def plot_eigenvalue_report(weight_directory,
unpruned_n_eigenvalues=None,
pruned_n_eigenvalues=None,
filter_norm=1,
figsize=(10, 5)):
weight_paths = get_weights_paths(weight_directory, norm=filter_norm)
is_slice = (unpruned_n_eigenvalues is not None
or pruned_n_eigenvalues is not None)
n_rows = 2 if is_slice else 1
_, axes = plt.subplots(n_rows, 2, squeeze=False, figsize=figsize)
axes[0][0].set_title('Unpruned')
plot_eigenvalues(weight_paths[True],
filter_norm=filter_norm,
ax=axes[0][0])
if is_slice:
plot_eigenvalues(weight_paths[True],
unpruned_n_eigenvalues,
filter_norm=filter_norm,
ax=axes[1][0])
axes[0][1].set_title('Pruned')
plot_eigenvalues(weight_paths[False],
filter_norm=filter_norm,
ax=axes[0][1])
if is_slice:
plot_eigenvalues(weight_paths[False],
pruned_n_eigenvalues,
filter_norm=filter_norm,
ax=axes[1][1])
def run_double_spectral_cluster(weight_directory,
with_shuffle=True,
n_clusters=4,
shuffle_method='layer',
n_samples=None,
n_workers=None,
with_shuffled_ncuts=False,
use_inv_avg_commute=False,
filter_norm=1,
random_state=RANDOM_STATE,
eigen_solver='arpack'):
weight_paths = get_weights_paths(weight_directory, norm=filter_norm)
return {
is_unpruned:
run_spectral_cluster(weight_path,
with_shuffle=with_shuffle,
n_clusters=n_clusters,
shuffle_method=shuffle_method,
n_samples=n_samples,
n_workers=n_workers,
with_shuffled_ncuts=with_shuffled_ncuts,
use_inv_avg_commute=use_inv_avg_commute,
filter_norm=filter_norm,
random_state=random_state,
eigen_solver=eigen_solver)
for is_unpruned, weight_path in weight_paths.items()
}
def build_weighted_dist_mat(model_path,
clustering_result,
filter_norm=1,
normalize_in_out=False):
weight_path = get_weights_paths(model_path,
norm=filter_norm)[False] # pruned
G = build_cluster_graph(
weight_path,
clustering_result[False], # pruned
normalize_in_out=normalize_in_out)
df = pd.DataFrame([{
'start': start,
'end': end,
'dist': _compute_weighted_dist(G, start, end)
} for start, end in it.combinations(G.nodes, 2)])
df = df[df != 0].dropna()
# The distance is normalized to [0, 1] inside the paths between two specific layers
# The max weighted sitance is one.
df['layers'] = df.apply(
lambda r: r['start'].split('-')[0] + '-' + r['end'].split('-')[0],
axis=1)
df['normalized_dist'] = df['dist'] / df.groupby(
'layers')['dist'].transform('max')
mat = df.pivot('start', 'end', 'normalized_dist')
return mat
def draw_mlp_clustering_report(weight_directory,
double_clustering_results,
n_cluster=4,
title=None,
figsize=(20, 30)):
weight_paths = get_weights_paths(weight_directory)
fig, axes = plt.subplots(2, 2, figsize=figsize)
if title is not None:
fig.suptitle(title)
axes[0][0].set_title('Unpruned')
draw_clustered_mlp(
weight_paths[True], # True represents **un**pruned
double_clustering_results[True],
n_clusters=n_cluster,
ax=axes[0][0])
draw_cluster_by_layer(weight_paths[True],
double_clustering_results[True],
n_clusters=n_cluster,
ax=axes[1][0])
axes[0][1].set_title('Pruned')
draw_clustered_mlp(weight_paths[False],
double_clustering_results[False],
n_clusters=n_cluster,
ax=axes[0][1])
draw_cluster_by_layer(weight_paths[False],
double_clustering_results[False],
n_clusters=n_cluster,
ax=axes[1][1])
def test_two_methods_cnn_clustering_pvalue():
# See comment above about BASE_PATH
config_updates = {'weights_path': get_weights_paths(get_model_path('CNN:MNIST',
model_base_path=BASE_PATH))[True],
'with_labels': False,
'with_shuffle': False,
'num_clusters': 4,
'is_testing': True}
experiment_run = clustering_experiment.run(config_updates=config_updates,
named_configs=['cnn_config'])
def run_activations_cluster_experiment(
activations_dir,
weights_dir,
exclude_inputs=True,
filter_norm=1,
with_shuffle=True,
n_clusters=10,
n_samples=None,
n_workers=None,
random_state=RANDOM_STATE,
):
