Kaggle 专利匹配比赛金牌方案赛后总结
比赛简介
在专利匹配数据集中，选手需要判断两个短语的相似度，一个是anchor ，一个是target
，然后输出两者在不同语义（context）的相似度，范围是0-1，我们队伍id为xlyhq，a榜rank 13，b榜ran12，非常感谢@heng zheng、@pythonlan,@leolu1998,@syzong四位队友的努力和付出，最后比较幸运的狗到金牌。
和其他前排核心思路差不多，我们在这里主要分享下我们的比赛历程以及相关实验的具体结果，以及有意思的尝试
文本处理
数据集主要有anchor、target和context字段，另外有额外的文本拼接信息，在比赛过程中我们主要是尝试了以下拼接的尝试：
- v1:test[‘anchor’] + ‘[SEP]’ + test[‘target’] + ‘[SEP]’ + test[‘context_text’]
- v2:test[‘anchor’] + ‘[SEP]’ + test[‘target’] + ‘[SEP]’ +test[‘context’]+ ‘[SEP]’ + test[‘context_text’],相当于直接把A47类似编码拼接上去
- v3:test[‘text’] = test[‘anchor’] + ‘[SEP]’ + test[‘target’] + ‘[SEP]’ + test[‘context’] + ‘[SEP]’ + test[‘context_text’] 获取更多的文本进行拼接，相当于把A47下面的子类别拼接上去，比如A47B，A47C
context_mapping = {
"A": "Human Necessities",
"B": "Operations and Transport",
"C": "Chemistry and Metallurgy",
"D": "Textiles",
"E": "Fixed Constructions",
"F": "Mechanical Engineering",
"G": "Physics",
"H": "Electricity",
"Y": "Emerging Cross-Sectional Technologies",
}
titles = pd.read_csv('./input/cpc-codes/titles.csv')
def process(text):
return re.sub(u"\(.*?\)|\{.*?}|\[.*?]", "", text)
def get_context(cpc_code):
cpc_data = titles[(titles['code'].map(len) <= 4) & (titles['code'].str.contains(cpc_code))]
texts = cpc_data['title'].values.tolist()
texts = [process(text) for text in texts]
return ";".join([context_mapping[cpc_code[0]]] + texts)
def get_cpc_texts():
cpc_texts = dict()
for code in tqdm(train['context'].unique()):
cpc_texts[code] = get_context(code)
return cpc_texts
cpc_texts = get_cpc_texts()
这个拼接方式可以得到不小的提升，但是文本长度变得更长，最大长度设置为300，导致训练更慢
- v4:核心的拼接方式：test[‘text’] = test[‘text’] + ‘[SEP]’ + test[‘target_info’]
# 拼接target info
test['text'] = test['anchor'] + '[SEP]' + test['target'] + '[SEP]' + test['context_text']
target_info = test.groupby(['anchor', 'context'])['target'].agg(list).reset_index()
target_info['target'] = target_info['target'].apply(lambda x: list(set(x)))
target_info['target_info'] = target_info['target'].apply(lambda x: ', '.join(x))
target_info['target_info'].apply(lambda x: len(x.split(', '))).describe()
del target_info['target']
test=test.merge(target_info,on=['anchor','context'],how='left')
test['text'] = test['text'] + '[SEP]' + test['target_info']
test.head()
这种拼接方式可以让模型cv和lb分数得到较大提升，通过v3和v4两种不同拼接方式的对比，我们可以发现选取质量更高的文本进行拼接对模型更有提升作用，v3方式中有很多冗余信息，而v4方式中有很多实体级别的关键信息。
数据划分
在比赛过程中，我们尝试了不同的数据划分方式，其中包括：
- StratifiedGroupKFold，这种拼接方式cv与lb线差比较小，分数稍微好一点
- StratifiedKFold：线下cv比较高
- 其他Kfold和GrouFold效果不好
损失函数
主要可以参考的损失函数有：
- BCE： nn.BCEWithLogitsLoss(reduction=“mean”)
- MSE：nn.MSELoss()
- Mixture Loss：MseCorrloss
class CorrLoss(nn.Module):
"""
use 1 - correlational coefficience between the output of the network and the target as the loss
input (o, t):
o: Variable of size (batch_size, 1) output of the network
t: Variable of size (batch_size, 1) target value
output (corr):
corr: Variable of size (1)
"""
def __init__(self):
super(CorrLoss, self).__init__()
def forward(self, o, t):
assert(o.size() == t.size())
# calcu z-score for o and t
o_m = o.mean(dim = 0)
o_s = o.std(dim = 0)
o_z = (o - o_m)/o_s
t_m = t.mean(dim =0)
t_s = t.std(dim = 0)
t_z = (t - t_m)/t_s
# calcu corr between o and t
tmp = o_z * t_z
corr = tmp.mean(dim = 0)
return 1 - corr
class MSECorrLoss(nn.Module):
def __init__(self, p = 1.5):
super(MSECorrLoss, self).__init__()
self.p = p
