def run(ref, model_path, num_clusters, num_cluster_samples, seed,
out_cluster_samples_file_hier, max_examples, out_cluster_samples_file,
data_path, view1_col, view2_col, label_col, sampling_strategy,
mvsc_no_unk):
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
id_to_token, token_to_id, vocab_size, word_emb_size, mvc_encoder = \
multiview_encoders.load_model(model_path)
print('loaded model')
print('loading dataset')
dataset = Dataset(data_path,
view1_col=view1_col,
view2_col=view2_col,
label_col=label_col)
n_cluster = len(dataset.id_to_label) - 1
print("loaded dataset, num of class = %d" % n_cluster)
idxes = dataset.trn_idx_no_unk if mvsc_no_unk else dataset.trn_idx
trn_idx = [x.item() for x in np.random.permutation(idxes)]
if max_examples is not None:
trn_idx = trn_idx[:max_examples]
num_clusters = n_cluster if num_clusters is None else num_clusters
print('clustering over num clusters', num_clusters)
mvsc = multiview.mvsc.MVSC(k=n_cluster)
latent_z1s, golds = transform(dataset, trn_idx, mvc_encoder, view='v1')
latent_z2s, _ = transform(dataset, trn_idx, mvc_encoder, view='v2')
print('running mvsc', end='', flush=True)
start = time.time()
preds, eivalues, eivectors, sigmas = mvsc.fit_transform(
[latent_z1s, latent_z2s], [False] * 2)
print('...done')
mvsc_time = time.time() - start
print('time taken %.3f' % mvsc_time)
lgolds, lpreds = [], []
for g, p in zip(golds, list(preds)):
if g > 0:
lgolds.append(g)
lpreds.append(p)
prec, rec, f1 = cluster_metrics.calc_prec_rec_f1(
gnd_assignments=torch.LongTensor(lgolds).to(device),
pred_assignments=torch.LongTensor(lpreds).to(device))
acc = cluster_metrics.calc_ACC(
torch.LongTensor(lpreds).to(device),
torch.LongTensor(lgolds).to(device))
silhouette = sklearn.metrics.silhouette_score(latent_z1s,
preds,
metric='euclidean')
davies_bouldin = sklearn.metrics.davies_bouldin_score(latent_z1s, preds)
print(
f'{datetime.datetime.now()} pretrain: eval prec={prec:.4f} rec={rec:.4f} f1={f1:.4f} '
f'acc={acc:.4f} sil={silhouette:.4f}, db={davies_bouldin:.4f}')
def calc_prec_rec_f1_acc(preds, golds):
lgolds, lpreds = [], []
for g, p in zip(golds, list(preds)):
if g > 0:
lgolds.append(g)
lpreds.append(p)
prec, rec, f1 = cluster_metrics.calc_prec_rec_f1(
gnd_assignments=torch.LongTensor(lgolds).to(device),
pred_assignments=torch.LongTensor(lpreds).to(device))
acc = cluster_metrics.calc_ACC(
torch.LongTensor(lpreds).to(device),
torch.LongTensor(lgolds).to(device))
return prec, rec, f1, acc
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data-path',
type=str,
default='./data/airlines_processed.csv')
parser.add_argument('--glove-path',
type=str,
default='./data/glove.840B.300d.txt')
parser.add_argument('--pre-model',
type=str,
choices=['ae', 'qt'],
default='qt')
parser.add_argument('--pre-epoch', type=int, default=0)
parser.add_argument('--pt-batch', type=int, default=100)
parser.add_argument('--model-path',
type=str,
help='path of pretrained model to load')
parser.add_argument('--way', type=int, default=5)
