def main():
train_df = pd.read_csv(TRAIN_PATH)
fold_df = pd.read_csv(FOLD_PATH)
n_train_df = len(train_df)
old_folds = pd.read_csv(FOLD_PATH_JIGSAW)
old_df = pd.read_csv(OLD_PATH)
old_df["target"] = old_df[["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]].sum(axis=1)
old_df["target"] = (old_df["target"] >= 1).astype("int8")
old_df = old_df[old_folds.fold_id != fold_id]
train_df = train_df.append(old_df).reset_index(drop=True)
del old_folds, old_df
gc.collect()
# y = np.where(train_df['target'] >= 0.5, 1, 0)
y = train_df['target'].values
identity_columns_new = []
for column in identity_columns + ['target']:
train_df[column + "_bin"] = np.where(train_df[column] >= 0.5, True, False)
if column != "target":
identity_columns_new.append(column + "_bin")
# Overall
#weights = np.ones((len(train_df),)) / 4
# Subgroup
#weights += (train_df[identity_columns].fillna(0).values >= 0.5).sum(axis=1).astype(bool).astype(np.int) / 4
# Background Positive, Subgroup Negative
#weights += (((train_df["target"].values >= 0.5).astype(bool).astype(np.int) +
# (1 - (train_df[identity_columns].fillna(0).values >= 0.5).sum(axis=1).astype(bool).astype(
# np.int))) > 1).astype(bool).astype(np.int) / 4
# Background Negative, Subgroup Positive
#weights += (((train_df["target"].values < 0.5).astype(bool).astype(np.int) +
# (train_df[identity_columns].fillna(0).values >= 0.5).sum(axis=1).astype(bool).astype(
# np.int)) > 1).astype(bool).astype(np.int) / 4
#loss_weight = 0.5
with timer('preprocessing text'):
# df["comment_text"] = [analyzer_embed(text) for text in df["comment_text"]]
train_df['comment_text'] = train_df['comment_text'].astype(str)
train_df = train_df.fillna(0)
with timer('load embedding'):
tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_PATH, cache_dir=None, do_lower_case=True)
X_text, train_lengths = convert_lines(train_df["comment_text"].fillna("DUMMY_VALUE"), max_len, tokenizer)
del train_lengths, tokenizer
gc.collect()
LOGGER.info(f"X_text {X_text.shape}")
X_old = X_text[n_train_df:].astype("int32")
X_text = X_text[:n_train_df].astype("int32")
#w_trans = weights[n_train_df:].astype("float32")
#weights = weights[:n_train_df].astype("float32")
y_old = y[n_train_df:].astype("float32")
y = y[:n_train_df].astype("float32")
train_df = train_df[:n_train_df]
with timer('train'):
train_index = fold_df.fold_id != fold_id
valid_index = fold_df.fold_id == fold_id
X_train, y_train = X_text[train_index].astype("int32"), y[train_index].astype("float32")
X_val, y_val = X_text[valid_index].astype("int32"), y[valid_index].astype("float32")
test_df = train_df[valid_index]
del X_text, y, train_index, valid_index, train_df
gc.collect()
model = BertForSequenceClassification.from_pretrained(WORK_DIR, cache_dir=None, num_labels=n_labels)
model.zero_grad()
model = model.to(device)
X_train = np.concatenate([X_train, X_old], axis=0)
y_train = np.concatenate([y_train, y_old], axis=0)
train_size = len(X_train)
del X_old, y_old
gc.collect()
train_dataset = torch.utils.data.TensorDataset(torch.tensor(X_train, dtype=torch.long),
torch.tensor(y_train, dtype=torch.float32))
valid = torch.utils.data.TensorDataset(torch.tensor(X_val, dtype=torch.long),
torch.tensor(y_val, dtype=torch.float32))
ran_sampler = torch.utils.data.RandomSampler(train_dataset)
len_sampler = LenMatchBatchSampler(ran_sampler, batch_size=batch_size, drop_last=False)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_sampler=len_sampler)
valid_loader = torch.utils.data.DataLoader(valid, batch_size=batch_size * 2, shuffle=False)
del X_train, y_train, X_val, y_val
gc.collect()
LOGGER.info(f"done data loader setup")
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
num_train_optimization_steps = int(epochs * train_size / batch_size / accumulation_steps)
total_step = int(epochs * train_size / batch_size)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=base_lr,
warmup=0.005,
t_total=num_train_optimization_steps)
LOGGER.info(f"done optimizer loader setup")
model, optimizer = amp.initialize(model, optimizer, opt_level="O1", verbosity=0)
criterion = torch.nn.BCEWithLogitsLoss().to(device)
#criterion = CustomLoss(loss_weight).to(device)
LOGGER.info(f"done amp setup")
for epoch in range(epochs):
LOGGER.info(f"Starting {epoch} epoch...")