self.mseLoss = nn.MSELoss()
self.corrLoss = CorrLoss()
def forward(self, o, t):
mse = self.mseLoss(o, t)
corr = self.corrLoss(o, t)
loss = mse + self.p * corr
return loss
我们实验采用的这个损失函数，效果稍微比BCE好一点
模型设计
为了提高模型的差异度，我们主要选择了不同模型的变体，其中包括以下五个模型：
- Deberta-v3-large
- Bert-for-patents
- Roberta-large
- Ernie-en-2.0-Large
- Electra-large-discriminator
具体cv分数如下：
deberta-v3-large：[0.8494,0.8455,0.8523,0.8458,0.8658] cv 0.85176
bertforpatents [0.8393, 0.8403, 0.8457, 0.8402, 0.8564] cv 0.8444
roberta-large [0.8183,0.8172,0.8203,0.8193,0.8398] cv 0.8233
ernie-large [0.8276,0.8277,0.8251,0.8296,0.8466] cv 0.8310
electra-large [0.8429,0.8309,0.8259,0.8416,0.846] cv 0.8376
训练优化
根据以往比赛经验，我们主要采用了以下模型训练优化方式：
- 对抗训练：尝试了FGM 对模型训练有提升效果
class FGM():
def __init__(self, model):
self.model = model
self.backup = {}
def attack(self, epsilon=1., emb_name='word_embeddings'):
# emb_name这个参数要换成你模型中embedding的参数名
for name, param in self.model.named_parameters():
if param.requires_grad and emb_name in name:
self.backup[name] = param.data.clone()
norm = torch.norm(param.grad)
if norm != 0 and not torch.isnan(norm):
r_at = epsilon * param.grad / norm
param.data.add_(r_at)
def restore(self, emb_name='emb.'):
# emb_name这个参数要换成你模型中embedding的参数名
for name, param in self.model.named_parameters():
if param.requires_grad and emb_name in name:
assert name in self.backup
param.data = self.backup[name]
self.backup = {}
- 模型泛化：加入了multidroout
- ema对模型训练有提升效果
class EMA():
def __init__(self, model, decay):
self.model = model
self.decay = decay
self.shadow = {}
self.backup = {}
def register(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
self.shadow[name] = param.data.clone()
def update(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.shadow
new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow[name]
self.shadow[name] = new_average.clone()
def apply_shadow(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.shadow
self.backup[name] = param.data
param.data = self.shadow[name]
def restore(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.backup
param.data = self.backup[name]
self.backup = {}
# 初始化
ema = EMA(model, 0.999)
ema.register()
# 训练过程中，更新完参数后，同步update shadow weights
def train():
optimizer.step()
ema.update()
# eval前，apply shadow weights；eval之后，恢复原来模型的参数
def evaluate():
ema.apply_shadow()
# evaluate
ema.restore()
没有用的尝试：
- AWP
- PGD
模型融合
根据线下交叉验证分数以及线上分数反馈，我们通过加权融合的方式进行平均融合
from sklearn.preprocessing import MinMaxScaler
MMscaler = MinMaxScaler()
predictions1 = MMscaler.fit_transform(submission['predictions1'].values.reshape(-1,1)).reshape(-1)
predictions2 = MMscaler.fit_transform(submission['predictions2'].values.reshape(-1,1)).reshape(-1)
predictions3 = MMscaler.fit_transform(submission['predictions3'].values.reshape(-1,1)).reshape(-1)
predictions4 = MMscaler.fit_transform(submission['predictions4'].values.reshape(-1,1)).reshape(-1)
predictions5 = MMscaler.fit_transform(submission['predictions5'].values.reshape(-1,1)).reshape(-1)
# final_predictions=(predictions1+predictions2)/2
# final_predictions=(predictions1+predictions2+predictions3+predictions4+predictions5)/5
# 5：2：1：1：1
final_predictions=0.5*predictions1+0.2*predictions2+0.1*predictions3+0.1*predictions4+0.1*predictions5
其他尝试
- two stage
前期我们做了不同预训练模型的微调，所以特征数量相对较多，我们尝试基于树模型的对文本统计特征以及模型预测做stacking尝试,当时模型是有比较不错的融合效果，下面含有部分代码