parser.add_argument('--num-epochs', type=int, default=100)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--save-model-path', type=str)
parser.add_argument('--view1-col', type=str, default='view1')
parser.add_argument('--view2-col', type=str, default='view2')
parser.add_argument('--label-col', type=str, default='label')
args = parser.parse_args()
np.random.seed(args.seed)
print('loading dataset')
dataset = Dataset(args.data_path,
view1_col=args.view1_col,
view2_col=args.view2_col,
label_col=args.label_col)
n_cluster = len(dataset.id_to_label) - 1
print("num of class = %d" % n_cluster)
if args.model_path is not None:
id_to_token, token_to_id, vocab_size, word_emb_size, model = multiview_encoders.load_model(
args.model_path)
print('loaded model')
else:
id_to_token, token_to_id, vocab_size, word_emb_size, model = \
multiview_encoders.from_embeddings(
args.glove_path, dataset.id_to_token, dataset.token_to_id)
print('created randomly initialized model')
print('vocab_size', vocab_size)
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
expressions = (model, optimizer)
pre_acc, pre_state, pre_state_epoch = 0., None, None
pretrain_method = {
'ae': pretrain.pretrain_ae,
'qt': pretrain.pretrain_qt,
}[args.pre_model]
for epoch in range(1, args.pre_epoch + 1):
model.train()
perm_idx = np.random.permutation(dataset.trn_idx)
trn_loss, _ = pretrain_method(dataset,
perm_idx,
expressions,
train=True)
model.eval()
_, tst_acc = pretrain_method(dataset,
dataset.tst_idx,
expressions,
train=False)
if tst_acc > pre_acc:
pre_state = copy.deepcopy(model.state_dict())
pre_acc = tst_acc
pre_state_epoch = epoch
print('{} epoch {}, train_loss={:.4f} test_acc={:.4f}'.format(
datetime.datetime.now(), epoch, trn_loss, tst_acc))
if args.pre_epoch > 0:
# load best state
model.load_state_dict(pre_state)
print(f'loaded best state from epoch {pre_state_epoch}')
# deepcopy pretrained views into v1 and/or view2
{
'ae': pretrain.after_pretrain_ae,
'qt': pretrain.after_pretrain_qt,
}[args.pre_model](model)
# reinitialiate optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
expressions = (model, optimizer)
print('applied post-pretraining')
kmeans = sklearn.cluster.KMeans(n_clusters=n_cluster,
max_iter=300,
verbose=0,
random_state=0)
z_v1, golds = transform(dataset, dataset.trn_idx, model, encoder='v1')
preds_v1 = kmeans.fit_predict(z_v1)
lgolds, lpreds = [], []
for g, p in zip(golds, list(preds_v1)):
if g > 0:
lgolds.append(g)
lpreds.append(p)
prec, rec, f1 = cluster_metrics.calc_prec_rec_f1(
gnd_assignments=torch.LongTensor(lgolds).to(device),
pred_assignments=torch.LongTensor(lpreds).to(device))
acc = cluster_metrics.calc_ACC(
torch.LongTensor(lpreds).to(device),
torch.LongTensor(lgolds).to(device))
print(
f'{datetime.datetime.now()} pretrain: test prec={prec:.4f} rec={rec:.4f} '
f'f1={f1:.4f} acc={acc:.4f}')
shot, way, query = 5, args.way, 15
preds_v2 = None
best_epoch, best_model, best_dev_f1 = None, None, None
for epoch in range(1, args.num_epochs + 1):
trn_loss = 0.
