def spectral_clustering(x, scale, n_nbrs=None, affinity='full', W=None):
'''
Computes the eigenvectors of the graph Laplacian of x,
using the full Gaussian affinity matrix (full), the
symmetrized Gaussian affinity matrix with k nonzero
affinities for each point (knn), or the Siamese affinity
matrix (siamese)
x: input data
n_nbrs: number of neighbors used
affinity: the aforementeiond affinity mode
returns: the eigenvectors of the spectral clustering algorithm
'''
if affinity == 'full':
W = K.eval(cf.full_affinity(K.variable(x), scale))
elif affinity == 'knn':
if n_nbrs is None:
raise ValueError('n_nbrs must be provided if affinity = knn!')
W = K.eval(cf.knn_affinity(K.variable(x), scale, n_nbrs))
elif affinity == 'siamese':
if W is None:
print('no affinity matrix supplied')
return
d = np.sum(W, axis=1)
D = np.diag(d)
# (unnormalized) graph laplacian for spectral clustering
L = D - W
Lambda, V = np.linalg.eigh(L)
return (Lambda, V)
def run_predict(params):
K.set_learning_phase(0)
input_shape = x.shape[1:]
y_labeled_onehot = np.empty((0, params['n_clusters']))
# spectralnet has three inputs -- they are defined here
inputs = {
'Unlabeled': Input(shape=input_shape, name='UnlabeledInput'),
'Labeled': Input(shape=input_shape, name='LabeledInput'),
'Orthonorm': Input(shape=input_shape, name='OrthonormInput'),
}
y_true = tf.placeholder(tf.float32,
shape=(None, params['n_clusters']),
name='y_true')
# Load Siamese network
if params['affinity'] == 'siamese':
siamese_input_shape = [params['n_clusters']]
siamese_inputs = {
'Unlabeled': Input(shape=siamese_input_shape,
name='UnlabeledInput'),
'Labeled': Input(shape=siamese_input_shape, name='LabeledInput'),
}
siamese_net = networks.SiameseNet(siamese_inputs, params['arch'],
params.get('siam_reg'), y_true,
params['siamese_model_path'])
else:
siamese_net = None
# Load Spectral net
spectralnet_model_path = os.path.join(params['model_path'], 'spectral_net')
spectral_net = networks.SpectralNet(inputs,
params['arch'],
params.get('spec_reg'),
y_true,
y_labeled_onehot,
params['n_clusters'],
params['affinity'],
params['scale_nbr'],
params['n_nbrs'],
batch_sizes,
spectralnet_model_path,
siamese_net,
train=False)
# get final embeddings
W_tensor = costs.knn_affinity(siamese_net.outputs['A'],
params['n_nbrs'],
scale=None,
scale_nbr=params['scale_nbr'])
x_spectralnet = spectral_net.predict_unlabelled(x)
W = spectral_net.run_tensor(x, W_tensor)
print('x_spectralnet', x_spectralnet.shape)
clustering_algo = joblib.load(
os.path.join(params['model_path'], 'spectral_net',
'clustering_aglo.sav'))
kmeans_assignments = clustering_algo.predict_cluster_assignments(
x_spectralnet)
y_spectralnet = clustering_algo.predict(x_spectralnet)
print_accuracy(kmeans_assignments, y, params['n_clusters'])
# x_dec = decode_data(x, params, params['dset'])
return x_spectralnet, y_spectralnet, x_spectralnet, W
def run_net(data, params):
#
# UNPACK DATA
#
x_train, y_train, x_val, y_val, x_test, y_test = data['spectral'][
'train_and_test']
x_train_unlabeled, y_train_unlabeled, x_train_labeled, y_train_labeled = data[
'spectral']['train_unlabeled_and_labeled']
x_val_unlabeled, y_val_unlabeled, x_val_labeled, y_val_labeled = data[
'spectral']['val_unlabeled_and_labeled']
if 'siamese' in params['affinity']:
pairs_train, dist_train, pairs_val, dist_val = data['siamese'][
'train_and_test']
x = np.concatenate((x_train, x_val, x_test), axis=0)
y = np.concatenate((y_train, y_val, y_test), axis=0)
if len(x_train_labeled):
y_train_labeled_onehot = OneHotEncoder().fit_transform(
y_train_labeled.reshape(-1, 1)).toarray()
else:
y_train_labeled_onehot = np.empty((0, len(np.unique(y))))
