def val_batch(atts, words, defns):
logits = m(defns, words)
val_loss = torch.sum(crit(logits, atts))
preds = crit.predict(logits)
acc_table = evaluate_accuracy(preds, atts.cpu().data.numpy())
acc_table['loss'] = val_loss.cpu().data.numpy()[None, :]
return acc_table.T.squeeze()
def eval(ckpt, use_emb=False):
# Recommended hyperparameters
args = ModelConfig(batch_size=64, ckpt=ckpt, dropout=0.5, use_emb=use_emb)
m = DictionaryModel(dict_field.vocab,
output_size=att_crit.input_size,
embed_input=args.use_emb,
dropout_rate=args.dropout)
m.load_state_dict(torch.load(args.ckpt)['m_state_dict'])
if torch.cuda.is_available():
m.cuda()
att_crit.cuda()
train_data.atts_matrix.cuda()
val_data.atts_matrix.cuda()
test_data.atts_matrix.cuda()
m.eval()
# Don't take the mean until the end
preds_ = []
labels_ = []
for val_b, (atts, words, defns, perm) in enumerate(tqdm(test_iter)):
preds_.append(att_crit.predict(m(defns, words))[perm])
labels_.append(atts.data.cpu().numpy()[perm])
preds = np.concatenate(preds_, 0)
labels = np.concatenate(labels_, 0)
acc_table = evaluate_accuracy(preds, labels).T.squeeze()
return acc_table, preds
def val_batch():
atts = val_data.atts_matrix
logits = m(val_data.embeds)
val_loss = torch.sum(crit(logits, atts))
preds = crit.predict(logits)
acc_table = evaluate_accuracy(preds, atts.cpu().data.numpy())
acc_table['loss'] = val_loss.cpu().data.numpy()[None, :]
return acc_table
def defn_to_atts(defn_type, use_emb=False, first_defn=True):
train_data, val_data, test_data = Attributes.splits(use_defns=True, cuda=False,
first_defn_at_test=first_defn)
enc_fn = {'bow': bowize, 'nbow': nbowize}[defn_type]
if first_defn:
# We want to oversample
balanced_train_inds = train_data._balanced_inds
X_train, Y_train = get_x(train_data, enc_fn, use_emb=use_emb)
X_train = X_train[balanced_train_inds]
Y_train = Y_train[balanced_train_inds]
X_val, Y_val = get_x(val_data, enc_fn, use_emb=use_emb)
X_test, Y_test = get_x(test_data, enc_fn, use_emb=use_emb)
else:
# We want to undersample
X_train, Y_train = get_stacked_x(train_data, enc_fn, use_emb=use_emb)
X_val, Y_val = get_stacked_x(val_data, enc_fn, use_emb=use_emb)
X_test, Y_test = get_stacked_x(test_data, enc_fn, use_emb=use_emb)
# cross validate
cs = np.power(10., [-3,-2,-1,0])
accs = defaultdict(list)
for c in cs:
for d, (dom_name, dom_size) in enumerate(train_data.domains):
M = LogisticRegression(C=c)
print("fitting {}".format(d))
M.fit(X_train, Y_train[:, d])
s = M.score(X_val, Y_val[:, d])
accs[d].append(s)
c_to_use = {d:cs[np.argmax(scores)] for d, scores in accs.items()}
print("Using c={}, acc of {:.3f} on val".format(
'\n'.join('{:2d}:{:.3f}'.format(d,c) for d,c in c_to_use.items()),
np.mean([max(accs[d]) for d in c_to_use.keys()])
))
# -----------------------------------------------
preds = []
for d, (dom_name, dom_size) in enumerate(train_data.domains):
M = LogisticRegression(C=c_to_use[d])
print("fitting {}".format(d))
M.fit(X_train, Y_train[:,d])
s = M.score(X_test, Y_test[:,d])
print("Score for {} is {}".format(dom_name, s))
preds.append(M.predict(X_test))
preds_full = np.array(preds).T
acc_table = evaluate_accuracy(preds_full, Y_test)
acc_table.index = ['{}{}({})'.format(defn_type, ' +GloVe' if use_emb else '',
'firstdefn' if first_defn else 'concat')]
np.save('{}{}.pkl'.format(defn_type, ' +GloVe' if use_emb else ''), preds_full)
