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294 lines
11 KiB
294 lines
11 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import random
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import os
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import sys
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from test_imperative_base import new_program_scope
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from paddle.fluid.dygraph.base import to_variable
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# Can use Amusic dataset as the DeepCF describes.
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DATA_PATH = os.environ.get('DATA_PATH', '')
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BATCH_SIZE = int(os.environ.get('BATCH_SIZE', 128))
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NUM_BATCHES = int(os.environ.get('NUM_BATCHES', 5))
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NUM_EPOCHES = int(os.environ.get('NUM_EPOCHES', 1))
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class DMF(fluid.Layer):
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def __init__(self, name_scope):
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super(DMF, self).__init__(name_scope)
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self._user_latent = fluid.FC(self.full_name(), 256)
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self._item_latent = fluid.FC(self.full_name(), 256)
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self._user_layers = []
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self._item_layers = []
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self._hid_sizes = [128, 64]
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for i in range(len(self._hid_sizes)):
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self._user_layers.append(
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self.add_sublayer(
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'user_layer_%d' % i,
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fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
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self._item_layers.append(
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self.add_sublayer(
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'item_layer_%d' % i,
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fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
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def forward(self, users, items):
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users = self._user_latent(users)
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items = self._item_latent(items)
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for ul, il in zip(self._user_layers, self._item_layers):
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users = ul(users)
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items = il(items)
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return fluid.layers.elementwise_mul(users, items)
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class MLP(fluid.Layer):
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def __init__(self, name_scope):
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super(MLP, self).__init__(name_scope)
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self._user_latent = fluid.FC(self.full_name(), 256)
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self._item_latent = fluid.FC(self.full_name(), 256)
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self._match_layers = []
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self._hid_sizes = [128, 64]
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for i in range(len(self._hid_sizes)):
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self._match_layers.append(
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self.add_sublayer(
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'match_layer_%d' % i,
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fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
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self._mat
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def forward(self, users, items):
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users = self._user_latent(users)
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items = self._item_latent(items)
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match_vec = fluid.layers.concat(
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[users, items], axis=len(users.shape) - 1)
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for l in self._match_layers:
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match_vec = l(match_vec)
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return match_vec
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class DeepCF(fluid.Layer):
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def __init__(self, name_scope, num_users, num_items, matrix):
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super(DeepCF, self).__init__(name_scope)
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self._num_users = num_users
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self._num_items = num_items
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self._rating_matrix = self.create_parameter(
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fluid.ParamAttr(trainable=False),
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matrix.shape,
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matrix.dtype,
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is_bias=False,
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default_initializer=fluid.initializer.NumpyArrayInitializer(matrix))
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self._rating_matrix.stop_gradient = True
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self._mlp = MLP(self.full_name())
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self._dmf = DMF(self.full_name())
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self._match_fc = fluid.FC(self.full_name(), 1, act='sigmoid')
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def forward(self, users, items):
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# users_emb = self._user_emb(users)
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# items_emb = self._item_emb(items)
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users_emb = fluid.layers.gather(self._rating_matrix, users)
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items_emb = fluid.layers.gather(
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fluid.layers.transpose(self._rating_matrix, [1, 0]), items)
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users_emb.stop_gradient = True
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items_emb.stop_gradient = True
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mlp_predictive = self._mlp(users_emb, items_emb)
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dmf_predictive = self._dmf(users_emb, items_emb)
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predictive = fluid.layers.concat(
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[mlp_predictive, dmf_predictive],
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axis=len(mlp_predictive.shape) - 1)
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prediction = self._match_fc(predictive)
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return prediction
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def get_data():
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user_ids = []
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item_ids = []
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labels = []
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NUM_USERS = 100
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NUM_ITEMS = 1000
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matrix = np.zeros([NUM_USERS, NUM_ITEMS], dtype=np.float32)
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for uid in range(NUM_USERS):
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for iid in range(NUM_ITEMS):
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label = float(random.randint(1, 6) == 1)
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user_ids.append(uid)
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item_ids.append(iid)
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labels.append(label)
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matrix[uid, iid] = label
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indices = np.arange(len(user_ids))
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np.random.shuffle(indices)
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users_np = np.array(user_ids, dtype=np.int32)[indices]
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items_np = np.array(item_ids, dtype=np.int32)[indices]
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labels_np = np.array(labels, dtype=np.float32)[indices]
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return np.expand_dims(users_np, -1), \
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np.expand_dims(items_np, -1), \
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np.expand_dims(labels_np, -1), NUM_USERS, NUM_ITEMS, matrix
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def load_data(DATA_PATH):
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sys.stderr.write('loading from %s\n' % DATA_PATH)
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likes = dict()
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num_users = -1
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num_items = -1
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with open(DATA_PATH, 'r') as f:
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for l in f.readlines():
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uid, iid, rating = [int(v) for v in l.split('\t')]
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num_users = max(num_users, uid + 1)