#
# SET UP INPUTS
#
# create true y placeholder (not used in unsupervised training)
y_true = tf.placeholder(tf.float32,
shape=(None, params['n_clusters']),
name='y_true')
batch_sizes = {
'Unlabeled': params['batch_size'],
'Labeled': params['batch_size'],
'Orthonorm': params.get('batch_size_orthonorm', params['batch_size']),
}
input_shape = x.shape[1:]
# spectralnet has three inputs -- they are defined here
inputs = {
'Unlabeled': Input(shape=input_shape, name='UnlabeledInput'),
'Labeled': Input(shape=input_shape, name='LabeledInput'),
'Orthonorm': Input(shape=input_shape, name='OrthonormInput'),
}
#
# DEFINE SIAMESE NET
#
# run only if we are using a siamese network
if params['affinity'] == 'siamese':
# set up the siamese network inputs as well
siamese_inputs = {
'A': inputs['Unlabeled'],
'B': Input(shape=input_shape),
'Labeled': inputs['Labeled'],
}
# generate layers
layers = []
layers += make_layer_list(params['arch'], 'siamese',
params.get('siam_reg'))
# create the siamese net
siamese_outputs = stack_layers(siamese_inputs, layers)
# add the distance layer
distance = Lambda(costs.euclidean_distance,
output_shape=costs.eucl_dist_output_shape)(
[siamese_outputs['A'], siamese_outputs['B']])
#create the distance model for training
siamese_net_distance = Model(
[siamese_inputs['A'], siamese_inputs['B']], distance)
#
# TRAIN SIAMESE NET
#
# compile the siamese network
siamese_net_distance.compile(loss=costs.contrastive_loss,
optimizer=RMSprop())
# create handler for early stopping and learning rate scheduling
siam_lh = LearningHandler(lr=params['siam_lr'],
drop=params['siam_drop'],
lr_tensor=siamese_net_distance.optimizer.lr,
patience=params['siam_patience'])
# initialize the training generator
train_gen_ = train_gen(pairs_train, dist_train,
params['siam_batch_size'])
# format the validation data for keras
validation_data = ([pairs_val[:, 0], pairs_val[:, 1]], dist_val)
# compute the steps per epoch
steps_per_epoch = int(len(pairs_train) / params['siam_batch_size'])
# train the network
hist = siamese_net_distance.fit_generator(
train_gen_,
epochs=params['siam_ne'],
validation_data=validation_data,
steps_per_epoch=steps_per_epoch,
callbacks=[siam_lh])
# compute the siamese embeddings of the input data
all_siam_preds = train.predict(siamese_outputs['A'],
x_unlabeled=x_train,
inputs=inputs,
y_true=y_true,
batch_sizes=batch_sizes)
#
# DEFINE SPECTRALNET
#
# generate layers
layers = []
layers = make_layer_list(params['arch'][:-1], 'spectral',
params.get('spec_reg'))
layers += [{
'type': 'tanh',
'size': params['n_clusters'],
'l2_reg': params.get('spec_reg'),
'name': 'spectral_{}'.format(len(params['arch']) - 1)
}, {
'type': 'Orthonorm',
'name': 'orthonorm'
}]
# create spectralnet
outputs = stack_layers(inputs, layers)
spectral_net = Model(inputs=inputs['Unlabeled'],
outputs=outputs['Unlabeled'])
#
# DEFINE SPECTRALNET LOSS
#
# generate affinity matrix W according to params
if params['affinity'] == 'siamese':
input_affinity = tf.concat(
[siamese_outputs['A'], siamese_outputs['Labeled']], axis=0)
x_affinity = all_siam_preds
elif params['affinity'] in ['knn', 'full']:
input_affinity = tf.concat([inputs['Unlabeled'], inputs['Labeled']],
axis=0)
x_affinity = x_train
# calculate scale for affinity matrix
scale = get_scale(x_affinity, batch_sizes['Unlabeled'],
params['scale_nbr'])
# create affinity matrix
if params['affinity'] == 'full':
W = costs.full_affinity(input_affinity, scale=scale)
elif params['affinity'] in ['knn', 'siamese']:
W = costs.knn_affinity(input_affinity,
params['n_nbrs'],
scale=scale,
scale_nbr=params['scale_nbr'])
# if we have labels, use them
if len(x_train_labeled):
# get true affinities (from labeled data)
W_true = tf.cast(tf.equal(costs.squared_distance(y_true), 0),
