达观杯--风险事件实验记录

达观杯–风险事件实验记录

官方代码

#!/usr/bin/env python
# coding: utf-8import pandas as pd
from sklearn.model_selection import train_test_splitimport sys
sys.path.append("./")# ### 加载数据集，并切分train/dev# In[2]:# 加载数据
df_train = pd.read_csv("./datasets/phase_1/splits/fold_0/train.txt")
df_train.columns = ["id", "text", "label"]
df_val = pd.read_csv("./datasets/phase_1/splits/fold_0/dev.txt")
df_val.columns = ["id", "text", "label"]
df_test = pd.read_csv("./datasets/phase_1/splits/fold_0/test.txt")
df_test.columns = ["id", "text", ]# 构建词表
charset = set()
for text in df_train['text']:for char in text.split(" "):charset.add(char)
id2char = ['OOV', '，', '。', '！', '？'] + list(charset)
char2id = {id2char[i]: i for i in range(len(id2char))}# 标签集
id2label = list(df_train['label'].unique())
label2id = {id2label[i]: i for i in range(len(id2label))}# ### 定义模型# In[3]:# 定义模型from tensorflow.keras.layers import *
from tensorflow.keras.models import *
MAX_LEN = 128
input_layer = Input(shape=(MAX_LEN,))
layer = Embedding(input_dim=len(id2char), output_dim=256)(input_layer)
layer = Bidirectional(LSTM(256, return_sequences=True))(layer)
layer = Flatten()(layer)
output_layer = Dense(len(id2label), activation='softmax')(layer)
model = Model(inputs=input_layer, outputs=output_layer)
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as npX_train, X_val, X_test = [], [], []
y_train = np.zeros((len(df_train), len(id2label)), dtype=np.int8)
y_val = np.zeros((len(df_val), len(id2label)), dtype=np.int8)for i in range(len(df_train)):X_train.append([char2id[char] for char in df_train.loc[i, 'text'].split(" ")])y_train[i][label2id[df_train.loc[i, 'label']]] = 1
for i in range(len(df_val)):X_val.append([char2id[char] if char in char2id else 0 for char in df_val.loc[i, 'text'].split(" ")])y_val[i][label2id[df_val.loc[i, 'label']]] = 1
for i in range(len(df_test)):X_test.append([char2id[char] if char in char2id else 0 for char in df_test.loc[i, 'text'].split(" ")])X_train = pad_sequences(X_train, maxlen=MAX_LEN, padding='post', truncating='post')
X_val = pad_sequences(X_val, maxlen=MAX_LEN, padding='post', truncating='post')
X_test = pad_sequences(X_test, maxlen=MAX_LEN, padding='post', truncating='post')# ### 模型训练# In[5]:model.fit(x=X_train, y=y_train, validation_data=(X_val, y_val), epochs=5, batch_size=32)# In[19]:y_val_pred = model.predict(X_val).argmax(axis=-1)
print(y_val_pred[: 20])
y_val = []
for i in range(len(df_val)):y_val.append(label2id[df_val.loc[i, 'label']])
y_val = [int(w) for w in y_val]
print(y_val[: 20])from sklearn.metrics import classification_report
results = {}
classification_report_dict = classification_report(y_val_pred, y_val, output_dict=True)
for key0, val0 in classification_report_dict.items():if isinstance(val0, dict):for key1, val1 in val0.items():results[key0 + "__" + key1] = val1else:results[key0] = val0import json
print(json.dumps(results, indent=2, ensure_ascii=False))y_pred = model.predict(X_test).argmax(axis=-1)
pred_labels = [id2label[i] for i in y_pred]
pd.DataFrame({"id": df_test['id'], "label": pred_labels}).to_csv("submission.csv", index=False)

baseline f1-score
0.36730954652

StratifiedKFold 有放回交叉验证
20210901

submit 随机word2vec bilstm max-pool -> 0.56353646

20210902

20210902

有点过拟合

20210907

20210908

*20210910
NEZHA 脱敏+counts

Roberta 脱敏 + 预训练 + counts