RankNet

RankNet 是一种用于学习排名的机器学习模型，由 Microsoft Research Asia 在 2005 年提出。它的主要目标是学习一个函数，该函数可以将输入的样本按照其重要性或优先级进行排名。RankNet 可以用于各种应用场景，例如信息检索、推荐系统、图像分类等。
RankNet 的基本思想是利用梯度下降法来优化一个基于损失函数的排名目标。在训练过程中，模型会尽可能地减小预测排名与真实排名之间的损失。为了解决梯度消失问题，RankNet 通常使用 Lambda 函数来平衡正负样本的权重。
要使用 RankNet，需要进行以下步骤：

1. 数据准备：收集并预处理输入数据，为模型提供训练样本。
2. 构建模型：搭建 RankNet 模型。RankNet 的基本结构包括两个神经网络：一个用于预测样本的得分，另一个用于根据得分对样本进行排序。
3. 训练模型：利用收集到的数据对模型进行训练，通过优化损失函数（如 Hinge 损失）来学习模型参数。
4. 评估模型：使用验证集对模型进行评估，根据评估结果调整模型参数以提高性能。
5. 应用模型：将训练好的模型应用于实际任务，例如信息检索、推荐系统等。
   总之，RankNet 是一种用于学习排名的机器学习模型，可以应用于各种排名相关的任务。通过数据准备、模型构建、训练和评估等步骤，可以利用 RankNet 解决实际问题。

Ch 12: Concept 01
Ranking by neural network
Import the relevant libraries

import tensorflow as tf
import numpy as np
import random

%matplotlib inline
import matplotlib.pyplot as plt
Let's fabricate some data. We'll call get_data() to generate two datasets: data_a and data_b.

We'll use the convention that points in data_a are ranked lower than those in data_b. So we need to learn a ranking function (i.e. utility function) that scores points in data_a lower.

n_features = 2

def get_data():
    data_a = np.random.rand(10, n_features) + 1
    data_b = np.random.rand(10, n_features)

    plt.scatter(data_a[:, 0], data_a[:, 1], c='r', marker='x')
    plt.scatter(data_b[:, 0], data_b[:, 1], c='g', marker='o')
    plt.show()

    return data_a, data_b

def get_data2():
    data_a = np.asarray([[0.1, 0.9], [0.1, 0.8]])
    data_b = np.asarray([[0.4,0.05], [0.45, 0.1]])

    plt.scatter(data_a[:, 0], data_a[:, 1], c='r', marker='x')
    plt.scatter(data_b[:, 0], data_b[:, 1], c='g', marker='o')
    plt.xlim([0, 0.5])
    plt.ylim([0, 1])
    plt.axes().set_aspect('equal')
    plt.show()


    return data_a, data_b

data_a, data_b = get_data()

Now, let's define our ranking model. It'll take in two items (x1 and x2), and return a score (s1 and s2) for each item.

Our model introduces a hyper-parameter called n_hidden to tweak the number of neurons in the hidden layer of the network.

n_hidden = 10
When defining the model, let's organize it into separate scopes. That way, the TensorBoard visualization will look very clean.

with tf.name_scope("input"):
    x1 = tf.placeholder(tf.float32, [None, n_features], name="x1")
    x2 = tf.placeholder(tf.float32, [None, n_features], name="x2")
    dropout_keep_prob = tf.placeholder(tf.float32, name='dropout_prob')


with tf.name_scope("hidden_layer"):
    with tf.name_scope("weights"):
        w1 = tf.Variable(tf.random_normal([n_features, n_hidden]), name="w1")
        tf.summary.histogram("w1", w1)
        b1 = tf.Variable(tf.random_normal([n_hidden]), name="b1")
        tf.summary.histogram("b1", b1)

    with tf.name_scope("output"):
        h1 = tf.nn.dropout(tf.nn.relu(tf.matmul(x1,w1) + b1), keep_prob=dropout_keep_prob)
        tf.summary.histogram("h1", h1)
        h2 = tf.nn.dropout(tf.nn.relu(tf.matmul(x2, w1) + b1), keep_prob=dropout_keep_prob)
        tf.summary.histogram("h2", h2)


with tf.name_scope("output_layer"):
    with tf.name_scope("weights"):
        w2 = tf.Variable(tf.random_normal([n_hidden, 1]), name="w2")
        tf.summary.histogram("w2", w2)
        b2 = tf.Variable(tf.random_normal([1]), name="b2")
        tf.summary.histogram("b2", b2)

    with tf.name_scope("output"):
        s1 = tf.matmul(h1, w2) + b2
        s2 = tf.matmul(h2, w2) + b2
The loss function will involve comparing s1 and s2.

Since we're trying to acheive the inequality Score(x1) < Score(x2), we need the loss function to insinuate s1 < s2.

In other words, the loss function tries to guarantee that s1 - s2 < 0.

with tf.name_scope("loss"):
    s12 = s1 - s2
    s12_flat = tf.reshape(s12, [-1])

    pred = tf.sigmoid(s12)
    lable_p = tf.sigmoid(-tf.ones_like(s12))

    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=tf.zeros_like(s12_flat), logits=s12_flat + 1)

    loss = tf.reduce_mean(cross_entropy)
    tf.summary.scalar("loss", loss)

with tf.name_scope("train_op"):
    train_op = tf.train.AdamOptimizer(0.001).minimize(loss)
Start the session and prepare peripheral ops.

sess = tf.InteractiveSession()
summary_op = tf.summary.merge_all()
writer = tf.summary.FileWriter("tb_files", sess.graph)
init = tf.global_variables_initializer()
sess.run(init)
Train the model with the training data.

for epoch in range(0, 10000):
    loss_val, _ = sess.run([loss, train_op], feed_dict={x1:data_a, x2:data_b, dropout_keep_prob:0.5})
    if epoch % 100 == 0 :
        summary_result = sess.run(summary_op, feed_dict={x1:data_a, x2:data_b, dropout_keep_prob:1})
        writer.add_summary(summary_result, epoch)
#         print("Epoch {}: Loss {}".format(epoch, loss_val))
Visualize the results on a grid by accumulating a list of points to test.

grid_size = 10
data_test = []
for y in np.linspace(0., 1., num=grid_size):
    for x in np.linspace(0., 1., num=grid_size):
        data_test.append([x, y])
Run the model on all the test points and visualize the utility scores of each point by a color.

def visualize_results(data_test):
    plt.figure()
    scores_test = sess.run(s1, feed_dict={x1:data_test, dropout_keep_prob:1})
    scores_img = np.reshape(scores_test, [grid_size, grid_size])
    plt.imshow(scores_img, origin='lower')
    plt.colorbar()
visualize_results(data_test)