python 代码实现NEURAL NETWORKS Uplift model for multiple correlated responses WITH LOWRANK REGULARIZATION

下面是使用Python实现NEURAL NETWORKS Uplift model for multiple correlated responses WITH LOWRANK REGULARIZATION的代码示例：

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

# 导入数据集
data = pd.read_csv('data.csv')
X = data.drop(['response', 'treatment'], axis=1).values
y = data['response'].values
t = data['treatment'].values

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test, t_train, t_test = train_test_split(X, y, t, test_size=0.2)

# 定义常量
n_features = X.shape[1]
n_components = 5
alpha = 0.01

# 定义占位符
X_ph = tf.placeholder(tf.float32, shape=[None, n_features], name='X')
y_ph = tf.placeholder(tf.float32, shape=[None], name='y')
t_ph = tf.placeholder(tf.float32, shape=[None], name='t')

# 定义神经网络模型
hidden_layer_1 = tf.layers.dense(X_ph, 64, activation=tf.nn.relu)
hidden_layer_2 = tf.layers.dense(hidden_layer_1, 32, activation=tf.nn.relu)
y_pred = tf.layers.dense(hidden_layer_2, 1, activation=tf.nn.sigmoid)

# 定义损失函数
treated = tf.cast(tf.equal(t_ph, 1), tf.float32)
control = 1 - treated
uplift = tf.reduce_mean((y_pred * treated - y_pred * control))
mse = tf.reduce_mean(tf.square(y_pred - y_ph))
lowrank = tf.reduce_sum(tf.svd(tf.transpose(hidden_layer_1), compute_uv=False))

loss = mse - alpha * uplift + alpha * lowrank

# 定义优化器
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_step = optimizer.minimize(loss)

# 定义会话
sess = tf.Session()
sess.run(tf.global_variables_initializer())

# 训练模型
for i in range(100):
    _, loss_value, uplift_value, mse_value, lowrank_value = sess.run([train_step, loss, uplift, mse, lowrank], feed_dict={X_ph: X_train, y_ph: y_train, t_ph: t_train})
    print('Epoch %d - Loss: %.4f, Uplift: %.4f, MSE: %.4f, Lowrank: %.4f' % (i, loss_value, uplift_value, mse_value, lowrank_value))

# 预测响应变量的值
y_train_pred = sess.run(y_pred, feed_dict={X_ph: X_train})
y_test_pred = sess.run(y_pred, feed_dict={X_ph: X_test})

# 计算控制组和干预组之间的差异
uplift_train = np.mean(y_train_pred[t_train == 1] - y_train_pred[t_train == 0])
uplift_test = np.mean(y_test_pred[t_test == 1] - y_test_pred[t_test == 0])

# 输出结果
print('Train uplift: %.2f' % uplift_train)
print('Test uplift: %.2f' % uplift_test)

请注意，这只是一个简单的示例，实际实现可能涉及更多的数据预处理和模型调整。