LOGGER.info(f"length {train_size} train...")
if epoch == 1:
for param_group in optimizer.param_groups:
param_group['lr'] = base_lr * gammas[1]
tr_loss, train_losses = train_one_epoch(model, train_loader, criterion, optimizer, device,
accumulation_steps, total_step, n_labels, base_lr,
gamma=gammas[2 * epoch])
LOGGER.info(f'Mean train loss: {round(tr_loss,5)}')
torch.save(model.state_dict(), '{}_dic_epoch{}'.format(exp, epoch))
torch.save(optimizer.state_dict(), '{}_optimizer_epoch{}.pth'.format(exp, epoch))
valid_loss, oof_pred = validate(model, valid_loader, criterion, device, n_labels)
LOGGER.info(f'Mean valid loss: {round(valid_loss,5)}')
if epochs > 1:
test_df_cp = test_df.copy()
test_df_cp["pred"] = oof_pred[:, 0]
test_df_cp = convert_dataframe_to_bool(test_df_cp)
bias_metrics_df = compute_bias_metrics_for_model(test_df_cp, identity_columns)
LOGGER.info(bias_metrics_df)
score = get_final_metric(bias_metrics_df, calculate_overall_auc(test_df_cp))
LOGGER.info(f'score is {score}')
del model
gc.collect()
torch.cuda.empty_cache()
test_df["pred"] = oof_pred[:, 0]
test_df = convert_dataframe_to_bool(test_df)
bias_metrics_df = compute_bias_metrics_for_model(test_df, identity_columns)
LOGGER.info(bias_metrics_df)
score = get_final_metric(bias_metrics_df, calculate_overall_auc(test_df))
LOGGER.info(f'final score is {score}')
test_df.to_csv("oof.csv", index=False)
xs = list(range(1, len(train_losses) + 1))
plt.plot(xs, train_losses, label='Train loss');
plt.legend();
plt.xticks(xs);
plt.xlabel('Iter')
plt.savefig("loss.png")
def main():
train_df = pd.read_csv(TRAIN_PATH)
train_df['male'] = np.load(
"../input/identity-column-data/male_labeled.npy")
train_df['female'] = np.load(
"../input/identity-column-data/female_labeled.npy")
train_df['homosexual_gay_or_lesbian'] = np.load(
"../input/identity-column-data/homosexual_gay_or_lesbian_labeled.npy")
train_df['christian'] = np.load(
"../input/identity-column-data/christian_labeled.npy")
train_df['jewish'] = np.load(
"../input/identity-column-data/jewish_labeled.npy")
train_df['muslim'] = np.load(
"../input/identity-column-data/muslim_labeled.npy")
train_df['black'] = np.load(
"../input/identity-column-data/black_labeled.npy")
train_df['white'] = np.load(
"../input/identity-column-data/white_labeled.npy")
train_df['psychiatric_or_mental_illness'] = np.load(
"../input/identity-column-data/psychiatric_or_mental_illness_labeled.npy"
)
fold_df = pd.read_csv(FOLD_PATH)
# y = np.where(train_df['target'] >= 0.5, 1, 0)
y = train_df['target'].values
y_aux = train_df[AUX_COLUMNS].values
identity_columns_new = []
for column in identity_columns + ['target']:
train_df[column + "_bin"] = np.where(train_df[column] >= 0.5, True,
False)
if column != "target":
identity_columns_new.append(column + "_bin")
# Overall
weights = np.ones((len(train_df), )) / 4
# Subgroup
weights += (train_df[identity_columns].fillna(0).values >= 0.5).sum(
axis=1).astype(bool).astype(np.int) / 4
# Background Positive, Subgroup Negative
weights += (
((train_df["target"].values >= 0.5).astype(bool).astype(np.int) +
(1 - (train_df[identity_columns].fillna(0).values >= 0.5).sum(
axis=1).astype(bool).astype(np.int))) > 1).astype(bool).astype(
np.int) / 4
# Background Negative, Subgroup Positive
weights += (
((train_df["target"].values < 0.5).astype(bool).astype(np.int) +
(train_df[identity_columns].fillna(0).values >= 0.5).sum(
axis=1).astype(bool).astype(np.int)) > 1).astype(bool).astype(
np.int) / 4
loss_weight = 0.5
with timer('preprocessing text'):
# df["comment_text"] = [analyzer_embed(text) for text in df["comment_text"]]
train_df['comment_text'] = train_df['comment_text'].astype(str)
train_df = train_df.fillna(0)
with timer('load embedding'):
tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_PATH,
cache_dir=None,
do_lower_case=True)
X_text = convert_lines_head_tail(