#
# if 'mlp' in str(weights_dir) and 'mlp' in str(activations_dir):
# named_configs = ['mlp_config']
# elif 'cnn' in str(weights_dir) and 'cnn' in str(activations_dir):
# named_configs = ['cnn_config']
# else:
# raise ValueError('Either mlp or cnn should be in path to determine the config.')
activations_path_dict = get_activations_paths(activations_dir)
activations_masks_path_dict = get_activation_masks_paths(activations_dir)
weight_path_dict = get_weights_paths(weights_dir, norm=filter_norm)
results_dicts = []
for is_unpruned in [True, False]:
config_updates = {
'activations_path': activations_path_dict[is_unpruned],
'activations_mask_path': activations_masks_path_dict[is_unpruned],
'weights_path': weight_path_dict[is_unpruned],
'exclude_inputs': exclude_inputs,
'with_shuffle': with_shuffle,
'seed': random_state,
'n_clusters': n_clusters
}
if n_samples is not None:
config_updates['n_samples'] = n_samples
if n_workers is not None:
config_updates['n_workers'] = n_workers
with suppress(), all_logging_disabled():
experiment_run = activations_experiment.run(
config_updates=config_updates)
results_dicts.append(experiment_run.result)
return {'unpruned': results_dicts[0], 'pruned': results_dicts[1]}
def run_double_spectral_cluster(
weight_directory,
with_shuffle=True,
n_clusters=4,
shuffle_method='layer',
n_samples=None,
n_workers=None,
with_shuffled_ncuts=False,
random_state=RANDOM_STATE,
):
weight_paths = get_weights_paths(weight_directory)
return {
is_unpruned:
run_spectral_cluster(weight_path, with_shuffle, n_clusters,
shuffle_method, n_samples, n_workers,
with_shuffled_ncuts, random_state)
for is_unpruned, weight_path in weight_paths.items()
}
def draw_mlp_clustering_report(weight_directory,
double_clustering_results,
n_cluster=4,
filter_norm=1,
is_first_square=True,
title=None,
figsize=(20, 30)):
weight_paths = get_weights_paths(weight_directory, norm=filter_norm)
if 'cnn' not in str(weight_directory).lower():
fig, axes = plt.subplots(2, 2, figsize=figsize)
if title is not None:
fig.suptitle(title)
axes[0][0].set_title('Unpruned')
draw_clustered_mlp(
weight_paths[True], # True represents **un**pruned
double_clustering_results[True],
n_clusters=n_cluster,
is_first_square=is_first_square,
ax=axes[0][0])
draw_cluster_by_layer(weight_paths[True],
double_clustering_results[True],
n_clusters=n_cluster,
ax=axes[1][0])
axes[0][1].set_title('Pruned')
draw_clustered_mlp(weight_paths[False],
double_clustering_results[False],
n_clusters=n_cluster,
is_first_square=is_first_square,
ax=axes[0][1])
draw_cluster_by_layer(weight_paths[False],
double_clustering_results[False],
n_clusters=n_cluster,
ax=axes[1][1])
else: # if it's a CNN
fig, axes = plt.subplots(1, 2, figsize=figsize)
if title is not None:
fig.suptitle(title)
axes[0].set_title('Unpruned')
draw_clustered_mlp(
weight_paths[True], # True represents **un**pruned
double_clustering_results[True],
n_clusters=n_cluster,
is_first_square=is_first_square,
ax=axes[0])
axes[1].set_title('Pruned')
draw_clustered_mlp(weight_paths[False],
double_clustering_results[False],
n_clusters=n_cluster,
is_first_square=is_first_square,
ax=axes[1])
def cluster_and_visualize(weights_dir,
activations_dir,
n_clusters=10,
corr_type='spearman',
filter_norm=1,
n_iters=20,
n_random=4,
side_len=28,
min_size=4,
max_prop=0.8):
assert corr_type in ['pearson', 'spearman']
results = {}
weight_path_dict = get_weights_paths(weights_dir, norm=filter_norm)
activations_path_dict = get_activations_paths(activations_dir)
activations_masks_path_dict = get_activation_masks_paths(activations_dir)
for is_unpruned in [True, False]:
# run clustering to get labels
# for a cnn, this will only get results for the conv layers
labels, _ = run_spectral_cluster(weight_path_dict[is_unpruned],
n_clusters=n_clusters,
with_shuffle=False)
# get the activations and the mask
with open(activations_path_dict[is_unpruned],
'rb') as f: # get stored correlation-based adjacency matrix
masked_activations = pickle.load(f)
with open(activations_masks_path_dict[is_unpruned], 'rb') as f:
activations_mask = pickle.load(f)
# the activations come pre-masked, so reconstruct them placing zeros for the units which were masked
activations = np.zeros(
(len(activations_mask), masked_activations.shape[-1]))
activations[activations_mask] = masked_activations
del masked_activations # take out the trash
# get the numbers of each type of unit
if 'cnn' in str(weights_dir): # if a cnn
cnn_params = CNN_VGG_MODEL_PARAMS if 'vgg' in str(
weights_dir).lower() else CNN_MODEL_PARAMS
unit_nums = [cl['filters'] for cl in cnn_params['conv']]
n_units = sum(unit_nums)
n_dense = sum(d['units'] for d in cnn_params['dense'])
n_outputs = 10
n_inputs = len(activations_mask) - n_units - n_dense - n_outputs
else: # if an mlp
n_inputs = 784
n_outputs = 10
unit_nums = [256, 256, 256, 256]
