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triplet_movielens.py
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triplet_movielens.py
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"""
Triplet loss network example for recommenders
"""
from __future__ import print_function
import numpy as np
import theano
import keras
from keras import backend as K
from keras.models import Sequential, Graph
from keras.layers.core import Dense, Lambda
from keras.optimizers import Adagrad, Adam
import data
import metrics
def identity_loss(y_true, y_pred):
return K.mean(y_pred - 0 * y_true)
def bpr_triplet_loss(X):
user_latent, item_latent = X.values()
positive_item_latent, negative_item_latent = item_latent.values()
# BPR loss
loss = - 1.0 / (1.0 + K.exp(-(K.sum(user_latent * positive_item_latent, axis=-1, keepdims=True)
- K.sum(user_latent * negative_item_latent, axis=-1, keepdims=True))))
return loss
def margin_triplet_loss(X):
user_latent, item_latent = X.values()
positive_item_latent, negative_item_latent = item_latent.values()
# Hinge loss: max(0, user * negative_item_latent + 1 - user * positive_item_latent)
loss = K.maximum(1.0
+ K.sum(user_latent * negative_item_latent, axis=-1, keepdims=True)
- K.sum(user_latent * positive_item_latent, axis=-1, keepdims=True),
0.0)
return loss
def get_item_subgraph(input_shape, latent_dim):
# Could take item metadata here, do convolutional layers etc.
model = Sequential()
model.add(Dense(latent_dim, input_shape=input_shape))
return model
def get_user_subgraph(input_shape, latent_dim):
# Could do all sorts of fun stuff here that takes
# user metadata in.
model = Sequential()
model.add(Dense(latent_dim, input_shape=input_shape))
return model
def get_graph(num_users, num_items, latent_dim):
batch_input_shape = (1,)
model = Graph()
# Add inputs
model.add_input('user_input', input_shape=(num_users,), batch_input_shape=batch_input_shape)
model.add_input('positive_item_input', input_shape=(num_items,), batch_input_shape=batch_input_shape)
model.add_input('negative_item_input', input_shape=(num_items,), batch_input_shape=batch_input_shape)
# Add shared-weight item subgraph
model.add_shared_node(get_item_subgraph((num_items,), latent_dim),
name='item_latent',
inputs=['positive_item_input',
'negative_item_input'],
merge_mode='join')
# Add user embedding
model.add_node(get_user_subgraph((num_users,), latent_dim),
name='user_latent',
input='user_input')
# Compute loss
model.add_node(Lambda(bpr_triplet_loss),
name='triplet_loss',
inputs=['user_latent', 'item_latent'],
merge_mode='join')
# Add output
model.add_output(name='triplet_loss', input='triplet_loss')
# Compile using a dummy loss to fit within the Keras paradigm
model.compile(loss={'triplet_loss': identity_loss}, optimizer=Adam())#Adagrad(lr=0.1, epsilon=1e-06))
return model
def count_inversions(model, user_features, posititve_item_features, negative_item_features):
loss = model.predict({'user_input': user_features,
'positive_item_input': posititve_item_features,
'negative_item_input': negative_item_features})['triplet_loss']
return (loss > 0).mean()
if __name__ == '__main__':
num_epochs = 5
# Read data
train, test = data.get_movielens_data()
num_users, num_items = train.shape
# Prepare the test triplets
test_uid, test_pid, test_nid = data.get_triplets(test)
test_user_features, test_positive_item_features, test_negative_item_features = data.get_dense_triplets(test_uid,
test_pid,
test_nid,
num_users,
num_items)
# Sample triplets from the training data
uid, pid, nid = data.get_triplets(train)
user_features, positive_item_features, negative_item_features = data.get_dense_triplets(uid, pid, nid, num_users, num_items)
model = get_graph(num_users, num_items, 256)
# Print the model structure
print(model.summary())
# Sanity check, should be around 0.5
print('AUC before training %s' % metrics.full_auc(model, test))
for epoch in range(num_epochs):
print('Epoch %s' % epoch)
model.fit({'user_input': user_features,
'positive_item_input': positive_item_features,
'negative_item_input': negative_item_features, 'triplet_loss': np.ones(len(uid))},
validation_data={'user_input': test_user_features,
'positive_item_input': test_positive_item_features,
'negative_item_input': test_negative_item_features, 'triplet_loss': np.ones(len(uid))},
batch_size=512,
nb_epoch=1,
verbose=2,
shuffle=True)
print('AUC %s' % metrics.full_auc(model, test))
print('Inversions percentage %s' % count_inversions(model,
test_user_features,
test_positive_item_features,
test_negative_item_features))