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Last active January 29, 2024 10:37
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Keras implementation of RankNet
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0_readme.md

This gist is the implementation of RankNet using Keras Functional Api

For details please check this blog post.

keywords: learning to rank | tensorflow | keras | custom training loop | ranknet | lambdaRank

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1_ranknet.py
import tensorflow as tf
from tensorflow.keras import layers, activations, losses, Model, Input
from tensorflow.nn import leaky_relu
import numpy as np
from itertools import combinations
from tensorflow.keras.utils import plot_model, Progbar
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
# model architecture
class RankNet(Model):
def __init__(self):
super().__init__()
self.dense = [layers.Dense(16, activation=leaky_relu), layers.Dense(8, activation=leaky_relu)]
self.o = layers.Dense(1, activation='linear')
self.oi_minus_oj = layers.Subtract()
def call(self, inputs):
xi, xj = inputs
densei = self.dense[0](xi)
densej = self.dense[0](xj)
for dense in self.dense[1:]:
densei = dense(densei)
densej = dense(densej)
oi = self.o(densei)
oj= self.o(densej)
oij = self.oi_minus_oj([oi, oj])
output = layers.Activation('sigmoid')(oij)
return output
def build_graph(self):
x = [Input(shape=(10)), Input(shape=(10))]
return Model(inputs=x, outputs=self.call(x))
# visualize model architecture
plot_model(RankNet().build_graph(), show_shapes=False)
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2_gen_data.py
# generate data
nb_query = 20
query = np.array([i+1 for i in range(nb_query) for x in range(int(np.ceil(np.abs(np.random.normal(0,scale=15))+2)))])
doc_features = np.random.random((len(query), 10))
doc_scores = np.random.randint(5, size=len(query)).astype(np.float32)
# put data into pairs
xi = []
xj = []
pij = []
pair_id = []
pair_query_id = []
for q in np.unique(query):
query_idx = np.where(query == q)[0]
for pair_idx in combinations(query_idx, 2):
pair_query_id.append(q)
pair_id.append(pair_idx)
i = pair_idx[0]
j = pair_idx[1]
xi.append(doc_features[i])
xj.append(doc_features[j])
if doc_scores[i] == doc_scores[j]:
_pij = 0.5
elif doc_scores[i] > doc_scores[j]:
_pij = 1
else:
_pij = 0
pij.append(_pij)
xi = np.array(xi)
xj = np.array(xj)
pij = np.array(pij)
pair_query_id = np.array(pair_query_id)
xi_train, xi_test, xj_train, xj_test, pij_train, pij_test, pair_id_train, pair_id_test = train_test_split(
xi, xj, pij, pair_id, test_size=0.2, stratify=pair_query_id)
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3_train_model.py
# train model using compile and fit
ranknet = RankNet()
ranknet.compile(optimizer='adam', loss='binary_crossentropy')
history = ranknet.fit([xi_train, xj_train], pij_train, epochs=50, batch_size=1, validation_data=([xi_test, xj_test], pij_test))
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4_plot_metric.py
# function for plotting loss
def plot_metrics(train_metric, val_metric=None, metric_name=None, title=None, ylim=5):
plt.title(title)
plt.ylim(0,ylim)
plt.plot(train_metric,color='blue',label=metric_name)
if val_metric is not None: plt.plot(val_metric,color='green',label='val_' + metric_name)
plt.legend(loc="upper right")
# plot loss history
plot_metrics(history.history['loss'], history.history['val_loss'], "Loss", "Loss", ylim=1.0)
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