n_units = sum(unit_nums)
labels = labels[n_inputs:n_inputs + n_units]
assert len(labels) == n_units
# get correlations
if corr_type == 'pearson':
corr_mat = np.corrcoef(activations[:n_inputs + n_units],
rowvar=True)
else: # spearman
corr_mat, _ = spearmanr(activations[:n_inputs + n_units], axis=1)
# get correlations between inputs and units
representations = corr_mat[n_inputs:, :n_inputs]
del corr_mat # take out the trash
representations[np.isnan(representations)] = 0
representations_by_layer = list(splitter(representations, unit_nums))
labels_by_layer = list(splitter(labels, unit_nums))
network_results = {}
for layer_i in range(len(unit_nums)): # for each layer
layer_reps = np.array(representations_by_layer[layer_i])
layer_reps_stds = np.std(layer_reps, axis=1)
layer_reps_valid = layer_reps[layer_reps_stds > 0]
n_valid = len(layer_reps_valid)
layer_labels = np.array(labels_by_layer[layer_i])
layer_size = unit_nums[layer_i]
max_size = max_prop * layer_size
layer_results = {}
for cluster_i in range(
n_clusters): # for each sub module within the layer
sm_reps = layer_reps[layer_labels == cluster_i]
sm_reps_stds = np.std(sm_reps, axis=1)
sm_reps = sm_reps[
sm_reps_stds >
0] # filter out ones that aren't responsive to anything
sm_size = len(sm_reps)
if sm_size < min_size or sm_size > max_size: # skip if too small or big
continue
sm_reps = align_reps(sm_reps, n_iters)
true_avg = np.reshape(np.mean(sm_reps, axis=0),
(-1, side_len, side_len))
if np.mean(true_avg) > 0: # align to have negative mean
true_avg *= -1
avgs = [true_avg] # first in the list will be the true one
for _ in range(n_random):
rdm_idxs = np.random.choice(np.array(range(n_valid)),
size=sm_size,
replace=False)
rdm_reps = layer_reps_valid[rdm_idxs]
rdm_reps = align_reps(rdm_reps, n_iters)
rdm_avg = np.reshape(np.mean(rdm_reps, axis=0),
(-1, side_len, side_len))
if np.mean(rdm_avg) > 0: # align to have negative mean
rdm_avg *= -1
avgs.append(rdm_avg)
layer_results[f'cluster_{cluster_i}'] = {
'ims': avgs,
'size': sm_size
}
network_results[f'layer_{layer_i}'] = layer_results
results[is_unpruned] = network_results
return results
def get_selectivities(run_dir,
dataset_name,
is_unpruned,
n_inputs=784,
n_outputs=10):
"""
selectivity here means a unit's linear correlation with an output category across a test set
this function will return the selectivities and assignments for output categories
that the units correlate the best and second best with
"""
weight_path = get_weights_paths(run_dir)[is_unpruned]
labels, _ = run_spectral_cluster(weight_path,
n_clusters=n_outputs,
with_shuffle=False)
act_path = get_activations_paths(run_dir)[is_unpruned]
act_mask_path = get_activation_masks_paths(run_dir)[is_unpruned]
with open(act_path, 'rb') as f:
activations = pickle.load(
f) # each row a unit and each col and example
with open(act_mask_path, 'rb') as f:
activations_mask = pickle.load(f)
if 'cnn' in str(run_dir).lower(): # for the cnns, only look at conv layers
if 'stacked' in str(run_dir).lower():
n_in = n_inputs * 2
else:
n_in = n_inputs
cnn_params = CNN_VGG_MODEL_PARAMS if dataset_name == 'cifar10_full' else CNN_MODEL_PARAMS
n_conv_filters = sum([cl['filters'] for cl in cnn_params['conv']])
n_start = np.sum(activations_mask[:n_in])
n_stop = n_start + np.sum(activations_mask[n_in:n_in + n_conv_filters])
activations = activations[n_start:n_stop, :]
labels = labels[activations_mask[n_in:n_in + n_conv_filters]]
else:
n_in = n_inputs
n_start = np.sum(activations_mask[:n_in])
activations = activations[n_start:-n_outputs, :]
labels = labels[n_inputs:-n_outputs]
labels = labels[activations_mask[n_in:-n_outputs]]
labels = labels[labels != -1]
activations = activations[labels != -1]
data_path = '.' + DATA_PATHS[dataset_name]
with open(data_path, 'rb') as f:
dataset = pickle.load(f)
y_test = dataset['y_test'][:activations.shape[1]]
y_test_onehot = np.array(
[y_test == label_i for label_i in range(n_outputs)])
corr_abs = np.abs(
np.corrcoef(np.vstack([activations, y_test_onehot]),
rowvar=True)[:activations.shape[0], :n_outputs])
assignments = np.argmax(corr_abs, axis=1)
selectivities = np.max(corr_abs, axis=1)
for i in range(len(assignments)):
corr_abs[i, assignments[i]] = 0
second_assignments = np.argmax(corr_abs, axis=1)
second_selectivities = np.max(corr_abs, axis=1)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
nmi = normalized_mutual_info_score(assignments, labels)
second_nmi = normalized_mutual_info_score(second_assignments, labels)
return assignments, selectivities, second_assignments, second_selectivities, nmi, second_nmi
def _perform_lesion_sub_experiment(dataset_path,
run_dir,
n_clusters=4,
n_shuffles=200,
n_side=28,
depth=1,
with_random=True,
model_params=None,
n_way=1,
n_way_type='joint',
unpruned=False,
true_as_random=False,
verbose=False):
if verbose:
print('Loading data...')
