Add recommendation system implementation with new API

python/paddle/fluid/tests/book/recommender_system/no_test_recommender_system.py

@@ -0,0 +1,298 @@
+#   Copyright (c) 2018 PaddlePaddle 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.
+
+import math
+import sys
+import os
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.framework as framework
+import paddle.fluid.layers as layers
+import paddle.fluid.nets as nets
+from functools import partial
+
+IS_SPARSE = True
+USE_GPU = False
+BATCH_SIZE = 256
+FEEDING_MAP = {
+    'user_id': 0,
+    'gender_id': 1,
+    'age_id': 2,
+    'job_id': 3,
+    'movie_id': 4,
+    'category_id': 5,
+    'movie_title': 6,
+    'score': 7
+}
+
+
+def get_usr_combined_features():
+    # FIXME(dzh) : old API integer_value(10) may have range check.
+    # currently we don't have user configurated check.
+
+    USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
+
+    uid = layers.data(name='user_id', shape=[1], dtype='int64')
+
+    usr_emb = layers.embedding(
+        input=uid,
+        dtype='float32',
+        size=[USR_DICT_SIZE, 32],
+        param_attr='user_table',
+        is_sparse=IS_SPARSE)
+
+    usr_fc = layers.fc(input=usr_emb, size=32)
+
+    USR_GENDER_DICT_SIZE = 2
+
+    usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
+
+    usr_gender_emb = layers.embedding(
+        input=usr_gender_id,
+        size=[USR_GENDER_DICT_SIZE, 16],
+        param_attr='gender_table',
+        is_sparse=IS_SPARSE)
+
+    usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
+
+    USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
+    usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
+
+    usr_age_emb = layers.embedding(
+        input=usr_age_id,
+        size=[USR_AGE_DICT_SIZE, 16],
+        is_sparse=IS_SPARSE,
+        param_attr='age_table')
+
+    usr_age_fc = layers.fc(input=usr_age_emb, size=16)
+
+    USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
+    usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
+
+    usr_job_emb = layers.embedding(
+        input=usr_job_id,
+        size=[USR_JOB_DICT_SIZE, 16],
+        param_attr='job_table',
+        is_sparse=IS_SPARSE)
+
+    usr_job_fc = layers.fc(input=usr_job_emb, size=16)
+
+    concat_embed = layers.concat(
+        input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)
+
+    usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
+
+    return usr_combined_features
+
+
+def get_mov_combined_features():
+
+    MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
+
+    mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
+
+    mov_emb = layers.embedding(
+        input=mov_id,
+        dtype='float32',
+        size=[MOV_DICT_SIZE, 32],
+        param_attr='movie_table',
+        is_sparse=IS_SPARSE)
+
+    mov_fc = layers.fc(input=mov_emb, size=32)
+
+    CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
+
+    category_id = layers.data(
+        name='category_id', shape=[1], dtype='int64', lod_level=1)
+
+    mov_categories_emb = layers.embedding(
+        input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
+
+    mov_categories_hidden = layers.sequence_pool(
+        input=mov_categories_emb, pool_type="sum")
+
+    MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
+
+    mov_title_id = layers.data(
+        name='movie_title', shape=[1], dtype='int64', lod_level=1)
+
+    mov_title_emb = layers.embedding(
+        input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
+
+    mov_title_conv = nets.sequence_conv_pool(
+        input=mov_title_emb,
+        num_filters=32,
+        filter_size=3,
+        act="tanh",
+        pool_type="sum")
+
+    concat_embed = layers.concat(
+        input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)
+
+    # FIXME(dzh) : need tanh operator
+    mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
+
+    return mov_combined_features
+
+
+def train_network():
+    usr_combined_features = get_usr_combined_features()
+    mov_combined_features = get_mov_combined_features()
+
+    inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
+    scale_infer = layers.scale(x=inference, scale=5.0)
+
+    label = layers.data(name='score', shape=[1], dtype='float32')
+    square_cost = layers.square_error_cost(input=scale_infer, label=label)
+    avg_cost = layers.mean(square_cost)
+