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num_items = max(num_items, iid + 1)
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if float(rating) > 0.0:
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likes[(uid, iid)] = 1.0
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user_ids = []
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item_ids = []
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labels = []
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matrix = np.zeros([num_users, num_items], dtype=np.float32)
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for uid, iid in likes.keys():
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user_ids.append(uid)
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item_ids.append(iid)
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labels.append(1.0)
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matrix[uid, iid] = 1.0
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negative = 0
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while negative < 3:
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nuid = random.randint(0, num_users - 1)
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niid = random.randint(0, num_items - 1)
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if (nuid, niid) not in likes:
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negative += 1
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user_ids.append(nuid)
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item_ids.append(niid)
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labels.append(0.0)
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indices = np.arange(len(user_ids))
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np.random.shuffle(indices)
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users_np = np.array(user_ids, dtype=np.int32)[indices]
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items_np = np.array(item_ids, dtype=np.int32)[indices]
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labels_np = np.array(labels, dtype=np.float32)[indices]
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return np.expand_dims(users_np, -1), \
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np.expand_dims(items_np, -1), \
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np.expand_dims(labels_np, -1), num_users, num_items, matrix
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class TestDygraphDeepCF(unittest.TestCase):
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def test_deefcf(self):
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seed = 90
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if DATA_PATH:
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(users_np, items_np, labels_np, num_users, num_items,
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matrix) = load_data(DATA_PATH)
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else:
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(users_np, items_np, labels_np, num_users, num_items,
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matrix) = get_data()
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startup = fluid.Program()
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startup.random_seed = seed
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main = fluid.Program()
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main.random_seed = seed
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scope = fluid.core.Scope()
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with new_program_scope(main=main, startup=startup, scope=scope):
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users = fluid.layers.data('users', [1], dtype='int32')
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items = fluid.layers.data('items', [1], dtype='int32')
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labels = fluid.layers.data('labels', [1], dtype='float32')
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deepcf = DeepCF('deepcf', num_users, num_items, matrix)
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prediction = deepcf(users, items)
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loss = fluid.layers.reduce_sum(
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fluid.layers.log_loss(prediction, labels))
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adam = fluid.optimizer.AdamOptimizer(0.01)
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adam.minimize(loss)
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exe = fluid.Executor(fluid.CPUPlace(
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) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
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exe.run(startup)
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for e in range(NUM_EPOCHES):
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sys.stderr.write('epoch %d\n' % e)
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for slice in range(0, BATCH_SIZE * NUM_BATCHES, BATCH_SIZE):
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if slice + BATCH_SIZE >= users_np.shape[0]:
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break
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static_loss = exe.run(
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main,
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feed={
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users.name: users_np[slice:slice + BATCH_SIZE],
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items.name: items_np[slice:slice + BATCH_SIZE],
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labels.name: labels_np[slice:slice + BATCH_SIZE]
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},
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fetch_list=[loss])[0]
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sys.stderr.write('static loss %s\n' % static_loss)
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with fluid.dygraph.guard():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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deepcf = DeepCF('deepcf', num_users, num_items, matrix)
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adam = fluid.optimizer.AdamOptimizer(0.01)
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for e in range(NUM_EPOCHES):
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sys.stderr.write('epoch %d\n' % e)
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for slice in range(0, BATCH_SIZE * NUM_BATCHES, BATCH_SIZE):
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if slice + BATCH_SIZE >= users_np.shape[0]:
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break
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prediction = deepcf(
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to_variable(users_np[slice:slice + BATCH_SIZE]),
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to_variable(items_np[slice:slice + BATCH_SIZE]))
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loss = fluid.layers.reduce_sum(
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fluid.layers.log_loss(prediction,
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to_variable(labels_np[
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slice:slice + BATCH_SIZE])))
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loss.backward()
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adam.minimize(loss)
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deepcf.clear_gradients()
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dy_loss = loss.numpy()
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sys.stderr.write('dynamic loss: %s %s\n' % (slice, dy_loss))
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with fluid.dygraph.guard():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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deepcf2 = DeepCF('deepcf', num_users, num_items, matrix)
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adam2 = fluid.optimizer.AdamOptimizer(0.01)
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backward_strategy = fluid.dygraph.BackwardStrategy()
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backward_strategy.sort_sum_gradient = True
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for e in range(NUM_EPOCHES):
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sys.stderr.write('epoch %d\n' % e)
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for slice in range(0, BATCH_SIZE * NUM_BATCHES, BATCH_SIZE):
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if slice + BATCH_SIZE >= users_np.shape[0]:
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break
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prediction2 = deepcf2(
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to_variable(users_np[slice:slice + BATCH_SIZE]),
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to_variable(items_np[slice:slice + BATCH_SIZE]))
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loss2 = fluid.layers.reduce_sum(
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fluid.layers.log_loss(prediction2,
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to_variable(labels_np[
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slice:slice + BATCH_SIZE])))
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loss2.backward(backward_strategy)
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adam2.minimize(loss2)
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deepcf2.clear_gradients()
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dy_loss2 = loss2.numpy()
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sys.stderr.write('dynamic loss: %s %s\n' %
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(slice, dy_loss2))
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self.assertEqual(static_loss, dy_loss)
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self.assertEqual(static_loss, dy_loss2)
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if __name__ == '__main__':
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unittest.main()
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