文章目錄

• • 1. 讀入數據
  • 2. 文本處理
  • 3. 建模
  • 4. 訓練
  • 5. 測試

練習地址：https://www.kaggle.com/c/ds100fa19
相關博文
[Kaggle] Spam/Ham Email Classification 垃圾郵件分類（spacy）
[Kaggle] Spam/Ham Email Classification 垃圾郵件分類（BERT）

1. 讀入數據

• 讀取數據，test集沒有標簽

import pandas as pd import numpy as np train = pd.read_csv("train.csv") test = pd.read_csv("test.csv") train.head()

• 數據有無效的單元

print(np.sum(np.array(train.isnull()==True), axis=0)) print(np.sum(np.array(test.isnull()==True), axis=0))

存在 Na 單元格

[0 6 0 0] [0 1 0]

• fillna 填充處理

train = train.fillna(" ") test = test.fillna(" ") print(np.sum(np.array(train.isnull()==True), axis=0)) print(np.sum(np.array(test.isnull()==True), axis=0))

填充完成，顯示 sum = 0

[0 0 0 0] [0 0 0]

• y 標簽 只有 0 不是垃圾郵件， 1 是垃圾郵件

print(train['spam'].unique()) [0 1]

2. 文本處理

• 郵件內容和主題合并為一個特征

X_train = train['subject'] + ' ' + train['email'] y_train = train['spam'] X_test = test['subject'] + ' ' + test['email']

• 文本轉成 tokens ids 序列

from keras.preprocessing.text import Tokenizer max_words = 300 tokenizer = Tokenizer(num_words=max_words, lower=True, split=' ') # 只給頻率最高的300個詞分配 id，其他的忽略 tokenizer.fit_on_texts(list(X_train)+list(X_test)) # tokenizer 訓練 X_train_tokens = tokenizer.texts_to_sequences(X_train) X_test_tokens = tokenizer.texts_to_sequences(X_test)

• pad ids 序列，使之長度一樣

# 樣本 tokens 的長度不一樣，pad maxlen = 100 from keras.preprocessing import sequence X_train_tokens_pad = sequence.pad_sequences(X_train_tokens, maxlen=maxlen,padding='post') X_test_tokens_pad = sequence.pad_sequences(X_test_tokens, maxlen=maxlen,padding='post')

3. 建模

embeddings_dim = 30 # 詞嵌入向量維度 from keras.models import Model, Sequential from keras.layers import Embedding, LSTM, GRU, SimpleRNN, Dense model = Sequential() model.add(Embedding(input_dim=max_words, # Size of the vocabularyoutput_dim=embeddings_dim, # 詞嵌入的維度input_length=maxlen)) model.add(GRU(units=64)) # 可以改為 SimpleRNN ， LSTM model.add(Dense(units=1, activation='sigmoid')) model.summary()

模型結構：

Model: "sequential_5" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding_2 (Embedding) (None, 100, 30) 9000 _________________________________________________________________ gru (GRU) (None, 64) 18432 _________________________________________________________________ dense_2 (Dense) (None, 1) 65 ================================================================= Total params: 27,497 Trainable params: 27,497 Non-trainable params: 0 _________________________________________________________________

4. 訓練

model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy']) # 配置模型 history = model.fit(X_train_tokens_pad, y_train,batch_size=128, epochs=10, validation_split=0.2) model.save("email_cat_lstm.h5") # 保存訓練好的模型

• 繪制訓練曲線

from matplotlib import pyplot as plt pd.DataFrame(history.history).plot(figsize=(8, 5)) plt.grid(True) plt.show()

5. 測試

pred_prob = model.predict(X_test_tokens_pad).squeeze() pred_class = np.asarray(pred_prob > 0.5).astype(np.int32) id = test['id'] output = pd.DataFrame({'id':id, 'Class': pred_class}) output.to_csv("submission_gru.csv", index=False)

• 3種RNN模型對比：
  

總結

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