_loss, preds_v2, tst_preds_v2 = run_one_side(model=model,
optimizer=optimizer,
preds_left=preds_v1,
pt_batch=args.pt_batch,
way=way,
shot=shot,
query=query,
n_cluster=n_cluster,
dataset=dataset,
right_encoder_side='v2')
trn_loss += _loss
_loss, preds_v1, tst_preds_v1 = run_one_side(model=model,
optimizer=optimizer,
preds_left=preds_v2,
pt_batch=args.pt_batch,
way=way,
shot=shot,
query=query,
n_cluster=n_cluster,
dataset=dataset,
right_encoder_side='v1')
trn_loss += _loss
dev_f1 = cluster_metrics.calc_f1(
gnd_assignments=torch.LongTensor(tst_preds_v1).to(device),
pred_assignments=torch.LongTensor(tst_preds_v2).to(device))
dev_acc = cluster_metrics.calc_ACC(
torch.LongTensor(tst_preds_v2).to(device),
torch.LongTensor(tst_preds_v1).to(device))
print('dev view 1 vs view 2: f1={:.4f} acc={:.4f}'.format(
dev_f1, dev_acc))
if best_dev_f1 is None or dev_f1 > best_dev_f1:
print('new best epoch', epoch)
best_epoch = epoch
best_dev_f1 = dev_f1
best_model = copy.deepcopy(model.state_dict())
best_preds_v1 = preds_v1.copy()
best_preds_v2 = preds_v2.copy()
lgolds, lpreds = [], []
for g, p in zip(golds, list(preds_v1)):
if g > 0:
lgolds.append(g)
lpreds.append(p)
prec, rec, f1 = cluster_metrics.calc_prec_rec_f1(
gnd_assignments=torch.LongTensor(lgolds).to(device),
pred_assignments=torch.LongTensor(lpreds).to(device))
acc = cluster_metrics.calc_ACC(
torch.LongTensor(lpreds).to(device),
torch.LongTensor(lgolds).to(device))
print(
f'{datetime.datetime.now()} epoch {epoch}, test prec={prec:.4f} rec={rec:.4f} '
f'f1={f1:.4f} acc={acc:.4f}')
print('restoring model for best dev epoch', best_epoch)
model.load_state_dict(best_model)
preds_v1, preds_v2 = best_preds_v1, best_preds_v2
lgolds, lpreds = [], []
for g, p in zip(golds, list(preds_v1)):
if g > 0:
lgolds.append(g)
lpreds.append(p)
prec, rec, f1 = cluster_metrics.calc_prec_rec_f1(
gnd_assignments=torch.LongTensor(lgolds).to(device),
pred_assignments=torch.LongTensor(lpreds).to(device))
acc = cluster_metrics.calc_ACC(
torch.LongTensor(lpreds).to(device),
torch.LongTensor(lgolds).to(device))
print(
f'{datetime.datetime.now()} test prec={prec:.4f} rec={rec:.4f} f1={f1:.4f} acc={acc:.4f}'
)
if args.save_model_path is not None:
preds_v1 = torch.from_numpy(preds_v1)
if preds_v2 is not None:
preds_v2 = torch.from_numpy(preds_v2)
state = {
'model_state': model.state_dict(),
'id_to_token': dataset.id_to_token,
'word_emb_size': word_emb_size,
'v1_assignments': preds_v1,
'v2_assignments': preds_v2
}
with open(expand(args.save_model_path), 'wb') as f:
torch.save(state, f)
print('saved model to ', args.save_model_path)
def run(data_path, model, pca_dims, view1_col, view2_col, label_col, no_idf,
mvsc_no_unk):
print('loading dataset')
dataset = Dataset(data_path,
view1_col=view1_col,
view2_col=view2_col,
label_col=label_col)
n_cluster = len(dataset.id_to_label) - 1
print("num of class = %d" % n_cluster)
vocab_size = len(dataset.token_to_id)
print('vocab_size', vocab_size)
if model == 'mvsc':
try:
import multiview
except Exception:
print('please install https://github.com/mariceli3/multiview')
return
print('imported multiview ok')
def run_pca(features):
print('fitting tfidf vectorizer', flush=True, end='')
vectorizer = TfidfVectorizer(token_pattern='\\d+',
ngram_range=(1, 1),
analyzer='word',
min_df=0.0,
max_df=1.0,
use_idf=not no_idf)