+    return avg_cost, scale_infer
+
+
+def inference_network():
+    usr_combined_features = get_usr_combined_features()
+    mov_combined_features = get_mov_combined_features()
+
+    inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
+    scale_infer = layers.scale(x=inference, scale=5.0)
+
+    return scale_infer
+
+
+def func_feed(feeding, data):
+    feed_tensors = {}
+    for (key, idx) in feeding.iteritems():
+        tensor = fluid.LoDTensor()
+        if key != "category_id" and key != "movie_title":
+            if key == "score":
+                numpy_data = np.array(map(lambda x: x[idx], data)).astype(
+                    "float32")
+            else:
+                numpy_data = np.array(map(lambda x: x[idx], data)).astype(
+                    "int64")
+        else:
+            numpy_data = map(lambda x: np.array(x[idx]).astype("int64"), data)
+            lod_info = [len(item) for item in numpy_data]
+            offset = 0
+            lod = [offset]
+            for item in lod_info:
+                offset += item
+                lod.append(offset)
+            numpy_data = np.concatenate(numpy_data, axis=0)
+            tensor.set_lod([lod])
+
+        numpy_data = numpy_data.reshape([numpy_data.shape[0], 1])
+        tensor.set(numpy_data, place)
+        feed_tensors[key] = tensor
+    return feed_tensors
+
+
+def train(use_cuda, save_path):
+    EPOCH_NUM = 1
+
+    feeding_map = {
+        'user_id': 0,
+        'gender_id': 1,
+        'age_id': 2,
+        'job_id': 3,
+        'movie_id': 4,
+        'category_id': 5,
+        'movie_title': 6,
+        'score': 7
+    }
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.movielens.train(), buf_size=8192),
+        batch_size=BATCH_SIZE)
+    test_reader = paddle.batch(
+        paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
+
+    def event_handler(event):
+        if isinstance(event, fluid.EndIteration):
+            if (event.batch_id % 10) == 0:
+                avg_cost = trainer.test(reader=test_reader)
+
+                print('BatchID {0:04}, Loss {1:2.2}'.format(event.batch_id + 1,
+                                                            avg_cost))
+
+                if avg_cost > 0.01:  # Low threshold for speeding up CI
+                    trainer.save_params(save_path)
+                    return
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.2)
+    trainer = fluid.Trainer(train_network, optimizer=sgd_optimizer, place=place)
+    trainer.train(
+        train_reader,
+        EPOCH_NUM,
+        event_handler=event_handler,
+        data_feed_handler=partial(func_feed, feeding_map))
+
+
+def infer(use_cuda, save_path):
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    inferencer = fluid.Inferencer(
+        inference_network, param_path=save_path, place=place)
+
+    def create_lod_tensor(data, lod=None):
+        tensor = fluid.LoDTensor()
+        if lod is None:
+            # Tensor, the shape is [batch_size, 1]
+            index = 0
+            lod_0 = [index]
+            for l in range(len(data)):
+                index += 1
+                lod_0.append(index)
+            lod = [lod_0]
+        tensor.set_lod(lod)
+
+        flattened_data = np.concatenate(data, axis=0).astype("int64")
+        flattened_data = flattened_data.reshape([len(flattened_data), 1])
+        tensor.set(flattened_data, place)
+        return tensor
+
+    # Generate a random input for inference
+    user_id = create_lod_tensor([[1]])
+    gender_id = create_lod_tensor([[1]])
+    age_id = create_lod_tensor([[0]])
+    job_id = create_lod_tensor([[10]])
+    movie_id = create_lod_tensor([[783]])
+    category_id = create_lod_tensor([[10], [8], [9]], [[0, 3]])
+    movie_title = create_lod_tensor([[1069], [4140], [2923], [710], [988]],
+                                    [[0, 5]])
+
+    results = inferencer.infer({
+        'user_id': user_id,
+        'gender_id': gender_id,
+        'age_id': age_id,
+        'job_id': job_id,
+        'movie_id': movie_id,
+        'category_id': category_id,
+        'movie_title': movie_title
+    })
+
+    print("infer results: ", results)
+
+
+def main(use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+    save_path = "recommender_system.inference.model"
+    train(use_cuda, save_path)
+    infer(use_cuda, save_path)
+
+
+if __name__ == '__main__':
+    main(USE_CUDA)