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Add recommendation system implementation with new API #10535
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bc6edc5
Add recommendation system implementation with new API
sidgoyal78 fce6034
Address review comments
sidgoyal78 24aaf98
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
sidgoyal78 eec0b18
Modify as per new API
sidgoyal78 a14423a
Modify train and test functions to enable data_feed_handler
sidgoyal78 cd788c6
Rename script to avoid same names for Cmake
sidgoyal78 d57afb6
Resolve merge conflict
sidgoyal78 cca4a55
Fix issues with data_feed_handler
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@@ -8,3 +8,4 @@ endforeach() | |
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add_subdirectory(fit_a_line) | ||
add_subdirectory(recognize_digits) | ||
add_subdirectory(recommender_system) |
7 changes: 7 additions & 0 deletions
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python/paddle/fluid/tests/book/high-level-api/recommender_system/CMakeLists.txt
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file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py") | ||
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}") | ||
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# default test | ||
foreach(src ${TEST_OPS}) | ||
py_test(${src} SRCS ${src}.py) | ||
endforeach() |
308 changes: 308 additions & 0 deletions
308
...ddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py
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# 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. | ||
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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 | ||
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IS_SPARSE = True | ||
USE_GPU = False | ||
BATCH_SIZE = 256 | ||
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def get_usr_combined_features(): | ||
# FIXME(dzh) : old API integer_value(10) may have range check. | ||
# currently we don't have user configurated check. | ||
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USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1 | ||
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uid = layers.data(name='user_id', shape=[1], dtype='int64') | ||
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usr_emb = layers.embedding( | ||
input=uid, | ||
dtype='float32', | ||
size=[USR_DICT_SIZE, 32], | ||
param_attr='user_table', | ||
is_sparse=IS_SPARSE) | ||
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usr_fc = layers.fc(input=usr_emb, size=32) | ||
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USR_GENDER_DICT_SIZE = 2 | ||
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usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64') | ||
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usr_gender_emb = layers.embedding( | ||
input=usr_gender_id, | ||
size=[USR_GENDER_DICT_SIZE, 16], | ||
param_attr='gender_table', | ||
is_sparse=IS_SPARSE) | ||
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usr_gender_fc = layers.fc(input=usr_gender_emb, size=16) | ||
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USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table) | ||
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64") | ||
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usr_age_emb = layers.embedding( | ||
input=usr_age_id, | ||
size=[USR_AGE_DICT_SIZE, 16], | ||
is_sparse=IS_SPARSE, | ||
param_attr='age_table') | ||
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usr_age_fc = layers.fc(input=usr_age_emb, size=16) | ||
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USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1 | ||
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64") | ||
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usr_job_emb = layers.embedding( | ||
input=usr_job_id, | ||
size=[USR_JOB_DICT_SIZE, 16], | ||
param_attr='job_table', | ||
is_sparse=IS_SPARSE) | ||
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usr_job_fc = layers.fc(input=usr_job_emb, size=16) | ||
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concat_embed = layers.concat( | ||
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1) | ||
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usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh") | ||
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return usr_combined_features | ||
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def get_mov_combined_features(): | ||
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MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1 | ||
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mov_id = layers.data(name='movie_id', shape=[1], dtype='int64') | ||
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mov_emb = layers.embedding( | ||
input=mov_id, | ||
dtype='float32', | ||
size=[MOV_DICT_SIZE, 32], | ||
param_attr='movie_table', | ||
is_sparse=IS_SPARSE) | ||
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mov_fc = layers.fc(input=mov_emb, size=32) | ||
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CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories()) | ||
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category_id = layers.data( | ||
name='category_id', shape=[1], dtype='int64', lod_level=1) | ||
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mov_categories_emb = layers.embedding( | ||
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE) | ||
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mov_categories_hidden = layers.sequence_pool( | ||
input=mov_categories_emb, pool_type="sum") | ||
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MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict()) | ||
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mov_title_id = layers.data( | ||
name='movie_title', shape=[1], dtype='int64', lod_level=1) | ||