X = vectorizer.fit_transform(features)
print(' ... done')
print('X.shape', X.shape)
print('running pca', flush=True, end='')
pca = TruncatedSVD(n_components=pca_dims)
X2 = pca.fit_transform(X)
print(' ... done')
return X2
golds = [dataset[idx][1] for idx in dataset.trn_idx]
if model in ['view1pca', 'view2pca', 'wholeconvpca']:
if model == 'view1pca':
utts = [dataset[idx][0][0] for idx in dataset.trn_idx]
utts = [' '.join([str(idx) for idx in utt]) for utt in utts]
elif model == 'view2pca':
convs = [dataset[idx][0][1] for idx in dataset.trn_idx]
utts = [[tok for utt in conv for tok in utt] for conv in convs]
utts = [' '.join([str(idx) for idx in utt]) for utt in utts]
elif model == 'wholeconvpca':
v1 = [dataset[idx][0][0] for idx in dataset.trn_idx]
convs = [dataset[idx][0][1] for idx in dataset.trn_idx]
v2 = [[tok for utt in conv for tok in utt] for conv in convs]
utts = []
for n in range(len(v1)):
utts.append(v1[n] + v2[n])
utts = [' '.join([str(idx) for idx in utt]) for utt in utts]
X2 = run_pca(utts)
print('running kmeans', flush=True, end='')
kmeans = sklearn.cluster.KMeans(n_clusters=n_cluster,
max_iter=300,
verbose=0,
random_state=0)
preds = kmeans.fit_predict(X2)
print(' ... done')
elif model == 'mvsc':
mvsc = multiview.mvsc.MVSC(k=n_cluster)
idxes = dataset.trn_idx_no_unk if mvsc_no_unk else dataset.trn_idx
v1 = [dataset[idx][0][0] for idx in idxes]
convs = [dataset[idx][0][1] for idx in idxes]
v2 = [[tok for utt in conv for tok in utt] for conv in convs]
v1 = [' '.join([str(idx) for idx in utt]) for utt in v1]
v2 = [' '.join([str(idx) for idx in utt]) for utt in v2]
v1_pca = run_pca(v1)
v2_pca = run_pca(v2)
print('running mvsc', end='', flush=True)
start = time.time()
preds, eivalues, eivectors, sigmas = mvsc.fit_transform(
[v1_pca, v2_pca], [False] * 2)
print('...done')
mvsc_time = time.time() - start
print('time taken %.3f' % mvsc_time)
lgolds, lpreds = [], []
for g, p in zip(golds, list(preds)):
if g > 0:
lgolds.append(g)
lpreds.append(p)
prec, rec, f1 = cluster_metrics.calc_prec_rec_f1(
gnd_assignments=torch.LongTensor(lgolds).to(device),
pred_assignments=torch.LongTensor(lpreds).to(device))
acc = cluster_metrics.calc_ACC(
torch.LongTensor(lpreds).to(device),
torch.LongTensor(lgolds).to(device))
print(
f'{datetime.datetime.now()} eval f1={f1:.4f} prec={prec:.4f} rec={rec:.4f} acc={acc:.4f}'
)
return prec, rec, f1, acc
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data-path', type=str, default='./data/airlines_processed.csv')
parser.add_argument('--glove-path', type=str, default='./data/glove.840B.300d.txt')
parser.add_argument('--pre-model', type=str, choices=['ae', 'qt'], default='qt')
parser.add_argument('--pre-epoch', type=int, default=0)
parser.add_argument('--pt-batch', type=int, default=100)
parser.add_argument('--model-path', type=str, help='path of pretrained model to load')
parser.add_argument('--way', type=int, default=5)
parser.add_argument('--num-epochs', type=int, default=100)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--save-model-path', type=str)
parser.add_argument('--view1-col', type=str, default='view1')
parser.add_argument('--view2-col', type=str, default='view2')
parser.add_argument('--label-col', type=str, default='tag')
args = parser.parse_args()
np.random.seed(args.seed)
print('loading dataset')