dtype='float32')
# replace lower right corner of W with W_true
unlabeled_end = tf.shape(inputs['Unlabeled'])[0]
W_u = W[:unlabeled_end, :] # upper half
W_ll = W[unlabeled_end:, :unlabeled_end] # lower left
W_l = tf.concat((W_ll, W_true), axis=1) # lower half
W = tf.concat((W_u, W_l), axis=0)
# create pairwise batch distance matrix Dy
Dy = costs.squared_distance(
tf.concat([outputs['Unlabeled'], outputs['Labeled']], axis=0))
else:
Dy = costs.squared_distance(outputs['Unlabeled'])
# define loss
spectral_net_loss = K.sum(W * Dy) / (2 * params['batch_size'])
# create the train step update
learning_rate = tf.Variable(0., name='spectral_net_learning_rate')
train_step = tf.train.RMSPropOptimizer(
learning_rate=learning_rate).minimize(
spectral_net_loss, var_list=spectral_net.trainable_weights)
#
# TRAIN SPECTRALNET
#
# initialize spectralnet variables
K.get_session().run(
tf.variables_initializer(spectral_net.trainable_weights))
# set up validation/test set inputs
inputs_test = {
'Unlabeled': inputs['Unlabeled'],
'Orthonorm': inputs['Orthonorm']
}
# create handler for early stopping and learning rate scheduling
spec_lh = LearningHandler(lr=params['spec_lr'],
drop=params['spec_drop'],
lr_tensor=learning_rate,
patience=params['spec_patience'])
# begin spectralnet training loop
spec_lh.on_train_begin()
for i in range(params['spec_ne']):
# train spectralnet
loss = train.train_step(return_var=[spectral_net_loss],
updates=spectral_net.updates + [train_step],
x_unlabeled=x_train_unlabeled,
inputs=inputs,
y_true=y_true,
batch_sizes=batch_sizes,
x_labeled=x_train_labeled,
y_labeled=y_train_labeled_onehot,
batches_per_epoch=100)[0]
# get validation loss
val_loss = train.predict_sum(spectral_net_loss,
x_unlabeled=x_val_unlabeled,
inputs=inputs,
y_true=y_true,
x_labeled=x[0:0],
y_labeled=y_train_labeled_onehot,
batch_sizes=batch_sizes)
# do early stopping if necessary
if spec_lh.on_epoch_end(i, val_loss):
print('STOPPING EARLY')
break
# print training status
print("Epoch: {}, loss={:2f}, val_loss={:2f}".format(
i, loss, val_loss))
print("finished training")
#
# EVALUATE
#
# get final embeddings
x_spectralnet = train.predict(outputs['Unlabeled'],
x_unlabeled=x,
inputs=inputs_test,
y_true=y_true,
x_labeled=x_train_labeled[0:0],
y_labeled=y_train_labeled_onehot[0:0],
batch_sizes=batch_sizes)
# get accuracy and nmi
kmeans_assignments, km = get_cluster_sols(x_spectralnet,
ClusterClass=KMeans,
n_clusters=params['n_clusters'],
init_args={'n_init': 10})
y_spectralnet, _ = get_y_preds(kmeans_assignments, y, params['n_clusters'])
print_accuracy(kmeans_assignments, y, params['n_clusters'])
from sklearn.metrics import normalized_mutual_info_score as nmi
nmi_score = nmi(kmeans_assignments, y)
print('NMI: ' + str(np.round(nmi_score, 3)))
if params['generalization_metrics']:
x_spectralnet_train = train.predict(
outputs['Unlabeled'],
x_unlabeled=x_train_unlabeled,
inputs=inputs_test,
y_true=y_true,
x_labeled=x_train_labeled[0:0],
y_labeled=y_train_labeled_onehot[0:0],
batch_sizes=batch_sizes)
x_spectralnet_test = train.predict(
outputs['Unlabeled'],
x_unlabeled=x_test,
inputs=inputs_test,
y_true=y_true,
x_labeled=x_train_labeled[0:0],
y_labeled=y_train_labeled_onehot[0:0],
batch_sizes=batch_sizes)
km_train = KMeans(
n_clusters=params['n_clusters']).fit(x_spectralnet_train)
from scipy.spatial.distance import cdist
dist_mat = cdist(x_spectralnet_test, km_train.cluster_centers_)
closest_cluster = np.argmin(dist_mat, axis=1)
print_accuracy(closest_cluster, y_test, params['n_clusters'],
' generalization')
nmi_score = nmi(closest_cluster, y_test)
print('generalization NMI: ' + str(np.round(nmi_score, 3)))
return x_spectralnet, y_spectralnet
