)
if run_dls_tsr:
print("Saving DLS_TSR", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_DLS_TSR_rescaled",
rescaledDLS_TSR)
if run_sg:
print("Saving SG", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_SG_rescaled",
rescaledSG)
if run_shapley_sampling:
print("Saving ShapleySampling", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type +
"_ShapleySampling_rescaled", rescaledShapleySampling)
if run_feature_permutation:
print("Saving FeaturePermutation", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type +
"_FeaturePermutation_rescaled", rescaledFeaturePermutation)
if run_feature_ablation:
print("Saving FeatureAblation", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type +
"_FeatureAblation_rescaled", rescaledFeatureAblation)
if run_occlusion:
print("Saving Occlusion", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type +
"_Occlusion_rescaled", rescaledOcclusion)
if run_fit:
print("Saving FIT", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_FIT_rescaled",
rescaledFIT)
if run_ifit:
print("Saving IFIT", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_IFIT_rescaled",
rescaledIFIT)
if run_wfit:
print("Saving WFIT", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_WFIT_rescaled",
rescaledWFIT)
if run_iwfit:
print("Saving IWFIT", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_IWFIT_rescaled",
rescaledIWFIT)