# ====================================================
# predictions1
# ====================================================
def get_fold_pred(CFG, path, model):
CFG.path = path
CFG.model = model
CFG.config_path = CFG.path + "config.pth"
CFG.tokenizer = AutoTokenizer.from_pretrained(CFG.path)
test_dataset = TestDataset(CFG, test)
test_loader = DataLoader(test_dataset,
batch_size=CFG.batch_size,
shuffle=False,
num_workers=CFG.num_workers, pin_memory=True, drop_last=False)
predictions = []
for fold in CFG.trn_fold:
model = CustomModel(CFG, config_path=CFG.config_path, pretrained=False)
state = torch.load(CFG.path + f"{CFG.model.split('/')[-1]}_fold{fold}_best.pth",
map_location=torch.device('cpu'))
model.load_state_dict(state['model'])
prediction = inference_fn(test_loader, model, device)
predictions.append(prediction.flatten())
del model, state, prediction
gc.collect()
torch.cuda.empty_cache()
# predictions1 = np.mean(predictions, axis=0)
# fea_df = pd.DataFrame(predictions).T
# fea_df.columns = [f"{CFG.model.split('/')[-1]}_fold{fold}" for fold in CFG.trn_fold]
# del test_dataset, test_loader
return predictions
model_paths = [
"../input/albert-xxlarge-v2/albert-xxlarge-v2/",
"../input/bert-large-cased-cv5/bert-large-cased/",
"../input/deberta-base-cv5/deberta-base/",
"../input/deberta-v3-base-cv5/deberta-v3-base/",
"../input/deberta-v3-small/deberta-v3-small/",
"../input/distilroberta-base/distilroberta-base/",
"../input/roberta-large/roberta-large/",
"../input/xlm-roberta-base/xlm-roberta-base/",
"../input/xlmrobertalarge-cv5/xlm-roberta-large/",
]
print("train.shape, test.shape", train.shape, test.shape)
print("titles.shape", titles.shape)
# for model_path in model_paths:
# with open(f'{model_path}/oof_df.pkl', "rb") as fh:
# oof = pickle.load(fh)[['id', 'fold', 'pred']]
# # oof = pd.read_pickle(f'{model_path}/oof_df.pkl')[['id', 'fold', 'pred']]
# oof[f"{model_path.split('/')[1]}"] = oof['pred']
# train = train.merge(oof[['id', f"{model_path.split('/')[1]}"]], how='left', on='id')
oof_res=pd.read_csv('../input/train-res/train_oof.csv')
train = train.merge(oof_res, how='left', on='id')
model_infos = {
'albert-xxlarge-v2': ['../input/albert-xxlarge-v2/albert-xxlarge-v2/', "albert-xxlarge-v2"],
'bert-large-cased': ['../input/bert-large-cased-cv5/bert-large-cased/', "bert-large-cased"],
'deberta-base': ['../input/deberta-base-cv5/deberta-base/', "deberta-base"],
'deberta-v3-base': ['../input/deberta-v3-base-cv5/deberta-v3-base/', "deberta-v3-base"],
'deberta-v3-small': ['../input/deberta-v3-small/deberta-v3-small/', "deberta-v3-small"],
'distilroberta-base': ['../input/distilroberta-base/distilroberta-base/', "distilroberta-base"],
'roberta-large': ['../input/roberta-large/roberta-large/', "roberta-large"],
'xlm-roberta-base': ['../input/xlm-roberta-base/xlm-roberta-base/', "xlm-roberta-base"],
'xlm-roberta-large': ['../input/xlmrobertalarge-cv5/xlm-roberta-large/', "xlm-roberta-large"],
}
for model, path_info in model_infos.items():
print(model)
model_path, model_name = path_info[0], path_info[1]
fea_df = get_fold_pred(CFG, model_path, model_name)
model_infos[model].append(fea_df)
del model_path, model_name
del oof_res
训练代码：
for fold_ in range(5):
print("Fold:", fold_)
trn_ = train[train['fold'] != fold_].index
val_ = train[train['fold'] == fold_].index