return acc_table
def deploy(data):
embs = data.embeds
preds = m(embs)
gt_atts = torch.cat(data.atts_list, 1)
loss = binary_cross_entropy_with_logits(preds, gt_atts, size_average=True)
# Now get the test results
acc_table = evaluate_accuracy(crit.predict(m(data.embeds)),
data.atts_matrix.cpu().data.numpy())
acc_table['loss'] = loss.cpu().data.numpy()[None,:]
return acc_table
def emb_to_atts(emb_type):
assert emb_type in TYPES
if emb_type != 'glove':
# Replace the matrices
vecs = np.load(os.path.join(retrofit_dir, emb_type + '.pkl'))
X_train = np.stack([vecs[v] for v in train_data.atts_df.index])
X_val = np.stack([vecs[v] for v in val_data.atts_df.index])
X_test = np.stack([vecs[v] for v in test_data.atts_df.index])
else:
X_train = embeds_train
X_val = embeds_val
X_test = embeds_test
print("train {} val {} test {}".format(X_train.shape, X_val.shape,
X_test.shape))
# cross validate
cs = np.power(10., [-3, -2, -1, 0, 1])
accs = defaultdict(list)
for c in cs:
for d, (dom_name, dom_size) in enumerate(train_data.domains):
M = LogisticRegression(C=c)
print("fitting {}".format(d))
M.fit(X_train, Y_train[:, d])
s = M.score(X_val, Y_val[:, d])
accs[d].append(s)
c_to_use = {d: cs[np.argmax(scores)] for d, scores in accs.items()}
print("Using c={}, acc of {:.3f} on val".format(
'\n'.join('{:2d}:{:.3f}'.format(d, c) for d, c in c_to_use.items()),
np.mean([max(accs[d]) for d in c_to_use.keys()])))
# -----------------------------------------------
preds = []
for d, (dom_name, dom_size) in enumerate(train_data.domains):
M = LogisticRegression(C=c_to_use[d])
print("fitting {}".format(d))
M.fit(X_train, Y_train[:, d])
s = M.score(X_test, Y_test[:, d])
print("Score for {} is {}".format(dom_name, s))
preds.append(M.predict(X_test))
preds_full = np.array(preds).T
accs = evaluate_accuracy(preds_full, Y_test)
accs.index = [emb_type]
return accs, preds_full
start = time.time()
l = train_batch(b_inds, optimizers=[optimizer])
train_l.append(l.data[0])
dur = time.time() - start
if b % 1000 == 0 and b >= 100:
print("e{:2d}b{:5d} Cost {:.3f} , {:.3f} s/batch".format(
epoch,
b,
np.mean(train_l),
dur,
),
flush=True)
dur_epoch = time.time() - start_epoch
print("Duration of epoch was {:.3f}/batch, overall loss was {:.3f}".format(
dur_epoch / b,
np.mean(train_l),
))
# Now get the test results
acc_table = evaluate_accuracy(crit.predict(m(test_data.embeds)),
test_data.atts_matrix.cpu().data.numpy())
acc_table.to_csv(os.path.join(args.save_dir, 'results.csv'))
torch.save(
{
'args': args.args,
'epoch': epoch,
'm_state_dict': m.state_dict(),
'optimizer': optimizer.state_dict(),
}, os.path.join(args.save_dir, 'ckpt_{}.tar'.format(epoch)))
"""
This model tries to predict the word from definition
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from data.attribute_loader import Attributes
from scipy.stats import mode
import pandas as pd
import numpy as np
from sklearn.metrics import f1_score
from lib.attribute_loss import evaluate_accuracy
train_data, val_data, test_data = Attributes.splits(use_defns=False,
cuda=False)
preds = mode(train_data.atts_matrix.data.numpy()).mode
print("Always predicting {}".format(preds))
Y_test = test_data.atts_matrix.data.numpy()
acc_table = evaluate_accuracy(
np.repeat(preds, Y_test.shape[0], axis=0),
test_data.atts_matrix.data.numpy(),
)
acc_table.to_csv('mfc.csv', float_format='%.2f')
