import functools import numpy import tensorflow as tf import toolz from tqdm import tqdm from evaluator import RecallEvaluator from sampler import WarpSampler from utils import citeulike, split_data def doublewrap(function): """ A decorator decorator, allowing to use the decorator to be used without parentheses if not arguments are provided. All arguments must be optional. """ @functools.wraps(function) def decorator(*args, **kwargs): if len(args) == 1 and len(kwargs) == 0 and callable(args[0]): return function(args[0]) else: return lambda wrapee: function(wrapee, *args, **kwargs) return decorator @doublewrap def define_scope(function, scope=None, *args, **kwargs): """ A decorator for functions that define TensorFlow operations. The wrapped function will only be executed once. Subsequent calls to it will directly return the result so that operations are added to the graph only once. The operations added by the function live within a tf.variable_scope(). If this decorator is used with arguments, they will be forwarded to the variable scope. The scope name defaults to the name of the wrapped function. """ attribute = '_cache_' + function.__name__ name = scope or function.__name__ @property @functools.wraps(function) def decorator(self): if not hasattr(self, attribute): with tf.variable_scope(name, *args, **kwargs): setattr(self, attribute, function(self)) return getattr(self, attribute) return decorator class CML(object): def __init__(self, n_users, n_items, embed_dim=20, features=None, margin=1.5, master_learning_rate=0.1, clip_norm=1.0, hidden_layer_dim=128, dropout_rate=0.2, feature_l2_reg=0.1, feature_projection_scaling_factor=0.5, use_rank_weight=True, use_cov_loss=True, cov_loss_weight=0.1 ): """ :param n_users: number of users i.e. |U| :param n_items: number of items i.e. |V| :param embed_dim: embedding size i.e. K (default 20) :param features: (optional) the feature vectors of items, shape: (|V|, N_Features). Set it to None will disable feature loss(default: None) :param margin: hinge loss threshold i.e. z :param master_learning_rate: master learning rate for AdaGrad :param clip_norm: clip norm threshold (default 1.0) :param hidden_layer_dim: the size of feature projector's hidden layer (default: 128) :param dropout_rate: the dropout rate between the hidden layer to final feature projection layer :param feature_l2_reg: feature loss weight :param feature_projection_scaling_factor: scale the feature projection before compute l2 loss. Ideally, the scaled feature projection should be mostly within the clip_norm :param use_rank_weight: whether to use rank weight :param use_cov_loss: use covariance loss to discourage redundancy in the user/item embedding """ self.n_users = n_users self.n_items = n_items self.embed_dim = embed_dim self.clip_norm = clip_norm self.margin = margin if features is not None: self.features = tf.constant(features, dtype=tf.float32) else: self.features = None self.master_learning_rate = master_learning_rate self.hidden_layer_dim = hidden_layer_dim self.dropout_rate = dropout_rate self.feature_l2_reg = feature_l2_reg self.feature_projection_scaling_factor = feature_projection_scaling_factor self.use_rank_weight = use_rank_weight self.use_cov_loss = use_cov_loss self.cov_loss_weight = cov_loss_weight self.user_positive_items_pairs = tf.placeholder(tf.int32, [None, 2]) self.negative_samples = tf.placeholder(tf.int32, [None, None]) self.score_user_ids = tf.placeholder(tf.int32, [None]) self.user_embeddings self.item_embeddings self.embedding_loss self.feature_loss self.loss self.optimize @define_scope def user_embeddings(self): return tf.Variable(tf.random_normal([self.n_users, self.embed_dim], stddev=1 / (self.embed_dim ** 0.5), dtype=tf.float32)) @define_scope def item_embeddings(self): return tf.Variable(tf.random_normal([self.n_items, self.embed_dim], stddev=1 / (self.embed_dim ** 0.5), dtype=tf.float32)) @define_scope def mlp_layer_1(self): return tf.layers.dense(inputs=self.features, units=self.hidden_layer_dim, activation=tf.nn.relu, name="mlp_layer_1") @define_scope def mlp_layer_2(self): dropout = tf.layers.dropout(inputs=self.mlp_layer_1, rate=self.dropout_rate) return tf.layers.dense(inputs=dropout, units=self.embed_dim, name="mlp_layer_2") @define_scope def feature_projection(self): """ :return: the projection of the feature vectors to the user-item embedding """ # feature loss if self.features is not None: # fully-connected layer output = self.mlp_layer_2 * self.feature_projection_scaling_factor # projection to the embedding return tf.clip_by_norm(output, self.clip_norm, axes=[1], name="feature_projection") @define_scope def feature_loss(self): """ :return: the l2 loss of the distance between items' their embedding and their feature projection """ loss = tf.constant(0, dtype=tf.float32) if self.feature_projection is not None: # the distance between feature projection and the item's actual location in the embedding feature_distance = tf.reduce_sum(tf.squared_difference( self.item_embeddings, self.feature_projection), 1) # apply regularization weight loss += tf.reduce_sum(feature_distance, name="feature_loss") * self.feature_l2_reg return loss @define_scope def covariance_loss(self): X = tf.concat((self.item_embeddings, self.user_embeddings), 0) n_rows = tf.cast(tf.shape(X)[0], tf.float32) X = X - (tf.reduce_mean(X, axis=0)) cov = tf.matmul(X, X, transpose_a=True) / n_rows return tf.reduce_sum(tf.matrix_set_diag(cov, tf.zeros(self.embed_dim, tf.float32))) * self.cov_loss_weight @define_scope def embedding_loss(self): """ :return: the distance metric loss """ # Let # N = batch size, # K = embedding size, # W = number of negative samples per a user-positive-item pair # user embedding (N, K) users = tf.nn.embedding_lookup(self.user_embeddings, self.user_positive_items_pairs[:, 0], name="users") # positive item embedding (N, K) pos_items = tf.nn.embedding_lookup(self.item_embeddings, self.user_positive_items_pairs[:, 1], name="pos_items") # positive item to user distance (N) pos_distances = tf.reduce_sum(tf.squared_difference(users, pos_items), 1, name="pos_distances") # negative item embedding (N, K, W) neg_items = tf.transpose(tf.nn.embedding_lookup(self.item_embeddings, self.negative_samples), (0, 2, 1), name="neg_items") # distance to negative items (N x W) distance_to_neg_items = tf.reduce_sum(tf.squared_difference(tf.expand_dims(users, -1), neg_items), 1, name="distance_to_neg_items") # best negative item (among W negative samples) their distance to the user embedding (N) closest_negative_item_distances = tf.reduce_min(distance_to_neg_items, 1, name="closest_negative_distances") # compute hinge loss (N) loss_per_pair = tf.maximum(pos_distances - closest_negative_item_distances + self.margin, 0, name="pair_loss") if self.use_rank_weight: # indicator matrix for impostors (N x W) impostors = (tf.expand_dims(pos_distances, -1) - distance_to_neg_items + self.margin) > 0 # approximate the rank of positive item by (number of impostor / W per user-positive pair) rank = tf.reduce_mean(tf.cast(impostors, dtype=tf.float32), 1, name="rank_weight") * self.n_items # apply rank weight loss_per_pair *= tf.log(rank + 1) # the embedding loss loss = tf.reduce_sum(loss_per_pair, name="loss") return loss @define_scope def loss(self): """ :return: the total loss = embedding loss + feature loss """ loss = self.embedding_loss + self.feature_loss if self.use_cov_loss: loss += self.covariance_loss return loss @define_scope def clip_by_norm_op(self): return [tf.assign(self.user_embeddings, tf.clip_by_norm(self.user_embeddings, self.clip_norm, axes=[1])), tf.assign(self.item_embeddings, tf.clip_by_norm(self.item_embeddings, self.clip_norm, axes=[1]))] @define_scope def optimize(self): # have two separate learning rates. The first one for user/item embedding is un-normalized. # The second one for feature projector NN is normalized by the number of items. gds = [] gds.append(tf.train .AdagradOptimizer(self.master_learning_rate) .minimize(self.loss, var_list=[self.user_embeddings, self.item_embeddings])) if self.feature_projection is not None: gds.append(tf.train .AdagradOptimizer(self.master_learning_rate) .minimize(self.feature_loss / self.n_items)) with tf.control_dependencies(gds): return gds + [self.clip_by_norm_op] @define_scope def item_scores(self): # (N_USER_IDS, 1, K) user = tf.expand_dims(tf.nn.embedding_lookup(self.user_embeddings, self.score_user_ids), 1) # (1, N_ITEM, K) item = tf.expand_dims(self.item_embeddings, 0) # score = minus distance (N_USER, N_ITEM) return -tf.reduce_sum(tf.squared_difference(user, item), 2, name="scores") BATCH_SIZE = 50000 N_NEGATIVE = 20 EVALUATION_EVERY_N_BATCHES = 30 EMBED_DIM = 100 def optimize(model, sampler, train, valid): """ Optimize the model. TODO: implement early-stopping :param model: model to optimize :param sampler: mini-batch sampler :param train: train user-item matrix :param valid: validation user-item matrix :return: None """ sess = tf.Session() sess.run(tf.global_variables_initializer()) if model.feature_projection is not None: # initialize item embedding with feature projection sess.run(tf.assign(model.item_embeddings, model.feature_projection)) # sample some users to calculate recall validation valid_users = numpy.random.choice(list(set(valid.nonzero()[0])), size=1000, replace=False) while True: # create evaluator on validation set validation_recall = RecallEvaluator(model, train, valid) # compute recall on validate set valid_recalls = [] # compute recall in chunks to utilize speedup provided by Tensorflow for user_chunk in toolz.partition_all(100, valid_users): valid_recalls.extend([validation_recall.eval(sess, user_chunk)]) print("\nRecall on (sampled) validation set: {}".format(numpy.mean(valid_recalls))) # TODO: early stopping based on validation recall # train model losses = [] # run n mini-batches for _ in tqdm(range(EVALUATION_EVERY_N_BATCHES), desc="Optimizing..."): user_pos, neg = sampler.next_batch() _, loss = sess.run((model.optimize, model.loss), {model.user_positive_items_pairs: user_pos, model.negative_samples: neg}) losses.append(loss) print("\nTraining loss {}".format(numpy.mean(losses))) if __name__ == '__main__': # get user-item matrix user_item_matrix, features = citeulike(tag_occurence_thres=5) n_users, n_items = user_item_matrix.shape # make feature as dense matrix dense_features = features.toarray() + 1E-10 # get train/valid/test user-item matrices train, valid, test = split_data(user_item_matrix) # create warp sampler sampler = WarpSampler(train, batch_size=BATCH_SIZE, n_negative=N_NEGATIVE, check_negative=True) # WITHOUT features # Train a user-item joint embedding, where the items a user likes will be pulled closer to this users. # Once the embedding is trained, the recommendations are made by finding the k-Nearest-Neighbor to each user. model = CML(n_users, n_items, # set features to None to disable feature projection features=None, # size of embedding embed_dim=EMBED_DIM, # the size of hinge loss margin. margin=1.9, # clip the embedding so that their norm <= clip_norm clip_norm=1, # learning rate for AdaGrad master_learning_rate=0.1, # whether to enable rank weight. If True, the loss will be scaled by the estimated # log-rank of the positive items. If False, no weight will be applied. # This is particularly useful to speed up the training for large item set. # Weston, Jason, Samy Bengio, and Nicolas Usunier. # "Wsabie: Scaling up to large vocabulary image annotation." IJCAI. Vol. 11. 2011. use_rank_weight=True, # whether to enable covariance regularization to encourage efficient use of the vector space. # More useful when the size of embedding is smaller (e.g. < 20 ). use_cov_loss=False, # weight of the cov_loss cov_loss_weight=1 ) #optimize(model, sampler, train, valid) # WITH features # In this case, we additionally train a feature projector to project raw item features into the # embedding. The projection serves as "a prior" to inform the item's potential location in the embedding. # We use a two fully-connected layers NN as our feature projector. (This model is much more computation intensive. # A GPU machine is recommended) model = CML(n_users, n_items, # enable feature projection features=dense_features, embed_dim=EMBED_DIM, margin=2.0, clip_norm=1.1, master_learning_rate=0.1, # the size of the hidden layer in the feature projector NN hidden_layer_dim=512, # dropout rate between hidden layer and output layer in the feature projector NN dropout_rate=0.3, # scale the output of the NN so that the magnitude of the NN output is closer to the item embedding feature_projection_scaling_factor=1, # the penalty to the distance between projection and item's actual location in the embedding # tune this to adjust how much the embedding should be biased towards the item features. feature_l2_reg=0.1, # whether to enable rank weight. If True, the loss will be scaled by the estimated # log-rank of the positive items. If False, no weight will be applied. # This is particularly useful to speed up the training for large item set. # Weston, Jason, Samy Bengio, and Nicolas Usunier. # "Wsabie: Scaling up to large vocabulary image annotation." IJCAI. Vol. 11. 2011. use_rank_weight=True, # whether to enable covariance regularization to encourage efficient use of the vector space. # More useful when the size of embedding is smaller (e.g. < 20 ). use_cov_loss=False, # weight of the cov_loss cov_loss_weight=1 ) optimize(model, sampler, train, valid)