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Add Correlation-Sensitive Next-Basket Recommendation (Beacon) Model (#…
…584) * Add beacon model * Add example * Update docs * Fixed data_iter * refactor code
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| # Copyright 2023 The Cornac Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================ | ||
|
|
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| from .recom_beacon import Beacon |
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| import numpy as np | ||
| import warnings | ||
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| # disable annoying tensorflow deprecated API warnings | ||
| warnings.filterwarnings("ignore", category=UserWarning) | ||
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| import tensorflow.compat.v1 as tf | ||
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| tf.logging.set_verbosity(tf.logging.ERROR) | ||
| tf.disable_v2_behavior() | ||
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| def create_rnn_cell(cell_type, state_size, default_initializer, reuse=None): | ||
| if cell_type == "GRU": | ||
| return tf.nn.rnn_cell.GRUCell(state_size, activation=tf.nn.tanh, reuse=reuse) | ||
| elif cell_type == "LSTM": | ||
| return tf.nn.rnn_cell.LSTMCell( | ||
| state_size, | ||
| initializer=default_initializer, | ||
| activation=tf.nn.tanh, | ||
| reuse=reuse, | ||
| ) | ||
| else: | ||
| return tf.nn.rnn_cell.BasicRNNCell( | ||
| state_size, activation=tf.nn.tanh, reuse=reuse | ||
| ) | ||
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| def create_rnn_encoder( | ||
| x, | ||
| rnn_units, | ||
| dropout_rate, | ||
| seq_length, | ||
| rnn_cell_type, | ||
| param_initializer, | ||
| seed, | ||
| reuse=None, | ||
| ): | ||
| with tf.variable_scope("RNN_Encoder", reuse=reuse): | ||
| rnn_cell = create_rnn_cell(rnn_cell_type, rnn_units, param_initializer) | ||
| rnn_cell = tf.nn.rnn_cell.DropoutWrapper( | ||
| rnn_cell, input_keep_prob=1 - dropout_rate, seed=seed | ||
| ) | ||
| init_state = rnn_cell.zero_state(tf.shape(x)[0], tf.float32) | ||
| # RNN Encoder: Iteratively compute output of recurrent network | ||
| rnn_outputs, _ = tf.nn.dynamic_rnn( | ||
| rnn_cell, | ||
| x, | ||
| initial_state=init_state, | ||
| sequence_length=seq_length, | ||
| dtype=tf.float32, | ||
| ) | ||
| return rnn_outputs | ||
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| def create_basket_encoder( | ||
| x, | ||
| dense_units, | ||
| param_initializer, | ||
| activation_func=None, | ||
| name="Basket_Encoder", | ||
| reuse=None, | ||
| ): | ||
| with tf.variable_scope(name, reuse=reuse): | ||
| return tf.layers.dense( | ||
| x, | ||
| dense_units, | ||
| kernel_initializer=param_initializer, | ||
| bias_initializer=tf.zeros_initializer, | ||
| activation=activation_func, | ||
| ) | ||
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| def get_last_right_output(full_output, max_length, actual_length, rnn_units): | ||
| batch_size = tf.shape(full_output)[0] | ||
| # Start indices for each sample | ||
| index = tf.range(0, batch_size) * max_length + (actual_length - 1) | ||
| # Indexing | ||
| return tf.gather(tf.reshape(full_output, [-1, rnn_units]), index) | ||
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| class BeaconModel: | ||
| def __init__( | ||
| self, | ||
| sess, | ||
| emb_dim, | ||
| rnn_units, | ||
| alpha, | ||
| max_seq_length, | ||
| n_items, | ||
| item_probs, | ||
| adj_matrix, | ||
| rnn_cell_type, | ||
| rnn_dropout_rate, | ||
| seed, | ||
| lr, | ||
| ): | ||
| self.scope = "GRN" | ||
| self.session = sess | ||
| self.seed = seed | ||
| self.lr = tf.constant(lr) | ||
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| self.emb_dim = emb_dim | ||
| self.rnn_units = rnn_units | ||
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| self.max_seq_length = max_seq_length | ||
| self.n_items = n_items | ||
