def find_pseudo_labels(
self,
labeled_file_path: str,
unlabeled_file_path: str,
temperature: int = 10,
batch_size: int = None,
**kwargs
):
labeled_data = load_data_jsonl(
labeled_file_path,
)
unlabeled_data = load_data_jsonl(
unlabeled_file_path,
)
if not batch_size:
batch_size = len(unlabeled_data)
unlabeled_data_chunks = chunks(unlabeled_data, batch_size)
n_batches = len(range(0, len(unlabeled_data), batch_size))
all_recovered = list()
labeled_embeddings = np.array(self.embedder.embed_sentences([d['sentence'] for d in labeled_data]))
for batch_ix, unlabeled_data_chunk in enumerate(unlabeled_data_chunks):
logger.info(f'Finding pseudo labels for batch {batch_ix + 1}/{n_batches}')
unlabeled_embeddings = np.array(self.embedder.embed_sentences([d['sentence'] for d in unlabeled_data_chunk]))
embeddings = np.concatenate((labeled_embeddings, unlabeled_embeddings), axis=0)
w = (1 - pairwise_distances(embeddings, embeddings, metric='cosine')).astype(np.float32)
all_recovered += get_nKNN_pseudo_labels(w, labeled_data, unlabeled_data_chunk, temperature=temperature)
return all_recovered
def find_pseudo_labels(
self,
labeled_file_path: str,
unlabeled_file_path: str,
batch_size: int = None,
**kwargs
):
self.fit(labeled_file_path)
unlabeled_data = load_data_jsonl(unlabeled_file_path)
if not batch_size:
batch_size = len(unlabeled_data)
unlabeled_data_chunks = chunks(unlabeled_data, batch_size)
n_batches = len(range(0, len(unlabeled_data), batch_size))
recovered = list()
for batch_ix, unlabeled_data_chunk in enumerate(unlabeled_data_chunks):
logger.info(f'Finding pseudo labels for batch {batch_ix + 1}/{n_batches}')
X = self.embedder.embed_sentences([str(d['sentence']) for d in unlabeled_data_chunk])
predictions = self.logreg.predict_proba(X)
pseudo_labels = predictions.argmax(1)
pseudo_labels_scores = predictions.max(1)
for original_data, pseudo_label, pseudo_label_score in zip(
unlabeled_data_chunk, pseudo_labels, pseudo_labels_scores):
recovered.append(dict(
data=original_data.copy(),
pseudo_label=self.labels_vocab(pseudo_label, rev=True),
pseudo_label_score=float(pseudo_label_score)
))
return recovered
def find_pseudo_labels(self,
labeled_file_path: str,
unlabeled_file_path: str,
temperature: int = 10,
batch_size: int = None,
**kwargs):
labeled_data = load_data_jsonl(labeled_file_path, )
unlabeled_data = load_data_jsonl(unlabeled_file_path, )
if not batch_size:
batch_size = len(unlabeled_data)
unlabeled_data_chunks = chunks(unlabeled_data, batch_size)
n_batches = len(range(0, len(unlabeled_data), batch_size))
all_recovered = list()
labeled_embeddings = np.array(
self.embedder.embed_sentences([d['input'] for d in labeled_data]))
for batch_ix, unlabeled_data_chunk in enumerate(unlabeled_data_chunks):
logger.info(
f'Finding pseudo labels for batch {batch_ix + 1}/{n_batches}')
unlabeled_embeddings = np.array(
self.embedder.embed_sentences(
[d['input'] for d in unlabeled_data_chunk]))
embeddings = np.concatenate(
(labeled_embeddings, unlabeled_embeddings), axis=0)
nn = NearestNeighbors(n_neighbors=10, metric='cosine')
nn.fit(embeddings)
graph = nn.kneighbors_graph().toarray()
w = (graph.T + graph > 0).astype(int)
# D
d = np.diag(w.sum(0))
d_half = fractional_matrix_power(d, -0.5)
# Normalized laplacian
l_sym = np.eye(len(w)) - d_half @ w @ d_half
# Eigen decomposition
eigs = eigh(l_sym, eigvals=(1, min(31, len(l_sym) - 1)))
normed_eigs = eigs[1] / np.sqrt(eigs[0])
# W_prime
w_prime = (normed_eigs @ normed_eigs.T).astype(np.float32)
all_recovered += get_nKNN_pseudo_labels(w_prime,
labeled_data,
unlabeled_data_chunk,
temperature=temperature)
return all_recovered
def find_pseudo_labels(
self,
labeled_file_path: str,
unlabeled_file_path: str,
temperature: int = 10,
batch_size: int = None,
**kwargs
):
labeled_data = load_data_jsonl(
labeled_file_path,
)
unlabeled_data = load_data_jsonl(
unlabeled_file_path,
)
if not batch_size:
batch_size = len(unlabeled_data)
unlabeled_data_chunks = chunks(unlabeled_data, batch_size)
n_batches = len(range(0, len(unlabeled_data), batch_size))
all_recovered = list()
labeled_embeddings = np.array(self.embedder.embed_sentences([d['sentence'] for d in labeled_data], detached=True))
for batch_ix, unlabeled_data_chunk in enumerate(unlabeled_data_chunks):
logger.info(f'Finding pseudo labels for batch {batch_ix + 1}/{n_batches}')
unlabeled_embeddings = np.array(self.embedder.embed_sentences([d['sentence'] for d in unlabeled_data_chunk], detached=True))
labels = [d['label'] for d in labeled_data] + ['' for _ in unlabeled_data_chunk]
labels_vocab = Vocab([d['label'] for d in labeled_data])
embeddings = np.concatenate((labeled_embeddings, unlabeled_embeddings), axis=0)
# Build similarity matrix
w = (1 - pairwise_distances(embeddings, embeddings, metric='cosine')).astype(np.float32)
# Extracts splits of W for each label. Will be used to compute score
w_label = dict()
for label in labels_vocab.labels:
labelled_global_indices = [ix for ix, d in enumerate(labeled_data) if d['label'] == label]
w_label[label] = w[:, labelled_global_indices]
# Build hierarchical tree, bottom to top
Z = linkage(embeddings, 'ward')
root_tree = to_tree(Z)
# Split tree, top to bottom
trees = get_unique_label_trees(root_tree=root_tree, labels=labels)
# Recover data
recovered = list()
for tree, path in trees:
output = list()
# Get all indices in the tree
order = tree.pre_order()
tree_labels = [labels[ix] for ix in order]
# Case when all elements of tree are unlabelled
if set(tree_labels) == {''}:
recovered += output
continue
# Case when samples are mixed (labeled & unlabeled), but with a unique label
# Get the label
pseudo_label = [l for o, l in zip(order, tree_labels) if len(labels[o])][0]
# Iterate over items
for ix in order:
# Case if item is unlabeled
if labels[ix] == '':
# Compute score
global_ix = ix
z_i = np.array([
w_label[label][global_ix].mean()
for label in labels_vocab.labels
])
# temperature
z_i *= temperature
z_i_bar = np.exp(z_i)
z_i_bar /= z_i_bar.sum()
pseudo_label_score = float(z_i_bar[labels_vocab(pseudo_label)])
# Output
dat = unlabeled_data_chunk[ix - len(labeled_data)].copy()
output.append(dict(
data=dat,
pseudo_label=pseudo_label,
pseudo_label_score=pseudo_label_score
))
recovered += output
all_recovered += recovered
return all_recovered