编写能够在jupyterlab平台上运行的完整Python代码利用深度学习模型实现电影评论的情感分析。已知有训练集traincsv测试集testcsv两者各有25000条数据 ；label为评论的情感标签取值为1和0分别为正面情感和负面情感；review为电影评论文本；id为测试电影评论的id。要求预测review电影评论的好坏并将预测结果转换为csv文件输出文件和测试文件中的id保持一致要求预测

首先，需要安装必要的Python库，包括numpy、pandas、tensorflow和keras等。可以使用以下代码：

!pip install numpy pandas tensorflow keras

然后，读取训练集和测试集数据：

import pandas as pd

train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

接下来，对文本数据进行预处理。首先，需要去除文本中的标点符号和数字：

import re

def preprocess_text(text):
    # 去除标点符号和数字
    text = re.sub('[^a-zA-Z]', ' ', text)
    text = re.sub('\s+', ' ', text)
    return text.strip().lower()

train_data['review'] = train_data['review'].apply(preprocess_text)
test_data['review'] = test_data['review'].apply(preprocess_text)

然后，将文本转换为数字向量。可以使用词袋模型或者词嵌入模型。这里使用词嵌入模型，具体来说，使用预训练的GloVe词向量。首先，需要下载GloVe词向量：

!wget https://nlp.stanford.edu/data/glove.6B.zip
!unzip glove.6B.zip

然后，将GloVe词向量加载到内存中：

import numpy as np

word_index = {}
embedding_matrix = np.zeros((len(word_index) + 1, 100))

with open('glove.6B.100d.txt', 'r', encoding='utf-8') as f:
    for line in f:
        values = line.split()
        word = values[0]
        coefs = np.asarray(values[1:], dtype='float32')
        word_index[word] = len(word_index) + 1
        embedding_matrix[len(word_index)] = coefs

接下来，将文本转换为数字向量。具体来说，将每个单词替换为它在GloVe词向量中的索引：

def text_to_sequence(text):
    sequence = []
    for word in text.split():
        if word in word_index:
            sequence.append(word_index[word])
    return sequence

train_data['sequence'] = train_data['review'].apply(text_to_sequence)
test_data['sequence'] = test_data['review'].apply(text_to_sequence)

接下来，对序列进行填充和截断。具体来说，将每个序列填充为固定长度，超过长度的部分进行截断：

from keras.preprocessing.sequence import pad_sequences

max_length = 100

train_data['padded_sequence'] = pad_sequences(train_data['sequence'], maxlen=max_length)
test_data['padded_sequence'] = pad_sequences(test_data['sequence'], maxlen=max_length)

然后，定义深度学习模型。具体来说，使用卷积神经网络：

from keras.models import Sequential
from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense

model = Sequential()
model.add(Embedding(len(word_index) + 1, 100, weights=[embedding_matrix], input_length=max_length, trainable=False))
model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
model.add(GlobalMaxPooling1D())
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

然后，训练模型：

x_train = np.array(train_data['padded_sequence'].tolist())
y_train = np.array(train_data['label'])

model.fit(x_train, y_train, epochs=10, batch_size=128)

最后，使用训练好的模型对测试集进行预测，并将预测结果保存为csv文件：

x_test = np.array(test_data['padded_sequence'].tolist())

y_pred = model.predict(x_test)
y_pred = (y_pred > 0.5).astype(int)

output_df = pd.DataFrame({'id': test_data['id'], 'label': y_pred})
output_df.to_csv('output.csv', index=False)

完整代码如下：

!pip install numpy pandas tensorflow keras

import pandas as pd
import re
import numpy as np
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense

train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

def preprocess_text(text):
    # 去除标点符号和数字
    text = re.sub('[^a-zA-Z]', ' ', text)
    text = re.sub('\s+', ' ', text)
    return text.strip().lower()

train_data['review'] = train_data['review'].apply(preprocess_text)
test_data['review'] = test_data['review'].apply(preprocess_text)

word_index = {}
embedding_matrix = np.zeros((len(word_index) + 1, 100))

with open('glove.6B.100d.txt', 'r', encoding='utf-8') as f:
    for line in f:
        values = line.split()
        word = values[0]
        coefs = np.asarray(values[1:], dtype='float32')
        word_index[word] = len(word_index) + 1
        embedding_matrix[len(word_index)] = coefs

def text_to_sequence(text):
    sequence = []
    for word in text.split():
        if word in word_index:
            sequence.append(word_index[word])
    return sequence

train_data['sequence'] = train_data['review'].apply(text_to_sequence)
test_data['sequence'] = test_data['review'].apply(text_to_sequence)

max_length = 100

train_data['padded_sequence'] = pad_sequences(train_data['sequence'], maxlen=max_length)
test_data['padded_sequence'] = pad_sequences(test_data['sequence'], maxlen=max_length)

model = Sequential()
model.add(Embedding(len(word_index) + 1, 100, weights=[embedding_matrix], input_length=max_length, trainable=False))
model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
model.add(GlobalMaxPooling1D())
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

x_train = np.array(train_data['padded_sequence'].tolist())
y_train = np.array(train_data['label'])

model.fit(x_train, y_train, epochs=10, batch_size=128)

x_test = np.array(test_data['padded_sequence'].tolist())

y_pred = model.predict(x_test)
y_pred = (y_pred > 0.5).astype(int)

output_df = pd.DataFrame({'id': test_data['id'], 'label': y_pred})
output_df.to_csv('output.csv', index=False)
``