| self.item_probs = item_probs | ||
| self.alpha = alpha | ||
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| with tf.variable_scope(self.scope): | ||
| # Initialized for n_hop adjacency matrix | ||
| self.A = tf.constant( | ||
| adj_matrix.todense(), name="Adj_Matrix", dtype=tf.float32 | ||
| ) | ||
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||
| uniform_initializer = ( | ||
| np.ones(shape=(self.n_items), dtype=np.float32) / self.n_items | ||
| ) | ||
| self.I_B = tf.get_variable( | ||
| dtype=tf.float32, | ||
| initializer=tf.constant(uniform_initializer, dtype=tf.float32), | ||
| name="I_B", | ||
| ) | ||
| self.I_B_Diag = tf.nn.relu(tf.diag(self.I_B, name="I_B_Diag")) | ||
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| self.C_Basket = tf.get_variable( | ||
| dtype=tf.float32, initializer=tf.constant(adj_matrix.mean()), name="C_B" | ||
| ) | ||
| self.y = tf.placeholder( | ||
| dtype=tf.float32, | ||
| shape=(None, self.n_items), | ||
| name="Target_basket", | ||
| ) | ||
|
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| # Basket Sequence encoder | ||
| with tf.name_scope("Basket_Sequence_Encoder"): | ||
| self.bseq = tf.sparse.placeholder( | ||
| dtype=tf.float32, | ||
| name="bseq_input", | ||
| ) | ||
| self.bseq_length = tf.placeholder( | ||
| dtype=tf.int32, shape=(None,), name="bseq_length" | ||
| ) | ||
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| self.bseq_encoder = tf.sparse.reshape( | ||
| self.bseq, shape=[-1, self.n_items], name="bseq_2d" | ||
| ) | ||
| self.bseq_encoder = self.encode_basket_graph( | ||
| self.bseq_encoder, self.C_Basket, True | ||
| ) | ||
| self.bseq_encoder = tf.reshape( | ||
| self.bseq_encoder, | ||
| shape=[-1, self.max_seq_length, self.n_items], | ||
| name="bsxMxN", | ||
| ) | ||
| self.bseq_encoder = create_basket_encoder( | ||
| self.bseq_encoder, | ||
| emb_dim, | ||
| param_initializer=tf.initializers.he_uniform(), | ||
| activation_func=tf.nn.relu, | ||
| ) | ||
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| # batch_size x max_seq_length x H | ||
| rnn_encoder = create_rnn_encoder( | ||
| self.bseq_encoder, | ||
| self.rnn_units, | ||
| rnn_dropout_rate, | ||
| self.bseq_length, | ||
| rnn_cell_type, | ||
| param_initializer=tf.initializers.glorot_uniform(), | ||
| seed=self.seed, | ||
| ) | ||
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| # Hack to build the indexing and retrieve the right output. # batch_size x H | ||
| h_T = get_last_right_output( | ||
| rnn_encoder, self.max_seq_length, self.bseq_length, self.rnn_units | ||
| ) | ||
|
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| # Next basket estimation | ||
| with tf.name_scope("Next_Basket"): | ||
| W_H = tf.get_variable( | ||
| dtype=tf.float32, | ||
| initializer=tf.initializers.glorot_uniform(), | ||
| shape=(self.rnn_units, self.n_items), | ||
| name="W_H", | ||
| ) | ||
|
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| next_item_probs = tf.nn.sigmoid(tf.matmul(h_T, W_H)) | ||
| logits = ( | ||
| 1.0 - self.alpha | ||
| ) * next_item_probs + self.alpha * self.encode_basket_graph( | ||
| next_item_probs, tf.constant(0.0) | ||
| ) | ||
|
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| with tf.name_scope("Loss"): | ||
| self.loss = self.compute_loss(logits, self.y) | ||
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| self.predictions = tf.nn.sigmoid(logits) | ||
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| # Adam optimizer | ||
| train_op = tf.train.RMSPropOptimizer(learning_rate=self.lr) | ||
|
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| # Op to calculate every variable gradient | ||
| self.grads = train_op.compute_gradients(self.loss, tf.trainable_variables()) | ||
| self.update_grads = train_op.apply_gradients(self.grads) | ||
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| def train_batch(self, s, s_length, y): | ||