ds = preprocess_dataset(dataset_path)
X, y = ds['X_test'], ds['y_test']
run_dir_path = Path(run_dir)
weight_paths = get_weights_paths(run_dir_path)
model_paths = get_model_paths(run_dir_path)
if unpruned:
model_path = str(model_paths[True])
weight_path = str(next(run_dir_path.glob('*-unpruned-weights.pckl')))
else:
model_path = str(model_paths[False])
weight_path = str(next(run_dir_path.glob('*-pruned-weights.pckl')))
if 'mlp' in model_path.lower() or 'poly' in model_path.lower():
network_type = 'mlp'
elif 'cnn' in model_path.lower():
network_type = 'cnn'
X = np.reshape(X, (-1, n_side, n_side, depth))
# assert model_params is not None, ('For CNN network type, '
# 'the model_param parameter should be given.')
else:
raise ValueError('Network type should be expressed explicitly '
'either mlp or cnn in run directory files.')
if 'poly' in model_path.lower():
task = 'regression'
else:
task = 'classification'
if verbose:
print('Running spectral clustering...')
labels, _ = run_spectral_cluster(weight_path,
n_clusters=n_clusters,
with_shuffle=False)
if verbose:
print('Loading model and extracting weights...')
# os.environ['CUDA_VISIBLE_DEVICES'] = ''
with suppress(), all_logging_disabled():
experiment_model = load_model2(model_path)
weights, biases = extract_weights(experiment_model, with_bias=True)
ignore_layers = False
if verbose:
print('Evaluate original model...')
evaluation = _evaluate(experiment_model, X, y, task)
if network_type == 'mlp':
layer_widths = extract_layer_widths(weights)
else:
layer_widths = []
weight_shapes = [layer_weights.shape for layer_weights in weights]
n_conv = sum(len(ws) == 4 for ws in weight_shapes)
layer_widths.extend([weight_shapes[i][-1] for i in range(n_conv)])
layer_widths.extend([ws[-1] for ws in weight_shapes[n_conv:]])
# omit non conv layers
weights = weights[:n_conv]
biases = biases[:n_conv]
layer_widths = layer_widths[:n_conv + 1]
if verbose:
print('Extract metadata...')
metadata = _extract_layer_label_metadata(network_type, layer_widths,
labels, ignore_layers)
if verbose:
print('Apply lesion trial on the true clustering...')
true_results = _apply_lesion_trial(X,
y,
network_type,
experiment_model,
weights,
biases,
layer_widths,
labels,
ignore_layers,
task,
to_shuffle=true_as_random,
n_way=n_way,
n_way_type=n_way_type,
verbose=verbose)
if with_random:
if verbose:
print('Apply lesion trial on the random clusterings...')
progress_iter = tqdm
else:
progress_iter = iter
all_random_results = []
for _ in progress_iter(range(n_shuffles)):
random_results = _apply_lesion_trial(X,
y,
network_type,
experiment_model,
weights,
biases,
layer_widths,
labels,
ignore_layers,
task,
to_shuffle=True,
n_way=n_way,
n_way_type=n_way_type,
verbose=verbose)
all_random_results.append(random_results)
else:
all_random_results = None
if n_way == 1:
true_results = _flatten_single_damage(true_results)
all_random_results = (
[_flatten_single_damage(result)
for result in all_random_results] if all_random_results else None)
return true_results, all_random_results, metadata, evaluation