train_df["comment_text"].fillna("DUMMY_VALUE"), max_len, head_len,
tokenizer)
X_text = np.array(X_text).astype("int32")
del tokenizer
gc.collect()
with timer('train'):
train_index = fold_df.fold_id != fold_id
valid_index = fold_df.fold_id == fold_id
X_train, y_train, y_aux_train, w_train = X_text[train_index], y[
train_index].astype("float32"), y_aux[train_index].astype(
"float32"), weights[train_index].astype("float32")
X_val, y_val, y_aux_val, w_val = X_text[valid_index], y[valid_index].astype("float32"),\
y_aux[valid_index].astype("float32"), weights[valid_index].astype("float32")
test_df = train_df[valid_index]
train_size = len(X_train)
del X_text, y, y_aux, weights, train_index, valid_index, train_df, fold_df
gc.collect()
model = BertForSequenceClassification.from_pretrained(
WORK_DIR, cache_dir=None, num_labels=n_labels)
model.zero_grad()
model = model.to(device)
y_train = np.concatenate(
(y_train.reshape(-1, 1), w_train.reshape(-1, 1), y_aux_train),
axis=1).astype("float32")
y_val = np.concatenate(
(y_val.reshape(-1, 1), w_val.reshape(-1, 1), y_aux_val),
axis=1).astype("float32")
del w_train, w_val, y_aux_train, y_aux_val
gc.collect()
train_dataset = torch.utils.data.TensorDataset(
torch.tensor(X_train, dtype=torch.long),
torch.tensor(y_train, dtype=torch.float32))
valid = torch.utils.data.TensorDataset(
torch.tensor(X_val, dtype=torch.long),
torch.tensor(y_val, dtype=torch.float32))
ran_sampler = torch.utils.data.RandomSampler(train_dataset)
len_sampler = LenMatchBatchSampler(ran_sampler,
batch_size=batch_size,
drop_last=False)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_sampler=len_sampler)
valid_loader = torch.utils.data.DataLoader(valid,
batch_size=batch_size * 2,
shuffle=False)
del X_train, y_train, X_val, y_val
gc.collect()
LOGGER.info(f"done data loader setup")
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
num_train_optimization_steps = int(epochs * train_size / batch_size /
accumulation_steps)
total_step = int(epochs * train_size / batch_size)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=base_lr,
warmup=0.005,
t_total=num_train_optimization_steps)
LOGGER.info(f"done optimizer loader setup")
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O1",
verbosity=0)
# criterion = torch.nn.BCEWithLogitsLoss().to(device)
criterion = CustomLoss(loss_weight).to(device)
LOGGER.info(f"done amp setup")
for epoch in range(epochs):
LOGGER.info(f"Starting {epoch} epoch...")
LOGGER.info(f"length {train_size} train...")
if epoch == 1:
for param_group in optimizer.param_groups:
param_group['lr'] = base_lr * gammas[1]
tr_loss, train_losses = train_one_epoch(model,
train_loader,
criterion,
optimizer,
device,
accumulation_steps,
total_step,
n_labels,
base_lr,
gamma=gammas[2 * epoch])
LOGGER.info(f'Mean train loss: {round(tr_loss,5)}')
torch.save(model.state_dict(),
'{}_epoch{}_fold{}.pth'.format(exp, epoch, fold_id))
valid_loss, oof_pred = validate(model, valid_loader, criterion,
device, n_labels)
LOGGER.info(f'Mean valid loss: {round(valid_loss,5)}')
del model
gc.collect()
torch.cuda.empty_cache()
test_df["pred"] = oof_pred[:, 0]
test_df = convert_dataframe_to_bool(test_df)
bias_metrics_df = compute_bias_metrics_for_model(test_df, identity_columns)
LOGGER.info(bias_metrics_df)
score = get_final_metric(bias_metrics_df, calculate_overall_auc(test_df))
LOGGER.info(f'final score is {score}')
test_df.to_csv("oof.csv", index=False)
xs = list(range(1, len(train_losses) + 1))
plt.plot(xs, train_losses, label='Train loss')
plt.legend()
plt.xticks(xs)
plt.xlabel('Iter')
plt.savefig("loss.png")
def main():
train_df = pd.read_csv(TRAIN_PATH).sample(train_size+valid_size, random_state=seed)
y = np.where(train_df['target'] >= 0.5, 1, 0)
y_aux = train_df[AUX_COLUMNS].values
identity_columns_new = []
for column in identity_columns + ['target']:
train_df[column + "_bin"] = np.where(train_df[column] >= 0.5, True, False)
if column != "target":
identity_columns_new.append(column + "_bin")
weights = np.ones((len(train_df),)) / 4
weights += (train_df[identity_columns].fillna(0).values >= 0.5).sum(axis=1).astype(bool).astype(np.int) / 4
weights += (((train_df["target"].values >= 0.5).astype(bool).astype(np.int) +
(train_df[identity_columns].fillna(0).values < 0.5).sum(axis=1).astype(bool).astype(np.int)) > 1).astype(
bool).astype(np.int) / 4
weights += (((train_df["target"].values < 0.5).astype(bool).astype(np.int) +
(train_df[identity_columns].fillna(0).values >= 0.5).sum(axis=1).astype(bool).astype(np.int)) > 1).astype(
bool).astype(np.int) / 4
loss_weight = 1.0 / weights.mean()
with timer('preprocessing text'):
#df["comment_text"] = [analyzer_embed(text) for text in df["comment_text"]]
train_df['comment_text'] = train_df['comment_text'].astype(str)
train_df = train_df.fillna(0)
with timer('load embedding'):
tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_PATH, cache_dir=None, do_lower_case=True)
X_text, train_lengths = convert_lines(train_df["comment_text"].fillna("DUMMY_VALUE"), max_len, tokenizer)
test_df = train_df[train_size:]
with timer('train'):
X_train, y_train, y_aux_train, w_train = X_text[:train_size], y[:train_size], y_aux[:train_size], weights[
:train_size]
X_val, y_val, y_aux_val, w_val = X_text[train_size:], y[train_size:], y_aux[train_size:], weights[
train_size:]
trn_lengths, val_lengths = train_lengths[:train_size], train_lengths[train_size:]
model = BertForSequenceClassification.from_pretrained(WORK_DIR, cache_dir=None, num_labels=n_labels)
model.zero_grad()
model = model.to(device)
y_train = np.concatenate((y_train.reshape(-1, 1), w_train.reshape(-1, 1), y_aux_train), axis=1)
y_val = np.concatenate((y_val.reshape(-1, 1), w_val.reshape(-1, 1), y_aux_val), axis=1)
train_dataset = torch.utils.data.TensorDataset(torch.tensor(X_train, dtype=torch.long),
torch.tensor(y_train, dtype=torch.float))
valid = torch.utils.data.TensorDataset(torch.tensor(X_val, dtype=torch.long),
torch.tensor(y_val, dtype=torch.float))
ran_sampler = torch.utils.data.RandomSampler(train_dataset)
len_sampler = LenMatchBatchSampler(ran_sampler, batch_size=batch_size, drop_last=False)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_sampler=len_sampler)
valid_loader = torch.utils.data.DataLoader(valid, batch_size=batch_size * 2, shuffle=False)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
num_train_optimization_steps = int(epochs * train_size / batch_size / accumulation_steps)
total_step = int(epochs * train_size / batch_size)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=2e-5,
warmup=0.05,
t_total=num_train_optimization_steps)
model, optimizer = amp.initialize(model, optimizer, opt_level="O1", verbosity=0)
#criterion = torch.nn.BCEWithLogitsLoss().to(device)
criterion = CustomLoss(loss_weight).to(device)
LOGGER.info(f"Starting 1 epoch...")
tr_loss, train_losses = train_one_epoch(model, train_loader, criterion, optimizer, device,
accumulation_steps, total_step, n_labels)
LOGGER.info(f'Mean train loss: {round(tr_loss,5)}')
torch.save(model.state_dict(), '{}_dic'.format(exp))
valid_loss, oof_pred = validate(model, valid_loader, criterion, device, n_labels)
del model
gc.collect()
torch.cuda.empty_cache()
test_df["pred"] = oof_pred[:, 0]
test_df = convert_dataframe_to_bool(test_df)
bias_metrics_df = compute_bias_metrics_for_model(test_df, identity_columns)
LOGGER.info(bias_metrics_df)
score = get_final_metric(bias_metrics_df, calculate_overall_auc(test_df))
LOGGER.info(f'final score is {score}')
test_df.to_csv("oof.csv", index=False)
xs = list(range(1, len(train_losses) + 1))
plt.plot(xs, train_losses, label='Train loss');
plt.legend();
plt.xticks(xs);
plt.xlabel('Iter')
plt.savefig("loss.png")