# print(train.iloc[val_].sort_values('id'))
trn_x, trn_y = train[train_features].iloc[trn_], train['score'].iloc[trn_]
val_x, val_y = train[train_features].iloc[val_], train['score'].iloc[val_]
# train_folds = folds[folds['fold'] != fold].reset_index(drop=True)
# valid_folds = folds[folds['fold'] == fold].reset_index(drop=True)
reg = lgb.LGBMRegressor(**params,n_estimators=1100)
xgb = XGBRegressor(**xgb_params, n_estimators=1000)
cat = CatBoostRegressor(iterations=1000,learning_rate=0.03,
depth=10,
eval_metric='RMSE',
random_seed = 42,
bagging_temperature = 0.2,
od_type='Iter',
metric_period = 50,
od_wait=20)
print("-"* 20 + "LightGBM Training" + "-"* 20)
reg.fit(trn_x, np.log1p(trn_y),eval_set=[(val_x, np.log1p(val_y))],early_stopping_rounds=50,verbose=100,eval_metric='rmse')
print("-"* 20 + "XGboost Training" + "-"* 20)
xgb.fit(trn_x, np.log1p(trn_y),eval_set=[(val_x, np.log1p(val_y))],early_stopping_rounds=50,eval_metric='rmse',verbose=100)
print("-"* 20 + "Catboost Training" + "-"* 20)
cat.fit(trn_x, np.log1p(trn_y), eval_set=[(val_x, np.log1p(val_y))],early_stopping_rounds=50,use_best_model=True,verbose=100)
imp_df = pd.DataFrame()
imp_df['feature'] = train_features
imp_df['gain_reg'] = reg.booster_.feature_importance(importance_type='gain')
imp_df['fold'] = fold_ + 1
importances = pd.concat([importances, imp_df], axis=0, sort=False)
for model, values in model_infos.items():
test[model] = values[2][fold_]
for model, values in uspppm_model_infos.items():
test[f"uspppm_{model}"] = values[2][fold_]
# for f in tqdm(amount_feas, desc="amount_feas 基本聚合特征"):
# for cate in category_fea:
# if f != cate:
# test['{}_{}_medi'.format(cate, f)] = test.groupby(cate)[f].transform('median')
# test['{}_{}_mean'.format(cate, f)] = test.groupby(cate)[f].transform('mean')
# test['{}_{}_max'.format(cate, f)] = test.groupby(cate)[f].transform('max')
# test['{}_{}_min'.format(cate, f)] = test.groupby(cate)[f].transform('min')
# test['{}_{}_std'.format(cate, f)] = test.groupby(cate)[f].transform('std')
# LightGBM
oof_reg_preds[val_] = reg.predict(val_x, num_iteration=reg.best_iteration_)
# oof_reg_preds[oof_reg_preds < 0] = 0
lgb_preds = reg.predict(test[train_features], num_iteration=reg.best_iteration_)
# lgb_preds[lgb_preds < 0] = 0
# Xgboost
oof_reg_preds1[val_] = xgb.predict(val_x)
oof_reg_preds1[oof_reg_preds1 < 0] = 0
xgb_preds = xgb.predict(test[train_features])
# xgb_preds[xgb_preds < 0] = 0
# catboost
oof_reg_preds2[val_] = cat.predict(val_x)
oof_reg_preds2[oof_reg_preds2 < 0] = 0
cat_preds = cat.predict(test[train_features])
cat_preds[xgb_preds < 0] = 0
# merge all prediction
merge_pred[val_] = oof_reg_preds[val_] * 0.4 + oof_reg_preds1[val_] * 0.3 +oof_reg_preds2[val_] * 0.3
# sub_reg_preds += np.expm1(_preds) / len(folds)
# sub_reg_preds += np.expm1(_preds) / len(folds)
sub_preds += (lgb_preds / 5) * 0.6 + (xgb_preds / 5) * 0.2 + (cat_preds / 5) * 0.2 #三个模型五折测试集预测结果
sub_reg_preds+=lgb_preds / 5 # lgb五折测试集预测结果
print("lgb",pearsonr(train['score'], np.expm1(oof_reg_preds))[0]) # lgb
print("xgb",pearsonr(train['score'], np.expm1(oof_reg_preds1))[0]) # xgb
print("cat",pearsonr(train['score'], np.expm1(oof_reg_preds2))[0]) # cat
print("xgb lgb cat",pearsonr(train['score'], np.expm1(merge_pred))[0]) # xgb lgb cat
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