dataset = Dataset(args.data_path, view1_col=args.view1_col, view2_col=args.view2_col, label_col=args.label_col)
n_cluster = len(dataset.id_to_label) - 1
print ("num of class = %d" %n_cluster)
if args.model_path is not None:
id_to_token, token_to_id, vocab_size, word_emb_size, model = multiview_encoders.load_model(args.model_path)
print('loaded model')
else:
id_to_token, token_to_id, vocab_size, word_emb_size, model = multiview_encoders.create_model_from_embeddings(
args.glove_path, dataset.id_to_token, dataset.token_to_id)
print('created randomly initialized model')
print('vocab_size', vocab_size)
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
expressions = (model, optimizer)
pre_acc, pre_state = 0., None
pretrain_method = {
'ae': pretrain.pretrain_ae,
'qt': pretrain.pretrain_qt,
}[args.pre_model]
for epoch in range(1, args.pre_epoch + 1):
model.train()
perm_idx = np.random.permutation(dataset.trn_idx)
trn_loss, _ = pretrain_method(dataset, perm_idx, expressions, train=True)
model.eval()
_, tst_acc = pretrain_method(dataset, dataset.tst_idx, expressions, train=False)
if tst_acc > pre_acc:
pre_state = copy.deepcopy(model.state_dict())
pre_acc = tst_acc
print('{} epoch {}, train_loss={:.4f} test_acc={:.4f}'.format(datetime.datetime.now(), epoch, trn_loss, tst_acc))
if args.save_model_path is not None:
save_model_path = f'{args.save_model_path}_pre_e{epoch}.dat'
state = {
'model_state': model.state_dict(),
'id_to_token': dataset.id_to_token,
'word_emb_size': word_emb_size
}
with open(expand(save_model_path), 'wb') as f:
torch.save(state, f)
print('saved model to ', save_model_path)
save_model_path = f'{args.save_model_path}_pre_best_e{epoch}.dat'
state = {
'model_state': pre_state,
'id_to_token': dataset.id_to_token,
'word_emb_size': word_emb_size
}
with open(expand(save_model_path), 'wb') as f:
torch.save(state, f)
print('saved model to ', save_model_path)
if args.pre_epoch > 0:
# load best state
model.load_state_dict(pre_state)
print('loaded best state')
# deepcopy pretrained views into v1 and/or view2
{
'ae': pretrain.after_pretrain_ae,
'qt': pretrain.after_pretrain_qt,
}[args.pre_model](model)
# reinitialiate optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
expressions = (model, optimizer)
print('applied post-pretraining')
kmeans = sklearn.cluster.KMeans(n_clusters=n_cluster, max_iter=300, verbose=0, random_state=0)
latent_z1s, golds = transform(dataset, dataset.trn_idx, model, encoder='v1')
pred1s = kmeans.fit_predict(latent_z1s)
lgolds, lpreds = [], []
for g, p in zip(golds, list(pred1s)):
if g > 0:
lgolds.append(g)
lpreds.append(p)
prec, rec, f1 = cluster_metrics.calc_prec_rec_f1(gnd_assignments=torch.LongTensor(lgolds).to(device), pred_assignments=torch.LongTensor(lpreds).to(device))
acc = cluster_metrics.calc_ACC(torch.LongTensor(lpreds).to(device), torch.LongTensor(lgolds).to(device))
silhouette, davies_bouldin = sklearn.metrics.silhouette_score(latent_z1s, pred1s, metric='euclidean'), sklearn.metrics.davies_bouldin_score(latent_z1s, pred1s)
print('{} pretrain: eval prec={:.4f} rec={:.4f} f1={:.4f} acc={:.4f} sil={:.4f}, db={:.4f}'.format(datetime.datetime.now(), prec, rec, f1, acc, silhouette, davies_bouldin))
perm_idx = dataset.trn_idx
pred2s, centroids1, centroids2, pred1s_perm_idx, preds2_perm_idx = None, None, None, None, None
for epoch in range(1, args.num_epochs + 1):
trn_loss = 0.
