写出使用Embedding+MLP 模型推荐用户出行目的地的代码，数据为用户历史出行记录（用户id，出发时间，出发地和目的地）。

好的，以下是使用Embedding+MLP模型推荐用户出行目的地的代码：

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split

# 加载数据
data = pd.read_csv('user_history.csv')

# 数据预处理
# 编码出发地和目的地
le = LabelEncoder()
data['source'] = le.fit_transform(data['source'])
data['destination'] = le.fit_transform(data['destination'])
# 处理出发时间
data['departure_time'] = pd.to_datetime(data['departure_time'])
data['season'] = (data['departure_time'].dt.month % 12 + 3) // 3  # 转化为季节
data['is_weekend'] = (data['departure_time'].dt.weekday >= 5).astype(int)  # 判断工作日/周末
# 编码用户id
data['user_id'] = data['user_id'].astype('category').cat.codes
# 划分训练集和测试集
train, test = train_test_split(data, test_size=0.2)

# 定义输入和输出
inputs = ['user_id', 'source', 'season', 'is_weekend']
output = 'destination'

# 定义Embedding层
def embedding_layer(name, input_dim, output_dim, input_length):
    input_layer = tf.keras.layers.Input(shape=(1,), dtype='int32', name=name+'_input')
    embedding_layer = tf.keras.layers.Embedding(input_dim=input_dim, output_dim=output_dim, input_length=input_length, name=name+'_embedding')(input_layer)
    flatten_layer = tf.keras.layers.Flatten(name=name+'_flatten')(embedding_layer)
    return input_layer, flatten_layer

# 定义MLP模型
def mlp_model(inputs, output):
    input_layers = []
    flatten_layers = []
    for name, input_dim in inputs.items():
        input_layer, flatten_layer = embedding_layer(name, input_dim=input_dim, output_dim=8, input_length=1)
        input_layers.append(input_layer)
        flatten_layers.append(flatten_layer)
    concat_layer = tf.keras.layers.Concatenate(name='concatenate')(flatten_layers)
    dense_layer1 = tf.keras.layers.Dense(64, activation='relu', name='dense1')(concat_layer)
    dense_layer2 = tf.keras.layers.Dense(32, activation='relu', name='dense2')(dense_layer1)
    output_layer = tf.keras.layers.Dense(1, activation='linear', name='output')(dense_layer2)
    model = tf.keras.models.Model(inputs=input_layers, outputs=output_layer)
    model.compile(optimizer='adam', loss='mse')
    return model

# 初始化模型
model = mlp_model(inputs={'user_id': len(data.user_id.unique()), 'source': len(data.source.unique()), 'season': len(data.season.unique()), 'is_weekend': 2},
                  output=output)

# 训练模型
history = model.fit([train.user_id, train.source, train.season, train.is_weekend], train[output], epochs=10, batch_size=128, validation_split=0.2)

# 模型评估
test_loss = model.evaluate([test.user_id, test.source, test.season, test.is_weekend], test[output], verbose=0)
print('Test loss:', test_loss)

# 模型应用
# 输入用户历史出行记录，获取推荐的目的地
user_id = 1
source = 10
season = 3
is_weekend = 1
destination = model.predict([np.array([user_id]), np.array([source]), np.array([season]), np.array([is_weekend])])[0][0]
destination_name = le.inverse_transform([int(destination)])[0]
print('User {} should travel to {}'.format(user_id, destination_name))

以上是使用Embedding+MLP模型推荐用户出行目的地的代码，希望能对你有所帮助。