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mov_title_emb = layers.embedding( | ||
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE) | ||
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mov_title_conv = nets.sequence_conv_pool( | ||
input=mov_title_emb, | ||
num_filters=32, | ||
filter_size=3, | ||
act="tanh", | ||
pool_type="sum") | ||
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concat_embed = layers.concat( | ||
input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1) | ||
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# FIXME(dzh) : need tanh operator | ||
mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh") | ||
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return mov_combined_features | ||
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def inference_program(): | ||
usr_combined_features = get_usr_combined_features() | ||
mov_combined_features = get_mov_combined_features() | ||
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inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features) | ||
scale_infer = layers.scale(x=inference, scale=5.0) | ||
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return scale_infer | ||
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def train_program(): | ||
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scale_infer = inference_program() | ||
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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) | ||
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return [avg_cost, scale_infer] | ||
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def func_feed(feeding, place, 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]) | ||
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numpy_data = numpy_data.reshape([numpy_data.shape[0], 1]) | ||
tensor.set(numpy_data, place) | ||
feed_tensors[key] = tensor | ||
return feed_tensors | ||
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def train(use_cuda, train_program, save_path): | ||
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() | ||
optimizer = fluid.optimizer.SGD(learning_rate=0.2) | ||
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 | ||
} | ||
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trainer = fluid.Trainer( | ||
train_func=train_program, place=place, optimizer=optimizer) | ||
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feed_order = [ | ||
'user_id', 'gender_id', 'age_id', 'job_id', 'movie_id', 'category_id', | ||
'movie_title', 'score' | ||
] | ||
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def event_handler(event): | ||
if isinstance(event, fluid.EndEpochEvent): | ||
test_reader = paddle.batch( | ||
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE) | ||
avg_cost_set = trainer.test( | ||
reader=test_reader, | ||
feed_order=feed_order, | ||
data_feed_handler=partial(func_feed, feeding_map, place)) | ||
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# get avg cost | ||
avg_cost = np.array(avg_cost_set).mean() | ||
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print("avg_cost: %s" % avg_cost) | ||
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if float(avg_cost) < 3: # Smaller value to increase CI speed | ||
trainer.save_params(save_path) | ||
else: | ||
print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, | ||
float(avg_cost))) | ||
if math.isnan(float(avg_cost)): | ||
sys.exit("got NaN loss, training failed.") | ||
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train_reader = paddle.batch( | ||
paddle.reader.shuffle( | ||
paddle.dataset.movielens.train(), buf_size=8192), | ||
batch_size=BATCH_SIZE) | ||
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trainer.train( | ||
num_epochs=1, | ||
event_handler=event_handler, | ||
reader=train_reader, | ||
feed_order=[ | ||
'user_id', 'gender_id', 'age_id', 'job_id', 'movie_id', | ||
'category_id', 'movie_title', 'score' | ||
], | ||
data_feed_handler=partial(func_feed, feeding_map, place)) | ||
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def infer(use_cuda, inference_program, save_path): | ||
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() | ||
inferencer = fluid.Inferencer( | ||
inference_program, param_path=save_path, place=place) | ||
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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) | ||
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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 | ||
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# 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]]) | ||
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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 | ||
}, | ||
return_numpy=False) | ||
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print("infer results: ", np.array(results[0])) | ||
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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=use_cuda, train_program=train_program, save_path=save_path) | ||
infer( | ||
use_cuda=use_cuda, | ||
inference_program=inference_program, | ||
save_path=save_path) | ||
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if __name__ == '__main__': | ||
main(USE_GPU) |
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Maybe we can write a new reader above the default train reader? But not use a function handle, we can process the data in the new reader
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I support this approach. Let the Trainer handle the train process to keep it compact and simple. I also feel that way it will be easier for the user to understand the flow of the fluid programming.
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I am working on this and it should be done by the end of Wed (May 23)
https://github.com/daming-lu/Paddle/tree/recommend_sys
Sid is on label_semantics, and Nicky on machine_translation, which might also need a new reader.