| bseq_indices, bseq_values, bseq_shape = self.get_sparse_tensor_info(s, True) | ||
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| [_, loss] = self.session.run( | ||
| [self.update_grads, self.loss], | ||
| feed_dict={ | ||
| self.bseq: (bseq_indices, bseq_values, bseq_shape), | ||
| self.bseq_length: s_length, | ||
| self.y: y, | ||
| }, | ||
| ) | ||
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| return loss | ||
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| def validate_batch(self, s, s_length, y): | ||
| bseq_indices, bseq_values, bseq_shape = self.get_sparse_tensor_info(s, True) | ||
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| loss = self.session.run( | ||
| self.loss, | ||
| feed_dict={ | ||
| self.bseq: (bseq_indices, bseq_values, bseq_shape), | ||
| self.bseq_length: s_length, | ||
| self.y: y, | ||
| }, | ||
| ) | ||
| return loss | ||
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| def predict(self, s, s_length): | ||
| bseq_indices, bseq_values, bseq_shape = self.get_sparse_tensor_info(s, True) | ||
| predictions = self.session.run( | ||
| self.predictions, | ||
| feed_dict={ | ||
| self.bseq: (bseq_indices, bseq_values, bseq_shape), | ||
| self.bseq_length: s_length, | ||
| }, | ||
| ) | ||
| return predictions.squeeze() | ||
|
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| def encode_basket_graph(self, binput, beta, is_sparse=False): | ||
| with tf.name_scope("Graph_Encoder"): | ||
| if is_sparse: | ||
| encoder = tf.sparse_tensor_dense_matmul( | ||
| binput, self.I_B_Diag, name="XxI_B" | ||
| ) | ||
| encoder += self.relu_with_threshold( | ||
| tf.sparse_tensor_dense_matmul(binput, self.A, name="XxA"), beta | ||
| ) | ||
| else: | ||
| encoder = tf.matmul(binput, self.I_B_Diag, name="XxI_B") | ||
| encoder += self.relu_with_threshold( | ||
| tf.matmul(binput, self.A, name="XxA"), beta | ||
| ) | ||
| return encoder | ||
|
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| def get_sparse_tensor_info(self, x, is_bseq=False): | ||
| indices = [] | ||
| if is_bseq: | ||
| for sid, bseq in enumerate(x): | ||
| for t, basket in enumerate(bseq): | ||
| for item_id in basket: | ||
| indices.append([sid, t, item_id]) | ||
| else: | ||
| for bid, basket in enumerate(x): | ||
| for item_id in basket: | ||
| indices.append([bid, item_id]) | ||
|
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| values = np.ones(len(indices), dtype=np.float32) | ||
| indices = np.array(indices, dtype=np.int32) | ||
| shape = np.array([len(x), self.max_seq_length, self.n_items], dtype=np.int64) | ||
| return indices, values, shape | ||
|
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| def compute_loss(self, logits, y): | ||
| sigmoid_logits = tf.nn.sigmoid(logits) | ||
|
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| neg_y = 1.0 - y | ||
| pos_logits = y * logits | ||
|
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| pos_max = tf.reduce_max(pos_logits, axis=1) | ||
| pos_max = tf.expand_dims(pos_max, axis=-1) | ||
|
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| pos_min = tf.reduce_min(pos_logits + neg_y * pos_max, axis=1) | ||
| pos_min = tf.expand_dims(pos_min, axis=-1) | ||
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| nb_pos, nb_neg = tf.count_nonzero(y, axis=1), tf.count_nonzero(neg_y, axis=1) | ||
| ratio = tf.cast(nb_neg, dtype=tf.float32) / tf.cast(nb_pos, dtype=tf.float32) | ||
|
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| pos_weight = tf.expand_dims(ratio, axis=-1) | ||
| loss = y * -tf.log(sigmoid_logits) * pos_weight + neg_y * -tf.log( | ||
| 1.0 - tf.nn.sigmoid(logits - pos_min) | ||
| ) | ||
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| return tf.reduce_mean(loss + 1e-8) | ||
|
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| def relu_with_threshold(self, x, threshold): | ||
| return tf.nn.relu(x - tf.abs(threshold)) |
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