shot, way, query = 5, args.way, 15
sampler1 = CategoriesSampler(pred1s, args.pt_batch, way, shot+query)
train1_batches = [[dataset[perm_idx[idx]] for idx in indices] for indices in sampler1]
trn_loss += do_pass(train1_batches, shot, way, query, expressions, encoder='v2')
latent_z2s, _ = transform(dataset, perm_idx, model, encoder='v2')
centroids2 = calc_centroids(latent_z2s, pred1s, n_cluster)
kmeans2 = sklearn.cluster.KMeans(n_clusters=n_cluster, init=centroids2, max_iter=10, verbose=0)
pred2s = kmeans2.fit_predict(latent_z2s)
pred2s_perm_idx = perm_idx.copy()
tst_latent_z2s, _ = transform(dataset, dataset.tst_idx, model, encoder='v2')
tst_pred2s = kmeans2.predict(tst_latent_z2s)
sampler2 = CategoriesSampler(pred2s, args.pt_batch, way, shot+query)
train2_batches = [[dataset[perm_idx[idx]] for idx in indices] for indices in sampler2]
trn_loss += do_pass(train2_batches, shot, way, query, expressions, encoder='v1')
perm_idx = np.random.permutation(dataset.trn_idx)
latent_z1s, golds = transform(dataset, perm_idx, model, encoder='v1')
centroids1 = calc_centroids(latent_z1s, pred2s, n_cluster)
kmeans1 = sklearn.cluster.KMeans(n_clusters=n_cluster, init=centroids1, max_iter=10, verbose=0)
pred1s = kmeans1.fit_predict(latent_z1s)
pred1s_perm_idx = perm_idx.copy()
tst_latent_z1s, _ = transform(dataset, dataset.tst_idx, model, encoder='v1')
tst_pred1s = kmeans1.predict(tst_latent_z1s)
f1 = cluster_metrics.calc_f1(gnd_assignments=torch.LongTensor(tst_pred1s).to(device), pred_assignments=torch.LongTensor(tst_pred2s).to(device))
acc = cluster_metrics.calc_ACC(torch.LongTensor(tst_pred2s).to(device), torch.LongTensor(tst_pred1s).to(device))
print('TEST f1={:.4f} acc={:.4f}'.format(f1, acc))
lgolds, lpreds = [], []
for g, p in zip(golds, list(pred1s)):
if g > 0:
lgolds.append(g)
lpreds.append(p)
prec, rec, f1 = cluster_metrics.calc_prec_rec_f1(gnd_assignments=torch.LongTensor(lgolds).to(device), pred_assignments=torch.LongTensor(lpreds).to(device))
acc = cluster_metrics.calc_ACC(torch.LongTensor(lpreds).to(device), torch.LongTensor(lgolds).to(device))
silhouette, davies_bouldin = sklearn.metrics.silhouette_score(latent_z1s, pred1s, metric='euclidean'), sklearn.metrics.davies_bouldin_score(latent_z1s, pred1s)
print('{} epoch {}, eval prec={:.4f} rec={:.4f} f1={:.4f} acc={:.4f} sil={:.4f}, db={:.4f}'.format(
datetime.datetime.now(), epoch, prec, rec, f1, acc, silhouette, davies_bouldin))
if args.save_model_path is not None:
pred1s = torch.from_numpy(pred1s)
if pred2s is not None:
pred2s = torch.from_numpy(pred2s)
state = {
'model_state': model.state_dict(),
'id_to_token': dataset.id_to_token,
'word_emb_size': word_emb_size,
'v1_assignments': pred1s,
'v2_assignments': pred2s,
'pred1s_perm_idx': pred1s_perm_idx,
'pred2s_perm_idx': pred2s_perm_idx
}
with open(expand(args.save_model_path), 'wb') as f:
torch.save(state, f)
print('saved model to ', args.save_model_path)
